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CoSpar identifies early cell fate biases from single-cell transcriptomic and lineage information

Nature Biotechnology volume 40, pages 1066–1074 (2022)Cite this article

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Abstract

A goal of single-cell genome-wide profiling is to reconstruct dynamic transitions during cell differentiation, disease onset and drug response. Single-cell assays have recently been integrated with lineage tracing, a set of methods that identify cells of common ancestry to establish bona fide dynamic relationships between cell states. These integrated methods have revealed unappreciated cell dynamics, but their analysis faces recurrent challenges arising from noisy, dispersed lineage data. In this study, we developed coherent, sparse optimization (CoSpar) as a robust computational approach to infer cell dynamics from single-cell transcriptomics integrated with lineage tracing. Built on assumptions of coherence and sparsity of transition maps, CoSpar is robust to severe downsampling and dispersion of lineage data, which enables simpler experimental designs and requires less calibration. In datasets representing hematopoiesis, reprogramming and directed differentiation, CoSpar identifies early fate biases not previously detected, predicting transcription factors and receptors implicated in fate choice. Documentation and detailed examples for common experimental designs are available at https://cospar.readthedocs.io/.

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Fig. 1: Integrative analysis of lineage tracing and transcriptome data.
Fig. 2: The CoSpar algorithm.
Fig. 3: Proof of concept with simulated data.
Fig. 4: Benchmarking CoSpar and prediction of progenitor bias in hematopoiesis.
Fig. 5: Progenitor bias in fibroblast reprogramming.
Fig. 6: Progenitor bias during human iPSC differentiation into endodermal lineages.

Data availability

All data analyzed in this article are publicly available through online sources.

The annotated data, results and Python implementation are available at https://cospar.readthedocs.io/. The raw data for the hematopoiesis dataset can be accessed at the Gene Expression Omnibus database with accession number GSE140802, the reprogramming dataset with accession number GSE99915 and the lung dataset with accession numbers GSE137805 and GSE137811.

Code availability

The results reported in this paper and our Python implementation are available at https://cospar.readthedocs.io/.

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Acknowledgements

S.-W.W. is a Damon Runyon Quantitative Biology Fellow supported by the Damon Runyon Cancer Research Foundation (02-20). A.M.K. acknowledges support by National Institutes of Health (NIH) grants R01HL14102-01 and R01-CA218579. K.H. would like to acknowledge funding from the Health Research Board Emerging Clinical Scientist Award ECSA-2020-011. D.N.K. is supported by NIH grants R01HL095993, U01TR001810 and N01 75N92020C00005. We thank T. Scully for helping with figures.

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Authors and Affiliations

  1. Department of Systems Biology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA

    Shou-Wen Wang & Allon M. Klein

  2. Center for Regenerative Medicine of Boston University and Boston Medical Center, Boston, MA, USA

    Michael J. Herriges & Darrell N. Kotton

  3. The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA, USA

    Michael J. Herriges & Darrell N. Kotton

  4. Department of Medicine, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin, Ireland

    Kilian Hurley

  5. Tissue Engineering Research Group, Royal College of Surgeons in Ireland, Dublin, Ireland

    Kilian Hurley

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Contributions

S.-W.W. and A.M.K. conceived the project. S.-W.W. devised the computational method, wrote the package and carried out CoSpar analyses. K.H. and D.N.K. designed and supervised, and M.J.H. carried out and analyzed, iPSC differentiation experiments. S.-W.W. and A.M.K. wrote the manuscript. A.M.K. supervised the project.

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Correspondence to Shou-Wen Wang or Allon M. Klein.

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Nature Biotechnology thanks Samantha A. Morris and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Wang, SW., Herriges, M.J., Hurley, K. et al. CoSpar identifies early cell fate biases from single-cell transcriptomic and lineage information. Nat Biotechnol 40, 1066–1074 (2022). https://doi.org/10.1038/s41587-022-01209-1

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