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Abstract

Germination involves highly dynamic transcriptional programs as the cells of seeds reactivate and express the functions necessary for establishment in the environment. Individual cell types have distinct roles within the embryo, so must therefore have cell type-specific gene expression and gene regulatory networks. We can better understand how the functions of different cell types are established and contribute to the embryo by determining how cell type-specific transcription begins and changes through germination. Here we describe a temporal analysis of the germinating Arabidopsis thaliana embryo at single-cell resolution. We define the highly dynamic cell type-specific patterns of gene expression and how these relate to changing cellular function as germination progresses. Underlying these are unique gene regulatory networks and transcription factor activity. We unexpectedly discover that most embryo cells transition through the same initial transcriptional state early in germination, even though cell identity has already been established during embryogenesis. Cells later transition to cell type-specific gene expression patterns. Furthermore, our analyses support previous findings that the earliest events leading to the induction of seed germination take place in the vasculature. Overall, our study constitutes a general framework with which to characterize Arabidopsis cell transcriptional states through seed germination, allowing investigation of different genotypes and other plant species whose seed strategies may differ.

Main

Germination is the process through which seeds begin to grow and establish in their environment, which is fundamental to agricultural production. Success requires that the seed monitors both its internal resources and the surrounding, external conditions to co-ordinate appropriate responses based on this information and ensure that the time and place are suitable for future plant growth. Germination and early seedling growth are consequently plastic, dynamic processes.

The seed is a complex structure comprising many tissues and cell types, which are established during embryogenesis. These cell types have individual functions and biochemistry that enable tight spatiotemporal control of growth and development. Variation in temperature is sensed and interpreted as a signal to germinate by just tens of cells in the embryonic radicle of dormant Arabidopsis seeds, in conjunction with the monolayer of cells in the endosperm4,5. Growth is then driven by cell expansion once germination commences, with both spatial and temporal variation in expansion rates. Initially, growth occurs in cells adjacent to the radicle tip, then proceeds to include cells further along the radicle and in the hypocotyl6. The end of germination is defined by emergence of the radicle through the endosperm and testa (seed coat), following which the cotyledons emerge and the seedling transitions from heterotrophic to photoautotrophic growth7.

Spatiotemporal control of growth and development requires correspondingly precise regulation of gene expression. The nuclei of mature Arabidopsis embryo cells condense and transition to a heterochromatic state by the end of seed development, repressing gene expression. This is thought to be an adaptation enabling seeds to tolerate the desiccation that occurs at the end of seed development9. The nuclei then reverse this process as the seed imbibes water and germination commences, decondensing and transitioning to the euchromatic state required for gene transcription9,11. However, germination considered in the strict sense (that is, to the point of radicle protrusion) is thought to be dependent only on translation, whereas de novo transcription during germination is non-essential12. Consistent with this, mature Arabidopsis seeds contain populations of stored transcripts that were transcribed during seed development, a subset of which are translated early in germination. During this time, the developing embryo draws on stored energy reserves, primarily in the form of lipids, but also from cell wall carbohydrates8,15,16,17,18. Nonetheless, transcription and de novo gene expression occur relatively early in germination, within 1-2 h of imbibition19,20. A cascade of transcription factors regulate gene expression at this time, which influences the rate at which germination progresses and is essential for a successful transition to post-germination seedling establishment5,13,19,21,22. Temporal changes occur in DNA methylation and small RNA abundance during germination, both of which are likely to be involved in gene regulation. Gene expression also varies spatially within seeds, reflecting the different functions of tissues and cell types1,4,25,26,27. However, the resolution of spatial studies, and consequently the insight they provide, have been limited by the precision and scale achieved by the predominant methods of hand isolation or laser capture microdissection.

Through understanding the spatiotemporal regulation of gene expression in seeds, we can better understand how the functions of different cell types are specified, and how these contribute to the functions of the seed as a whole. To this end, we investigated gene expression dynamics in the Arabidopsis embryo over the first 48 h of germination, as it transitions into a seedling, at single-cell resolution. We then interrogated the data to understand how the transcriptional state of cells progresses and how gene expression is regulated. We observed that most cells pass through a shared early transcriptional state before transitioning to cell type-specific transcriptional states. Once established, these cell type-specific transcriptional states were dynamic over germination and reflected changing functional properties of the cells. We constructed gene regulatory models for each cell type, from which we predicted key transcription factors that are active in individual cell types. This study provides insight into the different regulatory mechanisms operating as a seedling establishes itself within the environment.

Results

Generation of a single-cell expression atlas of germinating embryos

The major goals of our study were to characterize how gene expression differs between the cell types that constitute germinating Arabidopsis embryos and to determine how these patterns of gene expression may be regulated. To this end, we generated a single-cell RNA sequencing (scRNA-seq) atlas of germinating Arabidopsis embryos (Fig. 1). First, we established baseline germination dynamics in our system by performing a germination assay with Columbia-0 (Col-0) seeds, assessing testa and endosperm rupture over 48 h after transfer to light (Fig. 1a). Neither testa rupture nor endosperm rupture was observed at 12 h; at 24 h, there was 41% testa rupture and 12% endosperm rupture, followed at 36 h by 89% testa rupture and 71% endosperm rupture and at 48 h by 96% testa rupture and 92% endosperm rupture. We selected three time points for analysis from this assay (12, 24 and 48 h), corresponding to early germination (imbibed seed), mid-germination (the start of testa rupture) and the end of germination (endosperm rupture and radicle emergence) (Fig. 1a,b)13. Seeds were harvested at each time point and embryos were released from the seed coat and endosperm by physical disruption. Protoplasts of enriched embryo cells were then isolated and enriched to high purity to physically separate the cells from one another and make them amenable to microfluidic handling.

a, Arabidopsis seed germination analysis showing the percentage rupture of different stages during seed germination by measuring testa rupture (red line) and endosperm rupture (green line). Representative images for each time point are shown below. b, Germination procedure and sampling for transcriptomic analyses: (1) RNA-seq of whole isolated embryos without protoplast isolation; (2) RNA-seq of embryo protoplasts; and (3) scRNA-seq of individual embryo protoplasts. c, d, Expression changes of 1,202 genes in response to protoplast isolation (log ₂[fold change]). The transcriptional response to protoplast isolation was consistent across time points (heatmap ( c)) and included large fold-changes for well expressed genes (MA-plot ( d)). e, Venn diagram of the limited overlap in upregulated genes upon protoplast isolation between this study and a previous study by Birnbaum and colleagues28. CPM, counts per million; np, non-protoplast; p, protoplast.
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Isolation of protoplasts affects gene expression in sampled cells, creating a technical effect that must be controlled for in subsequent analyses. To enable this correction, we analysed the effect of protoplast isolation on embryo transcriptomes using bulk RNA-seq (that is, not scRNA-seq). We compared transcriptomes in whole, isolated embryos with those of our protoplast preparation, finding that 1,202 genes were differentially expressed between them (1% false discovery rate (FDR); log ₂[fold change] > 1.5; Fig. 1c,d and Supplementary Table 1). The genes responsive to protoplast isolation responded consistently across all time points (Fig. 1c) and the overall transcription dynamics during germination were preserved in protoplasts (only 0.4-2.5% of discordant changes between time points, where a gene that is upregulated in the whole-embryo data is downregulated in the protoplast data, or vice versa). Removing protoplast-responsive genes slightly improved the correlation between whole-embryo transcriptomes and single-cell pseudobulk transcriptomes at each time point (Pearson correlation coefficients of 0.80, 0.83 and 0.80 for the 12, 24 and 48 h time points, respectively, up from 0.79, 0.82 and 0.79) (Supplementary Table 2). These genes were excluded from our subsequent scRNA-seq data analyses.

Other plant single-cell gene expression studies have also used protoplast isolation to separate cells28,29,30,31,32,33,34,35,36,37,38. Approaches to correct for the effect of protoplast isolation have varied. Some studies have applied bulk RNA-seq in the same manner as ourselves29,30,34,35,37,38, whereas others have either used a single previously generated reference set of protoplast isolation-responsive genes or reported no correction at all32,33. Other scRNA-seq studies that have been comparably rigorous have detected similar numbers of protoplast-responsive genes (2,231 genes38 and 3,545 genes29), compared with the 1,202 genes that we detected in our study. It is possible that the population of protoplast isolation-responsive genes varies depending on experimental conditions, growth stage and other factors. We tested this by determining how many of the responsive genes were shared between our analysis and the previously generated reference set28. Only 75 of our 782 upregulated protoplast isolation-responsive genes were shared between the two datasets (Fig. 1e). This indicates that systematic parallel analysis and control for the effects of cell isolation is important when conducting scRNA-seq experiments.

Transcriptomes of germinating embryo protoplasts were analysed using the 10X Genomics Chromium platform to produce a single-cell atlas (Fig. 2 and Extended Data Fig. 1). Two biologically independent replicates were conducted per time point, with each sample a pool of hundreds of embryos. Samples in the first replicate comprised an equal mixture of Col-0 and Cape Verde Islands-0 (Cvi-0) cells for technical reasons, to enable real single cells to be distinguished from doublet cell capture events and background noise. Real single cells would contain only single nucleotide polymorphisms (SNPs) from a single ecotype, whereas doublets and background would contain a mixture of SNPs from both ecotypes. This was particularly helpful for the early time points, where cell messenger RNA (mRNA) content was low and default settings of the 10X Genomics data processing pipelines may have missed transcriptionally quiescent cells. Only Col-0 cells were used for all subsequent biological analyses, as the germination properties of Cvi-0 differ substantially. This yielded a total of 12,798 Col-0 cells, with an average 1,025 expressed genes per cell (Extended Data Fig. 1a,b). Cell recovery and the number of detected genes increased with time of germination, although equal numbers of cells were loaded per sample and each sample was sequenced at a similar depth (Extended Data Fig. 1a; 140-170 million reads per sample). It is likely that this occurred because the number of transcripts per cell was low in the early stages of germination (fewer unique molecular identifiers (UMIs) detected despite higher numbers of reads per cell; Extended Data Fig. 1e), affecting the detection of true cells from background, even after fine-tuning the cell detection parameters using the two ecotypes. We integrated data from all samples to minimize technical effects, then clustered cells according to their transcriptional profiles and visualized the resulting clusters in two dimensions with uniform manifold approximation and projection to assess the consistency between replicates and time points (Fig. 2a and Extended Data Fig. 1c-e). Cells from independent replicates of individual time points were co-located in the analysis, indicating that replicates were consistent with one another after data integration (Extended Data Fig. 1d). Fifteen distinct clusters of cells with similar transcriptional profiles were resolved among these data, which probably represented different cell types and states (Fig. 2a).

a, Uniform manifold approximation and projection (UMAP) dimensional reduction and visualization of 12,798 cells in 15 clusters. b, UMAP dimensional reduction and visualization of cells across three time points-12, 24 and 48 h after placement to light-showing the temporal changes in cell and cluster detection. c, Bubble plots showing the enrichment of expression of representative cell type-specific marker transcripts in 15 clusters and the percentage of cells from the two biological replicates (replicate 1, R1; replicate 2, R2) within each cluster at each time point. Marker transcripts were identified from published studies. d, Spatial distribution of cell clusters in the Arabidopsis embryo. SAM, shoot apical meristem; QC, quiescent centre.
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The cell clusters detected within embryos differed markedly between the 12, 24 and 48 h time points (Fig. 2b and Extended Data Fig. 1f). No cell division occurs in embryos during germination, so no new cells arose in the time period studied6. Consequently, the differences in cell clusters between time points indicate that the transcriptional states of individual cell types within the embryo change over time during germination. Additionally, cell types are established during seed development rather than during germination. This means that the cell clusters defined in our experiment correspond to cell states adopted by the various cells and tissues through the time course of germination, rather than to cell types directly. The consistency between replicates at individual time points was considered (Extended Data Fig. 1d,f). Largely, the same clusters were present in each replicate, but the proportions of cells in those clusters varied. As gene expression is highly dynamic at this time, even a very small shift in harvest time or environmental conditions might cause differences between replicates. Overall, however, the replicates corresponded well with one another.

Annotation of cell types in the expression atlas

We annotated the clusters within the embryo single-cell atlas so that we could interpret the changes occurring among them during germination (Fig. 2c and Supplementary Table 3). Annotation of single-cell data is often achieved by reference to an existing ground truth dataset of manually dissected or sorted cells from the organ of interest28,31,32,33. A comprehensive ground truth dataset does not exist that describes all cell types of the embryo during germination. To overcome this, we investigated the published literature and identified relevant marker genes from several studies, then compared them with genes expressed specifically in one or a small number of clusters (Supplementary Table 3).

We were able to infer identities for 13 of the 15 clusters (Fig. 2c,d). We detected the most abundant cell types of the cotyledon (mesophyll (clusters 3 and 13)), hypocotyl (cortex (clusters 1, 5 and 7), epidermis (cluster 6) and cortex/endodermis (cluster 2)) and radicle (epidermis (cluster 10), radicle apical meristem (cluster 14) and quiescent centre/columella (cluster 4)). Cell type markers were expressed clearly by cluster 2 (cortex/endodermis) and cluster 10 (epidermis), but we could not distinguish whether these were resident in the hypocotyl or radicle, probably reflecting that these cell types are continuous between the two organs at this developmental stage. Cells of the provasculature (protophloem (cluster 15), protoxylem (cluster 9) and provascular cells (cluster 12)) were mostly detected at the 48 h time point. Identities could not be assigned to clusters 8 and 11 because they did not show clear enrichment of expression for any marker genes from the literature, indicating that the clusters may represent some uncharacterized cell state or type. Clusters strongly expressing the marker transcripts of the shoot apical meristem WUSCHEL ( WUS; AT2G179500), CLAVATA3 ( CLV3; AT2G27250), SHOOT MERISTEMLESS ( STM; AT1G62360) and KNOTTED1-LIKE HOMEOBOX GENE 6 ( KNAT6; AT1G23380) were not detectable in the dataset. Some enrichment of KNAT6 and STM was detected in clusters 2, 4 and 12, suggesting that these clusters might include a small number of shoot apical meristem cells. The absence of a clear shoot apical meristem cluster probably occurred because these cells are very rare (approximately eight cells) relative to the total number of cells in an embryo. We also did not detect expression of endosperm or seed coat marker in any cluster, confirming the successful isolation and enrichment of embryos (Fig. 2c).

We validated annotations for clusters 9 and 14 using an independent experimental method (Fig. 3). We had annotated cluster 9 as protoxylem within the provasculature and present only at 48 h. The scRNA-seq analysis indicated that AT1G55210 expression was an independent marker for cluster 9, which had not been used in the initial literature-based annotation of the cluster, so we determined the location of its transcripts by RNA in situ hybridization (Fig. 3a, Supplementary Tables 4 and 5 and Extended Data Fig. 2). Correspondingly, a signal was detected specifically within the protoxylem at 48 h but was not detected at 24 h. We also validated the annotation of cluster 14 as radicle apical meristem cells, present only at 48 h. The location of the independent marker transcript AT3G20470 was examined. A signal was specifically detected in cells of the radicle apical meristem region and only at 48 h, but with the signal weaker in the radicle cortex cells (Fig. 3b, Supplementary Tables 4 and 5 and Extended Data Fig. 2). This observation corresponded with our annotation of cluster 14 from known marker transcripts, which indicated the presence of epidermis, endodermis, atrichoblast and root cap marker transcripts (Fig. 2c). The marker gene validation results for both clusters were also consistent with the changes in the detection of clusters over time described above, further illustrating the dynamic nature of cell transcriptomes during germination.

a, b, Expression domains of independent marker transcripts of cluster 9 ( a) and cluster 14 ( b) confirmed the physical location of the cells in these clusters. UMAP dimensional reduction graph visualized the expression of marker transcript at 48 h. The hybridization signals were detected at 48 but not 24 h, also confirming the temporal detection of these clusters in scRNA-seq data. For each time point, the results of hybridization with antisense probes (that is, the test) and sense probes (negative controls) are shown. Representative images are shown (more than ten embryos were examined). Scale bars, 200 μm.

Embryo cells undergo extensive transcriptional reprogramming

The growth properties and development of individual cells within the embryo change as germination progresses5,6. We investigated how underlying dynamic gene expression might contribute to changes in the functional properties of cells during this time and how this relates to the transcriptional states observed in our data. We focused on the hypocotyl cortex cells because these were detected as three distinct clusters (1, 5 and 7) present in different proportions at each time point across germination (Fig. 4). Cells of cluster 5 were the most abundant at 12 h, accompanied by a small number of cluster 1 cells (Fig. 4a). Cells of clusters 1 and 7 were most abundant at 24 h, whereas at 48 h very few cells were detected from any of the clusters, but cells of cluster 1 were the most abundant (Fig. 4a). The existence of three distinct clusters of hypocotyl cortex cells indicated that there were populations of hypocotyl cortex cells whose transcriptomes differed. It was not possible to identify single marker transcripts clearly specific to individual clusters, suggesting that the differences between clusters were relatively subtle and related to quantitative differences in the expression of many genes rather than the complete presence or absence of certain transcripts (Extended Data Fig. 3 and Supplementary Table 6). However, marker transcripts strongly specific to all three of clusters 1, 5 and 7 combined were readily identified (Extended Data Fig. 4 and Supplementary Table 7). AT4G16410 was a specific marker transcript of clusters 1, 5 and 7 at the 12 and 24 h time points in our scRNA-seq data. Expression of the transcript was detected by RNA in situ hybridization in lower and middle hypocotyl cortex cells at 12 and 24 h, confirming the cell type annotation of clusters 1, 5 and 7 (Fig. 4b and Extended Data Fig. 5).

a, UMAP dimensional reduction and visualization of clusters 1, 5 and 7 cells across three timepoints, 12 h, 24 h and 48 h. b, Confirmation of the locations of clusters 1, 5 and 7 using RNA in situ hybridization of a marker transcript specific to these clusters at 12 and 24 h. Left, UMAP dimensional reduction plots of the expression of the marker transcript of AT4G16410 across all cells at each time point. Right, locations of the hypocotyl cortex cell type. Representative images are shown (more than ten embryos were examined). Scale bars, 200 μm. c, Cells of clusters 1, 5 and 7 form a contiguous group together. They sit upon a temporal trajectory from 12 h to 24 h to 48 h, which corresponds to the transition from cluster 5 cells, through cluster 7 to cluster 1 cells (top graph). Reconstruction of pseudotime (bottom graph) follows a trajectory that corresponds to the real time of germination (middle graph). On the trajectory, branch points are denoted by black circles and leaves (outcome cell states) are denoted by grey circles, while numbers are for reference purposes only. d, Co-expressed gene modules across the pseudotime trajectory of cluster 1, 5 and 7 cells. Early pseudotime is equivalent to early germination. The module annotations are major representative Gene Ontology terms associated with the modules, assessed using Gene Ontology term reduction.
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We conducted a detailed analysis of the transcriptomes of cells within each of the three hypocotyl cortex clusters (1, 5 and 7) to understand their similarities and differences. We performed a new dimension reduction analysis only for cells annotated to hypocotyl clusters (1, 2, 5, 6, 7 and 10) to remove the influence of the large transcriptional differences from cells of other organs or tissues, thereby allowing us to focus on the smaller differences between hypocotyl cells (Fig. 4c). Cells of the hypocotyl cortex clusters again formed a contiguous group even at this focused scale, confirming that the transcriptomes of clusters 1, 5 and 7 were highly similar (Fig. 4c). The temporal ordering of the group was preserved, transitioning from cells harvested at 12 h (cluster 5), through 24 h (clusters 7 and 1) to 48 h (cluster 1; Fig. 4a). These observations suggested that clusters 1, 5 and 7 represent hypocotyl cortex cells transitioning through different transcriptional states over time during germination.

We further examined the proposal that clusters 1, 5 and 7 represent hypocotyl cortex cells at different stages of germination by assembling the hypocotyl cortex cells onto a pseudotime trajectory. The cells of a single cell type within an individual embryo become active at different times during germination, initiating a dynamic gene expression program5,39. Consequently, the cells of a single cell type are spread across a shared developmental trajectory of gene expression, with each cell in a slightly different expression state. Furthermore, the precise time at which germination begins under uniform conditions differs between genetically identical seeds40. Each sample in our experiment was a pool of hundreds of embryos. Therefore, many cells of each cell type would have been present in each of our samples, and these cells would have been distributed across a continuum of that cell type's developmental trajectory. This experimental design is advantageous because it allows us to reconstruct the developmental trajectory of gene expression using a technique called pseudotime analysis. Pseudotime analysis arranges the cells of a single-cell experiment into order according to their expression states, and thereby developmental progression. In our case, early pseudotime should correspond to early germination and late pseudotime to late germination, and the cells are ordered on a continuum between these points. This ordering of cells enables examination of how gene expression changes dynamically during germination.

Reconstruction of the pseudotime trajectory supported the notion that clusters 1, 5 and 7 represent hypocotyl cortex cells transitioning through different stages of germination, with pseudotime following a similar path to the true time of germination (Fig. 4c). Furthermore, the dynamics observed in transcriptomes across hypocotyl cortex cell clusters indicated that the functional properties of these cells change during germination. To examine this, we identified modules of genes that were co-expressed and assessed their functions and timing of expression. There were 25 distinct modules of co-expressed genes across the pseudotime trajectory of clusters 1, 5 and 7 (Fig. 4d and Supplementary Table 8). These were broadly categorized as early (modules 4, 6, 11, 12, 14, 15, 17 and 24), mid- (1, 2, 5, 19, 20 and 22) and late (3, 7, 8, 9, 10, 13, 16, 18, 21, 23 and 25) on the pseudotime trajectory, which can be considered as equivalent to early, mid- and late germination. Genes co-expressed during early germination were enriched for functions related to chromatin accessibility, transcription, RNA splicing and translation; during mid-germination, functions related to translation, protein maturation and photosynthesis were more strongly evident; and in late germination, photosynthesis was the dominant function. These analyses indicate that dynamic gene expression drives functional changes in hypocotyl cortex cells across germination.

An initial cell transcriptional state is established early in germination

Next, we sought to understand how initial cell transcriptional states are established as cells commence activity. The earliest germination time point, 12 h, was dominated by cells of clusters 8 and 11 (26.74 and 37.27% of cells captured at 12 h, respectively; Fig. 5 and Extended Data Fig. 1f). Like many other clusters, the presence of clusters 8 and 11 was dynamic across germination, being greatest at 12 h, and with the clusters being almost entirely absent by 48 h (Fig. 5a). We were unable to identify known cell type marker transcripts from the published literature with which clusters 8 and 11 could be annotated. Cluster 8 expressed marker transcripts of mature embryos and dry seeds, suggesting that it might comprise cells in an early germination state that have not previously been characterized (Fig. 2c)41. To test this idea, we assessed the similarity of the cluster 8 and 11 transcriptomes with the transcriptomes of whole seeds at earlier germination time points (Fig. 5b). To do so, we used a dataset of whole (bulk) seed RNA-seq that included 1, 6 and 12 h germination time points (that is, earlier than in our scRNA-seq analysis) and calculated identity (module) scores between each cell and the bulk seed transcriptomes13. Cluster 8 cells identified strongly (more than all other clusters) with the transcriptomes of 1 and 6 h bulk seeds, whereas cluster 11 cells did not. This suggested that the biological properties of clusters 8 and 11 are distinct.

a, Top, UMAP dimensional reduction and visualization of all cells across three time points (that is, 12, 24 and 48 h), showing the temporal changes in cell and cluster detection. Bottom, UMAP dimensional reduction and visualization of cluster 8 and 11 cells across three time points. b, Comparison of transcriptomes of each cell, grouped by cluster, against whole (bulk) seed transcriptomes from early time points during germination. The transcriptomes of cluster 8 cells are strongly similar to the transcriptomes of bulk seeds at 1 and 6 h of germination. c, d, RNA in situ hybridization to confirm the locations of clusters 8 ( c) and 11 ( d) at 1, 6 and 12 h after 48 h of stratification using two cluster-specific marker transcripts for each. Illustrated sketches indicate the area of expression. The expression of each marker is shown in an adjacent UMAP dimensional reduction plot that displays all of the cells detected at 12 h, some of which belong to other clusters. Representative images are shown (more than ten embryos were examined). Scale bars, 200 μm. The arrows indicate regions where signals were detected.
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The physical locations of cells in clusters 8 and 11 were determined to better understand their biological properties. We examined the localization of two marker transcripts for each cluster using RNA in situ hybridization. Expression of cluster 8 marker transcripts (AT4G25140 and AT5G35660) was detected throughout the whole embryo (cotyledon, hypocotyl, radicle and provasculature) at 12 h (Fig. 5c, Supplementary Tables 5 and 9 and Extended Data Fig. 5). We also examined the expression domain of cluster 8 marker transcripts at 1 and 6 h, given the similarity of this cluster to earlier bulk germination time points, embryos and dry seeds41. The same whole-embryo expression pattern was detected at 1 and 6 h (Fig. 5c and Extended Data Fig. 5). Contrastingly, marker transcripts of cluster 11 (AT1G27130 and AT5G10520) were detected only in the provasculature cells at 1, 6 and 12 h (Fig. 5d, Supplementary Tables 5 and 9 and Extended Data Fig. 5). This expression domain of cluster 11 marker transcripts corresponds to a defined region of abscisic acid and gibberellic acid signalling, which is proposed to regulate the decision to germinate in dormant seeds-an event that precedes the germination events covered by our experiments4. Considered together, these data indicate that cluster 8 represents a general cell transcriptional state through which most cells of the embryo pass early in germination. In contrast, cluster 11 probably represents the set of cells where the decision to germinate was made, which appear to have an early germination transcriptional state that is different from all of the other cells.

Individual gene regulatory programs are active in each cell type

The many cell types of an embryo each have different roles and contribute at different times to the success of germination1,4,5,27. This is achieved by cell types having distinct functional properties, which must be determined by the particular complement of genes that these cells express. Consequently, each cell type of the germinating embryo should have a unique and dynamic gene regulatory program. We examined the properties of these gene regulatory programs by analysing gene expression dynamics and predicting the transcription factors that putatively regulate programs for each cell cluster (that is, cell type or state) identified in our scRNA-seq dataset (Fig. 6, Extended Data Figs. 6- 9 and Supplementary Tables 10- 14). We used a method called continuous state hidden Markov models-transcription factor (CSHMM-TF) because it assembles pseudotime trajectories from which expression dynamics can be analysed and also predicts which transcription factors are putative regulators of the observed gene expression dynamics42. CSHMM-TF predicts regulatory transcription factors based on both their expression and the expression of their target genes. This is achieved by integrating transcription factor binding data, here provided as experimentally validated target genes for >500 Arabidopsis transcription factors from genome-wide in vitro protein-DNA binding assays43. CSHMM-TF also identifies where the gene expression of groups of cells diverges substantially during pseudotime, splitting cells with different expression states onto different paths that might indicate subpopulations of cells within a cluster.

a, Pseudotime models of cell developmental trajectories for clusters 8 and 11, constructed using CSHMM-TF. P indicates paths and D indicates nodes. Nodes demarcate the start and end of each path. Mutants with mutation of the genes encoding the transcription factors highlighted in red were selected for germination assay. b, Germination assay of T-DNA mutant lines predicted in CSHMM-TF of clusters 8 and 11. The percentage of germination of seeds (endosperm rupture) at 24 h is shown. Each data point represents one biological replicate of 50 scored seeds. Seeds from two individual parent plants (_1 and _2) of each mutant line were included. Four biological replicates are included for each mutant line. Asterisks denote significant differences compared with wild-type Col-0. Statistical significance was determined by two-tailed paired Student's t-test ( P < 1 × 10 −5). Mutants with mutation of the genes highlighted in red were selected for RNA-seq. c, Number of DEGs (FDR < 0.05) in mutants at 24 h compared with wild-type Col-0. Positive values represent upregulation in the mutant and negative values represent downregulation in the mutant. d, Active transcription factors (TFs) in every CSHMM-TF model of the 15 clusters identified, comprising a total of 81 unique transcription factors, 39 of which are active in one cell cluster only.
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Gene expression was dynamic and complex across germination in all cell clusters. Each cluster exhibited distinct patterns of gene co-expression across germination and the functional annotations enriched among expressed genes changed across germination, suggesting changes in cell biological properties (Fig. 6a, Extended Data Fig. 5 and Supplementary Tables 10 and 11). For example, cells of cluster 8 (early germination cell state) first expressed genes involved in the utilization of energy resources (ATP and oil bodies; path P0), followed by RNA processing, translation and hypoxia (path P1). The expression of energy biology functions earliest probably indicates the initiation of metabolism, whose resumption when mitochondrial function is being re-established would result in the observed hypoxia signature at this phase of germination44. Consistent with this, rice and barley grains express similar functions in the earliest phase of germination45,46. Cells then split along two gene expression paths, with path P2 cells expressing genes involved in mRNA metabolism and energy biology, but path P3/P4 cells expressed more protein processing and translation functions. The patterns of gene expression differed in cluster 11 cells (germination decision centre) compared with cluster 8 (Fig. 6a and Supplementary Tables 10 and 11). For example, genes associated with hypoxia were already expressed at the earliest phase of the cluster 11 model (P0). This would be consistent with metabolism in cluster 11 cells becoming active earlier, as would be expected for the cells where the decision to germinate is first made. Cluster 11 cells then split along three gene expression paths (P1, P2/3 and P4), with the expression of functions involved in translation and ribosome biogenesis featured in two of these (P1 and P2/3). For clusters 8 and 11, the pseudotime arrangement was consistent with the actual germination time of cells (Fig. 6a). Similarly complex and distinct patterns of gene expression over germination were observed for all clusters, indicating that gene regulatory programs differ between each cluster and therefore between each cell type or state (Fig. 6, Extended Data Figs. 6- 9 and Supplementary Tables 10- 14).

Differing gene regulatory programs require the activity of different transcription factors. We first compared the candidate regulatory transcription factors predicted by our models for the gene regulatory programs of clusters 8 and 11. Seven out of 15 predicted regulatory transcription factors were unique to cluster 8, whereas 15 out of 23 were unique to cluster 11 and eight were shared between the two clusters (Fig. 6a). Several of these transcription factors had previous evidence implicating them in germination or early seedling growth. Regulators of brassinosteroid hormone responses were among the transcription factors shared between clusters (BES1-INTERACTING MYC-LIKE PROTEIN 2 (BIM2; AT1G69010), BES1/BZR1 HOMOLOG 3 (BEH3; AT4G18890) and BES1/BZR1 HOMOLOG 4 (BEH4; AT1G78700)), which is notable because brassinosteroids play an important role in cell division and growth47. Three transcription factors specific to cluster 8 have functions in embryo development, seed maturation and lipid accumulation (ABA-RESPONSIVE ELEMENT BINDING PROTEIN 3 (AREB3; AT3G56850), FUSCA3 (AT3G26790) and HEAT SHOCK FACTOR 3 (HSF3; AT5G16820))47,48,49,50. Another cluster 8 specific transcription factor (GT-1; AT1G13450) promotes light-responsive gene expression, consistent with the recent exposure of the seeds to light as a germination trigger51. The cluster 11-specific transcription factor ERF55/translucent green (TG; AT1G36060) suppresses light-induced seed germination52. Others have roles in mucilage production and root hair growth (DF1; AT1G76880), which occurs early in germination, as well as calcium signalling (CALMODULIN-BINDING TRANSCRIPTION ACTIVATOR 1 (CAMTA1; AT5G09410) and CAMTA5 (AT4G16150)) and auxin-mediated morphogenesis (HAT2; AT5G47370), both of which are signalling pathways that influence seed development and germination53,54,55,56,57,58,59. These analyses indicate that cells of clusters 8 and 11 execute distinct gene regulatory programs involving different sets of transcription factors.

We assessed the utility of our model as a tool with which to predict transcription factors that influence germination phenotypes. Our analysis focused on the predicted regulatory transcription factors specific to cluster 8 (7 out of 15) and cluster 11 (15 out of 23). We obtained transfer DNA (T-DNA) fragments conferring homozygous mutations expected to disrupt either promoters or coding sequences of the transcription factors from cluster 8 (four independent T-DNA fragments disrupting three genes: areb3 for AREB3, gt1-1 and gt1-2 for GT1, and hsf3 for HSF3) and cluster 11 (seven independent T-DNA fragments disrupting five genes: dof5.8 for AT5G66940, tg-1 and tg-2 for TG, tga6 and tga6-1 for TGA6 (AT3G12250), tga9 for TGA9 (AT1G08320) and wrky3 for AT2G03340; Supplementary Table 15). These genes were selected for analysis based on the availability of single-insertion mutation-causing lines from the Arabidopsis Biological Resource Center targeting the intended region of genes. Progeny from two parent lines were collected for each T-DNA fragment for quantitative PCR with reverse transcription (qRT-PCR) analysis to confirm knockout or knockdown of the target genes. The results indicated that fragments targeting AREB3, HSF3, GT1 and DOF5.8 caused knockdown of the genes, whereas fragments targeting TGA9, WRKY3, TG and TGA6 caused knockout of the genes. The second allele for GT1, for which the insertion of T-DNA was in the promoter region, caused overexpression of this gene in the mutant (Extended Data Fig. 10a). The germination rates of the mutated plants (as determined by testa rupture) were compared with those of wild-type Col-0 after 24 h exposure to light, across four independent biological replicates (Fig. 6b). The germination rates were faster for all mutants with mutations affecting four out of the eight transcription factors (AREB3, GT1, TGA9 and WRKY3). For cluster 8, the germination rates were consistently faster in all progeny lines of areb3, gt1-1 and gt1-2 mutants. The germination rates of the hsf3 progeny lines were inconsistent. For cluster 11, the germination rates were consistently faster in all progeny lines of tga9 and wrky3, whereas the germination rate of dof5.8 did not differ from that of the wild type. tg-1 exhibited an increased germination rate but tg-2 did not, suggesting a difference of effect between these two mutations in the same gene. The germination rate was increased in one of two tga6 progeny lines and both tga6-1 progeny lines, potentially resulting from a relatively weak phenotypic effect of tga6. Overall, these results indicate that our modelling approach is able to identify transcription factors that influence germination phenotypes when altered through mutation of their encoding genes.

We also investigated the effects of mutating genes encoding predicted regulatory transcription factors on global gene expression. A subset of mutants were selected to represent a spectrum of phenotypes: areb3 and wrky3 with strong effects on germination rate; tg-1 with a moderate effect; and hsf3 and tga6 with weak or inconsistent effects (Fig. 6b; selected lines are indicated in red). The transcriptomes of these mutants were compared with those of wild-type plants at 24 h of germination using bulk RNA-seq (Fig. 6c and Supplementary Table 16). Expression values of the target mutated genes were consistent with qRT-PCR results for each line. The mutants areb3 and wrky3, which had the strongest increases in germination rates, also had the largest numbers of differentially expressed genes (DEGs) (859 for areb3 and 959 for wrky3), whereas only 31 genes were misregulated in tg-1. There was a large and significant overlap in DEGs between mutants (Extended Data Fig. 10b,c; P < 1 × 10 −5; hypergeometric test), which is consistent with both similar gene networks being affected and also the common effect of accelerated germination. The downregulated genes in the areb3 mutant were significantly enriched for targets of the AREB3 transcription factor (27 out of the 369 DEGs from 536 total known AREB3 targets; P < 1 × 10 −5; hypergeometric test), but the DEGs of the other mutants were not significantly enriched for the known targets of those transcription factors43. Overall, these differential expression results combined with the early germination phenotypes indicate that AREB3 and WRKY3 may influence gene expression during germination, further supporting the utility of our modelling approach. The bulk RNA-seq approach used here lacks sensitivity given that we expect these transcription factors to operate within defined cell subsets and at specific times, so the results suggest that analysis of mutants at the single-cell level may be able to resolve the cell state-specific roles of these transcription factors in germination.

Having validated the utility of our modelling approach to identify candidate transcription factors that may influence germination phenotypes, we examined the characteristics of the gene regulatory program models of all cell clusters (Fig. 6d, Extended Data Figs. 6- 9 and Supplementary Tables 10- 14). A total of 81 transcription factors were predicted to be active across the models of all cell clusters (Fig. 6d and Supplementary Table 17). Mutations in genes encoding 15 of the 81 transcription factors led to independently reported phenotype changes relevant to seed development, germination and early seedling growth (Supplementary Table 17). These include the previously discussed mutations of AREB3 and TG. Mutations of WRKY DNA-BINDING PROTEIN 18 ( WRKY18; AT4G31800) confer altered sensitivity to abscisic acid and hence an altered germination rate60. Mutation of DOF AFFECTING GERMINATION 2 ( DAG2; AT2G46590) results in a decreased germination rate in the light, suggesting that DAG2 is a positive regulator of light-mediated seed germination with expression confined to vascular tissue and induced by imbibition61,62. MYB HYPOCOTYL ELONGATION-RELATED ( MYBH; AT5G47390) overexpression caused increased hypocotyl elongation of seedlings grown in the light63. Thirty-nine of the 81 transcription factors were unique to a single cluster, such as PHLOEM EARLY DOF 1 (PEAR1; AT2G37590) and PHLOEM EARLY DOF 2 (PEAR2; AT5G02460), which were uniquely predicted to regulate gene expression in the protophloem (cluster 15) and which are known regulators of protophloem development64 (Supplementary Table 17). In contrast, other transcription factors were shared between many cell types, such as BIM2 (a regulator of brassinosteroid signalling and growth; eight clusters) and MYBS2 (AT5G08520; a known regulator of glucose and abscisic acid signalling; nine clusters)47,65.

Overall, these analyses indicate that the different cell types of a germinating embryo express unique and dynamic gene regulatory programs that are probably governed by specific sets of transcription factors, and support the ability of our models to identify relevant candidate transcription factors involved in germination that may be used as a resource in other studies.

Discussion

How cell type-specific patterns of gene expression are established and change in individual embryo cells during germination are notable unanswered questions in seed biology. In this study, we have comprehensively described gene expression dynamics between 12 and 48 h of germination in the individual cells of the Arabidopsis embryo. We observed that gene expression is highly dynamic within individual cell types and that cells transition through distinct transcriptional states as germination progresses. The earliest specific transcriptional activity is detected in the root tip vasculature, supporting the definition of this region as the germination decision centre4. Aside from this, nearly all other embryo cells pass through a common transcriptional state at the start of germination. This was evident from our observations that more than one-third of cells at 12 h of germination belonged to a single cluster (cluster 8) and that marker transcripts for this cluster were broadly expressed across the embryo when observed using RNA in situ hybridization. We could rule out that the decreased transcriptome complexity observed at 12 h was due to undersampling as the cells at this time point had the highest number of reads per cell and the highest sequencing saturation. Additionally, cells with similarly low UMI counts from later time points clustered away from the early germination clusters. This confirmed that the RNA content of cells at 12 h is genuinely low and more homogeneous than at later time points.

Subsequently, the many cell type-specific gene expression programs of the embryo emerge. Why cells need to express this shared transcriptional program upon first activity remains to be discovered, especially given that their cell identity has already been defined during embryogenesis. The emergence of specialized transcriptional states from a common ground state also poses the question of which factors provide the memory of cell identity after embryo development and seed dessication. These may include factors such as specific mRNAs stored at low abundance, stored transcription factors or preserved epigenetic differences between cell types.

The transcriptomes of each embryo cell type changed substantially as germination progressed, resulting in changes to the molecular pathways and functions expressed by each cell type over time. The hypocotyl cortex cells were an example of this, expressing genes involved in mRNA splicing and transcriptional functions early in germination, progressing to protein maturation and the establishment of photosynthesis in mid-germination and then chloroplast organization and cell growth in late germination when the seed-seedling transition occurs. Similar dynamics were observed in every cell type, but in each case the functions expressed and the sequence of changes were specific to the individual cell type. This presumably reflects the unique role of each embryo cell type during germination. Underlying these expression dynamics were cell type-specific gene regulatory networks, defined by groups of transcription factors. Although some transcription factors were predicted to be active across multiple cell types, a subset of transcription factors were specific to individual cell types or transcriptional states. This indicates that distinct groups of transcription factors govern the dynamic functional changes of each embryo cell type as germination progresses. Mutations of a subset of transcription factors from two clusters (cluster 8 (common transcriptional state) and cluster 11 (germination decision centre)) resulted in faster germination. This suggests that genes expressed in clusters 8 and 11 are enriched for germination inhibitors that are active at the earliest stages of germination. While it may initially appear counter-intuitive that genes encoding germination inhibitors are induced during germination, this may be explained by those genes undergoing feedback inhibition. In this case, an increase in transcription of the germination inhibitor genes would result in suppression of their activity. Feedback inhibition is common and well documented among germination genes, including many components of gibberellin synthesis, degradation and response4,66. These observations also highlight that transcript abundance alone cannot predict gene function. The transcriptional induction of germination inhibitors at the early stages of the germination program may represent a regulatory mechanism that merits further examination.

Overall, we illustrate that the cells of the embryo progress through specific transcriptional states as germination progresses. This enables individual cell types to express the genes that define the changing functions of those cell types at the appropriate time, thereby contributing to the successful seed-seedling transition. Seed structures and resources vary remarkably between plant species, requiring different cell types, functions and dynamics. Our study provides a framework for the analysis of functional variation in seeds between species and for investigation of how different species establish cell transcriptional states in the embryo. The interactive version of the atlas from this study, available at https://scgerminationatlas.latrobe.edu.au/, can be mined as a resource for future detailed studies of germination and cell types.

Methods

Plant material and growth conditions

Col-0 and Cvi-0 seeds were sown on MS media plates (containing 3% sucrose). Seeds were sterilized and subjected to 48 h of cold (4° C), dark stratification before being transferred to continuous light (at 22° C), then collected after 12, 24 or 48 h in the light. Two biological replicates were collected and analysed.

Germination assay

The germination of Col-0 was scored by measuring both testa rupture and endosperm rupture after 12, 24, 36 and 48 h in the light. Four biological replicates consisting of 50 seeds were included for each time point for the measurement. Testa rupture was scored when visible slits formed on the surface of the seed coat, whereas endosperm rupture was scored after the radicle tip emerged out of the seed coat1.

Dissociation of Arabidopsis embryos into single cells

Approximately 1,000 seeds per individual sample were sandwiched between two glass slides and embryos were released mechanically from seed coats by pressing the slides together. Ruptured seed coats and embryos were collected into microcentrifuge tubes and separated from each other using a Percoll gradient. In brief, the samples were resuspended in MC buffer (10 mM potassium phosphate (pH 7.0), 50 mM NaCl and 0.1 M sucrose) and loaded onto a 1 ml 60% Percoll cushion. The tubes were then centrifuged at 800 g for 5 min and the embryo fraction (at the bottom of the tubes) was collected and resuspended in 0.6 ml MC buffer. A second Percoll gradient was repeated to obtain pellets of embryos without any seed coats. Enriched embryos were cut with razor blades and digested in protoplasting buffer (2% wt/vol cellulase, 3% wt/vol macerozyme, 0.4 M d-mannitol, 20 mM MES, 20 mM KCl in Milli-Q water (with the pH adjusted to 5.7 using 1 M Tris/HCL (pH 7.5)), 0.1% wt/vol bovine serum albumin, 10 mM CaCl ₂ and 5 mM β-mercaptoethanol). After 3 h of digestion, protoplasts were filtered through a 70 μm cell strainer, followed by a 40 μm cell strainer to remove debris, and centrifuged at 500 g for 5 min. The supernatant was removed and the pellet was washed with 2 ml protoplasting buffer without enzymes or mercaptoethanol and centrifuged at 500 g for 5 min. The pellet was resuspended in 50 μl protoplasting buffer without enzymes or mercaptoethanol. Protoplasts were counted using a haemocytometer and adjusted to a final concentration of 800-1,200 protoplasts per μl.

Bulk RNA-seq library preparation

Col-0 seeds were sown and collected at the 12, 24 and 48 h time points as above in three independent replicates (batches) of the experiment. Embryos were released from seed coats and enriched by performing a Percoll gradient. For bulk RNA-seq, embryos were collected either without protoplasting or with protoplasting. RNA was extracted using a Qiagen RNeasy Plant Mini Kit. RNA quality and integrity were determined using a Qubit fluorometer. Libraries were prepared using a TruSeq Stranded mRNA Library Prep Kit and sequenced with an Illumina NextSeq 500 sequencer using 75 bp single-end kits.

Bulk RNA-seq analysis

Raw Illumina reads were trimmed for quality and adaptor sequences with TrimGalore version 0.4.5. Trimmed fastq files were quality checked with FastQC67 and aligned to the Arabidopsis thaliana Col-0 TAIR10 assembly with HISAT2 version 2.1.0 (ref. 68). Exonic counts aggregated by genes were calculated with FeatureCounts from the Rsubread package (version 1.6.2)69 using the Araport11 annotation70. Differential expression analysis between the dissociated and non-dissociated embryos was performed in R 3.5.1 (ref. 71) with the edgeR package72,73. The design matrix was defined as model.matrix (time + protoplast) and generalized linear models for each gene were fit with glmFit. Genes differentially expressed by the dissociation treatment were identified by performing a likelihood ratio test on the protoplast factor with glmLRT. We imposed a 1% FDR and a minimum absolute log ₂[fold change] of 1.5 to call genes as significantly induced or repressed by the dissociation. Similarly, to evaluate the coherence of the transcriptional dynamics of whole embryos versus isolated protoplasts through germination, we identified developmentally regulated genes by performing a likelihood ratio test on the time factor with glmLRT for whole and dissociated embryos separately. We imposed a 5% FDR and a minimum absolute log ₂[fold change] of 1.5 successive time points to call genes as significantly induced or repressed in that time interval. We then quantified the number of genes with discordant changes between whole and dissociated embryos.

Single-cell RNA-seq library preparation

Six thousand protoplasts per time point and replicate were loaded onto a Chromium Single Cell instrument (10X Genomics, Millennium Science) to generate single-cell Gel bead-in-EMulsions (GEMs). scRNA-seq libraries were generated with the Chromium Single Cell 3′ Library & Gel Bead Kit v2 (10X Genomics, Millennium Science) according to the user guide (Chromium Single Cell 3′ Reagent Kits v2). The resulting libraries were checked on an Agilent 2100 Bioanalyzer and quantified with the KAPA Library Quantification Kit for Illumina Platforms (Millennium Science). The libraries were sequenced on an Illumina NextSeq 500 using two 150 bp paired-end kits with 15% PhiX. The raw scRNA-seq dataset comprised 26 bp read 1, 116 bp read 2 and 8 bp i7 index reads.

Single-cell RNA-seq analysis

CellRanger count (version 1.3.0) was used to generate raw UMI count matrices for each sample separately, mapping to the TAIR10 A. thaliana genome masked for Cvi-0 SNPs with the STAR options --alignIntronMin = 10 --alignIntronMax = 5000 --scoreDelOpen = −1 --scoreDelBase = −1 --scoreInsOpen = −1 --scoreInsBase = −1 and using the Araport11 AtRTD2 gene transfer format (GTF) file.

Single cells for the first replicate, containing Cvi-0 and Col-0 cells, were genotyped by first counting the UMIs containing Col-0 or Cvi-0 SNPs for each cell barcode, followed by density-based clustering with DBSCAN. These steps are included in the sctools package ( https://github.com/timoast/sctools; version 0.7). The clustering parameters were optimized for each sample: ϵ background = 0.5 and ϵ margin = 0.3 for 12 h replicate 1; ϵ background = 0.4 and ϵ margin = 0.3 for 24 h replicate 1; and ϵ background = 0.4 and ϵ margin = 0.15 for 48 h replicate 1. Only cells genotyped as Col-0 were retained for subsequent biological analysis.

We applied emptyDrops74 from the DropletUtils package (version 1.6.1) following the guide to distinguish real cells. Further quality control was performed using scater (version 1.14.6)75 to remove cells with: (1) more than three median absolute deviations of the log ₁₀ read counts below the median values; and (2) more than three median absolute deviations of the log ₁₀ genes detected below the median. Then, calculateAverage was used to remove low-abundance genes with an average count below 0.

Pseudobulk expression by time point was calculated with Seurat's AggregateExpression function. The correlation of normalized log[counts per million] between pseudobulk and non-protoplasted bulk controls was calculated with the Pearson method, with and without removing protoplast-responsive genes.

Genes induced during protoplast isolation were removed before applying the normalization method calculateSumFactors with pool-based size factors used from scran (version 1.10.2)76. Highly variable genes were selected by modelGeneVar and getTopHVGs with the biological variance threshold set to 0. FastMNN77 was then performed using highly variable genes to integrate datasets from each sample. Mutual nearest neighbours dimension reductions were applied to construct a shared nearest neighbour graph with the function provided in scran, and the Louvain algorithm from igraph (version 1.2.2)78 was followed to group cells into clusters. Mutual nearest neighbours dimension reductions were also applied to generate a two-dimensional uniform manifold approximation and projection for visualization.

Cluster and cell type annotation

To identify cluster-enriched genes, genes upregulated in each cluster were calculated using FindMarkers from Seurat (4.0.5) with the P value < 0.01 (ref. 79) (Supplementary Table 2). The cell type of each cluster was manually annotated and assigned using known cell type marker genes from the literature (Supplementary Table 3). Well-characterized endosperm and seed coat marker genes were included to show exclusion of these two tissues and enrichment of embryos in the current data.

Comparison with bulk RNA-seq data

We also compared the scRNA-seq data with the published time series sequencing bulk RNA-seq profiles of seed germination13. The samples used for bulk RNA-seq were collected after 48 h of dark stratification, followed by 1, 6, 12 and 24 h of exposure to continuous light. Differential expression analysis between samples from a specific time point to others was performed with the limma package (version 3.38.3)80. With the design matrix defined as model.matrix (0 + time), precision weights were calculated by voom81 and used in eBayes for statistical testing. Contrasts were made between data from every time point to the average of data from other time points. Genes that were differentially expressed with the log ₂[fold change] above 1.5 and FDR < 5% were retained as differentially expressed genes. We then filtered out genes that were regarded as differentially expressed genes at more than one time point. Then, the lists of unique differentially expressed genes of individual time points were used in the scRNA-seq data to calculate their average expression in each of the cells with the function AddModuleScore82.

RNA in situ hybridization

Seeds were harvested and fixed in ice-cold Farmer's fixative (3:1 ethanol:acetic acid). The samples were placed in a cold room (4° C) overnight. The following day, the fixed tissues were dehydrated using a Leica TP1020 Semi-Enclosed Benchtop Tissue Processor (Leica Biosystems) at room temperature in a graded series of ethanol (1 h each at 75, 85, 100, 100 and 100% vol/vol). The tissues were then transferred to a graduated ethanol:xylene series (1 h 20 min each at 75%:25%, 50%:50% and 25%:75% vol/vol), finished with a xylene series (1 h each at 100 and 100% vol/vol). Tissue was then added to molten Surgipath Paraplast Paraffin (Leica Biosystems) twice for 2 h each at 65° C. Paraplast blocks were then prepared with dozens of seeds in each block using the Leica EG1150 H Heated Paraffin Embedding Module with the added Leica EG1150 C Cold Plate for Modular Tissue Embedding System (Leica Biosystems) with vacuum infiltration. Embedded tissues were cut at 8 µm sections and in situ hybridization was carried out according to a modified protocol from Jackson83: 50° C hybridization temperature and 0.2× saline-sodium citrate (SSC) washes. Transcripts of interest were amplified using designed primers (Supplementary Table 5) and cloned into pGEM-T Easy vector (Promega). Digoxigenin-labelled antisense and sense RNA probes were transcribed from the T7 or SP6 promoter of pGEM-T Easy vector (Promega) according to the manufacturer's instructions. All hybridization results were observed and photographed using a Zeiss Axio Observer A1 microscope (Carl Zeiss). Representative images are shown with more than ten embryos examined.

Trajectory inference analysis

Monocle 3 (1.0.0)84 was used to construct single-cell trajectories. Cells from annotated hypocotyl clusters were extracted and reprocessed (including normalization and batch effect correction77, dimensionality reduction and clustering) with Monocle 3. This resulted in three distinct partitions and we learned the trajectory for each of the partitions. We selected the beginning nodes of the trajectory where more adjacent cells come from 12 h. Modules of co-regulated genes were then calculated using Louvain community analysis based on genes with the function find_gene_modules. Aggregated expression of all genes in each module across cells along pseudotime was plotted in a heatmap. After grouping modules based on their expression pattern according to the pseudotime stage, we assessed enriched Gene Ontology terms using all genes from each stage. Gene Ontology analysis was performed using the topGo package (version 2.34.0)85.

Inferring transcription factor activation order with CSHMM-TF

CSHMM-TF (commit 417bd71; running Python 2.7)42 was used for analysis of time series single-cell expression data with information about transcription factors (transcription factor binding data from ref. 43). Cells from each cluster were extracted separately and their raw count matrix and information about their collected time were used as input. find_gene_modules was used to find gene modules across individual clusters. Aggregated expression was then calculated based on assigned time blocks of activation along the learned path by CSHMM-TF. Gene Ontology analysis was performed in each module of genes separately and the results are shown in dot plots, with size representing the ratio of provided genes to all genes in a specific term and colour representing adjusted enrichment P  values.

Gene Ontology analysis

Gene Ontology analysis was performed using the topGO package85. Whole gene sets without protoplasting genes were used as the background. Adjusted P values of gene enrichment were obtained from multiple P  values generated from topGO with the number of tests run in each enrichment analysis.

Characterization and bulk RNA-seq of T-DNA lines

T-DNA lines86 were genotyped by PCR with gene-specific primers designed using T-DNA express ( http://signal.salk.edu/cgi-bin/tdnaexpress) and T-DNA primers for the different mutant collections87,88 (Supplementary Table 15). Target gene expression in T-DNA mutants was validated by qRT-PCR (Supplementary Table 15). The germination rates of homozygous mutant lines were then assessed after 24 h in light. Seeds from two individual parent plants of each mutant line were included. Endosperm rupture was counted in four biological replicates of 50 seeds. Seeds of mutant lines and the Col-0 wild type were then collected at 24 h for RNA extraction using the Spectrum Plant Total RNA Kit (Sigma-Aldrich), followed by bulk RNA-seq library preparation and analysis.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Acknowledgements

Work in M.G.L.'s laboratory is funded by the Australian Research Council (ARC) Discovery Program (grant DP220102840). M.G.L. and R.L. are funded by ARC CE230100015. Q.G. is funded by a National Health and Medical Research Council Investigator Grant (GNT2007996). J.W. and L.C.L. were funded by the ARC Discovery Program (grant DP210103258). This study was supported by the La Trobe University Genomics Platform. This work was also supported by National Health and Medical Research Council Investigator Grant GNT1178460 and ARC grants DP210104058, LE170100225 and CE140100008 (all to R.L.). L.A. was supported by a La Trobe University Graduate Research Scholarship and a La Trobe University Full Fee Research Scholarship. We thank B. Wakely and O. Nath for expertise and assistance with building the interactive web browser.

Funding

Open access funding provided by La Trobe University.

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Author notes

These authors contributed equally: Lim Chee Liew, Yue You.

Authors and Affiliations

1. La Trobe Institute for Sustainable Agriculture and Food, AgriBio, La Trobe University, Melbourne, Victoria, Australia

Lim Chee Liew, Lucas Auroux, Marta Peirats-Llobet, Sophia Ng, Muluneh Tamiru-Oli, Oliver Berkowitz, Uyen Vu Thuy Hong, Asha Haslem, James Whelan, Quentin Gouil & Mathew G. Lewsey

2. Epigenetics and Development Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia

Yue You, Matthew E. Ritchie & Quentin Gouil

3. Department of Medical Biology, The University of Melbourne, Melbourne, Victoria, Australia

Yue You, Matthew E. Ritchie & Quentin Gouil

4. Australian Research Council Centre of Excellence in Plant Energy Biology, School of Molecular Sciences, The University of Western Australia, Perth, Western Australia, Australia

Marina Oliva, Tim Stuart & Ryan Lister

5. Australian Research Council Centre of Excellence in Plants for Space, School of Molecular Sciences, The University of Western Australia, Perth, Western Australia, Australia

Marina Oliva & Ryan Lister

6. Australian Research Council Research Hub for Medicinal Agriculture, AgriBio, La Trobe University, Melbourne, Victoria, Australia

Sophia Ng, Muluneh Tamiru-Oli, Oliver Berkowitz, Uyen Vu Thuy Hong, James Whelan & Mathew G. Lewsey

7. Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore

Tim Stuart

8. School of Life Sciences, University of Warwick, Coventry, UK

George W. Bassel

9. Harry Perkins Institute of Medical Research, Queen Elizabeth II Medical Centre and Centre for Medical Research, The University of Western Australia, Perth, Western Australia, Australia

Ryan Lister

10. Australian Research Council Centre of Excellence in Plant Energy Biology, AgriBio Building, La Trobe University, Melbourne, Victoria, Australia

James Whelan

11. College of Life Sciences, Zhejiang University, Hangzhou, People's Republic of China

James Whelan

12. Australian Research Council Centre of Excellence in Plants for Space, AgriBio, La Trobe University, Melbourne, Victoria, Australia

Mathew G. Lewsey

Contributions

L.C.L., J.W., Q.G. and M.G.L. conceived of and planned the project. R.L., T.S. and M.O. provided novel methods and reagents. L.C.L., L.A., M.O., M.P.-L., S.N., M.T.-O., O.B. and U.V.T.H. contributed to sample preparation, protoplast isolation and RNA in situ hybridization. L.C.L., A.H. and T.S. carried out library preparation. L.C.L., Y.Y., L.A., M.E.R. and Q.G. processed and analysed the data. L.C.L., Y.Y., L.A., Q.G., M.G.L. and G.W.B. interpreted the results and drafted the manuscript. R.L., M.E.R. and J.W. provided critical feedback on data interpretation. All authors reviewed and contributed to the final manuscript.

Corresponding authors

Correspondence to James Whelan, Quentin Gouil or Mathew G. Lewsey.

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Liew, L.C., You, Y., Auroux, L. et al. Establishment of single-cell transcriptional states during seed germination. Nat. Plants 10, 1418-1434 (2024). https://doi.org/10.1038/s41477-024-01771-3
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DOI: https://doi.org/10.1038/s41477-024-01771-3

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Establishment of single-cell transcriptional states during seed germination - Nature Plants

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Germination involves highly dynamic transcriptional programs as the cells of seeds reactivate and express the functions necessary for establishment in the environment. Individual cell types have distinct roles within the embryo, so must therefore have cell type-specific gene expression and gene regulatory networks.

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Introduction

Increases in temperature and carbon dioxide and changes in precipitation over the 21st century pose a threat to agricultural productivity in the coming decades (Batley and Edwards 2016; Vogel et al. 2019; Wing et al. 2021). Climate change will negatively affect global food supplies, so research on improving agricultural output under deteriorating climate conditions is necessary to ensure global food security. Despite traditional breeding efforts, certain stages of the crop life cycle remain particularly sensitive to climate factors, including flowering, pollination, seed development, germination, and seedling growth. Due to reduced sequencing and genotyping costs, genomics and advanced phenotyping are transforming breeding strategies and the development of new cultivars of resilient crops (Edwards and Batley 2010; Voss-Fels and Snowdon 2016). For the majority of crops, seeds are the delivery system for transferring advanced genetics into the production field. In particular, rapid and synchronous seed germination and seedling growth are particularly important to agricultural output because they are essential for the establishment of seedlings in the field. Here, we discuss how climate change is predicted to affect seed germination and vigor and how recent advances in understanding these processes can be applied to enable high agricultural productivity under these changing conditions. In this review, we cover recent advances in identifying mechanisms that would be amenable to use for selection for improved seed vigor.

Seed vigor is a complex trait that encompasses aging tolerance, seed dormancy, viability, rapid germination, and seedling establishment, especially in suboptimal conditions. As seeds age, they progressively lose their vigor and become increasingly sensitive to stress upon germination, which occurs between imbibition and the emergence of the radicle from the seed (Bewley et al. 2013). Though in many seeds time is required to attain competency for germination in a dry after-ripening process (Fig. 1), seeds experiencing prolonged aging eventually lose the capacity to germinate (i.e., seed viability) entirely. Mechanistically, seed aging results from damage to cellular processes (Zinsmeister et al. 2020). It largely depends upon environmental storage conditions, seed genetics, and the maternal environment (Bewley et al. 2013). Although aging reduces viability, seeds are generally more resistant to environmental stressors than seedlings. To minimize both aging and the exposure of seedlings to environmental stressors, seeds must regulate germination carefully. Seed dormancy is responsible for defining the environmental conditions in which a seed can germinate (Finch-Savage and Leubner-Metzger 2006). Depth of seed dormancy determines the timing of germination, and seeds cannot germinate until dormancy is alleviated (Fig. 1). Seed viability is the percentage of seeds producing normal seedlings in an ideal growing environment, assuming removal of dormancy before testing (Basra 1995). Viability provides a user-friendly guide to growers looking to assess seed lot quality. Environmental stressors at any time from seed development to seedling establishment can reduce stand density, increase the variation in time to harvest, and reduce harvestable crop yield. Seed vigor, therefore, encompasses all of these components in the context of environment and seed genetics to assess the performance of a seed lot.

During seed development, genotype and environment influence the biosynthesis of abscisic acid (ABA) in the seed, inducing differing depths of primary dormancy. Dormancy is alleviated in these seeds with light, temperature, after-ripening, or chilling. Different genotypes or environmental conditions during seed filling can cause less ABA accumulation in the seed, leading to non-dormant seeds and removing the need for breaking the primary dormancy. Non-dormant seeds in conditions meeting their water and temperature requirements for germination then shift their gibberellin (GA) to ABA ratio higher, promoting germination. Seeds in which primary dormancy has been removed or non-dormant seeds under temperature extremes, anoxia, light conditions, or aging stress can experience relative dormancy, and with extended time in these conditions, can induce secondary dormancy. Secondary dormancy and relative dormancy can be alleviated with time under proper light, temperature, after-ripening, and/or chilling conditions.

Dormancy and germination

Dormancy is an adaptive trait in wild plants for avoiding germination under conditions that would not be conducive to the survival of the seedlings (Klupczyńska and Pawłowski 2021). Dormancy can be primary, in which seeds at maturity require specific conditions, such as moist chilling or dry after-ripening, before they become capable of germinating, or secondary, in which non-dormant seeds in unfavorable environmental conditions revert to a state of dormancy (Fig. 1). Primary dormancy can be imposed through physical features (e.g. water-resistant seed coats in many pulses, which prevent hydration of zygotic tissues) (Bolingue et al. 2010), and physiologically, through hormones (Bewley et al. 2013). In crops, residual primary dormancy at sowing time can reduce germination percentages and rates (inverse of times to germination), which in turn affects stand uniformity. In bulk-harvested crops, stand uniformity is essential, because plants with delayed development may not produce a harvestable product. Therefore, domestication has selected against crop seed dormancy in many cases, favoring faster germination and an increased range of threshold temperatures and water potentials of crop species (Durr et al. 2015). Breeding programs have benefitted from investigations into the genetic networks underlying dormancy, especially when considering dormancy induced by temperature extremes; however, complete removal of dormancy is detrimental to agriculture, since this can result in pre-harvest sprouting. Pre-harvest sprouting occurs when untimely rain before harvest causes seeds to germinate before being shed from the mother plant and is due to insufficient dormancy. Pre-harvest sprouting drastically reduces seed quality and longevity (Gualano et al. 2014; Soltani et al. 2021), where longevity is defined as seed viability after dry storage (Bewley et al. 2013). Pre-harvest sprouting and its underlying genetics and regulation have been reviewed elsewhere (Rodríguez et al. 2015; Vetch et al. 2019).

The process of germination involves bidirectional interactions between the embryo and the endosperm, with the endosperm acting as an environmental sensor that regulates the growth of the embryo and the embryo controlling the degradation of the endosperm (Yan et al. 2014). Additionally, multiple interacting physical and hormonal factors control germination (Chahtane et al. 2017). Regulation of xyloglucan biosynthesis in the embryo and endosperm plays a key role in endosperm weakening, allowing germination to occur (Nonogaki 2019). Additionally, the cutin coat that lies between the endosperm and the testa negatively influences germination, while the sheath between the endosperm and the embryo facilitates germination (Nonogaki 2019). Since the embryo, endosperm, and maternal tissues have different genetic compositions that contribute to both the physical and genetic basis of dormancy, it is important to consider tissue-level differences when searching for genes associated with germination and dormancy; however, most quantitative trait locus (QTL) mapping studies ignore these tissue-level distinctions. Despite this shortcoming, work to isolate the impact of each of these tissues on dormancy has been conducted in rice (Gu et al. 2015). The researchers were able to isolate three seed dormancy loci, each one unique to either embryo, endosperm, or maternal tissues. The QTL associated with endosperm-imposed dormancy had an additive effect on germination and contained OsGA20ox2, a gibberellin synthesis gene whose expression in the endosperm is believed to control primary dormancy through a gibberellin-regulated mechanism associated with the timing of dehydration (Supplementary Table S1; Ye et al. 2015).

Hormonal control of dormancy and germination

The transition from dormancy to competency for germination is dictated by the balance between abscisic acid (ABA) and gibberellin (GA) levels in the seed, with a lower ABA/GA ratio required for alleviating dormancy and permitting germination (Fig. 1). These hormones are mutually antagonistic, with each downregulating the other's metabolism (Fig. 2; Bewley et al. 2013). While the mechanism controlling the balance between ABA and GA and the role of this balance in seed germination remains to be fully elucidated, recent work has suggested the presence of an ABA repressor complex (Nonogaki and Zhang 2020). Such a complex could initiate germination through coordinated suppression of ABA signaling. Weighted gene correlation network analysis has shown that the genetic regulation of the ABA/GA ratio contains numerous genes involved in seed vigor that are expressed in the endosperm of tomato seeds (Bizouerne et al. 2021).

Abscisic acid (ABA) maintains seed dormancy through a gene expression network of ABSCISIC ACID INSENSITIVE (ABI) transcription factors. Gibberellins (GA) promote seed germination through GA signaling pathway genes like SLY1 and GID1. Rice GID2 is orthologous to Arabidopsis SLY1. Drought stress and high-temperature conditions can induce ABA biosynthesis, leading to elevated dormancy. Low temperatures promote ABI3 expression and downregulate GA biosynthesis through DOG1. Auxin signaling, which is under miR160 regulation through ARFs, leads to the biosynthesis of ABA and the promotion of ABA signaling genes. MiRNAs such as miR9678 in wheat can play roles in maintaining hormonal balance. DOG1 DELAY OF GERMINATION 1, NCED 9-CIS-EPOXYCAROTENOID DIOXYGENASE, ARF AUXIN RESPONSE FACTOR, SLY1 SLEEPY1, GID1 GIBBERELLIN INSENSITIVE DWARF1, GA-ox GA OXIDASE.

Response to the seed production environment involves numerous overlapping networks of genes in both maternal and zygotic tissues (Penfield and MacGregor 2017). Environmental factors associated with climate change affect the network of genes and hormones controlling germination in grains such as rice (Liu et al. 2019; Suriyasak et al. 2020) and wheat (Izydorczyk et al. 2018) and vegetables such as lettuce (Huo et al. 2013) and tomato (Geshnizjani et al. 2018). Temperature is a major environmental factor that affects the degree of dormancy, with even 1 °C difference in sensitive ranges capable of determining dormancy (Springthorpe and Penfield 2015). Dormancy is commonly greater in seeds that develop at temperature extremes (Penfield and MacGregor 2017; Toh et al. 2008). In rice, researchers have shown that high temperatures and drought stress cause ABA accumulation through the induced expression of 9-CIS-EPOXYCAROTENOID DIOXYGENASEs ( NCEDs) (Liu et al. 2019; Suriyasak et al. 2020), which mediate seed dormancy, plant growth, abiotic stress tolerance, and leaf senescence (Huang et al. 2018). In many plants and tissues, ABA biosynthesis is rate-limited by NCED enzymes (Nambara and Marion-Poll 2005). As such, this class of genes presents numerous candidates for manipulating temperature-induced dormancy to maximize agricultural yield in a changing climate. In Arabidopsis, NCED genes and their functions are well-characterized. Five NCED genes ( AtNCED2/3/5/6/9) are likely involved in ABA biosynthesis (Lefebvre et al. 2006). AtNCED6 is expressed specifically in the endosperm (Lefebvre et al. 2006), and its induction during seed development is sufficient for increasing dormancy (Martínez-Andújar et al. 2011). AtNCED3 and AtNCED5 are drought-inducible ABA biosynthesis genes important for regulating ABA levels during water stress (Tan et al. 2003; Ruggiero et al. 2004; Frey et al. 2012). AtNCED5, AtNCED6, and AtNCED9 synthesize ABA in the embryo and endosperm during the onset of primary dormancy (Lefebvre et al. 2006; Seo et al. 2006; Martínez-Andújar et al. 2011; Frey et al. 2012). ABA produced in the endosperm can be transported into the embryo: export of ABA from the endosperm is controlled by ATP-BINDING CASSETTE TRANSPORTERS, SUBFAMILY G ( ABCG) genes AtABCG25 and AtABCG31, and import into the embryo is controlled by AtABCG30 and AtABCG40 (Kang et al. 2015). AtNCED2, AtNCED5, and AtNCED9 are induced by high temperature in imbibed seeds, and mutating them leads to a loss of thermoinhibition (Seo et al. 2006; Toh et al. 2008).

Because each NCED gene has an overlapping yet distinct function, manipulating these genes and their promoters may permit fine-tuning seed dormancy to specific field conditions (Frey et al. 2012). However, specific NCEDs are not highly conserved between crops. As such, it is difficult to identify which NCEDs to target for alleviating crop dormancy and improving germination without first characterizing these genes. One tool that has proven useful for this task is QTL mapping. For example, using QTL mapping, researchers have investigated the NCED gene family as potential targets for mitigating heat-induced dormancy in lettuce, a species highly susceptible to thermoinhibition (Huo and Bradford 2015). By using a recombinant inbred line (RIL) population from a thermotolerant lettuce cultivar and a heat-susceptible cultivar, the researchers found a QTL in which they identified LsNCED4 (Argyris et al. 2011). Gain or loss of function of LsNCED4 can mediate seed thermoinhibition (Huo et al. 2013; Bertier et al. 2018).

In addition to ABA synthesis genes, ABA response genes play key roles in seed dormancy. ABSCISIC ACID-INSENSITIVE (ABI) genes are primarily responsible for carrying the ABA signal through to the dormancy phenotype (Fig. 2), and abi mutant plants can have a significantly reduced response to ABA treatment (Söderman et al. 2000). ABI3, ABI4, and ABI5 are key ABA-related transcription factors promoting seed dormancy, and expression of ABI3, ABI4, and ABI5 is higher in dormant seeds than in non-dormant seeds (Shu et al. 2013; Huang et al. 2017; Skubacz and Daszkowska‐Golec 2017). A recent study in tomato showed that ABI4 could be associated with the acquisition of seed vigor in the embryo (Bizouerne et al. 2021). ABI5, a transcription factor shown to enhance dormancy in Arabidopsis (Wu et al. 2015), is important in downregulating PHOSPHATE1, a gene involved in reducing sensitivity to ABA (Huang et al. 2017).

While ABA and GA are the primary regulators of seed dormancy and germination, other hormones can impact the expression of key genes in a complex regulatory network. For example, exogenous auxin application in soybean represses germination by increasing the ABA/GA ratio (Fig. 2; Shuai et al. 2017). Furthermore, overexpressing the auxin signaling down-regulator microRNA 160 (miR160) or mutating auxin receptors or auxin biosynthesis genes releases seed dormancy (Liu et al. 2013). This dormancy release occurs through stimulation of ABI3 expression, and AUXIN RESPONSE FACTOR 10 and 16, which are auxin signaling genes regulated by miR160, are required for the expression of ABI3 in Arabidopsis (Fig. 2; Liu et al. 2013). ABA/GA signaling can also be affected by microRNAs. MiR9678, a miRNA specifically expressed in scutellum tissue of developing seeds, can affect seed germination: overexpression of miR9678 in wheat leads to delayed germination by reducing GA level, and miR9678 silencing improves germination (Fig. 2; Guo et al. 2018).

DELAY OF GERMINATION 1 ( DOG1) is a key seed-specific regulator for the ABA and GA cross-talk that represses GA biosynthesis (Bentsink et al. 2006; Née et al. 2017) and determines the initial depth of seed dormancy (Footitt et al. 2020). DOG1 is responsive to temperature, allowing it to interface environmental signals with the ABA/GA network (Fig. 2). It is upregulated in response to cold (Kendall et al. 2011) and therefore may impact the germination of seeds planted in springtime. DOG1 also acts as a link between ABA and GA for crosstalk between ABI5 and ABI3 (Dekkers et al. 2016) and GA biosynthesis genes (Graeber et al. 2014), likely mediating the balance between these two hormones to allow a coordinated hormonal shift during dormancy alleviation and germination (Fig. 2). DOG1 also influences the levels of miR156 and miR172 that regulate the plant life cycle progression, coordinating seed dormancy and flowering phenotypes with environmental factors (Huo et al. 2016). The researchers showed that overexpression of miR172 promotes early flowering and reduces seed dormancy in Arabidopsis and that this effect requires functional DOG1. Because DOG1 determines the initial depth of seed dormancy, allelic variants can provide species growing across multiple climates the capacity to adapt dormancy and germination timing to local conditions (Kerdaffrec et al. 2016; Martínez-Berdeja et al. 2020). Orthologs of DOG1 are common to many plant species. Researchers have investigated DOG1-Like ( DOG1L) genes in cereals and identified 5 DOG1L groups, four of which are functionally orthologous to AtDOG1 (Ashikawa et al. 2013). The numerous DOG1L orthologs common in many species may provide a library of possible genes for manipulating seed dormancy through transgenic approaches. This tactic has been employed in wheat using Triticeae DOG1L genes (Ashikawa et al. 2014).

Seed vigor

Defining seed vigor

While seed viability tests are user-friendly and helpful for growers looking for a rapid assessment of seed lot quality, viability is a poor predictor of a seed lot's performance in field conditions. This is because, in an ideal growing environment, seeds of the same cultivar from different sources or production environments may have similarly high germination percentages and therefore high levels of seedling establishment; however, when placed in stressful environments like the variable conditions common in the field, these same seeds can have drastically different capacities for establishing into healthy seedlings. This capacity to germinate and emerge quickly, particularly under stressful conditions, has been termed "seed vigor". Seed vigor is a complex agronomic trait that includes the performance in the field as well as seed storage history. The International Seed Testing Association (ISTA) has defined seed vigor as the sum of those properties that determine the activity and performance of seed lots of acceptable germination in a wide range of environments (Seed Vigour Testing 2021). This definition does not interpret seed vigor to be a single quantifiable property, but rather a trait that includes the seed's ability to still germinate after storage and under adverse conditions, in addition to the seedlings' ability to grow normally and uniformly. Therefore, seed vigor describes the performance potential of viable seeds in an agricultural context. Because seed vigor is based upon complex interactions between genes and the environment, it can be modified by numerous environmental factors (e.g., soil water availability, temperature, developmental stage at harvest, and harvest and storage conditions (Figs. 3 and 4; Sun et al. 2007). Some environmental stressors are common to agricultural seedbeds: for example, after sowing, soil conditions tend to deteriorate with time as moisture recedes and soil strength increases. Seeds that are genetically optimized for these common field environment conditions, and have been handled and stored properly, have higher vigor. High vigor seeds, especially in small-seeded crops, implement a stress avoidance strategy in deteriorating soil conditions through rapid seedling establishment while soil conditions are adequate. The success of this approach depends on fast germination upon sowing, rapid root growth to reach receding moisture, and upward shoot growth through the soil to reach the surface (Finch-Savage et al. 2010).

Drought stress or heat stress during grain filling is expected to reduce seed vigor. Drought and heat stress during seed storage, seed germination, and early seedling growth will also lead to further deterioration of seed vigor. Additionally, flooding during germination can reduce seed vigor.

Stressors associated with climate change such as temperature and moisture extremes reduce seed vigor. If these stressors are present during seed filling, they can lead to seed variation and deep primary dormancy. During storage, they can cause residual dormancy and excess aging and during germination, they lead to thermoinhibition and delayed/variable germination. Each of these effects can reduce the viability and performance of a seed lot, reducing yield. Breeding for climate-resilient cultivars is likely to improve seed vigor in the face of climate change stressors. Manipulating genetic factors associated with dormancy provides an avenue for breeding climate change-resilient crops and may be used to directly alleviate deep primary dormancy, residual dormancy, and thermoinhibition. Manipulation of genes associated with seed longevity such as certain annexins and miRNAs can reduce excess aging. Seed priming can help remove residual dormancy and thermoinhibition and can improve the speed and uniformity of seedling establishment. Improved seed testing will better inform farmers, allowing them to more accurately predict the field performance of a seed lot and plan sowing density accordingly.

Measuring seed vigor

Historically, seed vigor has been difficult to quantify. While no single test has been developed to measure all aspects of seed vigor, and few tests apply to all crops, the performance potential of seeds can be determined in several manners. One common approach for assessing seed vigor is to first subject seeds to rapid aging under controlled conditions by elevating the temperature and seed moisture content. Differences in vigor between seed lots are enhanced under these conditions and can be detected through a standard germination test (Finch-Savage and Bassel 2016). The difference in seed germination over accelerated aging (AA) timepoints can be used to fit a survival curve and calculate a viability constant useful for assessing seed lot aging dynamics (Ellis and Roberts 1980). This approach is useful in small-seeded crops such as lettuce (Kraak and Vos 1987). Additionally, because AA reduces seed vigor, studies have used AA to manipulate the vigor of a seed lot (Baek et al. 2018). Marcos-Filho ( 2015) describes this and other seed vigor tests in detail. However, results at elevated moisture contents can differ from those at lower moisture contents (Schwember and Bradford 2010), and for prediction of seed storage life, moisture contents lower than those in equilibrium with 60% relative humidity (RH) are recommended (Walters et al. 2020). At these moisture contents, elevated temperatures (up to 60 °C) can be used to speed aging. Accelerated aging at higher RH, such as the test at 100% RH and (41-45 °C) approved by ISTA for soybean (TeKrony 2005), can rapidly reduce seed vigor and viability, but the mechanism of aging is distinct from that occurring during seed storage conditions. Recently, advancement for assessing viability and vigor using computerized image analysis has proven efficient in numerous crops including soybean, (Lee et al. 2017), carrot (De Marchi, Cicero ( 2017)), maize (Castan et al. 2018), onion (Gonçalves et al. 2017) and wheat (Fan et al. 2020). Additionally, Brassica oleracea is a species susceptible to "blindness," which causes failure at the growing seedling apex early in seedling development, and researchers are developing tools for predicting susceptibility to this vigor-reducing condition by combining multispectral imaging, chlorophyll fluorescence, and oxygen consumption on a seed-by-seed basis (Bello and Bradford 2021). The development of rapid screening and sorting of high vigor seeds may provide a tool for ensuring that only the highest quality seeds are used for sowing (Nehoshtan et al. 2021; Seed-X 2021).

The seed germination trait most closely associated with vigor is the speed of germination (Wheeler and Ellis 1992; Bradford et al. 1993) and has long been recognized as a key trait for seed vigor testing (McDonald 1975). Vigorous seeds have high germination rates ( GR), or the inverse of the time to radicle emergence of a specific percentage ( GR g or 1/ t g), and the values of GR g decline as seeds age and vigor decreases. Recently, GR has informed genetic analyses of seed vigor in small, direct-seeded crops such as Brassica oleracea (Bettey et al. 2000) and rice (Guo et al. 2019; Yang et al. 2021), as well as large-seeded crops such as field pea (Lamichaney, Parihar, et al. 2021). Unfortunately, this measure of seed vigor is underutilized as it requires repeated observations of germination percentages at frequent intervals, which is too labor-intensive for routine seed testing. Early counts of normal seedlings are included in ISTA rules and represent at least one point during the germination time course indicating the speed of germination (Matthews et al. 2011; Ilbi et al. 2020). Seedling growth tests are also often used as indicators of seed vigor. However, seedling growth rates after radicle emergence are often independent of germination timing (Tarquis and Bradford 1992), so differences in seedling size at a specific time after imbibition are primarily due to the differences among seeds in their times to germination, as seedlings from earlier germinating seeds have longer times to grow until the end of the test (Matthews and Khajeh-Hosseini 2007). It has also been proposed that the incidence of abnormal seedlings can be deduced from analysis of germination time courses (Bradford et al. 1993), as the lag period before germination commences indicates the extent of repair processes due to damage sustained during aging (Matthews and Khajeh-Hosseini 2007). Thus, assessing germination rates would be a single vigor index that could largely replace other labor-intensive and more subjective vigor assays. Automated imaging methods enabling repeated observations (Matthews and Powell 2011; Colmer et al. 2020) or measurements correlated with radicle emergence such as single-seed respiration rates (Bello and Bradford 2016) will likely enable wider use of seed germination time courses and of GR as a broad purpose seed vigor index soon.

Seed dormancy and vigor overlap

Seed dormancy functions to ensure germination occurs only during the right season that will allow the successful seedling establishment and thereby survival of the species (Chahtane et al. 2017). It is induced in the freshly produced seeds during the maturation process on the mother plant (Finch-Savage and Leubner-Metzger 2006). Dormancy can be released by a period of dry storage or after-ripening, most likely by the accumulation and action of reactive oxygen species which can degrade germination-inhibiting molecules like lipids, mRNAs, and proteins (El-Maarouf-Bouteau et al. 2013). Most orthodox seeds possess remarkable desiccation tolerance and can retain viability for considerable periods, even for centuries in some instances (Walters et al. 2010; Rajjou et al. 2012). Though after-ripening may be required to break primary dormancy and allow germination, prolonged aging also leads to seed deterioration and loss of viability due to oxidative damage (Schwember and Bradford 2011; Groot et al. 2012; Morscher et al. 2015). Prolonged aging can lead to delay and failure of germination, and seeds also become more sensitive to stresses during germination, resulting in poor seedling development and establishment (Roberts and Ellis 1989; De Vitis et al. 2020). Oxidation during storage plays a major role in increasing the rate of aging as seeds stored in anoxia retain viability longer (Schwember and Bradford 2011; Groot et al. 2015). Aging-driven seed longevity is also affected by the temperature and moisture content of the seeds (Roberts and Ellis 1989; De Vitis et al. 2020). Seed vigor measurements include testing for traits such as seed longevity, germination capacity, and early stress tolerance. These parameters indicate the ability of seeds to germinate and result in seedlings that can give rise to a healthy and vigorous plant. Thus, it is pertinent to stress the fact that seed dormancy and vigor represent aspects of a physiological continuum that begins on the mother plant during seed maturation, reaches peak vigor after the loss of primary dormancy, and ends eventually with the loss of seed viability.

Environmental factors affecting seed vigor

The environment directly affects seed vigor throughout seed development, storage, and pre-seedling emergence (Fig. 3). For example, high-temperature stress during seed filling reduces the germination and vigor of soybean (Egli et al. 2005) and field pea (Lamichaney, Parihar, et al. 2021). Temperature stress during seed filling can reduce seed vigor of hybrid rice, possibly by affecting starch accumulation and structure (Wang et al. 2020). High-temperature and humidity stress during soybean seed development reduce seed vigor by negatively impacting key signaling pathways (e.g., ABA-mediated, MAPK, G protein-mediated, calcium-mediated, and phosphatidylinositol), metabolic pathways, plant physiology, and biochemistry, and high seed vigor can be maintained under high-temperature and humidity conditions in part by enhancing protein synthesis and nutrient storage in the cotyledons (Wei et al. 2020). Heat stress also impacts seeds in storage (Fig. 3). For example, oat seeds stored at 50 °C versus 35 °C for less than 2 days had significantly decreased seed vigor, and subsequent proteomic analysis showed that heat-responsive protein species and mitochondrial respiration were sensitive to heat stress (Chen et al. 2016).

Like high temperatures, drought stress during seed development can severely diminish seed vigor (Fig. 3). Drought stress during seed filling reduces seed vigor in soybean (Dornbos et al. 1989; Samarah et al. 2009; Wijewardana et al. 2019) and barley (Samarah and Alqudah 2011). Crop seeds are also particularly susceptible to drought and salinity stress during germination, including maize (Khodarahmpour 2011; Liu et al. 2015), sunflower (Kaya et al. 2006) tomato (Ishola Esan et al. 2018), and cucumber (Bakhshandeh et al. 2021).

In many crops, seed vigor also declines rapidly in excess moisture. Because many crops have been bred for rapid imbibition upon sowing to hasten germination, they are particularly susceptible to flood damage, which limits the availability of oxygen (Fig. 3). This is a major issue, especially in pulses (Soltani et al. 2017), which are often grown as rainy season crops.

While the effects of other abiotic factors associated with climate change have been explored, there is relatively little published research on the effect of elevated CO ₂ levels on seed vigor. CO ₂ concentration does not significantly affect vigor during seed storage, since seeds are often stored in a CO ₂-rich environment for pest control, and this environment does not reduce seed quality (Shekar et al. 2018). The effects of elevated CO ₂ during seed filling are variable between species (Lamichaney and Maity 2021). One study showed reduced seed vigor at elevated CO ₂ concentrations (>610 ppm) in rice (Lamichaney et al. 2019). However, the susceptibility of seed vigor to elevated CO ₂ appears dependent on the species. For example, in a chickpea study, elevated CO ₂ concentrations (566-630 ppm) did not affect seed vigor (Lamichaney, Tewari, et al. 2021). Additional work is necessary to determine whether elevated CO ₂ levels could impact seed vigor more prominently in combination with other factors.

Seed vigor in the context of climate change

Traditionally, germination percentage has been used to determine the quality of a seed supply, and farmers select the time to sow their crops to maximize the growing season and the potential yield of their crop using this percentage to gauge the sowing density. However, because seeds must often be sown at suboptimal times for crop establishment, this germination percentage is not indicative of the actual seedling emergence under adverse conditions and can lead farmers to underestimate their seed requirements, leading to a loss in overall yield. Climate change is likely to worsen the conditions into which seeds must be sown in the field as farmers face temporal constraints for maximizing the growing season (Fig. 4). Therefore, seed vigor is becoming an increasingly essential agronomic trait in maintaining high seedling emergence. Additionally, seed germination under stressful conditions reduces the uniformity of establishment time within a seed lot, and this variation in time to establishment often leads to variation in the maturity of plants within a crop stand. Because stand uniformity and achieving expected plant populations per unit area are essential in obtaining a high yield in bulk-harvested crops, low vigor seeds can cause significant losses in overall harvestable yield. Climate change will likely increase the variability in seed establishment time by reducing vigor, leading to a lack of uniformity of the crop stand and losses in yield (Fig. 4). Reduction in seed vigor has been shown to detrimentally impact the uniformity, growth, development, and yield of crops such as soybean (Ebone et al. 2020). As a result, robust seeds with improved vigor are necessary for improving stand uniformity in the stressful environmental conditions encountered in the field, and improved seed vigor will therefore help to increase overall harvestable agricultural product. Successful stand establishment is also important for forming a crop canopy, which reduces weed growth and therefore herbicide usage. As such, seed vigor plays an important role in the yield and profitability of the crop and can help reduce grower costs.

Climate change is expected to have a more pronounced effect during the early stages of plant development-germination and seedling establishment-than in the adult stages (Lloret et al. 2004; Fernández-Pascual et al. 2015). Germination of imbibed seeds exposed to high temperatures is either inhibited (thermoinhibition) or prevented (thermodormancy). Thermoinhibited seeds can germinate immediately under favorable temperatures; however, thermodormancy induced by prolonged exposure to higher temperatures may not allow germination even when the temperatures are lowered. This phenomenon has been reported in several crop species like tomato (Geshnizjani et al. 2018), lettuce (Huo et al. 2013), barley (Leymarie et al. 2009), and several forest species including Pinus (Guo et al. 2020). With rising global temperatures and increasingly variable precipitation, more crops are expected to become susceptible to either thermoinhibition or thermodormancy during germination. Thus, research into understanding the genetic aspects of seed germination and vigor and characterizing the genes that can alleviate the effects of elevated temperatures and precipitation variability is critical for crop establishment and food security.

Current approaches to improve seed vigor

Seed priming/treating

One strategy that has proven effective in speeding germination is seed priming, which is a seed prehydration and drying treatment applied before sowing (Halmer 2004). Seed priming is now a widespread commercial practice to increase seed performance, primarily by reducing the time to germination and improving uniformity. Priming can also allow seeds to be sown earlier to maximize the growing season: seed priming has been reported to improve cold tolerance in maize primed with salicylic acid (Farooq et al. 2008) or chitosan (Guan et al. 2009), capsicum primed with thiourea, hydrogen peroxide (Yadav et al. 2011), and tobacco primed with putrescine (Xu et al. 2011). Seed priming has also proven useful in large-seeded crops that require time to imbibe before germination, especially in pulses (Arif et al. 2008). While seed priming can improve seedling establishment under stressful conditions, it can make seeds more susceptible to deterioration in storage, depending upon the storage conditions (Hill et al. 2007). Priming cannot compensate for poor seed vigor due to the genetic background of plants, and it should be used in coordination with seeds bred for high vigor to increase stand establishment under stressful conditions or uniformity under optimal conditions (e.g., for transplants).

Genetic dissection of seed vigor traits using linkage and association mapping

Genetic background is an essential determining factor in seed performance (Saux et al. 2020). However, because of its complexity, many aspects of seed vigor have remained elusive targets for those seeking to improve it through traditional breeding methods. Advances in genomics have sprung open the door to the improvement of seed vigor through QTL pyramiding and marker-assisted breeding.

In rice, researchers have identified and fine-mapped QTLs associated with seed vigor, providing tightly linked DNA markers for breeding (Supplementary Table S1). RILs have proven useful for identifying QTLs for seed vigor based on seed maturity stage at harvest (Liu et al. 2014; Lai et al. 2016), for seed longevity (Raquid et al. 2021), and early seedling vigor (Zhang et al. 2017). In coordination with genome-wide association studies (GWAS), RILs have helped identify specific candidate seed vigor genes (Guo et al. 2019). Differential gene expression analysis paired with QTL mapping provides an additional avenue for identifying candidate vigor genes (Yang et al. 2021). Recently, QTLs associated with vigor are also being identified in wild rice varieties and provide additional options for backcrossing seed vigor QTLs into domesticated cultivars for high yielding, high vigor rice (Jin et al. 2018).

Although most QTL studies for seed vigor have been conducted in rice, numerous studies have explored seed vigor in other cereals (Supplementary Table S1). In wheat, QTL mapping and candidate gene analysis have provided a basis for functional identification of related candidate genes for seed vigor (Shi et al. 2020). In maize, RILs have been used to identify seed vigor QTLs associated with seed maturity stage at harvest (Jing-bao et al. 2011), seed longevity (Han et al. 2018), tolerance of stressful germination conditions (Han et al. 2014), and after artificial aging (Wu et al. 2020). In barley and oat, QTLs have been paired with GWAS to identify candidate vigor genes (Thabet et al. 2018; Huang et al. 2020).

Relatively few QTL studies assessing seed vigor have been conducted outside cereal crops. However, some work has identified QTLs associated with seedling vigor in tomato (Supplementary Table S1; Khan et al. 2012) and Brassica rapa (Supplementary Table S1; Basnet et al. 2015). QTLs associated with low-temperature stress in rapeseed have also been identified (Luo et al. 2021). In common bean, a QTL analysis identified a locus associated with reduced physical seed dormancy in domesticated plants (Supplementary Table S1; Soltani et al. 2021). Numerous QTLs for longevity in soybean (Dargahi et al. 2014; Zhang et al. 2019), lettuce (Schwember and Bradford 2010), and rapeseed (Wang et al. 2018) provide a basis for breeding improved seed storage life of these crops.

Genetic engineering and gene editing

Now that significant progress has been made in the mapping of QTLs associated with seed vigor and the identification of the candidate genes involved, this data has begun to inform strategies for crop improvement. Outside of targeting seed dormancy, genome editing is an uncommon approach to improving the complex trait of seed vigor in part due to a large number of small effect loci. However, advances in understanding the genetic basis of seed vigor have proven effective at both exploring the function of specific candidate genes and for improving seed vigor. For example, among the proteins identified in a proteomic study of soybean was annexin, GmANN (Wei et al. 2020). Annexins are proteins important in plant stress responses, but they have not been found previously in developing seeds. The researchers transferred the GmANN gene to Arabidopsis, and the transgenic line had greater resistance and higher seed vitality under high-temperature and humidity stress compared to wild-type seeds (Wei et al. 2019). MiRNAs have also been targeted for improving seed vigor. MiR168 and miR164 are both important plant regulatory miRNAs, and overexpression of miR168a combined with silencing of miR164c results in higher seed germination percentages in AA seeds in rice (Zhou et al. 2020), demonstrating that the genetic manipulation of miRNAs may improve seed longevity. Additional work to improve seed vigor through genetic modification has been reviewed (Wu et al. 2017).

Conclusion

While maintaining food security in the coming decades poses a challenge due to increased demand and climate change, advancement in genetics and genomics is opening the door for improving seed vigor and other key agricultural traits involved in seed quality and marketable yield (Fig. 4). Research into the genetic underpinnings of seed dormancy and vigor will continue to identify key candidate genes associated with seed development, longevity, and field germination speed, and subsequent studies targeting these genes for manipulation will help uncover their role in improving seed vigor as well as their value for improving agricultural productivity.

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Acknowledgements

Research in our laboratory is supported by the Western Regional Seed Physiology Research Group (WRSPRG), the Shurl and Kay Curci Foundation (SKCF) Faculty Scholars Program award from Innovative Genomics Institute, Berkeley, and a generous start-up grant from the Department of Plant Sciences, UC Davis. We apologize to our colleagues and other researchers whose work we could not include in this review article due to space limitations.

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Authors and Affiliations

1. Seed Biotechnology Center, Department of Plant Sciences, University of California, Davis, CA, USA

Reagan C. Reed, Kent J. Bradford & Imtiyaz Khanday

2. Innovative Genomics Institute, University of California, Berkeley, CA, USA

Imtiyaz Khanday

Contributions

IK conceptualized the review article. RCR, KJB, and IK reviewed the literature, RCR drafted the initial manuscript. All the authors contributed to writing and revising the manuscript and approved the final version of the review article.

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Correspondence to Imtiyaz Khanday.

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Reed, R.C., Bradford, K.J. & Khanday, I. Seed germination and vigor: ensuring crop sustainability in a changing climate. Heredity 128, 450-459 (2022). https://doi.org/10.1038/s41437-022-00497-2
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DOI: https://doi.org/10.1038/s41437-022-00497-2

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Seed germination and vigor: ensuring crop sustainability in a changing climate - Heredity

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In the coming decades, maintaining a steady food supply for the increasing world population will require high-yielding crop plants which can be productive under increasingly variable conditions. Maintaining high yields will require the successful and uniform establishment of plants in the field under altered environmental conditions.

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Abstract

Information regarding the germination and seedling growth behavior of a potential weed species is an important tool to manage weeds without the use of agricultural chemicals that cause harmful effects on human health and the environment. A series of experiments were directed to investigate the influence of different environmental factors (temperature, pH, NaCl, moisture stress, and seed burial depth) on germination and seedling emergence of perennial ryegrass ( Lolium perenne L.) under controlled conditions. Results suggested that 25 °C is the optimum temperature for maximum germination (95%) and seedling growth of perennial ryegrass, however, a quick decline was observed at 35 °C. Seed germination was unaffected by pH levels ranging from 5 to 10. The 92% seed germination was recorded where no salt stress was applied and germination was reduced by 87% at 250 mMNaCl concentration. Seed germination was unaffected by osmotic potential ranges from 0 to − 0.4 MPa thereafter declined and completely inhibited at − 0.8 or − 1.0 MPa. No seed emerged at the soil surface or a soil depth of 6 or 7 cm and 90% emergence occurred at 1 cmsoil depth. The germination and seedlings parameters like time to initial germination, mean germination time, time taken to 50% germination and germination index, root and shoot length, and fresh and dry weight of root and shoot are significantly affected with the environmental factors. The information obtained in this study will be helpful to develop better management strategies for germination and the emergence of perennial ryegrass in areas where it has the ability to rapidly colonize.

Introduction

Perennial ryegrass ( Lolium perenne L.) is an important pasture and forage weed that often grows in temperate regions and it is native to Europe and Africa1,2,3. Perennial ryegrass has little or no juvenility and can be vernalized in the seed4 The Inflorescence of perennial ryegrass is un-branched; each spikelet has only one glume and 4 to 14 florets without awns. Though it was introduced by pastoralists as cultivated species for livestock grazing and fodder but from sown pastures, it has spread to occupy footpaths, roadsides, sand dunes, river beds, and waste places. In many parts of the world, the species is considered an environmental weed as it has many weedy characteristics like rapid adaptation to the environment, producing a large number of seeds, and is easily dispersed5. Due to high seed production and effective dispersal capacity, perennial ryegrass has the ability to rapidly colonize new areas where conditions are favorable.

The germination and emergence of weed seeds are two of the most critical phases in plant development and determine the success of a weed in an agro-ecosystem6. Germination and emergence are mediated by various environmental factors such as temperature7, pH6, NaCl8, water stress and seed burial depth6. Previous studies showed that initiation or inhibition of seed germination is dependent on these factors9,10,11. It is further claimed that temperature is a main determinant of germination when other factors are not limiting and temperature effects are variable for species within the genera12. Moisture stress may delay, reduce or prevent the weed seed germination however, the ability of weed seeds to germinate under low moisture conditions gives them a competitive advantage, which enables them to outcompete the germination or growth of many corps. The pH influenced the germination of seeds and availability of nutrients but several weed species tolerate a varied range of pH levels13. Weed seed location in the soil affected germination and emergence6, by affecting moisture, light and temperature11. According to Javaid et al.14 seed burial depth and food reserves are the most important factors affecting seed germination. Salinity is one of the main problems of soil degradation. Osmotic and ionic effects occur due to salinity that inhibits plant growth15. The seed germination affected by salinity depends upon the type of species and environmental characteristics16 and a reduction in germination was seen when the salt concentration exceeds a threshold level10,17. Salt stress generally decreases the germination percentage and retards the onset of germination. Overall germination of several species may be influenced more by low osmotic potential than by specific ion effects18. The specific ion effect strongly inhibited radicle growth and effective seedling formation depending on the frequency and the total rainfall to germinate and grow19.

A little work has been done on the germination ecology of this weed. The ecology information can play a key role in developing management or control programs, otherwise, control measures can be wasted. Therefore, the present research was planned to investigate the impact of temperature, soil pH, NaCl concentration, osmotic stress and seed burial depth on seed germination and seedling growth of perennial ryegrass. This information can help to characterize the germination niche and the habitat in which it is likely to germinate and develop.

Materials and methods

Site description and collection of seed

The experiments were performed in the Laboratory of Agronomy Department, College of Agriculture, University of Sargodha, Pakistan (32.0°N and 72.6°E) in 2018. Mature seeds were collected during March 2017 and 2018 from several fallow farms in Layyah, and Sargodha Punjab, Pakistan, and a bulked sample was prepared. At the time of collection, seeds were collected by breaking the stem of the plant about 10 cm in length with a spike each year separately. After that paper bags were used to transport the seeds into the laboratory, seeds were detached from the spike of the plant, cleaned, and air-dried at room temperature for 7 days.

Working samples were drawn from the composite sample. After that seeds were kept in air-tight glass bottles until used in the germination experiments.

General germination test protocol

Before the commencement of subsequent germination or emergence tests, seeds of perennial ryegrass were sterilized in 1% sodium hypochlorite (NaClO) for 5 min and then rinsed with distilled water 5 times20. Germination of perennial ryegrass was determined by placing 20 seeds in a Petri plate of 9 cm diameter having a Whatman filter paper No. 10, moistened with 3 mL distilled water or the applicable treatment solution14. Para-film was used to seal the Petri plates to prevent the loss of water. The Petri plates were kept in a germination cabinet (Seedburo Equipment Company, Chicago, IL, USA). Cool white fluorescent bulbs (FL40SBR; National, Tokyo, Japan) were used to produce a photosynthetic photon flux density of 200 µmol m −2 s −1, set to a 12-h alternating light/dark cycle for all the experiments. All experimental trials were performed at the day and night temperature of 25 °C except for the temperature experiment. Seeds were conceived to be germinated when the radicle attained 2 mm in length. The germinated seeds were counted daily for 30 days. However, in the experiment of seed burial depths, when cotyledon was visible at the soil surface then seedlings were considered to have emerged. Each experiment was repeated twice using the seeds collected in two different years. Each treatment was replicated four times in each experiment.

Impact of temperature

To investigate the impact of temperature on germination of perennial ryegrass, twenty seeds were placed uniformly in a Petri plate, lined with filter paper beneath the seed moistened with distilled water of 3 mL and then retained in an incubator at constant temperatures of 20, 25, 30 and 35 °C for 15 days.

Impact of pH

The impact of pH on seed germination of perennial ryegrass was evaluated by using buffer solutions of pH 5, 6, 7, 8, 9 and 10 which were made according to the method defined by Chachalis and Reddy21. A 2 mM solution of MES [2-( N-morpholino) ethanesulfonic acid] was adjusted to pH 5 or 6 with 1 N hydrochloric acid (HCl), and a 2 mM solution of HEPES [ N-(2-hydroxy-methyl) piperazine- N-(2-ethanesulfonic acid)] was adjusted to pH 7 or 8 with 1 N NaOH. Buffer solutions of pH 9 and 10 were prepared with 2 mM TRICINE [ N Tris (hydroxymethyl) methylglycine] and adjusted to each respective pH value with 1 N NaOH. Unbuffered deionized water (pH 6.2) was used as a control.

Impact of salt stress

To determine the influence of salt stress on seed germination of perennial ryegrass 20 seeds were placed in the sealed Petri dishes containing various sodium chloride (NaCl) concentrations at 0, 50, 100, 150, 200, 250, and 300 mM. However, distilled water was used as a control treatment.

Impact of osmotic stress

Perennial ryegrass seeds were placed in Petri plates with the osmotic potential of 0, − 0.2, − 0.4, − 0.6, − 0.8 and − 1.0 MPa. Osmotic potentials were made by using polyethylene glycol (PEG 8000; Sigma-Aldrich Co., 3050, Spruce St., MO 63130) in distilled water. The equation described by Michel and Kaufmann22 was used for the calculation of water potential from a known concentration of PEG 6000. Distilled water was used as the control treatment.

Water potential = − (1.18 × 10 ⁻²) C − (1.18 × 10 ⁻⁴) C ² + (2.67 × 10 ⁻⁴) 18 CT + (8.39 × 10 ⁻⁷) C ² T. Where: T represents the temperature in centigrade while C is the PEG concentration.

Impact of seed burial depth

The impact of seed burial depth on seed emergence was investigated in the greenhouse at the College of Agriculture, University of Sargodha, Pakistan. Twenty seeds of perennial ryegrass were placed on the soil surface or covered with soil (30% silt, 30% clay and 40% sand) at sowing depths of 0, 1, 2, 3, 4, 5, 6 and 7 in 15 cm diameter of plastic pots. In the entire experiment, the greenhouse temperature was maintained at 25 ± 2 °C during the day and night. Pots were watered as and when required to maintain sufficient soil moisture. Seedlings were considered to have emerged when cotyledons were visible at the soil surface.

Germination or emergence percentage data of perennial ryegrass obtained from the experiments regarding osmotic stress, NaCl concentration and seed burial depth, were subjected to non-linear regression analysis. Germination percentage data at various concentrations of osmotic potential and NaCl were fixed to a 3-parameter logistic model by using software Sigma Plot 2008 (version 11.0, SyStat Software GmbH, Schimmelbuschstrasse 25 D-40699 Erkrath Germany)). The fitted model was:

$$ G(\% ) = \frac{{\mathop G\nolimits_{\max } }}{{1 + \mathop {\left( {\frac{x}{{\mathop x\nolimits_{50} }}} \right)}\nolimits^{g} }} $$

where G is the total germination percentage at concentration x, x ₅₀ is the osmotic potential or NaCl concentration for 50% suppression of the maximum germination and g denotes the slope and G max is the maximum germination percentage.

A three-parameter logistic model:

$$ E\left( \% \right) = \frac{{E_{max} }}{{1 + \left( {\frac{x}{{x_{50} }}} \right)^{e} }} $$

was fixed to the seedling emergence percentage gained at various burial depths of 0 to 7 cm, where E is the total seedling emergence percentage at burial depth x, x ₅₀ is the burial depth for 50% suppression of the maximum seedling emergence and e denotes the slope, E max is the maximum seedling emergence percentage.

Time to initial germination or emergence (Ti) was noted when the first seed germinated or seedling emerged. The time taken to 50% germination or emergence ( T ₅₀ or E ₅₀) was estimated by using a formula described by Coolbear et al.23:

$$ T_{50} \;or\;E_{50} = t_{i} \frac{{\left( {\frac{N}{2} - n_{i} } \right)(t_{j} - t_{i} )}}{{(n_{j} - n_{i} )}} $$

where N is the final number of sprouted or emerged seeds, and n j and n i are the cumulative number of seeds germinated by adjacent counts at times t j (day) and t i, (day), respectively, when n i <  N/ 2 <  n j.

Mean germination or emergence time ( MGT or MET), which is a measure of the speed of germination or emergence, was calculated after Ellis and Roberts24:

$$ MGT\;or\;MET = \frac{{\sum {Dn} }}{\sum n } $$

where n is the number of seeds that had germinated on day D and n is the number of days counted from the beginning of the germination experiment.

The germination or emergence index ( GI or EI), which is a measure of the percentage and rate of germination was calculated as described by the Association of Official Seed Analysis25 using the following formula:

$$ GI\;{\text{or}}\;EI = \frac{{No\;of\;germinated\;{\text{or}}\;emerged\;seedlings}}{Days\;of\;first\;count} + \cdots + { }\frac{{No\;of\;gerimnated\;{\text{or}}\;emerged\;seedlings}}{Days\;of\;final\;count} $$

Data regarding root length (cm), shoot length (cm), root fresh weight (g) root dry weight (g), shoot fresh weight (g) shoot dry weight of perennial ryegrass was collected throughout the study by using standard procedures.

Statistical analysis

All the experiments were carried out in a completely randomized design (CRD) with four replications and each experiment was repeated twice. The collected data were subjected to one-way ANOVA. The significance of treatment means was practiced by using the least significant difference (LSD) test at a 5% level of probability26.

Research ethics

Experimental research and field studies on plants (either cultivated or wild), including the collection of plant material, complied with relevant institutional, national, and international guidelines and legislation. Prior permission was undertaken from farm owner and Offices of Research, Innovation and Commercialization, University of Sargodha, Pakistan.

Results

Impact of temperature

Germination (%) of perennial ryegrass varied significantly in response to tested temperature (Fig. 1). The highest germination percentage of perennial ryegrass was observed at 25 °C. The germination at 20 and 30 °C temperatures were statistically similar. However, a strong reduction in germination was seen when the temperature was raised to 35 °C and reduces the germination by 85% compared to 25 °C.

Influence of temperature on germination of perennial ryegrass. Nails on the vertical bars represent the standard error of the means.

The time to initial germination (Ti) of perennial ryegrass was 1 to 2 days earlier at 25 °C than other tested temperatures and was 2 days. The slowest Ti was measured at 30 °C (Table 1).

Table 1 Effect of temperature, pH, NaCl concentration, osmotic potential and seed burial depths on germination parameters of perennial ryegrass.

The minimum time to reach 50% of the maximum germination ( T ₅₀) was recorded at 25 °C and the lowest mean germination time ( MGT) was measured under the same temperature. The germination index ( GI) represents the rate of germination and the highest GI was observed at 25 °C (Table 1). The seedlings' growth of perennial ryegrass was affected significantly by tested temperatures (Table 2).

Table 2 Effect of temperature, pH, NaCl concentration, osmotic potential and seed burial depths on seedling growth of perennial ryegrass.

Among all the temperatures, the length, fresh and dry weight of root and shoot was maximum at 25 °C followed by 20 °C temperature and minimum values of these parameters were observed at 35 °C (Table 2).

Impact of pH

The pH did not affect seed germination of perennial ryegrass and germination remained more than 87% within pH range of 5 to 10 (Fig. 2).

Influence of pH on germination of perennial ryegrass. Nails on the vertical bars represent standard error of the means.

The higher seed germination of perennial ryegrass under a varied range of pH showed that pH would not be a limiting factor for its germination. However, the highest and lowest seed germination was recorded at pH 6 and 8, respectively (Fig. 2). The shortest Ti was observed at pH 7 and control (pH 6.2) (Table 1). The lowest and highest T ₅₀ were measured at the control and pH 8, respectively, however, a similar trend was also observed for MGT.

Data in Table 2 specified that maximum root length and fresh weight were attained at control (pH 6.2). The root dry weight was non-significant. There was no deviation in root length of perennial ryegrass from pH 5-8. The minimum values of root length and fresh weight were recorded at pH 10. However, the peak value of shoot length, fresh weight and dry weight of perennial ryegrass were recorded at pH 5 (Table 2).

Impact of NaCl

A 3-parameter sigmoid model (G (%) = 85.7 [1 + (x/170.6) 4.2], R ² = 0.96) was fixed to the germination data of perennial ryegrass in response to various concentrations of NaCl (Fig. 3). The model showed that seed germination was reduced linearly with a rise in NaCl concentration from 0 to 250 mM. The highest seed germination was recorded where no salt stress was applied. Only 15% germination was observed at 250 mM NaCl concentration.

Influence of NaCl concentration on seed germination of perennial ryegrass. Bold line represents a three-parameter logistic model (G (%) = G max/[1 + (x/x ₅₀) g]) fitted to the data. The vertical dash line represents X-axis value at 50% of the maximum germination. Dotted liens show 95% confidence intervals. Vertical bars represent ± standard error of the mean.

No germination was seen at 300 mM NaCl concentration (Fig. 3). The model estimated that 50% germination of the maximum occurred at 170.6 mM NaCl concentration. Compared to control, Ti of perennial ryegrass was postponed nearly to 1 day at 50, 100, 150, 200 and nearly to 5 days at 250 mM NaCl concentration (Table 1). The T ₅₀ and MGT were greater in control (distilled water) as compared to each NaCl concentration, showing less germination of perennial ryegrass in reply to increasing concentration of NaCl (Table 1). For the rate of germination, GI was higher in control than all other concentrations of NaCl used. The seedling growth parameters of L. perenne indicated that by increasing salt stress the fresh weight, dry weight and length of root and shoot decreased significantly (Table 2). Compared to all NaCl concentrations, the highest perennial ryegrass fresh and dry weight of root as well as root and shoot length was recorded in control (distilled water) (Table 2).

Impact of osmotic potential

Seed germination percentage decreased with an increase in osmotic potential. The seed germination was decreased from 92 to 27% when the osmotic potential was decreased from 0 to − 0.6 MPa.

No germination was observed where osmotic potential − 0.8 to − 1.0 was applied. The model estimated that 50% germination of the maximum was obtained at − 0.38 MPa (Fig. 4). The Ti, T ₅₀ and MGT of perennial ryegrass were delayed with decreasing osmotic potential (Table 1). Compared to distilled water, Ti, T ₅₀ and MGT were delayed nearly to 2-4 days. The maximum GI was recorded with distilled water, whilst it decreased when the osmotic potential was reduced from 0 to − 0.6 MPa (Table 1). In the case of seedling growth, data showed that decrease in osmotic potential from 0 to − 0.6 MPa, and the seedling growth parameters were reduced. The highest root length, fresh weight, and dry weight of perennial ryegrass were observed in distilled water. The shoot length, and fresh and dry weight of perennial ryegrass were also maximum in distilled water and the lowest values of these parameters were recorded at − 0.4 MPa (Table 2).

Influence of osmotic potential on seed germination of perennial ryegrass. Bold line represents a three-parameter logistic model (G (%) = G max/[1 + (x/x ₅₀) g]) fitted to the data. Vertical dash line represents X-axis value at 50% of the maximum germination. Dotted liens show 95% confidence intervals. Vertical bars represent ± standard error of the mean.

Impact of seed burial depth

No seedling emergence was observed when seeds were placed at the soil surface. The seed emergence was reduced linearly from 1 to 5 cm and inhibited completely at 6 or 7 cm burial depth.

The maximum seedling emergence occurred at 1 cm depth and declined quickly to 55 and 28% at 4 and 5 cm seedling depths, respectively. The model estimated that 50% emergence of the maximum was obtained at 4.33 cm seed burial depth (Fig. 5). The seed emergence parameters such as Ti, E ₅₀, MET were directly proportional to seed burial depth (Table 1). At 1 or 2 cm depth, seed emergence started in 3.2 and 3.7 days, respectively; however, with increasing burial depth from 3 to 5 cm, Ti was delayed by 1 to 3 days. The E ₅₀ and MET were minimum at 1 cm and maximum at 5 cm seed burial depth (Table 1). The emergence index ( EI) was reduced with increasing seed burial depth. The seed placed at 1 cm depth recorded the highest value (3.5) of El that gradually decreased to 1.3 and 0.6 at 4 and 5 cm burial depths, respectively. The seedling growth of perennial ryegrass presented in Table 2 indicated that maximum root and shoot length were observed at 2 cm burial depth. The highest fresh and dry weight of root and shoot were also recorded at the same seeding depth (Table 2).

Influence of seed burial depth on seed emergence of perennial ryegrass.. Bold line represents a three-parameter logistic model (E (%) = E max/[1 + (x/x ₅₀) g]) fitted to the data. The vertical dash line represents X-axis value at 50% of the maximum germination. Dotted liens show 95% confidence intervals. Vertical bars represent ± standard error of the mean. No emergence was recorded at 0 cm seed burial depth so, the data of 0 cm were not included in the fitted model.

Discussion

Impact of temperature

The temperature has a major influence on the occurrence and speed of Lolium species germination by affecting the seed deterioration, loss of dormancy and germination processes27. Our data showed that the optimum temperature for maximum germination of perennial ryegrass is 25 °C. It is the winter season weed and the temperature in November and December is about 25 °C. During these months, this weed has the ability to spread in different localities. The low germination at 35 °C may have biological significance because, in field conditions, soil warms slowly, resulting in significant germination causing a problem for growers28. Previous work showed that perennial ryegrass germination was typically reduced below a soil temperature of 15 °C, however, low temperature has little influence on the potential survival of seeds29. The germination rate parameters like Ti, T ₅₀ and MGT were lower and the germination index was higher at 25 °C, indicating the most suitable temperature for the germination requirement of this weed. Germination of perennial ryegrass was started on the second day at 25 °C and prolonged to four days with an increase of decrease in temperature. According to the Association of Official Seed Analysts30, the first and final counts of germination should take place on day 5 and 14 for most of the Lolium species, however, germination may have been delayed to 14 days at low-temperature regimes. Root and shoot length, fresh and dry weight were also maximum at 25 °C. Root length and shoot length varied with varying temperatures because temperature affected the growth and development of plants beyond optimum temperature31.

Impact of pH

Our results indicated that germination of perennial ryegrass was not affected by pH and it can grow on a wide range of pH from 5 to 10. Rather than the addition of NaOH or HCl to water, we used MES, HEPES and TRICINE for the perpetration of desired pH buffer to avoid change in pH during the experiment. It is notable that although different buffers have different chemicals, have no significant effects on the germination of perennial ryegrass. According to Thomas et al.32, many weed species germinate over a wide range of pH from 6 to 9 but some species showed the problem in germination when exposed to an acidic range (pH 5-6)33,34. Germination at high pH suggested that germination of this species will be inhibited under CO ₃ 2− and HCO ₃− salts. Likewise, germination, the values of germination parameters over time, T ₅₀, and MGT of perennial ryegrass were not affected by pH ranges For seedling parameters of perennial ryegrass, our findings are parallel to those of Chejara et al.33 who reported that some species showed minute differences in root and shoot length over the wide ranges of pH. Our results showed that pH has a negative correlation with shoot length might be due to the increased concentration of salts with an increasing pH value. The ability to germinate over a wide range of pH buffers (5-10) may not provide a reliable indicator of the effects of alkaline or acidic soil on seed germination.

Impact of salinity

Salinity decreased germination rate progressively with an increase in concentration, suggesting that this species may not be established well in highly saline areas. It is documented that a higher concentration of NaCl can inhibit the germination of many weed species and may die at some stages6. In contrast, some weed species are resistant to NaCl concentration and can germinate successfully under salinity stress9. In the case of perennial ryegrass, the germination percentage, Ti, T ₅₀, and MGT were reduced with an increase in salt concentration might be due to less water uptake and providing suitable environments for the entrance of noxious ions in the embryo. According to Chachalis et al.35, seed germination was significantly affected by salinity and may generate low water potential and provide suitable environments for the access of noxious ions to the embryo. Higher NaCl concentration (300 mM) reduced the germination index and Ti which ultimately reduced the germination capability of many weed seeds36. Similar to our results for GI, Tanveer et al.34 concluded that GI of Cucumis melo was maximum at a lower salinity level. The results suggested that salt stress caused a reduction in root and shoot growth parameters of perennial ryegrass, which might be due to less water uptake under high concentrations. Salinity decreased the ease with which seeds take up moisture or facilitated the intake of toxic ions, leading to changes in hormonal and enzymatic activity of seeds and resulting in inhibition in seed germination. An example of the first mechanism, Prisco et al.37 found that germination of red kidney bean ( Phaseolus vulgaris L.) was inhibited by the osmotic effect of NaCl at greeter than − 0.8 MPa osmotic potential. Evidence of the second mechanism was described by Kim and Park38 who found salinity reduced the biosynthesis of gibberellic acid, an essential hormone for breaking dormancy and controlling the growth of seedlings. Resultantly, salinity might affect some physiological processes in plants but it is unclear whether the reduction in germination is directly due to osmotic effects, or to some non-toxic germination repression caused by the salt.

Impact of osmotic potential

The seed germination of perennial ryegrass was decreased by increasing osmotic potential from 0 to − 0.6 and suppressed completely from − 0.8 to − 1 MPa might be due to drought condition that fails the embryo to develop radicle and plumule. The process of water imbibition remains incomplete in drought-sensitive seeds. Dry seeds need more water for cellular metabolism and the amount of water differs from species to species39. It has been postulated that metabolites of starch such as glucose are critical for seed germination as they are involved as osmolytes for cellular turgor maintenance and energy sources40. Under water-deficit conditions, starch metabolism is reduced, leading to poor germination41. Drought affected the Ti, T ₅₀, MGT and GI of perennial ryegrass and the findings of our experiment are similar to the results of Tanveer et al.42 who stated that the highest Ti (3.54 days) of Lathyrus aphaca was measured at the osmotic potential of − 0.6 MPa. In our experiment, fresh and dry weight, as well as shoot and root length of perennial ryegrass, decreased as osmotic potential increased. Contrary to our results, Norsworthy and Oliveira43 stated that the shoot length of sicklepod showed resistance to osmotic potential ranges from 0.0 to − 1.0 MPa. The results showed that the osmotic potential of − 0.4 MPa declined the germination of perennial ryegrass up to 40% whereas, the salinity at 100 mM (~ 0.45 MPa) recorded the germination of 80%. This variation in germination might be due to the fact described by Fukuda et al.44 that moderate salt levels promote germination and it has been suggested that salt can be compartmentalized and used as cellular osmotica, allowing seeds to germinate under osmotic conditions which would otherwise preclude it. It is further supported by Fukuda et al.44 that compartmentalization of salt into vacuoles aid the growth and survival of vegetative plants. Moreover, in the osmotic potential case, the PEG is either unable to cross the cell membrane or can do so only slowly while in the case of salt stress, salt might be compartmentalized and the seed avoids the negative effect of low water potential.

Impact of seed burial depth

Seeding depth also suppresses the emergence of many weed species6. In our study, there was no seed emergence of perennial ryegrass observed at the soil surface, which might be due to a lack of moisture at the soil surface. No germination at the soil surface favored perennial ryegrass weed in the no-till system. These results are in line with those of Javaid and Tanveer11 who reported that seeds of Emex australis sown on the soil surface had delayed and reduced germination. Emergence was detected at 1 to 5 cmof seed burial depth and maximum emergence was recorded at 1 cmdepth which was probably due to its small seed size and inadequate food reserves, which enabled the seeds to arise from bigger depths. The reduced emergence at greater depth might be due to small amounts of gaseous diffusion in soil and hypoxia45. Different weeds germinate to different degrees mainly depending on their seed size and seeding depth46. Our study revealed that the deeper the sowing depth of perennial ryegrass, the more will be Ti, E ₅₀ and MET. As deeper the seeds were sown of dove weed, E ₅₀ tended to decline and significantly reduced from 2 to 6 cm47. Seed burial depth reduced the E ₅₀ and MET 48. Root and shoot length, and fresh and dry weight of perennial ryegrass decreased as seeding depth increased. Boyd and Van Acker49 also reported that the root and shoot fresh weight of numerous weed species decreased with increasing seed burial depth.

Conclusions

The temperature had a significant impact on the seed germination of perennial ryegrass and the optimum temperature for its germination was 25 °C. It can withstand a wide range of salinity and have the potential to tolerate extreme salinity up to 250 mM NaCl concentration. Osmotic potential reduces its germination and growth beyond − 0.6 MPa and this weed proved to be unaffected by different pH ranges from 5 to 10. It is also revealed that the seed of perennial ryegrass was not germinated at the soil surface, beyond 1-2 cm burial depth a significant reduction was observed and complete inhibition in germination occurred below 5 cm burial depth. The germination and seedling traits were also influenced by these environmental factors.

Acknowledgements

The authors extend their appreciation to Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R20), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Author information

Authors and Affiliations

1. Department of Agronomy, College of Agriculture, University of Sargodha, Sargodha, 40100, Pakistan

Muhammad Mansoor Javaid, Hasnain Waheed, Imran Haider, Muhammad Ather Nadeem & Bilal Ahmad Khan

2. Department of Agronomy, University of Agriculture Faisalabad, Faisalabad, 38040, Pakistan

Athar Mahmood

3. Department of Biology, College of Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia

Dalal S. Alshaya & Muneera D. F. AlKahtani

4. College of Agriculture, Bahauddin Zakariya University, Bahadur Sub-Campus, Layyah, 31200, Pakistan

Allah Wasaya & Sajid Fiaz

5. Department of Plant Breeding and Genetics, The University of Haripur, Haripur, 22620, Pakistan

Sher Aslam Khan

6. Department of Botany, University of Agriculture Faisalabad, Faisalabad, 38040, Pakistan

Maria Naqve

7. Horticultural Sciences Department, UF/IFAS, North Florida Research and Education Center Quincy 32351, University of Florida, Gainesville, USA

Muhammad Adnan Shahid

8. Agriculture Extension and Adaptive Research, Agriculture Department, Government of Punjab, Lahore, Pakistan

Saira Azmat

9. Department of Horticulture, College of Agriculture, University of Sargodha, Sargodha, 40100, Pakistan

Rashad Mukhtar Balal

10. Center of Excellence in Biotechnology Research, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia

Kotb A. Attia

Contributions

M.M.J. conceived the idea and conducted research. The investigation was carried out by A.M. and H.W. Data analysis was carried out by M.N., M.A.S., I.H., A.W. and K.A.A., D.S.A., M.D.F.A., S.A.K. and S.F. provided technical expertise and revised the article as per reviewer comments. M.M.J., A.M., H.W., R.M.B., M.A.N., S.A. and B.A.K. helped in the writing of the original draft. All authors carefully read, revise, and approved the article for submission.

Corresponding authors

Correspondence to Athar Mahmood, Dalal S. Alshaya or Sajid Fiaz.

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Competing interests

The authors declare no competing interests.

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Javaid, M.M., Mahmood, A., Alshaya, D.S. et al. Influence of environmental factors on seed germination and seedling characteristics of perennial ryegrass ( Lolium perenne L.). Sci Rep 12, 9522 (2022). https://doi.org/10.1038/s41598-022-13416-6
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DOI: https://doi.org/10.1038/s41598-022-13416-6

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Influence of environmental factors on seed germination and seedling characteristics of perennial ryegrass (Lolium perenne L.) - Scientific Reports

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Information regarding the germination and seedling growth behavior of a potential weed species is an important tool to manage weeds without the use of agricultural chemicals that cause harmful effects on human health and the environment.

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20 mL: Germinaron 20

Tamaños: 12 cm, 7.5 cm, 6 cm, 8 cm, 5 cm, 8.5 cm, 4.5 cm, 8 cm, 8 cm, 7. 9 cm, 7.7 cm, 6.5 cm, 7 cm, 7.9 cm, 6 cm, 7 cm, 6 cm, 7 cm, 7 cm, 6 cm.

10 mL: Germinaron 9

Tamaños: 8.5 cm, 5.5 cm, 6.3 cm, 6.6 cm, 8 cm, 7.8 cm, 3 cm, 7.3 cm, 3 cm

30 mL: Germinaron 11

Tamaños: 5.5 cm, 6.5 cm, 4.3 cm, 5 cm, 4.7 cm, 5 cm, 6.2 cm, 6.5 cm, 4.5 cm, 2.3 cm, 7 cm.

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Diana Marcela Riveros Diaz

Laura Valentina Caceres Gonzalez

Sandra Milena Gonzalez

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Bueno este es nuestro ensayo espero sea del agrado, modificamos ciertas partes las cuales suene mas técnico y a su vez tratamos de basarnos en videos y en los artículos los cuales los comparto ya en otra publicación

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por si gustan leer a si sea el resumen

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ARTICULOS LOS CUALES NOS BASAMOS PARA PODER FORTALECER EL ENSAYO

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y por ultimo un video el cual habla sobre el estrés del potencial hibrico

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Ensayo- Tema : Fundamentos y termodinamica del agua 

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Ensayo- Tema : Fundamentos y termodinamica del agua 

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Zoey Vega Jaime

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Ensayo fisiologia Vegetal- Zoey Vega Jaime

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https://doi.org/10.1016/j.earscirev.2024.104775

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En este video relacionamos los componentes del potencial hídrico con las plantas bajo condiciones en ambientes con condiciones extremas como el desierto y su relación con los procesos de fotosíntesis. También analizamos los resultados de laboratorio

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Componentes del potencial hídrico en condiciones extremas + laboratorio 

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BIBLIOGRAFIAS:

Biology Campbell, N. A., Urry, L. A., Cain, M. L., Wasserman, S. A., Minorsky, P. V., & Orr, R. B. (2021). Biology (12th ed.). Pearson.

Plant Physiology and Development Taiz, L., Zeiger, E., Møller, I. M., & Murphy, A. (2015). Plant physiology and development (6th ed.). Sinauer Associates.

FILMORA(PROGRAMA DE EDICION)

ECHO POR:David Guzman,Ana Sofia Diaz,Michael rojas

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Resultados Practica de laboratorio - potencial hídrico. Cindy Rodriguez, Wendy Daza, Daniel Forero

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Laboratorio potencial hidrico. Cindy Rodriguez, Wendy Daza, Daniel Forero

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Potencial Hídrico en papa

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Informe guía de laboratorio: Potencial hídrico

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Integrantes: Laura Valentina Cáceres, Diana Marcela Riveros y Sandra Milena González

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Laboratorio potencial hídrico

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Determinación del potencial hídrico celular.

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Integrantes:

Valentina Caceres

Sandra González

Marcela Riveros

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Roger Beltrán

Isabel Cárdenas

Juan Garzón

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INTEGRANTES:

ANA SOFIA DIAZ

MICHAEL ROJAS

DAVID GUZMAN

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Infografia - Alejandro García, María José Riaño y Tamara Conrado 

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Infografia - Alejandro García, María José Riaño y Tamara Conrado 

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Fase Luminica La energia radiante excita la clorofila para la hidrolisis del agua para generar O2, ATP y NADPH

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Infografía sobre Cuidado de Plantas de Interior Ilustrativo Verde Claro_20260527_104553_0000

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Fotosíntesis y desarrollo de la planta | Genially

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El potencial hídrico es la energía potencial del agua en una planta en comparación con el agua pura. Se mide en unidades de presión (como Megapascales, MPa) y determina la capacidad del agua para moverse, siendo el agua pura su valor máximo. En las plantas, este valor siempre es negativo debido a la presencia de sales y azúcares (solutos).

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Infografia fisiología vegetal- Zoey Vega y Alexandra Guzmán (grupo 5)

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Infografia fisiología vegetal- Zoey Vega y Alexandra Guzmán (grupo 5)

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Borrador infografia-grupo 5- Alexandra Guzmán y Zoey Vega