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Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data

Nature Biotechnology volume 40, pages 527–538 (2022)Cite this article

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Abstract

Single-cell RNA sequencing (scRNA-seq) distinguishes cell types, states and lineages within the context of heterogeneous tissues. However, current single-cell data cannot directly link cell clusters with specific phenotypes. Here we present Scissor, a method that identifies cell subpopulations from single-cell data that are associated with a given phenotype. Scissor integrates phenotype-associated bulk expression data and single-cell data by first quantifying the similarity between each single cell and each bulk sample. It then optimizes a regression model on the correlation matrix with the sample phenotype to identify relevant subpopulations. Applied to a lung cancer scRNA-seq dataset, Scissor identified subsets of cells associated with worse survival and with TP53 mutations. In melanoma, Scissor discerned a T cell subpopulation with low PDCD1/CTLA4 and high TCF7 expression associated with an immunotherapy response. Beyond cancer, Scissor was effective in interpreting facioscapulohumeral muscular dystrophy and Alzheimer’s disease datasets. Scissor identifies biologically and clinically relevant cell subpopulations from single-cell assays by leveraging phenotype and bulk-omics datasets.

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Fig. 1: The workflow of Scissor and its performance in applications with known phenotype-associated cell subpopulations.
Fig. 2: Scissor identification results on lung cancer cells guided by TCGA-LUAD survival outcomes.
Fig. 3: Scissor identification results on lung cancer cells guided by TP53 mutation status.
Fig. 4: Scissor identification results on melanoma T cells.
Fig. 5: Scissor identification results on FSHD cells.
Fig. 6: Scissor identification results on AD.

Data availability

All datasets analyzed in this study were published previously. The corresponding descriptions and pre-processing steps are described in the Supplementary Materials.

Software availability

The open-source Scissor R package and tutorial are available at GitHub: https://github.com/sunduanchen/Scissor.

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Acknowledgements

This work was supported by the following funding: NIH 5K01LM012877 (to Z.X.); NIH 1R21HL145426 (to Z.X.); NIH 1R01CA207377 (to D.Z.Q.); NIH NIGMS MIRA R35GM124704 (to A.C.A.); the Medical Research Foundation of Oregon (to Z.X.); NCI R01 CA251245, P50 CA097186, P50 CA186786, P50 CA186786-07S1 and Department of Defense Impact Award W81XWH-16-1-0597 (to J.J.A.); and NCI R01CA244576 (to A.V.D.). We thank W. Anderson and A. Hill for editing the manuscript. The resources of the Exacloud high-performance computing environment, developed jointly by Oregon Health & Science University (OHSU) and Intel, and the technical support of the OHSU Advanced Computing Center are gratefully acknowledged.

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Authors and Affiliations

  1. Computational Biology Program, Oregon Health & Science University, Portland, OR, USA

    Duanchen Sun, Xiangnan Guan, Paul T. Spellman & Zheng Xia

  2. Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA

    Duanchen Sun, Xiangnan Guan & Zheng Xia

  3. Department of Cell, Developmental and Cancer Biology, Oregon Health & Science University, Portland, OR, USA

    Amy E. Moran & Pepper Schedin

  4. Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA

    Amy E. Moran, David Z. Qian, Pepper Schedin, Andrew C. Adey, Paul T. Spellman & Zheng Xia

  5. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China

    Ling-Yun Wu

  6. Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA

    Mu-Shui Dai, Andrew C. Adey & Paul T. Spellman

  7. City of Hope National Medical Center, Duarte, CA, USA

    Alexey V. Danilov

  8. Department of Internal Medicine, Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA

    Joshi J. Alumkal

  9. Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, OR, USA

    Zheng Xia

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Contributions

D.S. and Z.X. conceived the idea, implemented the algorithm and performed the analyses. D.S., G.X., P.T.S. and Z.X. interpreted the results. A.E.M., L.Y.W, D.Z.Q., P.S., M.D., A.V.D., J.J.A. and A.C.A. provided scientific insights on the applications. Z.X. supervised the study. D.S. and Z.X. wrote the manuscript with feedback from all other authors. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Zheng Xia.

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Competing interests

A.E.M. discloses receipt of a sponsored research agreement from AstraZeneca. A.V.D. reports consultancy from Abbvie, Beigene, Celgene, Curis, Janssen, Karyopharm, Nurix, Seattle Genetics, Teva Oncology and TG Therapeutics; research funding from Aptose Biosciences, Bristol Myers Squibb, Gilead Sciences and Takeda Oncology; and consultancy and research funding from AstraZeneca, Bayer Oncology, Genentech and Verastem Oncology. J.A.A. has received consulting income from Janssen Biotech, Merck Sharp & Dohme and Dendreon and honoraria for speaker’s fees from Astellas. All other authors declare no competing interests.

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Supplementary information

Supplementary Information

Dataset descriptions, Supplementary Figs. 1–10 and Supplementary Tables 7–14.

Reporting Summary

Supplementary Table 1

Additional information for the LUAD cancer survival analysis, including differentially expressed genes, signature genes, enriched pathways, motifs and the descriptions of the validation datasets.

Supplementary Table 2

Additional information for the LUAD cancer cell TP53 mutation analysis, including differentially expressed genes, signature genes and enriched pathways.

Supplementary Table 3

Additional information for the melanoma T cell immunotherapy response analysis, including differentially expressed genes, signature genes and enriched pathways.

Supplementary Table 4

Additional information for the FSHD muscle analysis, including differentially expressed genes, signature genes and enriched pathways.

Supplementary Table 5

Additional information for the Alzheimer’s disease analysis, including differentially expressed genes, signature genes and enriched pathways.

Supplementary Table 6

Description of the ten experiments analyzed by Scissor.

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Sun, D., Guan, X., Moran, A.E. et al. Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data. Nat Biotechnol 40, 527–538 (2022). https://doi.org/10.1038/s41587-021-01091-3

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