Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Massively multiplex single-cell Hi-C

This article has been updated


We present single-cell combinatorial indexed Hi-C (sciHi-C), a method that applies combinatorial cellular indexing to chromosome conformation capture. In this proof of concept, we generate and sequence six sciHi-C libraries comprising a total of 10,696 single cells. We use sciHi-C data to separate cells by karyotypic and cell-cycle state differences and identify cell-to-cell heterogeneity in mammalian chromosomal conformation. Our results demonstrate that combinatorial indexing is a generalizable strategy for single-cell genomics.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Figure 1: sciHi-C integrates in situ Hi-C with combinatorial cellular indexing to generate signal-rich bulk Hi-C maps that can be decomposed into single-cell Hi-C maps.
Figure 2: Cellular indices generated through sciHi-C are overwhelmingly species specific and can be separated by cell type.
Figure 3: sciHi-C of nocadazole-arrested HeLa S3 cells enable in silico sorting by cell-cycle progression.

Similar content being viewed by others

Accession codes

Primary accessions

Gene Expression Omnibus

Change history

  • 10 February 2017

    In the version of this article initially published online, the Gene Expression Omnibus (GEO) accession containing all processed data and raw reads (except for HeLa data) was not provided; the correct accession, GSE84920, has now been included. The error has been corrected for the print, PDF and HTML versions of this article as of 10 February 2017.


  1. Ramani, V., Shendure, J. & Duan, Z. Genomics Proteomics Bioinformatics 14, 7–20 (2016).

    Article  Google Scholar 

  2. Cremer, T. & Cremer, C. Nat. Rev. Genet. 2, 292–301 (2001).

    Article  CAS  Google Scholar 

  3. van Steensel, B. & Dekker, J. Nat. Biotechnol. 28, 1089–1095 (2010).

    Article  CAS  Google Scholar 

  4. Söderberg, O. et al. Nat. Methods 3, 995–1000 (2006).

    Article  Google Scholar 

  5. Ramani, V., Qiu, R. & Shendure, J. Nat. Biotechnol. 33, 980–984 (2015).

    Article  CAS  Google Scholar 

  6. Dekker, J., Rippe, K., Dekker, M. & Kleckner, N. Science 295, 1306–1311 (2002).

    Article  CAS  Google Scholar 

  7. Lieberman-Aiden, E. et al. Science 326, 289–293 (2009).

    Article  CAS  Google Scholar 

  8. Duan, Z. et al. Nature 465, 363–367 (2010).

    Article  CAS  Google Scholar 

  9. Nagano, T. et al. Nature 502, 59–64 (2013).

    Article  CAS  Google Scholar 

  10. Cusanovich, D.A. et al. Science 348, 910–914 (2015).

    Article  CAS  Google Scholar 

  11. Klein, A.M. et al. Cell 161, 1187–1201 (2015).

    Article  CAS  Google Scholar 

  12. Macosko, E.Z. et al. Cell 161, 1202–1214 (2015).

    Article  CAS  Google Scholar 

  13. Rotem, A. et al. Nat. Biotechnol. 33, 1165–1172 (2015).

    Article  CAS  Google Scholar 

  14. Rao, S.S.P. et al. Cell 159, 1665–1680 (2014).

    Article  CAS  Google Scholar 

  15. Deng, X. et al. Genome Biol. 16, 152 (2015).

    Article  Google Scholar 

  16. Adey, A. et al. Nature 500, 207–211 (2013).

    Article  CAS  Google Scholar 

  17. Essletzbichler, P. et al. Genome Res. 24, 2059–2065 (2014).

    Article  CAS  Google Scholar 

  18. Naumova, N. et al. Science 342, 948–953 (2013).

    Article  CAS  Google Scholar 

  19. Imakaev, M. et al. Nat. Methods 9, 999–1003 (2012).

    Article  CAS  Google Scholar 

  20. Sanborn, A.L. et al. Proc. Natl. Acad. Sci. USA 112, E6456–E6465 (2015).

    Article  CAS  Google Scholar 

  21. Vitak et al. Nat. Methods http://dx.doi/10.1038/NMETH.4154 (2017).

  22. Ramani, V., Duan, Z. & Shendure, J. Massively multiplex single-cell Hi-C. Protocol Exchange (2017).

  23. Jin, W. et al. Nature 528, 142–146 (2015).

    Article  CAS  Google Scholar 

  24. Servant, N. et al. Genome Biol. 16, 259 (2015).

    Article  Google Scholar 

  25. Carette, J.E. et al. Nature 477, 340–343 (2011).

    Article  CAS  Google Scholar 

Download references


The authors thank S. Kasinathan, members of the UW Center for Nuclear Organization and Function, and members of the Shendure lab (particularly M. Kircher), for helpful discussions. HeLa S3 cells were used as part of this study. Henrietta Lacks, and the HeLa cell line that was established from her tumor cells in 1951, have made significant contributions to scientific progress and advances in human health. We are grateful to Henrietta Lacks, now deceased, and to her surviving family members for their contributions to biomedical research. Primary MEF aliquots were a gift from C. Ware (University of Washington), HeLa S3 aliquots were a gift from the Malik Lab (Fred Hutchinson Cancer Research Center), and Patski cell aliquots were a gift from the Disteche lab (University of Washington). This work was funded by grants from the NIH (5T32HG000035 to V.R.; DP1HG007811 and 5R01HG006283 to J.S.; and U54DK107979 to X.D., C.M.D., W.S.N., Z.D., and J.S.). J.S. is an Investigator of the Howard Hughes Medical Institute.

Author information

Authors and Affiliations



V.R., Z.D., and J.S. conceived of the project. V.R., X.D., R.Q., and Z.D. carried out experiments. C.M.D. and W.S.N. provided invaluable critical input. K.L.G. and F.J.S. were part of initial discussions on novel approaches to single-cell Hi-C. V.R., Z.D., and J.S. wrote the paper.

Corresponding authors

Correspondence to Zhijun Duan or Jay Shendure.

Ethics declarations

Competing interests

K.L.G. and F.J.S. are employees of Illumina Inc.

Integrated supplementary information

Supplementary Figure 1 Nuclei remain intact through proximity ligation in the combinatorial single cell Hi-C protocol

Phase contrast microscopy of HeLa S3 and HAP1 nuclei following proximity ligation and serial dilution shows that nuclei remain intact throughout the combinatorial single cell Hi-C protocol (scale bar = 100 μm).

Supplementary Figure 2 Coverage of combinatorial single cell Hi-C cellular indices follows a bimodal distribution.

Examining a histogram of the coverage (i.e. # of unique reads) of combinatorial single cell Hi-C cellular indices in two replicate libraries reveals a bimodal distribution, where low coverage cellular indices likely represent barcoding of free DNA in solution, rather than intact nuclei.

Supplementary Figure 3 Coverage of cellular indices is not correlated between replicate experiments

Scatter plot of coverage per cellular index for all cellular indices with at least 1 unique read in both replicate combinatorial single cell Hi-C libraries. A Pearson’s r of -0.03 suggests that there is minimal intrinsic bias (i.e. “barcode” effect) biasing coverage of particular cellular indices.

Supplementary Figure 4 Single cellular indices demonstrate high cis:trans ratios.

Histogram of the cis:trans ratios for cellular indices over two biological replicates. High cis:trans ratio suggest that nuclei remain intact during the protocol, and hint at a single-cellular origin for the majority of cellular indices.

Supplementary Figure 5 Quality control statistics for PL1 and PL2 libraries are similar to primary experiment libraries.

a.) Violin plots showing the distribution of ligation types across all cellular indices with at least 1,000 reads in libraries PL1 and PL2. b.) Species specificity for both libraries.

Supplementary Figure 6 The HeLa genotype enables further filtration of potential barcode collisions in combinatorial single cell Hi-C datasets.

We examined all homozygous non-reference sites determined by Adey et al and tabulated the fraction of sites where the non-reference allele was found in our sequencing reads, with the expectation that single HeLa cells should have very high (i.e. >=99%) homozygous non-reference alleles at those sites, with reduced fractions indicating contamination by HAP1. For this study, we drew conservative cutoffs of 57% and 99% for each species (i.e. any cellular indices falling between these values were discarded).

Supplementary Figure 7 Raw single cell matrices used as input for PCA.

To generate these matrices, we took single-cell contact maps and vectorized them, such that each cell is represented by a vector of non-redundant contact counts between two loci. For interchromosomal analysis, each vector contained the log10 transformed number of raw counts between two chromosomes; for intrachromosomal analysis, each vector contained a 1 if a contact between two 10 Mb intrachromosomal windows was observed, and 0 if not. These vectors were then concatenated to form the heatmaps above. The pairwise bin ID simply represents a label for each pair of interacting windows represented in the heat maps. a.) A heat map representation of a portion (250 cells) of the input interchromosomal matrix for PCA. Rows represent single human cells, while columns represent pairwise interactions between two whole chromosomes. For this analysis, raw counts were used, and n = 3,609 cells. b.) Heat map representation of a portion (2,500 cells) of the input intrachromosomal matrix for PCA. Here, interchromosomal counts were ignored, and interaction frequencies between discrete 10 Mb windows genome-wide were reduced to a binary representation (i.e. 1 if present, 0 if absent). Again, n = 3,609 cells.

Supplementary Figure 8 The first component of PCA using both interchromosomal contacts and 10 Mb windowed intrachromosomal contacts strongly correlates with coverage.

a.) Correlation between the principal component 1 (PC1) and coverage for interchromosomal interactions (ρ = -0.917). b.) Correlation between the principal component 1 (PC1) and coverage for interacting 10 Mb intrachromosomal windows (ρ = 0.897).

Supplementary Figure 9 Analysis of principal component loadings for interchromosomal separation experiment reveals that translocations contribute to cell type separation in principal component space.

a.) Heat map of loadings for principal component 2 after all known translocations (blacked out entries) are removed from the analysis. b.) After removing all entries corresponding to known translocations from the interchromosomal single-cell Hi-C contact matrix, cell-type separation using PC1 and PC2 is qualitatively worse but still apparent, suggesting that cell-type specific interchromosomal contacts may contribute to the observed separation pattern. Percentages shown are the percentage of variance explained by each plotted PC.

Supplementary Figure 10 PCA using an alternate feature set still enables separation between HAP1 and K562.

Shown is a projection of principal component 2 and principal component 3 from PCA on the intrachromosomal single cell contact matrix (n = 3,609 cells). For this analysis, only intrachromosomal contacts between 10 Mb windows were used. The matrix used for this computation is shown in Supplementary Figure 7b. Percentages shown are the percentage of variance explained by each plotted PC.

Supplementary Figure 11 Separation of cell types by PCA is consistent across biological replicate combinatorial single cell Hi-C experiments.

Across 4 different libraries, the separation of single HeLa S3 and HAP1 cells is evident, suggesting that this is not simply a technical artifact or batch effect.

Supplementary Figure 12 PCA of single-cell interchromosomal contacts using cells from 4 different human cell types results in separation of HeLa S3 from other cell lines.

A fifth experiment (Library ML3) containing K562 and GM12878 cells was lightly sequenced and combined with an existing HeLa S3 and HAP1 dataset (Library ML1), resulting in n = 1,394 cells. Projection of single cells onto PC2 and PC3 results in separation of HeLa S3 from the remaining three cell types, but weak separation of K562, GM12878, and HAP1. Percentages shown are the percentage of variance explained by each plotted PC.

Supplementary Figure 13 Combinatorial single cell Hi-C captures cell-to-cell heterogeneity masked by bulk measurement.

a.) Decay in contact probability for all primary experiment (ML libraries) cells with at least 10,000 unique contacts (n = 769 cells). Plotted is the mean contact probability for each bin (purple), along with standard deviation (blue). Shuffled controls where all cellular index assignments have been randomized demonstrate strikingly lower variance compared to observed single cells, for both mouse and human. b) Scaling coefficients calculated for a.), for distances between 50 kb and 8 Mb. Shuffled controls demonstrate a tighter distribution of coefficients compared to the observed single human cells. c.) Single-cell scaling coefficients reproducibly demonstrate positive correlation with single-cell cis:trans ratios in both mouse and human cells.

Supplementary Figure 14 Correlation between single cell cis:trans ratios and single-cell scaling coefficients is reproducible across combinatorial single-cell Hi-C experiments.

We observe a correlation between high cis:trans ratios and shallow scaling coefficients in both mouse and human cells in both the PL2 (Pearson’s R = 0.199; Spearman’s ρ = 0.0713) and ML3 (Pearson’s R = 0.643; Spearman’s ρ = 0.175) experiments. It is possible that the lack of correlation / weaker correlation shown in PL1 (Pearson’s R = 0.0649; Spearman’s ρ = -0.0500) and PL2, respectively, are a result of shallower sequencing, or sampling (i.e. perhaps related to the relative abundance of unsynchronized cells in each phase of the cell cycle).

Supplementary Figure 15 “Programmed” barcoding approaches enable association of cell types with unique first round barcodes.

By loading unique cell types into programmed wells during the first round of indexing, we are able to validate cell types in silico. This schematic shows how libraries PL1 and PL2 were generated, wherein only one cell type was present per cell. By contrast, for ML1, ML2 and ML3, subsets of wells contained mixtures of one human and one mouse cell type.

Supplementary information

Supplementary Text and Figures

Supplementary Figure 1–15, Supplementary Table 1 and Supplementary Protocol (PDF 3550 kb)

Supplementary Data

sciHi-C barcode sequences (XLSX 68 kb)

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ramani, V., Deng, X., Qiu, R. et al. Massively multiplex single-cell Hi-C. Nat Methods 14, 263–266 (2017).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing