Abstract
Genome instability and aberrant alterations of transcriptional programs both play important roles in cancer. Single-cell RNA sequencing (scRNA-seq) has the potential to investigate both genetic and nongenetic sources of tumor heterogeneity in a single assay. Here we present a computational method, Numbat, that integrates haplotype information obtained from population-based phasing with allele and expression signals to enhance detection of copy number variations from scRNA-seq. Numbat exploits the evolutionary relationships between subclones to iteratively infer single-cell copy number profiles and tumor clonal phylogeny. Analysis of 22 tumor samples, including multiple myeloma, gastric, breast and thyroid cancers, shows that Numbat can reconstruct the tumor copy number profile and precisely identify malignant cells in the tumor microenvironment. We identify genetic subpopulations with transcriptional signatures relevant to tumor progression and therapy resistance. Numbat requires neither sample-matched DNA data nor a priori genotyping, and is applicable to a wide range of experimental settings and cancer types.
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Data availability
The scRNA-seq and WGS validation data from the WASHU MM study can be accessed through SRA (PRJNA694128). The scRNA-seq data from the MDA CopyKAT study can be accessed through GEO (GSE148673) and SRA (PRJNA625321). The NCI-N87 scDNA-seq and scRNA-seq datasets are available on GEO (GSE142750) and SRA (PRJNA498809). The HCA collection of reference expression profiles can be obtained from Synapse under ID syn21041850. The 1000G phasing panel can be downloaded from the IGSR FTP site (http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release). The TOPMed phasing panel can be accessed through the TOPMed Imputation Server (https://imputation.biodatacatalyst.nhlbi.nih.gov/).
Code availability
The Numbat algorithm is available at https://github.com/kharchenkolab/numbat. The analysis scripts and notebooks used to reproduce results included in the paper are available at https://github.com/kharchenkolab/NumbatAnalysis.
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Acknowledgements
P.V.K., R.S. and T.G. were supported by Synergy grant no. 85629 (KILL-OR-DIFFERENTIATE) from the European Research Council. P.-R.L. was supported by NIH grant no. DP2 ES030554, a Burroughs Wellcome Fund Career Award at the Scientific Interfaces and the Next Generation Fund at the Broad Institute of MIT and Harvard.
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P.V.K. and T.G. formulated the study and the overall approach. A.K. carried out proof-of-concept tests of population-based phasing. T.G. developed the detailed algorithms with advice from P.V.K., R.S., H.S. and P.-R.L. T.G. implemented the Numbat package with help from E.B. T.G. and P.V.K. drafted the manuscript. All authors provided suggestions and corrections on the manuscript text.
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P.V.K. is an employee of Altos Labs and serves on the Scientific Advisory Board to Celsius Therapeutics, Inc. and Biomage, Inc. The remaining authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Haplotype-aware Hidden Markov models.
a, Phase switch probability as a function of genetic distance, estimated from alleles phased from LoH regions in TNBC4. Genetic distance is measured in centimorgan (cM). Error bar represents 95% CI derived from a binomial test. The center of the error bar represents the observed fraction of phase switches. b, Schematic of conventional and haplotype-aware allele HMM. t, copy number state transition probability. ps, phase transition probability. c, Schematic of the Numbat joint HMM. Only three copy number states (neutral, deletion, amplification) are included for illustrative purposes.
Extended Data Fig. 2 Probabilistic model of gene expression and allele counts from transcriptome sequencing experiments.
cm, number of maternal chromosome copies. cp, number of paternal chromosome copies. λi, observed gene expression magnitude of gene i. \(\lambda _i^ \ast\), reference gene expression magnitude of gene i. μ and σ2, global bias and variance in gene expression. πj, fraction of paternal alleles of SNP j. γ, global inverse overdispersion of allele-specific detection. l, library size. mj, total allele count of SNP j. Xi, observed molecule counts for gene i. Yj, observed paternal allele count for SNP j.
Extended Data Fig. 3 WGS validation of Numbat CNV calls from scRNA-seq data.
For each sample, the DNA profile (top) is juxtaposed with the copy number profile inferred by the Numbat joint HMM (bottom). Gray vertical bars represent centromeres and gap regions. logR, log coverage ratio. BAF, B-allele frequency. logFC, log expression fold-change. pHF, paternal haplotype frequency. BAMP, balanced amplification.
Extended Data Fig. 4 Tumor versus normal cell classification accuracy of Numbat joint model, Numbat expression-only model, and CopyKAT.
Each dot represents a distinct sample (TNBC, n = 5; ATC, n = 4; MM, n = 8). Center line, mean. ATC5 was excluded from the benchmark due to lack of clear expression of tumor marker KRT8.
Extended Data Fig. 5 Numbat reliably distinguishes tumor and normal cells (TNBC series).
The aneuploidy probability is shown as a color gradient (red: high, blue: low). For each sample (row), the series of figures (columns) respectively show the aneuploidy probabilities by expression evidence, those by allele evidence, those by combined evidence, CopyKAT prediction (binary 0 or 1), and marker gene expression in a t-SNE embedding of gene expression profiles.
Extended Data Fig. 6 Numbat reliably distinguishes tumor and normal cells (ATC series).
The aneuploidy probability is shown as a color gradient (red: high, blue: low). For each sample (row), the series of figures (columns) respectively show the aneuploidy probabilities by expression evidence, those by allele evidence, those by combined evidence, CopyKAT prediction (binary 0 or 1), and marker gene expression in a t-SNE embedding of gene expression profiles.
Extended Data Fig. 7 Numbat reliably distinguishes tumor and normal cells (MM series).
The aneuploidy probability is shown as a color gradient (red: high, blue: low). For each sample (row), the series of figures (columns) respectively show the aneuploidy probabilities by expression evidence, those by allele evidence, those by combined evidence, CopyKAT prediction (binary 0 or 1), and marker gene expression in a t-SNE embedding of gene expression profiles.
Extended Data Fig. 8 CNV detection performance as a function of tumor cell fraction.
At each tumor cell fraction, tumor cells were subsampled and mixed with randomly sampled normal cells at the corresponding proportion. Precision, recall and F1 scores were calculated based on the detected segments from scRNA-seq data and the ground truth copy number profiles (from WGS) in 5 multiple myeloma samples. For Numbat, two methods are compared: pseudobulk joint HMM (Numbat-HMM) and iterative optimization (Numbat-iterative) with no minimum pseudobulk size limit. a, Performance for all event types (amplification, deletion, and CNLoH). b, Performance for amplifications. c, Performance for deletions.
Extended Data Fig. 9 Numbat analysis of gastric cell line (NCI-N87) scRNA-seq data and validation by scDNA-seq.
a, Single-cell copy number landscape and subclonal structure reconstructed by scDNA-seq data. Gray vertical bars represent gap regions. A rooted hierarchical clustering tree is shown on the left. Three subclones were defined by cutting the tree with k=3. Red asterisks denote salient subclonal events. b, Single-cell CNV landscape and subclonal structure inferred from the paired scRNA-seq data by Numbat. The original prediction was composed of four subclones. The uppermost two clones were merged and denoted as the ‘major’ clone. Red asterisks denote validated subclonal events. c, Subclone-specific copy number profiles. For each subclone, the top track shows CNV calls made by clone-specific Numbat HMM; the bottom track shows DNA copy number profile of a representative cell from that subclone. Gray vertical bars represent gap regions. d, Numbat recapitulates clonal fractions measured by scDNA-seq. e, Stability and accuracy of Numbat CNV calls for each subclone with respect to parameter variations. F1 scores were computed by comparing DNA profiles for each subclone with the best-matching subclone CNV profiles predicted by Numbat. Circles denote F1 score from initialization with a random tree. Red triangles mark default parameter values.
Extended Data Fig. 10 Single-cell copy number profile and phylogeny reconstructed by Numbat (TNBC and ATC).
Branch lengths correspond to the number of CNV events. Blue dashed line separates predicted tumor and normal cells. Confident subclones are highlighted and marked by red dashed rectangles. The vertical bar on the left of each panel shows cell type ground truth. In TNBC5 and ATC2, the second vertical bar on the left of the panel shows variant allele frequency of a clone-associated mitochondrial mutation. For ATC2, results from the subsampled dataset (including aneuploid cells and 50 randomly sampled normal cells) are shown. In ATC5, some tumor cells were likely mis-annotated as normal in the original annotation.
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Gao, T., Soldatov, R., Sarkar, H. et al. Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes. Nat Biotechnol 41, 417–426 (2023). https://doi.org/10.1038/s41587-022-01468-y
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DOI: https://doi.org/10.1038/s41587-022-01468-y
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