Abstract
Bulk and single-cell DNA sequencing has enabled reconstructing clonal substructures of somatic tissues from frequency and cooccurrence patterns of somatic variants. However, approaches to characterize phenotypic variations between clones are not established. Here we present cardelino (https://github.com/single-cell-genetics/cardelino), a computational method for inferring the clonal tree configuration and the clone of origin of individual cells assayed using single-cell RNA-seq (scRNA-seq). Cardelino flexibly integrates information from imperfect clonal trees inferred based on bulk exome-seq data, and sparse variant alleles expressed in scRNA-seq data. We apply cardelino to a published cancer dataset and to newly generated matched scRNA-seq and exome-seq data from 32 human dermal fibroblast lines, identifying hundreds of differentially expressed genes between cells from different somatic clones. These genes are frequently enriched for cell cycle and proliferation pathways, indicating a role for cell division genes in somatic evolution in healthy skin.
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Data availability
scRNA-seq data have been deposited in the ArrayExpress database at EMBL-EBI (www.ebi.ac.uk/arrayexpress) under accession number E-MTAB-7167. WES data is available through the HipSci portal (www.hipsci.org). The lines used in this study have the identifiers: euts, fawm, feec, fikt, garx, gesg, heja, hipn, ieki, joxm, kuco, laey, lexy, naju, nusw, oaaz, oilg, pipw, puie, qayj, qolg, qonc, rozh, sehl, ualf, vass, vils, vuna, wahn, wetu, xugn, zoxy. Metadata, processed data and large results files are available at https://doi.org/10.5281/zenodo.1403510
Code availability
The cardelino methods are implemented in an open-source, publicly available R package (github.com/single-cell-genetics/cardelino). The code used to process and analyse the data is available (github.com/davismcc/fibroblast-clonality), with a reproducible workflow implemented in Snakemake64. Descriptions of how to reproduce the data processing and analysis workflows, with html output showing code and figures presented in this paper, are available at davismcc.github.io/fibroblast-clonality. Docker images providing the computing environment and software used for data processing (hub.docker.com/r/davismcc/fibroblast-clonality/) and data analyses in R (hub.docker.com/r/davismcc/r-singlecell-img/) are publicly available.
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Acknowledgements
We thank D. Jörg for highly constructive discussions and P. Qiao for valuable comments on the manuscript. We acknowledge the Wellcome Sanger Institute Cellular Genetics and Phenotyping teams (in particular, A. Alderton, C. Gomez, R. Boyd, S. Patel and S. Barnett) and DNA pipelines for their invaluable assistance in generating the data for this study. We thank G. Kildisiute for assisting in CNV analysis of the fibroblast lines. This project was supported by Wellcome Sanger core funding (WT206194) and the Human Induced Pluripotent Stem Cell Initiative. Research in the Stegle laboratory is supported by the BMBF, the Volkswagen Foundation, the Chan Zuckerberg Initiative and the EU (ERC project DECODE, grant agreement 732546). D.J.M. is supported by the National Health and Medical Research Council of Australia (grants APP1112681 and APP1162829), seed funding from the Baker Foundation and the Holyoake Research Fellowship at St Vincent's Institute of Medical Research and the University of Melbourne. R.R. is supported the BBSRC Doctoral Training Programme. Y.H. is supported by the University of Cambridge and EMBL-EBI through an EBPOD postdoctoral fellowship. D.J.K. is supported by the Wellcome Trust under grants 203828/Z/16/A and 203828/Z/16/Z. T.H. is supported by a Human Frontier Science Program Fellowship, an EMBO Long-term Fellowship and an EMBO Advanced Fellowship.
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R.R., T.H. and S.A.T. conceived and planned the experiments. R.R. and T.H. carried out the experiments. Y.H., D.J.M. and O.S. developed the computational methods. Y.H. developed the statistical model and the implementation. Y.H. and D.J.M. wrote the software. Y.H. carried out all simulation experiments and benchmarked alternative methods. The HipSci Consortium provided the cell lines and exome sequencing data. P.D. conducted somatic variant calling from exome sequencing data. D.J.G. advised on somatic variant calling approaches and the mutational signatures analysis carried out by R.R. D.J.M. and M.J.B. developed data processing workflows and D.J.M. processed the fibroblast scRNA-sequencing data. R.L. and D.J.M. processed the melanoma scRNA-sequencing data. D.J.K. conducted the selection analyses, supervised by B.D.S. D.J.M. and Y.H. carried out clonal inference and cell-assignment analyses. D.J.M. conducted differential gene and pathway expression analyses and integrated the computational analyses into a reproducible workflow. D.J.M. and R.R. took the lead in writing the manuscript. D.J.M., R.R. and Y.H. drafted the manuscript and designed the figures. W.W. suggested improvements to somatic variant calling and DE analyses. S.A.T. and O.S. conceived of the study, planned and supervised the work. All authors contributed to the interpretation of results and commented on and approved the final manuscript. The HipSci Consortium generated and provided early access to the fibroblast lines used in this work (see Supplementary Note for a full list of consortium members).
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Supplementary Figure 1 Graphical representation of the cardelino model.
The clonal tree configuration matrix C is a random variable and follows a Bernoulli distribution encoded by an input tree configuration Ω that is provided to the model (for example estimated from bulk or single-cell DNA-seq data using existing methods such as Canopy) as well as an error rate ξ, which follows a beta prior distribution with hyper parameters 𝜅. The indicator matrix I defines the assignment of cells to clones, which is another unknown variable, and assumed to follow a multinomial prior with fixed parameter 𝜋 for each cell. The clone configuration C and cell identity I together encode the genotype ci,Ij of each variant i in each cell j. If ci,Ij is 1, the alternative allelic read count will follow a binomial distribution with gene specific parameter 𝜃i, otherwise with error related parameter 𝜃0. Both 𝜃i and 𝜃0 have a beta prior distribution, but with different parameters. Shaded nodes represent observed variables; unshaded nodes represent unknown variables; yellow circled nodes represent fixed hyper parameters.
Supplementary Figure 2 Distribution of key data characteristics from experimental scRNA-seq data from 32 fibroblast cell lines, used as the basis of parameter settings in simulations.
(a) Number of clones inferred from bulk exome-seq data. (b) The median number of variants per clonal branch; (c) The overall coverage of variants, namely the fraction of variants with at least one read. (d) Scatter plot between the mean number of reads per variant per cell and the overall coverage of variants in the same line. The default parameters used in simulations are highlighted with the red line.
Supplementary Figure 3 Simulation results evaluating the inferred relax (error) rate in the configuration of variants in the guide clonal tree.
(a) The estimated relax rate as a function of the simulated error rates. Errors are simulated by uniformly swapping the mutation states in the configuration matrix of the guide clonal tree, which means that a clone may contain false mutations in the guide clonal tree provided to cardelino (except in the case of the base clone which has no mutations under any simulation conditions). (b) The estimated relax rate across different fractions of variants that have wrong branch configuration. Errors are added by swapping branches for variants.
Supplementary Figure 4 Additional results from assessing cardelino and alternative methods using simulated data.
Assessment of cell assignment to clones across a variety of simulation settings, considering SingleCellGenotyper (SCG), Demuxlet, cardelino and its two versions: cardelino-free without any informative clone configuration prior and cardelino-fixed assuming that the clone configuration prior is correct (Methods; Supplementary Note). All methods were applied to simulated data with known ground truth, varying (a) the number of informative variants per clonal branch, (b) the fraction of informative variants covered (that is, nonzero scRNA-seq read coverage), (c) the total number of clones, (d) the precision (i.e., inverse variance) of allelic ratio across genes; lower precision means more genes with high allelic imbalance, (e) the rate of general errors of mutation states in the clone configuration matrix, (f) the fraction of wrongly clustered variants in the input clonal tree branch. Default parameter values are marked with an asterisk and are retained when varything other parameters. (g) The effects of the tree topology on the cell assignment accuracy. In the simulations there are 50 repeats for each parameter, where one of the tree topology candidates is randomly selected in each repeat. For the four-clone configurations, there are four different tree topologies (upper panel), and their performance (area under the precision-recall curve) for the five different methods are splitted (bottom panel).
Supplementary Figure 5 Estimated mutational signature exposures based upon the tri-nucleotide context of somatic SNVs called from whole-exome sequencing (WES) data for n=32 HipSci human fibroblast lines.
The x-axis shows 30 COSMIC mutational signatures, in order, and the y-axis shows estimated exposures (mutation fraction) using the sigfit package (Methods), with significant signatures highlighted in blue. Across lines, the only significant signatures are Signature 7 (UV mutagenic process) and Signature 11.
Supplementary Figure 6 Variant allele frequency (VAF) distributions for somatic variants called from whole exome sequencing data for the 32 fibroblast lines.
The grey lines indicate the minimum allele-frequency threshold (0.05) used for variants for this analysis (Methods). The blue lines indicate the model (neutral/selected) inferred by SubClonalSelection (shading 95% confidence interval). Donors with a selection probability below 0.3 are classified as ‘neutral’, above 0.7 as ‘selected’. Donors which are neither ‘selected’ nor ‘neutral’ remain ‘undetermined’. High confidence ‘selected’ lines (selection probability >0.7 and >100 somatic variants) are: joxm, wahn, garx, vass, ualf, euts, pipw, oilg, feec, fikt, qolg, and puie.
Supplementary Figure 7 Comparison of five methods on simulated data matching 32 fibroblast cell lines and estimated error rate and cell assignability with cardelino from experimental data for 32 fibroblast lines.
(a) Assessment of cell assignment to clones across a variety of simulation settings, considering SingleCellGenotyper (SCG), Demuxlet, cardelino, cardelino-free and cardelino-fixed (Methods; Supplementary Note). Considered are simulated data based on empirical characteristics observed in 32 fibroblast lines. For each line, the sequence coverage, clone configuration (i.e., number of clones, variants on each branch), and allelic imbalance parameters were obtained to derive simulation parameters. 200 cells are synthesised per line and a guide clonal tree with 10% errors in allocation of variants to clones. (b) Estimated error rate in the clonal tree configuration derived from bulk exome-seq data (based on cardelino) for each of 32 lines versus fraction of confidently assigned cells (>90% of cells assigned for 23 lines; at cardelino posterior probability P>0.5 for most-probable clone).
Supplementary Figure 8 Comparison of cell assignment between five methods on experimental data across 32 fibroblast lines.
(a) The fraction of assignable cells (i.e., highest P > thresholds) when varying the thresholds from 0.5 to 0.95. Shown are box plots depicting median and the first and third quantiles of the 32 lines. (b) The adjusted Rand index of cell assignment to clones between the five considered methods. The values are averaged across 32 fibroblast lines. (c) Scatter plot between the uncertainty of the inferred tree from cardelino-free (x-axis) and the mean absolute difference of the assignment probability between cardelino-free and cardelino (y-axis). The output posterior clonal configuration matrix from cardelino-free consists of the probability of each variant being present in each clone. A completely uninformative clonal tree would have all entries equal to 0.5. Thus, we measure the uncertainty of the output tree from cardelino-free by taking 0.5 minus the mean absolute difference of the posterior probability configuration matrix and the uninformative configuration probability matrix of all of entries equal to 0.5. With this measure, a value of 0.5 indicates a posterior configuration indistinguishable from the uninformative configuration and a value of 0 indicates very high-confidence from the model in the posterior configuration. (d) The comparison of cell assignment for one representative line (feec) when using different guide clonal trees sampled from Canopy’s posterior distribution as input. Each violin plot shows the adjusted Rand index of cell assignment between each of 435 tree pairs combining the 30 most probable trees from bulk exome-seq for the feec line. (e) Cell assignment similarity for each of the 32 lines when using different guide clonal trees, quantified with adjusted Rand index values between different pairs of guide clonal trees. For each line, we take the 30 most probable posterior trees from Canopy, and then each dot in the box plot denotes the average adjusted Rand index value for one line, calculated from 435 of these pairwise comparisons.
Supplementary Figure 9 ICell-clone assignment rates from cardelino.
(a) Scatter plot of the fraction of cells assigned in each cell line using cardelino (at posterior probability > 0.5) as a function of the minimum number of clone-specific variants for the corresponding line (minimum Hamming distance between clones for a given donor), for 32 fibroblast lines. Total number of cells that were considered for this analysis (QC passed) per line indicated by colour. (b) Scatter plot of recall (assignment rate) versus precision (assignment accuracy) when assigning cells using cardelino (at posterior probability > 0.5). Shown are data from for 32 simulated lines, using parameters that match the observed data characteristics in the set of 32 real fibroblast lines (Methods). The average number of variants per clonal branch (i.e., #variant/(#clone - 1)) is shown by point colour (slightly different from Supplementary Fig. 4 which uses the minimum number of variants distinguishing between pairs of clones, as shown in Fig. 3a). Lines with fewer informative variants per branch tend to have lower assignment rates, but the precision remains high.
Supplementary Figure 10 Clone prevalence estimates from WES data (x-axis; using Canopy) versus the fraction of single-cell transcriptomes assigned to the clone (y-axis; using cardelino), for each clone across lines.
Points are coloured by the overall fraction of single-cell transcriptomes assigned for a given line (i.e. cells with posterior P>0.5 for assignment).
Supplementary Figure 11 Direct effects of somatic variants on genes overlapping the variant.
Volcano plot showing negative log P values versus log2-fold change from testing differential expression for genes with a somatic mutation between cells with the mutation and cells without the mutation, faceted by VEP annotation category (Methods). Each point represents a gene, and box plots show the overall log2-fold change distribution for each annotation category. DE tests (two-sided QL F test in edgeR) are conducted within each line (donor) separately, and the results shown here are aggregated across n=32 lines. Genes are categorised by simplified functional annotations from VEP of the somatic mutation, and genes significantly DE at an FDR threshold of 20% are shown in red.
Supplementary Figure 12 Gene set enrichment results for fibroblast data from n=32 lines.
(a) Heatmap showing Spearman correlation between gene set enrichment results for the 16 most frequently enriched MSigDB Hallmark gene sets across 31 lines. Colour indicates the correlation between pairs of gene sets and is only shown if the correlation is significant (P < 0.05). (b) Heatmap showing proportion of overlap in genes between pairs of gene sets (matching those in left panel). (c) Heatmap showing the direction (first listed clone relative to second listed clone; in colour) and strength of enrichment (-log10(P) as degree of shading) for Hallmark gene sets tested with camera (Methods) for all pairwise comparisons between clones across n=31 lines. Gene sets that are significantly enriched at an FDR threshold of 5% are indicated with dots. Gene sets are shown if significant in at least one line and are ordered by number of lines in which they are significant.
Supplementary Figure 13 Results from five human melanoma samples.
(a) Number of cells assigned by cardelino to each inferred clone for five melanoma patients, stratified by cell type identified using gene expression of marker genes as in the original publication 37. (b) Gene set enrichment analysis results when comparing gene expression in clone1 cells to cells in other clones, within each patient, including cells from all cell types. Given that immune cells and cancer-associated fibroblast (CAF) cells are almost all assigned to clone1, this comparison effectively reflects expression differences between melanoma and immune cells. (c) Gene set enrichment analysis results when considering all pairwise comparisons between clones consisting of melanoma cells only. The heatmaps in (b) and (c) depict signed P-values of gene set enrichment (n=31 cell lines; two-sided test using camera) for Hallmark gene sets found to be significantly enriched (FDR<0.05) in at least one comparison. Dots denote significant enrichments. For details on the cell assignment and gene set enrichment analyses see Supplementary Note. (d) Heatmap showing correlations between gene set enrichment results when using all cells (across melanoma, immune and cancer-associated fibroblast cell types) assigned to clones across five melanoma patients and comparing expression of cells assigned to clone1 to those assigned to other clones. (e) Heatmap showing correlations between gene set enrichment results when using all melanoma cells assigned to clones across five melanoma patients and comparing expression of cells between all pairs of clones (for which the clones have sufficiently many cells assigned). For both (d) and (e), the eatmap shows Spearman correlation between gene set enrichment results for the 16 most frequently enriched MSigDB Hallmark gene sets across n=5 patients. Colour indicates the correlation between pairs of gene sets and is only shown if the correlation is significant (P < 0.05).
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Cardelino cell-clone assignment results for 32 fibroblast lines
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McCarthy, D.J., Rostom, R., Huang, Y. et al. Cardelino: computational integration of somatic clonal substructure and single-cell transcriptomes. Nat Methods 17, 414–421 (2020). https://doi.org/10.1038/s41592-020-0766-3
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DOI: https://doi.org/10.1038/s41592-020-0766-3
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