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Multimodal single-cell and whole-genome sequencing of small, frozen clinical specimens


Single-cell genomics enables dissection of tumor heterogeneity and molecular underpinnings of drug response at an unprecedented resolution1,2,3,4,5,6,7,8,9,10,11. However, broad clinical application of these methods remains challenging, due to several practical and preanalytical challenges that are incompatible with typical clinical care workflows, namely the need for relatively large, fresh tissue inputs. In the present study, we show that multimodal, single-nucleus (sn)RNA/T cell receptor (TCR) sequencing, spatial transcriptomics and whole-genome sequencing (WGS) are feasible from small, frozen tissues that approximate routinely collected clinical specimens (for example, core needle biopsies). Compared with data from sample-matched fresh tissue, we find a similar quality in the biological outputs of snRNA/TCR-seq data, while reducing artifactual signals and compositional biases introduced by fresh tissue processing. Profiling sequentially collected melanoma samples from a patient treated in the KEYNOTE-001 trial12, we resolved cellular, genomic, spatial and clonotype dynamics that represent molecular patterns of heterogeneous intralesional evolution during anti-programmed cell death protein 1 therapy. To demonstrate applicability to banked biospecimens of rare diseases13, we generated a single-cell atlas of uveal melanoma liver metastasis with matched WGS data. These results show that single-cell genomics from archival, clinical specimens is feasible and provides a framework for translating these methods more broadly to the clinical arena.

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Fig. 1: Comparison of sequencing quality metrics, TCR clonotype overlap, and copy number alterations, between fresh and frozen samples.
Fig. 2: Evolution of the tumor ecosystem during anti-PD1 therapy.
Fig. 3: Spatial intra-lesional heterogeneity in response to anti-PD1 therapy.
Fig. 4: An atlas of uveal melanoma liver metastases.

Data availability

Processed data are available on the Gene Expression Omnibus, accession no. GSE192402. Raw data are available on dbGAP, accession no. phs003097.v1.p1.

Code availability

Code is publicly available via and (


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We thank H. Hibshoosh at Columbia University for fruitful discussions. We thank the members of the CUIMC Human Immune Monitoring Core for technical advice. Y.W. is supported by National Institutes of Health (NIH), National Institute of Allergy and Infectious Disease training grant (no. T32AI148099). B.I. is supported by the NIH, National Cancer Institute (NCI) (grant nos. K08CA222663, R37CA258829, R01CA266446 and U54CA225088), a Burroughs Wellcome Fund Career Award for Medical Scientists, a Velocity Fellows Award, the Louis V. Gerstner, Jr. Scholars Program and a Young Investigator Award by the Melanoma Research Alliance. R.D.C., E.A. and B.I. are supported by an NCI grant (no. R21CA263381) and a Columbia University Research Initiatives in Science & Engineering Award. E.A. was supported by an NCI grant (no. R00CA230195) and NSF grant (no. CBET-2144542). J.L.F. acknowledges support from the Columbia University Van C. Mow fellowship. G.A.-R. and A.R. are supported by the Parker Institute for Cancer Immunotherapy and an NIH grant (no. P01CA168585). A.M.T. is supported by the NCI (grant no. 5K22CA237733-03). This work was supported by an NIH/NCI Cancer Center Support grant (no. P30CA013696), the Molecular Pathology Shared Resource and its Tissue Bank at Columbia University and the Flow-cytometry Core Facility supported by a grant (no. S10OD020056).

Author information

Authors and Affiliations



B.I. conceived the study. B.I. and E.A. jointly provided overall supervision of the study. J.C.M., A.D.A., Y.G., I.B., P.H., L.C. and S.B. performed experiments. Y.W., J.L.F., J.C.M., Y.G., S.T., G.A-R., S.H., Y.J., J.B., M.H. and A.M.T. performed analyses. S.A.K., B.S.H., A.R., G.K.S. and R.D.C. provided clinical specimens. R.D.C., E.Z.M., F.C., A.M.T. and G.K.S. provided additional supervision. Y.W., J.L.F, J.C.M., E.A. and B.I. wrote the manuscript. All authors reviewed, contributed to and approved the manuscript.

Corresponding authors

Correspondence to Elham Azizi or Benjamin Izar.

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Competing interests

B.I. has received consulting fees from Volastra Therapeutics Inc, Merck, AstraZeneca and Janssen Pharmaceuticals and has received research funding to Columbia University from Alkermes, Arcus Biosciences, Checkmate Pharmaceuticals, Compugen, Immunocore and Synthekine. G.A.-R. has received honoraria from consulting with Arcus Biosciences. A.R. has received honoraria from consulting with Amgen, Bristol Myers Squibb, Chugai, Genentech, Merck, Novartis, Roche and Sanofi, is or has been a member of the scientific advisory board and holds stock in Arcus, Compugen, CytomX, Highlight, ImaginAb, Isoplexis, Kite-Gilead, Lutris, Merus, PACT, RAPT, Synthekine and Tango Therapeutics. A.M.T. receives research support from Ono Pharmaceuticals. B.S.H. participated in advisory boards for AstraZeneca and Ideaya. R.D.C. is a consultant for Alkermes, Bristol Myers Squibb, Castle Biosciences, Delcath, Eisai, Hengrui, Ideaya, Immunocore, InxMed, Iovance, Merck, Novartis, Oncosec, Pierre Fabre, PureTech Health, Regeneron, Sanofi Genzyme, Sorrento Therapeutics and Trisalus, serves on clinical/scientific advisory boards for Aura Biosciences, Chimeron and Rgenix Research, and has received research funding to Columbia University from Amgen, Astellis, AstraZeneca, BioMed Valley, Bolt, Bristol Myers Squibb, Corvus, Cstone, Foghorn, Ideaya, Immatics, Immunocore, InxMed, Iovance, Merck, Mirati, Novartis, Pfizer, Plexxikon, Regeneron and Roche/Genentech. B.I. and J.C.M. filed a patent describing the generation of high-quality single-cell genomics data from frozen tissues. The remaining authors declare no competing interests.

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Nature Genetics thanks Jeffrey Sosman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Gating strategy and additional sequencing quality metrics for fresh and frozen samples.

a, Gating strategy for sorting viable CD45+ and CD45− cells from freshly digested surgical specimens. Final sorting gates indicated in red. b-d, Violin plots and boxplots indicating percent of mitochondrial reads across samples and different experimental settings in (b) NSCLC, (c) cutaneous melanoma, (d) uveal melanoma. e, Genes detected per cell in the NSCLC sample using procedures presented here compared to two previously reported protocols (CST and TST, indicated in green) after adjustment for sequencing saturation (Methods). Upper and lower edges of boxplot indicate 75th and 25th percentiles respectively, and middle line indicates median.

Extended Data Fig. 2 TCR clonality and marker gene expression in melanoma samples.

a, UMAP clustering of T cells, with projected clonality (top), sequencing source (middle) and cell cycle markers and T cell dysfunction and stemness markers (bottom) in primary uveal melanoma. b, same as (a) for cutaneous melanoma.

Extended Data Fig. 3 Comparison of different integration methods.

Application of three integration methods (STACAS, top row; scVI, middle row; Seurat, bottom row) in NSCLC samples. Resulting UMAP embeddings are colored by sample (left column), mean LISI score (middle column) and cell type (right row).

Extended Data Fig. 4 Comparison of different integration methods.

Application of three integration methods (STACAS, scVI and Seurat) across cutaneous and uveal melanoma. Resulting UMAP embeddings are colored by sample (left column), mean LISI score (middle column) and cell type (right row).

Extended Data Fig. 5 Cell type composition changes in fresh and frozen samples.

a, Stacked bar plots indicating proportions of all cell types in NSCLC, cutaneous and uveal melanoma across different methods. b, Simpson diversity index for immune cells in the same samples as (a). c, Stacked bar plots of malignant and non-malignant cell fractions in the same samples as (a).

Extended Data Fig. 6 Additional sequencing quality metrics, T cell marker gene expression and spatial cell type distribution in anti-PD-1 therapy samples.

a, Timing of sequentially collected specimens in a patient on anti-PD1 therapy. b,c, Violin plots and boxplots of (b) genes per cell detected (left) and percent of mitochondrial reads (right), and (c) expression of artifactual signature across samples collected over different time points. Upper and lower edges of boxplot indicate 75th and 25th percentiles respectively, and middle line indicates median. d, e, UMAP representation of CD8+ T cells across all time points and with projected TCR clonality, and (e) cell cycle markers (top) and T cell dysfunction and Stemness markers (bottom). f, Starfysh inferred proportions of cells corresponding to the CD8+ T cell activation and CD8+ T cell dysfunction signatures (columns), for data collected at the pre-treatment time point. g, Major cell types deconvolved using RCTD in on-treatment and on-treatment (later) timepoints. h, IGV plots of copy number changes measured in WES and US-WGS analysis of a pre-treatment melanoma specimen (presented in Fig. 2).

Extended Data Fig. 7 Cell type proportion, gene expression and regulatory program changes in anti-PD-a therapy samples.

a, b, Stacked bar plots indicating proportion of (a) all cell types across sequentially collected anti-PD1 therapy tissue specimens and (b) malignant and non-malignant fractions. c, Heatmap of selected genes (rows) and their gene expression (normalized expression) in individual cells (column). Indicated on the bottom are time points of sample collection (pre, on, on_later) and clones (0–3) as defined in Fig. 2. d, Gene set enrichment analysis (GSEA) of genes differentially expressed in clone 2. e, Violin plots of the distribution of p-values from Fisher’s exact test in pre, on and on_later timepoints (9969, 6651, and 4447 cells respectively), testing for association of recurrent copy number alterations in each cell with ICR signature gene location. f, Top: Histogram of gene density across genome for positive and negative genes in ICR signature. Bottom: Plot of inferCNV inferred copy number alterations in on timepoint sample. g, Transcription factors associated with Clone 2 in pre, on-treatment and on-treatment later timepoints, based on Wilcoxon rank-sum test of AUCell scores in Clone 2 cells vs. non-Clone 2 cells () from SCENIC analysis.

Extended Data Fig. 8 Sequencing quality metrics and MRI scans of uveal melanoma liver metastasis samples.

a, Schematic of tissue collection and indicated number of specimens per time point. MEKi, MEK-inhibitor (Selumetinib). b, Violin plots and boxplots indicating number of genes detected per cell (top lane), percent of mitochondrial reads (middle lane) and expression of a stress signature (bottom lane) across 20 uveal melanoma specimens. Upper and lower edges of boxplot indicate 75th and 25th percentiles respectively, and middle line indicates median. Yellow color indicates a specimen sequenced with 10 × 3′ V3 chemistry, with lower quality, while data for the remainder of samples (indicated in red) were generated with 5′ chemistry. c, Exemplary baseline and post-treatment MRI of the abdomen showing moderate response of a liver metastatic lesion in a uveal melanoma patient treated with selumetinib.

Extended Data Fig. 9 Concurrent WGS in uveal melanoma liver metastasis samples.

a, Schematic design of generation of (low-pass) whole-genome sequencing from the same cell/nucleus pool that was also used for single-nucleus RNA and TCR sequencing. b, Inferred CNAs (columns) across samples (indicated by bar on the left) in the uveal melanoma cohort. c, Exemplary whole-genome sequencing result (top) showing copy number alterations (y axis, log2 ratio) with amplifications in red, deletions in green and unaltered chromosome regions in blue. Inference of CNAs of the using snRNA-seq that was generated from the same starting cell/nucleus pool as WGS.

Supplementary information

Supplementary Information

Reporting Summary

Supplementary Table

Supplementary Table 1: Sample overview. Supplementary Table 2: Quality control metrics. Supplementary Table 3: TCR Gini’s coefficient. Supplementary Table 4: Markers and signatures. Supplementary Table 5: Buffers, oligos, Slide-seq v.2. Supplementary Table 6: AXL, MITF and ICR signatures. Supplementary Table 7: Signatures for snRNA-seq/ST integration.

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Wang, Y., Fan, J.L., Melms, J.C. et al. Multimodal single-cell and whole-genome sequencing of small, frozen clinical specimens. Nat Genet 55, 19–25 (2023).

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