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LÁMINA NUCLEAR
Hay dominios LAD (Dominios asociados a membrana) de la cromatina, cercanas a lámina nuclear. Con poca expresión de genética y abundante GC.

COMPLEJO DE PORO NUCLEAR
Dentro del complejo hay un tipo de proteínas FG-NUPS que se integran con proteínas reguladoras para la expresión de algún gen.

NUCLEOLO
Distintos segmentos de cromosomas expresan los ARNr necesarios para la composición de los ribosomas.

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Factores limitantes:

Sensitividad: Capacidad de captar luz por el microscopio

Resolución: Depende del tamaño del tipo de sonda utilizada

Fosmid probes (~40kb): para sodear regiones mayores a 100 kb

OligoNTPs probes: para sondear regiones menores a 100 kb

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Nota 1: es clave la evidencia empírica de la inducción de la formación de cross-links entre regiones cercanas tridimensionalmente de ADN gracias al formaldehído.

Nota 2: 3C y sus derivados son técnicas de inferencia, ya que por probabilidad, si dos regiones de ADN están cerca en un espacio tridimensional, es más probable que se formen estos cross-links.

Nota 3: A diferencia de FISH, esta técnica se aplica a todo un cultivo celular.

La técnica consiste en los siguientes pasos:

1. Formación de cross-links: inducido por formaldehído

2. Digestión: se extrae el ADN y se digiera con enzimas de restrcción --> las enzimas de restricción no cortan los cross-links.

3. Ligación: estos extremos de sobran de los cross-links se ligan con una ligasa.

4. Detección por PCR: se rompen los cross-links y estos templates de uniones en espacios 3D se amplifican con PCR.

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Transcription regulation in higher eukaryotes is controlled by regulatory elements such as enhancers that are recognized by transcription factors. In many cases regulatory elements can be located at distances up to several megabases from their target genes. Recent evidence shows that long-range control of gene expression can be mediated through direct physical interactions between genes and these regulatory elements. Such looping interactions can be detected using the chromosome conformation capture (3C) methodology. Although 3C is experimentally straightforward, to draw meaningful conclusions one must carefully design 3C experiments and implement the conscientious use of controls. The general guidelines presented here should help experimental design and minimize misinterpretation of 3C experiments.

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The three 'C' s of chromosome conformation capture: controls, controls, controls - Nature Methods

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Transcription regulation in higher eukaryotes is controlled by regulatory elements such as enhancers that are recognized by transcription factors. In many cases regulatory elements can be located at distances up to several megabases from their target genes1,2. Recent evidence shows that long-range control of gene expression can be mediated through direct physical interactions between genes and these regulatory elements.

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Se sigue la misma rut de trabajo de 3C pero se agregan los siguientes pasos:

Self-circularization: Se forman librerías de estas interacciones 3D del genoma por medio de una doble ligación de los extremos (miniplásmidos).

PCR inversa: se targetean las regiones de contacto y se analiza en microarrays.

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Authors and Affiliations

1. Department of Cell Biology and Genetics, Erasmus Medical Centre, PO Box 2040, Rotterdam, 3000 CA, The Netherlands

Marieke Simonis, Petra Klous, Erik Splinter & Wouter de Laat

2. Department of Biochemistry, Erasmus Medical Centre, PO Box 2040, Rotterdam, 3000 CA, The Netherlands

Yuri Moshkin

3. Department of Clinical Genetics, Erasmus Medical Centre, PO Box 2040, Rotterdam, 3000 CA, The Netherlands

Rob Willemsen

4. Division of Molecular Biology, Netherlands Cancer Institute, Plesmanlaan 121, Amsterdam, 1066 CX, The Netherlands

Elzo de Wit & Bas van Steensel

Contributions

M.S. set up, performed and analyzed 4C experiments and wrote the paper; P.K. performed cryo-FISH experiments; E.S. helped set up 4C; Y.M. helped with statistical analysis; R.W. prepared cryosections; E.d.W. and B.v.S. provided probe sequences and helped with statistical analysis and W.d.L. designed experiments and wrote the paper.

Corresponding author

Correspondence to Wouter de Laat.

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Nuclear organization of active and inactive chromatin domains uncovered by chromosome conformation capture-on-chip (4C) - Nature Genetics

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The spatial organization of DNA in the cell nucleus is an emerging key contributor to genomic function1,2,3,4,5,6,7,8,9,10,11,12. We developed 4C technology (chromosome conformation capture (3C)-on-chip), which allows for an unbiased genome-wide search for DNA loci that contact a given locus in the nuclear space.

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5C: Chromosome Conformation Capture Carbon Copy

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5C: Chromosome Conformation Capture Carbon Copy

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Lo novedoso de esta técnica es la utilización de primers universales y múltiples (como una PCR múltiple) para la posterior detección por microarrays

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Nota 1: Estas técnicas y sus derivados tienen mayor resolución que 3C, 4C y 5C.

Nota 2: Hi-C y ChIA-PET se analizan mediante high-throughput sequencing.

Nota 3: Se contruyen mapas de contacto cuya resolución en la actualidad llega niveles menore a 100 pares de bases.

Hi - C:
Lo peculiar de esta técnica es que previo a la ligación, se agregan adaptadores marcados con biotina
Posterior a esto, se filtran los fragmentos captando únicamente los marcados biotina, dándole así una mayor especificidad a comparación de 3C, 4C y 5C.

GCC y TCC: GCC es la aplicación de Hi-C al análisis estructural de genomas (se han mapeado genomas de bacterias como E. coli)
TCC (Tethered) utiliza métodos estadísticos para mejorar la relación señal/ruido de la técnica de Hi-C

ChIA - PET:
Utiliza ChIP (Chromatin Inmuno-Precipitation) para filtrar las interacciones proteína-ADN.

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Hi-C (or standard Hi-C) is a high-throughput genomic and epigenomic technique first described in 2009 by Lieberman-Aiden et al. to capture chromatin conformation. In general, Hi-C is considered as a derivative of a series of chromosome conformation capture technologies, including but not limited to 3C (chromosome conformation capture), 4C (chromosome conformation capture-on-chip/circular chromosome conformation capture), and 5C (chromosome conformation capture carbon copy). Hi-C comprehensively detects genome-wide chromatin interactions in the cell nucleus by combining 3C and next-generation sequencing (NGS) approaches and has been considered as a qualitative leap in C-technology (chromosome conformation capture-based technologies) development and the beginning of 3D genomics.

Similar to the classic 3C technique, Hi-C measures the frequency (as an average over a cell population) at which two DNA fragments physically associate in 3D space, linking chromosomal structure directly to the genomic sequence. The general procedure of Hi-C involves first crosslinking chromatin material using formaldehyde. Then, the chromatin is solubilized and fragmented, and interacting loci are re-ligated together to create a genomic library of chimeric DNA molecules. The relative abundance of these chimeras, or ligation products, is correlated to the probability that the respective chromatin fragments interact in 3D space across the cell population. While 3C focuses on the analysis of a set of predetermined genomic loci to offer "one-versus-some" investigations of the conformation of the chromosome regions of interest, Hi-C enables "all-versus-all" interaction profiling by labeling all fragmented chromatin with a biotinylated nucleotide before ligation. As a result, biotin-marked ligation junctions can be purified more efficiently by streptavidin-coated magnetic beads, and chromatin interaction data can be obtained by direct sequencing of the Hi-C library.

Analyses of Hi-C data not only reveal the overall genomic structure of mammalian chromosomes, but also offer insights into the biophysical properties of chromatin as well as more specific, long-range contacts between distant genomic elements (e.g. between genes and regulatory elements). In recent years, Hi-C has found its application in a wide variety of biological fields, including cell growth and division, transcription regulation, fate determination, development, disease, and genome evolution. By combining Hi-C data with other datasets such as genome-wide maps of chromatin modifications and gene expression profiles, the functional roles of chromatin conformation in genome regulation and stability can also be delineated.

At its inception, Hi-C was a low-resolution, high-noise technology that was only capable of describing chromatin interaction regions within a bin size of 1 million base pairs (Mb). The Hi-C library also required several days to construct, and the datasets themselves were low in both output and reproducibility. Nevertheless, Hi-C data offered new insights for chromatin conformation as well as nuclear and genomic architectures, and these prospects motivated scientists to put efforts to modify the technique over the past decade.

Between 2012 and 2015, several modifications to the Hi-C protocol have taken place, with 4-cutter digestion or adapted deeper sequencing depth to obtain higher resolution. The use of restriction endonucleases that cut more frequently, or DNaseI and Micrococcal nucleases also significantly increased the resolution of the method. More recently (2017), Belaghzal et al. described a Hi-C 2.0 protocol that was able to achieve kilobase (kb) resolution. The key adaptation to the base protocol was the removal of the SDS solubilization step after digestion to preserve nuclear structure and prevent random ligation between fragmented chromatin by ligation within the intact nuclei, which formed the basis of in situ Hi-C. In 2021, Hi-C 3.0 was described by Lafontaine et al., with higher resolution achieved by enhancing crosslinking with formaldehyde followed by disuccinimidyl glutarate (DSG). While formaldehyde captures the amino and imino groups of both proteins and DNA, the NHS-esters in DSG react with primary amines on proteins and can capture amine-amine interactions. These updates to the base protocol allowed the scientists to look at more detailed conformational structures such as chromosomal compartment and topologically associating domains (TADs), as well as high-resolution conformational features such as DNA loops.

To date, a variety of derivatives of Hi-C have already emerged, including in situ Hi-C, low Hi-C, SAFE Hi-C, and Micro-C, with distinctive features related to different aspects of standard Hi-C, but the basic principle has remained the same.

First published by Lieberman-Aiden, et al. in 2009, the outline of the classical Hi-C workflow is as follows: cells are cross-linked with formaldehyde; chromatin is digested with a restriction enzyme that generates a 5' overhang; the 5' overhang is filled with biotinylated bases and the resulting blunt-ended DNA is ligated. The ligation products, with biotin at the junction, are selected for using streptavidin and further processed to prepare a library ready for subsequent sequencing efforts.

Standard Hi-C gives data on pairwise interactions at the resolution of 1 to 10 Mb, requires high sequencing depth and the protocol takes around 7 days to complete.

Cell and nuclear membranes are highly permeable to formaldehyde. Formaldehyde cross-linking is frequently employed for the detection and quantification of DNA-protein and protein-protein interactions. Of interest in the context of Hi-C, and all 3C-based methods, is the ability of formaldehyde to capture cis chromosomal interactions between distal segments of chromatin. It does so by forming covalent links between spatially adjacent chromatin segments. Formaldehyde can react with macromolecules in two steps: first it reacts with a nucleophilic group on a DNA base for example, and forms a methylol adduct, which is then converted to a Schiff base. In the second step, the Schiff base, which can decompose rapidly, forms a methylene bridge with another functional group on another molecule. It can also make this methylene bridge with a small molecule in solution such as glycine, which is used in excess to quench formaldehyde in Hi-C. Quenchers can typically exert an effect on formaldehyde from outside the cell. A key feature of this two-step formaldehyde crosslinking reaction is that all the reactions are reversible, which is vital for chromatin capture.

Crosslinking is a pivotal step of the chromatin capture workflow as the functional readout of the technique is the frequency at which two genomic regions are crosslinked to each other. Thus, the standardization of this step is important and for that, one must consider potential sources of variation. Presence of serum, which contains a high concentration of protein, in culture media can decrease the effective concentration of formaldehyde available for chromatin crosslinking, by sequestering it in the culture media. Therefore, in cases where serum is used in culture, it should be removed for the crosslinking step. The nature of cells, i.e., whether they are suspension or adherent, is also a pertinent consideration for the crosslinking step. Adherent cells bind to surfaces with the help of molecular mechanisms of cytoskeletons. It has been shown that there is a link between cytoskeleton-maintained nuclear and cellular morphology which, if altered, may negatively impact global nuclear organization. Adherent cells therefore, should be crosslinked while still attached to their culture surface.

Lysis, restriction digest and biotinylation

Cells are lysed on ice with cold hypotonic buffer containing sodium chloride, Tris-HCl at pH 8.0, and non-ionic detergent IGEPAL CA-630, supplemented with protease inhibitors. The protease inhibitors and incubation on ice help preserve the integrity of crosslinked chromatin complexes from endogenous proteases. The lysis step helps to release the nucleic material from the cells.

Following cell lysis, chromatin is solubilized with dilute SDS in order to remove proteins that have not been crosslinked and to open chromatin and make it more accessible for subsequent restriction endonuclease-mediated digestion. If the incubation with SDS exceeds the recommended 10 minutes, the formaldehyde crosslinks can be reversed and so the incubation with SDS must be immediately followed by an incubation on ice. A non-ionic detergent called Triton X-100 is used to quench SDS in order to prevent enzyme denaturation in the next step.

Any restriction enzyme that generates a 5' overhang, such as HindIII can be used to digest the now accessible chromatin overnight. This 5' overhang provides the template required by the Klenow fragment of DNA Polymerase I to add biotinylated CTP or ATP to the digested ends of chromatin. This step allows for the selection of Hi-C ligation products for library preparation.

A dilution ligation is performed on DNA fragments that are still crosslinked to one another in order to favor the intramolecular ligation of fragments within the same chromatin complex instead of ligation events between fragments across different complexes. Since this ligation step occurs between blunt-ended DNA fragments (since the sticky ends have been filled in with biotin-labeled bases), the reaction is allowed to go on for up to 4 hours to make up for its inherent inefficiency. As a result of proximity ligation, the terminal HindIII sites are lost and an NheI site is generated.

Biotin removal, DNA shearing, size selection and end repair

The biotin-labeled ligation products can be purified using phenol-chloroform DNA extraction. To remove any fragments with biotin-labeled ends that have not been ligated, T4 DNA Polymerase with 3' to 5' exonuclease activity is used to remove nucleotides from the ends of such fragments. This step ensures that none of these unligated fragments are selected for library preparation. The reaction is stopped with EDTA and the DNA is purified once again using phenol-chloroform DNA extraction.

The ideal size of DNA fragments for the sequencing library depends on the sequencing platform that will be used. DNA can first be sheared to fragments around 300-500 bp long using sonication. Fragments of this size are suitable for high-throughput sequencing. Following sonication, fragments can be size selected using AMPure XP beads from Beckman Coulter to obtain ligation products with a size distribution between 150 and 300 bp. This is the optimal fragment size window for HiSeq cluster formation.

DNA shearing causes asymmetric DNA breaks and must be repaired before biotin pulldown and sequencing adaptor ligation. This is achieved by using a combination of enzymes that fill in 5' overhangs, and add 5' phosphate groups and adenylate to the 3' ends of fragments to allow for ligation of sequencing adaptors.

Using an excess of streptavdin beads, such as the My-One C1 streptavidin bead solution from Dynabeads, biotinylated Hi-C ligation products can be pulled-down and enriched for. Ligation of the Illumina paired-end adapters is performed while the DNA fragments are bound to the streptavidin beads. Adsorption to the beads increases efficiency of the ligation of these blunt-ended DNA fragments to the adaptors, as it decreases their mobility.

After the ligation of the adaptors is complete, PCR amplification of the library is performed. The PCR step can introduce high number of duplicates in a low complexity Hi-C ligation product sample as a result of over-amplification. This results in very few interactions being captured and oftentimes, this is because the input sample size had a low amount of cells. It is important to titrate the number of cycles required to get at least 50 ng of Hi-C library DNA for sequencing. Fewer the cycle number, the better so that there are no PCR artifacts (such as off-target amplicons, non-specificity, etc.). The ideal range of PCR cycles is 9-15 and it is more ideal to pool multiple PCR reactions to get enough DNA for sequencing, than to increase the number of cycles for one PCR reaction. The PCR products are purified again using AMPure beads to remove primer dimers and then quantified before being sequenced. Regions of chromatin that interact with each other are then identified by paired-end sequencing of the biotinylated, ligated products.

Any platform that can allow for the ligated fragments to be sequenced across the NheI junction ( Roche 454) or by paired-end or mate-paired reads ( Illumina GA and HiSeq platforms) would be suitable for Hi-C. Before high-throughput sequencing, the quality of the library should be verified using Sanger sequencing, wherein the long sequencing read will read through the biotin junction. Thirty-six or 50 bp reads are sufficient to identify most chromatin interacting pairs using Illumina paired-end sequencing. Since the average size of fragments in the library is 250 bp, 50bp paired-end reads have been found to be optimum for Hi-C library sequencing.

Standard Hi-C has a high input cell number cost, requires deep sequencing, generates low-resolution data, and suffers from formation of redundant molecules that contribute to low complexity libraries when cell numbers are low. To combat these issues in order to be able to apply this technique in contexts where cell number is a limiting factor, for example, with primary human cell work, several Hi-C variants have been developed since the first conceptualization of Hi-C.

The resolution that can be achieved with in situ Hi-C can be up to 950 to 1000 bp compared to the 1 to 10 Mb resolution of standard Hi-C and the 100 kb resolution of DNase Hi-C. While standard Hi-C makes use of a 6-bp cutter such as HindIII for the restriction digest step, in situ Hi-C uses a 4-bp cutter such as MboI or its isoschizomer DpnII (which is not sensitive to CpG methylation) to increase efficiency and resolution (as the restriction sites of MboI and DpnII are more frequently occurring in the genome). Data between replicates for in situ Hi-C is consistent and highly reproducible, with very less background noise and demonstrating clear chromatin interactions. It is however possible that some of the captured interactions may not be accurate intermolecular interactions since the nucleus is densely packed with protein and DNA so performing proximity ligations in intact nuclei may pull down confounding interactions that may only form due to the nature of nuclear packaging and not so much unique chromosomal interactions with cellular functional impact. It also requires an extremely high sequencing depth of around 5 billion paired-end reads per sample to achieve the resolution of data described by Rao et al. Several techniques that have adapted the concept of in situ Hi-C exist, including Sis Hi-C, OCEAN-C and in situ capture Hi-C. Described below are two of the most prominent in situ Hi-C based techniques.

SAFE Hi-C, or simplified, fast, and economically efficient Hi-C, generates sufficient ligated fragments without amplification for high-throughput sequencing. In situ Hi-C data that has been published indicates that amplification (at the PCR step for library preparation) introduces distance-dependent amplification bias, which results in a higher noise to signal ratio against genomic distance. SAFE Hi-C was successfully used to generate an amplification-free, in situ Hi-C ligation library from as low as 250 thousand K562 cells. Ligation fragments are anywhere between 200 and 500 bp long, with an average at about 370 bp. All ligation product libraries were sequenced using the Illumina HiSeq platform (2x150 bp paired-end reads). Although SAFE Hi-C can be used for a cell input as low as 250 thousand, Niu et al. recommend using 1 to 2 million cells. Samples produce enough ligates to be sequenced on one-fourth of a lane. SAFE Hi-C has been demonstrated to increase library complexity due to the removal of PCR duplicates which lower the overall percentage of unique paired reads. Overall, SAFE Hi-C preserves the integrity of chromosomal interactions while also reducing the need to have high sequencing depth and saving overall cost and labor.

Micro-C is a version of Hi-C that includes a micrococcal nuclease (MNase) digestion step to look at interactions between pairs of nucleosomes, thus enabling resolution of sub-genomic TAD structures at the 1 to 100 nucleosome scale. It was first developed for use in yeast and was shown to conserve the structural data obtained from a standard Hi-C but with greater signal-to-noise ratio. When used with human embryonic stem cells and fibroblasts, 2.6 to 4.5 billion uniquely mapped reads were obtained per sample. Hsieh et al. analyzed 2.64 billion reads from mouse embryonic stem cells and demonstrated that there was increased power for detecting short-range interactions.

Hi-C has also been adapted for use with single cells but these techniques require high levels of expertise to perform and are plagued with issues such as low data quality, coverage, and resolution.

Hi-C data produced by deep sequencing is in the form of a traditional FASTQ file, and the reads can be aligned to the genome of interest using sequence alignment software (e.g. Bowtie, bwa, etc.). Because Hi-C ligation products may span hundreds of megabases and may bridge loci on different chromosomes, Hi-C read alignment is often chimeric in the sense that different parts of a read may be aligned to loci distant apart, possibly in different orientations. Long-read aligners (e.g. minimap2) often support chimeric alignment and can be directly applied to long-read Hi-C data. Short-read Hi-C alignment is more challenging.

Several variations of read mapping techniques are implemented in many bioinformatics pipelines, such as ICE, HiC-Pro, HIPPIE, HiCUP, and TADbit, to map two portions of a paired end read separately, in the case that the two portions match distinct genomic positions, thus addressing the challenge where reads span the ligation junctions.

With increased read length, more recent pipelines (e.g. Juicer and the 4D-Nucleosome Data Portal) often align short Hi-C reads with an alignment algorithm capable of chimeric alignment, such as bwa-mem, chromap and dragmap. This procedure calls alignment once and is simpler than iterative mapping.

The mapped reads are then each assigned a single genomic alignment location according to its 5' mapped position in the genome. For each read pair, a location is assigned to only one of the restriction fragments, thus should fall in close proximity to a restriction site and less than the maximum molecule length away. Reads mapped more than the maximum molecule length away from the closest restriction sites are the results of physical breakage of the chromatin or non-canonical nuclease activities. Because these reads also instruct information on chromatin interactions, they are not discarded, but appropriate filtering must take place after assigning genomic locations to remove technical noise in the dataset.

The remaining paired reads mapped to distinct restriction fragments are also filtered to discard identical/redundant PCR products, and this is achieved by removing reads sharing the exact same sequence or 5' alignment positions. Additional levels of filtering could also be applied to fit the experimental purpose. For example, potential undigested restriction sites could be specifically filtered out, rather than passively identified, by removing reads mapped to the same chromosomal strand with a small distance (user-defined, experience-based) in between.

Bin-level filtering, just like fragment-level filtering, also takes place to shed experimental artifacts from the obtained data. Bins with high noise and low signals are removed as they typically represent highly repetitive genomic contents around the telomeres and centromeres. This is done by comparing the individual bin sums to the sum of all bins and removing the bottom 1% of bins, or by using the variance as a measure of noise. Low-coverage bins, or bins three standard deviations below the center of a log-normal distribution (which fits the total number of contacts per genomic bin), are removed using the MAD-max (maximum allowed median absolute deviation) filter. After binning, Hi-C data will be stored in a symmetrical matrix format.

More recently, many approaches have been proposed to predetermine the optimal bin size for different Hi-C experiments. Li et al. in 2018 described deDoc, a method where bin size is selected as the one at which the structural entropy of the Hi-C matrix reaches a stable minimum. QuASAR, on the other hand, offers a bit more quality assessment, and compares replicate scores of the samples (given that replicates are indeed included for the experimental purpose) to find the maximum usable resolution. Some publications also tried to score interaction frequencies at the single-fragment level, where a higher coverage can be achieved even with a lower number of reads. HiCPlus, a tool developed by Zhang et al. in 2018, is able to impute Hi-C matrices similar to the original ones using only 1/16 of the original reads.

Balancing/normalization

Balancing refers to the process of bias correction of the obtained Hi-C data, and can be either explicit or implicit. Explicit balancing methods require the explicit definitions of biases known to be associated with Hi-C reads (or any high-throughput sequencing technique in general) including the read mappability, GC content, as well as individual fragment length. A correction factor is first computed for each of the considered biases, followed by each of their combination, and then applied to the read counts per genomic bin.

However, some biases can come from an unknown origin, in which case an implicit balancing approach is used instead. Implicit balancing relies on the assumption that each genomic locus should have "equal visibility", which suggests that the interaction signal at each genomic locus in the Hi-C data should add up to the same total amount. One approach called iterative correction uses the Sinkhorn-Knopp balancing algorithm and attempts to balance the symmetrical matrix using the aforementioned assumption (by equalizing the sum of each and every row and column in the matrix). The algorithm iteratively alternates between two steps: 1) dividing each row by its mean, and 2) dividing each column by its mean, which are guaranteed to converge in the end and leave no obviously high rows or columns in the interaction matrix. Other computational methods also exist to normalize the biases inherent to Hi-C data, including sequential component normalization (SCN), the Knight-Ruiz matrix-balancing approach, and eigenvector decomposition (ICE) normalization. In the end, both the explicit and the implicit bias correction methods yield comparable results.

With a binned, genome-wide interaction matrix, common interaction patterns observed in mammalian genomes can be identified and interpreted biologically, while more rare, less frequently observed patterns such as circular chromosomes and centromere clustering, may require additional specially-tailored methods to be identified.

1. Cis/trans interaction ratio

Cis/ trans interactions are one of the two strongest interaction patterns observed in Hi-C maps. They are not locus-specific, and thus are considered as a genome-level pattern. Typically, a higher interaction frequency is observed, on average, for pairs of loci residing on the same chromosome (in cis) than pairs of loci residing on different chromosomes (in trans). In Hi-C interaction matrices, cis/trans interactions appear as square blocks centered along a diagonal, matching individual chromosomes at the same time. Because this pattern is relatively consistent across different species and cell types, it can be used to assess the quality of the data. A noisier experiment, due to random background ligation or any unknown factor, will result in a lower cis to trans interaction ratio (as the noise is expected to affect both cis and trans interactions to a similar extent), and high-quality experiments typically have a cis/trans interaction ratio between 40 and 60 for the human genome.

This pattern refers to the distance-dependent decay of interaction frequencies on a genome level, and represents the second one of the two strongest Hi-C interaction patterns. As the interaction frequencies between cis-interacting loci decrease (as a result of further distance between them), a gradual decrease of interaction frequency can be observed moving away from the diagonal in the interaction matrix.

The strongest locus-specific pattern found in Hi-C maps is chromatin compartments, which takes the shape of a plaid or "checker-board"-like pattern on the interaction matrix, with alternating blocks that range between 1 and 10 Mb in size (which makes them easy to extract even in experiments with very low sampling) in the human genome. This pattern can be found at both high and low frequencies. Because chromosomes consist of two types of genomic regions that alternate along the length of individual chromosomes, the interaction frequencies between two regions of the same type and interaction frequencies between two regions of different types can be quite different.

The definition of the active (A) and inactive (B) chromatin compartments is based on principal component analysis, first established by Lieberman-Aiden et al. in 2009. Their approach calculated the correlation of the Hi-C matrix of observed vs. expected signal (obtained from a distance-normalized contact matrix) ratio, and used the sign of the first eigenvector to denote positive and negative parts of the resulting plot as A and B compartments, respectively. Many genomic studies have indicated that chromatin compartments are correlated with chromatin states, such as gene density, DNA accessibility, GC content, replication timing, and histone marks. Therefore, type A compartments are more specifically defined to represent the gene-dense regions of euchromatin, while type B compartments represent heterochromatic regions with less gene activities. Overall, chromatin compartments offer insights on the general organization principles of the genome of interest.

More and more bioinformatics tools capable of performing compartment calling have been developed over the past decade, including HOMER, HiTC R, and CscoreTool. Although they each has their own differences and optimizations made on the original 2009 approach, their base protocols still rely on principal component analysis.

4. Topologically associating domains (TADs)

TADs are sub-Mb structures that may harbor gene-regulatory features, such as local promoter- enhancer interactions. More generally, TADs are considered as an emergent property of underlying biological mechanisms, which defines TADs as loop extrusions, compartmentalizations, or any dynamic genomic pattern rather than a static structural feature of the genome. Thus, TADs represent regulatory microenvironments and usually show up on a Hi-C map as blocks of highly self-interacting regions in which interaction frequencies within the region are significantly higher than interaction frequencies between two adjacent regions. In Hi-C interaction matrices, TADs are square blocks of elevated interaction frequencies centred along the diagonal. However, this is merely an oversimplified description, and identifying the actual pattern requires much more statistical processing and estimation.

Another approach is to calculate the average interaction frequencies crossing over each bin, again within some predetermined genomic range. The resulting value is referred to as the insulation score and can be thought of as the average of a square sliding along the diagonal of the matrix (Crane et al.). This value is expected to be lower at TAD boundaries; thus, one can use standard statistical techniques to find local minima (boundaries), and define regions between consecutive boundaries to be TADs.

Biologically, regulatory interactions usually occur at much smaller scale than TADs, and two genomic elements can activate/inhibit the expression of a gene within as small a distance as 1 kb. Therefore, point interactions are important in interpreting Hi-C maps, and are expected to appear as local enrichments in contact probability. However, current methodologies for the identification of point interactions are all implicit in nature, in that they do not instruct what a point interaction should look like. Instead, point mutations are identified as outliers with higher interaction frequencies than expected within the Hi-C matrix, given that the background model consists only of the strongest signals such as the distance-decay functions. The background model can be estimated and constructed using both local signal distributions and global approaches (i.e. chromosome-wide/genome-wide). Many of the aforementioned bioinformatics packages incorporate algorithms to identify point interactions. In short, the significance of individual pairwise interaction is calculated, and significantly high outliers are corrected for multiple testing before they are recognized as truly informative point interactions. It is helpful to compliment identified point interactions with additional evidence such as analysis of enrichment scores and biological replicates, to indicate that these interactions are indeed of biological significance.

Hi-C can reveal chromatin conformation changes during cell division. In interphase, chromatins are generally loose and vivacious so that transcription regulation and other regulatory activities could take place. When entering mitosis and cell division, chromatins become compactly folded into dense cylindrical chromosomes. Within the past five years, the development of single-cell Hi-C has enabled the depiction of the entire 3D structural landscape of chromatins/chromosomes throughout the cell cycle, and many studies have discovered that these identified genomic domains remain unchanged in interphase, and are erased by silencing mechanisms when the cell enters mitosis. When mitotic division is completed and the cell re-enters the interphase, chromatin 3D structures are observed to be re-established, and transcription regulation is restored.

It has been suspected that the differentiation of embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) into various mature cell lineages is accompanied by global changes in chromosomal structures and consequently interaction dynamics to allow for the regulation of transcriptional activation/silencing. Standard Hi-C can be used to investigate this research question.

In 2015, Dixon et al. applied standard Hi-C to capture global 3D dynamics in human ESCs during their differentiation into high five cells. Due to the ability of Hi-C to depict dynamic interactions in differentiation-related TADs, the researchers discovered increases in the number of DHS sites, CTCF binding ability, active histone modifications, and target gene expressions within these TADs of interest, and found significant participation of major pluripotency factors such as OCT4, NANOG, and SOX2 in the interaction network during somatic cell reprogramming. Since then, Hi-C has been recognized as one of the standard methods to probe for transcriptional regulatory activities, and has confirmed that chromosome architecture is closely related to cell fate.

Mammalian somatic growth and development starts with the fertilization of sperm and oocyte, followed by the zygote stage, the 2-cell, 4-cell, and the 8-cell stage, the blastocyst stage, and finally the embryo stage. Hi-C made it possible to explore the comprehensive genomic architecture during growth and development, as both sis-Hi-C and in situ Hi-C have reported that TADs and genomic A and B compartments are not obviously present and appear to be less well-structured in oocyte cells. These structural features of the chromatin only gradually establish from weaker frequencies to cleaner and more frequent datapoints after fertilization, as developmental stages progress.

As data on 3D genome structures becomes more and more prevalent in recent years, Hi-C begins to be used as a means to track evolutionary structural features/changes. Genomic single nucleotide polymorphisms (SNPs) and TADs are typically conserved across species, along with the CTCF factor in the chromatin domain evolution. Other factors, however, have been revealed by Hi-C techniques to experience structural evolutions in 3D architecture. These include codon usage frequency similarity (CUFS), paralog gene co-regulation, and spatially co-evolving orthologous modules (SCOMs). For large-scale domain evolution, chromosomal translocations, syntenic regions, as well as genomic rearrangement regions were all relatively conserved. These findings imply that Hi-C technologies is capable of providing an alternative point of view in the eukaryotic tree of life.

Several studies have employed the use of Hi-C to describe and study chromatin architecture in different cancers and their impact on disease pathogenesis. Kloetgen et al. used in situ Hi-C to study T cell acute lymphoblastic leukemia (T-ALL) and found a TAD fusion event that removed a CTCF insulation site, allowing for the oncogene MYC's promoter to directly interact with a distal super enhancer. Fang et al. have also shown how there are T-ALL specific gain or loss of chromatin insulation, which alters the strength of TAD architecture of the genome, using in situ Hi-C. Low-C has been used to map the chromatin structure of primary B cells of a diffuse large B-cell lymphoma patient and was used to find high chromosome structural variation between the patient and healthy B-cells. Overall, the application of Hi-C and its variants in cancer research provides unique insight into the molecular underpinnings of the driving factors of cell abnormality. It can help explain biological phenomena (high MYC expression in T-ALL) and help aid drug development to target mechanisms unique to cancerous cells.

1. Hoffman, Elizabeth A.; Frey, Brian L.; Smith, Lloyd M.; Auble, David T. (30 October 2015). "Formaldehyde Crosslinking: A Tool for the Study of Chromatin Complexes". The Journal of Biological Chemistry. 290 (44): 26404-26411. doi:10.1074/jbc.R115.651679. ISSN 0021-9258. PMC . PMID 26354429.

2. ^ Li, Heng (2013). "Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM". arXiv: [q-bio.GN].

3. ^ Sauria, Michael EG; Taylor, James (14 November 2017). "QuASAR: Quality Assessment of Spatial Arrangement Reproducibility in Hi-C Data": 204438. doi:10.1101/204438. S2CID 90376810.

4. ^ Knight, Philip; Ruiz, Daniel (26 October 2012). "A fast algorithm for matrix balancing". IMA Journal of Numerical Analysis. 33 (3): 1029-1047. doi:10.1093/imanum/drs019.

5. Ou, Horng D.; Phan, Sébastien; Deerinck, Thomas J.; Thor, Andrea; Ellisman, Mark H.; O'Shea, Clodagh C. (28 July 2017). "ChromEMT: Visualizing 3D chromatin structure and compaction ninterphase and mitotic cells". Science. 357 (6349): eaag0025. doi:10.1126/science.aag0025. ISSN 0036-8075. PMC . PMID 28751582.

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Hi-C (genomic analysis technique) - Wikipedia

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Hi-C (or standard Hi-C) is a high-throughput genomic and epigenomic technique first described in 2009 by Lieberman-Aiden et al. to capture chromatin conformation. In general, Hi-C is considered as a derivative of a series of chromosome conformation capture technologies, including but not limited to 3C (chromosome conformation capture), 4C (chromosome conformation capture-on-chip/circular chromosome conformation capture), and 5C (chromosome conformation capture carbon copy).

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La disrupción de los reguladores clave de la arquitectura del genoma puede tener consecuencias graves. Se ha demostrado que mutaciones en CTCF y cohesina están asociadas con enfermedades genéticas y trastornos del desarrollo.

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La combinación de enfoques basados en 3C con otros tipos de datos epigenómicos puede ayudar a cerrar la brecha de conocimiento y revelar principios fundamentales subyacentes a la estructura y función del genoma.

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La comprensión de la estructura tridimensional del genoma en el núcleo es fundamental para comprender la regulación génica y cómo los cambios en la organización del genoma pueden contribuir a enfermedades y trastornos genéticos.

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Técnicas como Hi-C y FISH se utilizan para estudiar la estructura tridimensional del genoma en el núcleo y han revelado información sobre la organización espacial de los cromosomas y la formación de compartimentos y subdominios.

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La organización tridimensional del genoma en el núcleo puede influir en la regulación génica al permitir la interacción de elementos reguladores con sus genes objetivo.

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Territorios cromosomicos
Regiones específicas de los cromosomas que contienen genes y elementos reguladores que controlan procesos biológicos dentro del núcleo celular. Estos se dividen en a nivel jerarquico segun su nivel de compactación en Territorios cromosómicos, compartimentos A y B, regiones TAD y subTADs, y por último los loops.

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Reguladores de la organización del genoma.

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Reguladores de la organización del genoma.

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CTCF induce la formación de LADs
Interacción intra e intercromosomal

COHESINAS y CTCF  están en los límites de los dominios de asociación topológica (TADS) que son microambientes funcionales de reguladores y targets

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El objetivo de las técnica de análisis tipo 3C es analizar el comportamiento de la conformación a nivel celular; sin embargo, algunas limitaciones encontradas se atribuyen a las diferencias significativas de la conformación de la cromatina en células individuales que pueden presentar variaciones según el ciclo celular y estado celular ya que, los datos de los análisis 3C generan patrones de interacción de cromatina promediados que no otorgan información sobre las fuerzas de interacción y estabilidad cromosómica de cada célula en caso ocurran.

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El mejoramiento del mapeo 3D del genoma significa mejorar la profundidad de secuenciación a partir de Bibliotecas Hi-C. Para ello, se emplean técnicas más avanzadas como técnicas 4C y 5C. Una mayor calidad de Bibliotecas Hi-C conlleva a tener una mejor profundidad de secuenciación y esto permite tener una mejor resolución de datos. La captura del genoma completo o selectivo puede aumentar  el número de lecturas utilizables Hi-C. Uno de los métodos utilizados para la captura selectiva del genoma puede darse a partir del uso de oligonucleótidos específicos para unir bibliotecas Hi-C y observar las interacciones intra o intercromosómicas. Este método es una tecnología 3C de cuarta generación.

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Esquema de la metodología de la técnica T2C

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Técnica de mejoramiento del mapeo 3D del genoma (ejemplo)

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Técnica de mejoramiento del mapeo 3D del genoma (ejemplo)

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La técnica T2C de captura de cromatina dirigida es de alta resolución y de alto rendimiento. Es una técnica de cuarta generación donde semplean oligonucleótidos específicos para unir bibliotecas Hi-C y observar las posibles interacciones cromosómicas.
La cromatina entrecruzada aislada pasa por una digestión primaria, se liga y vuelve a pasar por una segunda digestión por enzima de registrión donde ocurre una reparación y se liga a sus adaptadores correpondientes para dar paso a la hibridación por oligonucleótidos. Finalmente, ocurre la secuenciación por pares. El mapa de interacción de cromatina representa las interacciones entre dos fragmentos del genoma.

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La agrupación de secuencias cortas en contigs sigue siendo un desafío para el ensamblaje de genomas. El tamaño pequeño y la baja calidad de las lecturas dificultan el ensamblaje. Para  la construcción de modelos genómicos basados en secuencias cortas, se hacen uso de mapas físicos como es el caso de los que se encuentran en el Proyecto Genoma Humano, aún así, existen contigs que no se encuentran referenciados, y en otros organismos aún no se han establecidos genomas de referencia. Para contrarrestar estas dificultades se buscan diversos enfoques de secuenciación profunda actuales.

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Tal es el caso del programa LACHESIS "ligadura de cromatina adyacente permite el andamiaje in situ", un método para ensamblar genomas de Novo a partir de datos de secuenciación shotgun y lecturas Hi-C.
a. La entrada es un grupo de contigs y datos de interacción cromosómica (enlaces Hi-C) del genoma de referencia. Las interacciones cromosómicas se muestran en líneas naranjas agrupadas.
b. Los contigs en el mismo cromosoma (mismo color: azul, verde, rojo) presentan mayor número de interacciones cromosómicas, es decir, hay mayor número de interacciones intracromosómicas que intercromosómicas.
c. En el mismo cromosoma, los contigs más cercanos, muestran más enlaces Hi-C que contigs el número de enlaces entre contigs más separados. Esto permite que el programa órdenes los contigs por grupo cromosómico.
d. La posición de los enlaces permite el ordenamiento según la orientación de cada uno de los contigs.

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Debido  que cada cromosoma se pliega en un lugar distinto incluso el par de cromosomas homólogos y es posible que el mayor número de interacciones cromosómicas y contactos de la cromatina provengan del interior de cada cromosoma, las nuevas tecnologías buscan la separación de cromosomas homólogos empleando la técnica Hi-C y datos de secuenciación del genoma completo. Esto podría ayudar en la reconstrucción de haplotipos celulares en base a los datos proporcionados por interacciones en Hi-C.

Asimismo, también existen técnicas 3C con enfoque metagenómico (meta 3C) que puede analizar la diversidad de la organización cromosómica de los microorganismos en una comunidad. Este método puede caracterizar genomas individuales y genomas de las especies contenidas en una mezcla de microorganismos.

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1. La organización tridimensional del genoma en el núcleo celular es esencial para la regulación génica y la función del genoma.
2. Los reguladores clave, como CTCF y cohesina, desempeñan un papel crucial en la organización y regulación del genoma.
3. Se necesitan más investigaciones y tecnologías avanzadas para comprender completamente la estructura y función del genoma en el núcleo celular.

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