Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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

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/.

Your institute does not have access to this article

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

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/.

References

  1. Woodworth, M. B., Girskis, K. M. & Walsh, C. A. Building a lineage from single cells: genetic techniques for cell lineage tracking. Nat. Rev. Genet. 18, 230–244 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. Wagner, D. E. & Klein, A. M. Lineage tracing meets single-cell omics: opportunities and challenges. Nat. Rev. Genet. 21, 410–427 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. Kester, L. & van Oudenaarden, A. Single-cell transcriptomics meets lineage tracing. Cell Stem Cell 23, 166–179 (2018).

    CAS  PubMed  Article  Google Scholar 

  4. Bendall, S. C. et al. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157, 714–725 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408–1414 (2020).

    CAS  PubMed  Article  Google Scholar 

  6. Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016).

    CAS  PubMed  Article  Google Scholar 

  7. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  8. Qiu, X. et al. Mapping vector field of single cells. Preprint at bioRxiv https://doi.org/10.1101/696724 (2019).

  9. Schiebinger, G. et al. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell 176, 928–943 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. Tritschler, S. et al. Concepts and limitations for learning developmental trajectories from single cell genomics. Development 146, dev170506 (2019).

  12. Weinreb, C., Rodriguez-Fraticelli, A., Camargo, F. D. & Klein, A. M. Lineage tracing on transcriptional landscapes links state to fate during differentiation. Science 367, eaaw3381 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. Weinreb, C., Wolock, S., Tusi, B. K., Socolovsky, M. & Klein, A. M. Fundamental limits on dynamic inference from single-cell snapshots. Proc. Natl Acad. Sci. USA 115, E2467–E2476 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. Alemany, A., Florescu, M., Baron, C. S., Peterson-Maduro, J. & van Oudenaarden, A. Whole-organism clone tracing using single-cell sequencing. Nature 556, 108–112 (2018).

    CAS  PubMed  Article  Google Scholar 

  15. Biddy, B. A. et al. Single-cell mapping of lineage and identity in direct reprogramming. Nature 564, 219–224 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. Bowling, S. et al. An engineered CRISPR/Cas9 mouse line for simultaneous readout of lineage histories and gene expression profiles in single cells. Cell 181, 1410–1422 (2019).

  17. Chan, M. M. et al. Molecular recording of mammalian embryogenesis. Nature 570, 77–82 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. Rodriguez-Fraticelli, A. E. et al. Single-cell lineage tracing unveils a role for TCF15 in haematopoiesis. Nature 583, 585–589 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. Spanjaard, B. et al. Simultaneous lineage tracing and cell-type identification using CRISPR–Cas9-induced genetic scars. Nat. Biotechnol. 36, 469–473 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. Raj, B. et al. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nat. Biotechnol. 36, 442–450 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. Lopez-Garcia, C., Klein, A. M., Simons, B. D. & Winton, D. J. Intestinal stem cell replacement follows a pattern of neutral drift. Science 330, 822–825 (2010).

    CAS  PubMed  Article  Google Scholar 

  22. Hurley, K. et al. Reconstructed single-cell fate trajectories define lineage plasticity windows during differentiation of human PSC-derived distal lung progenitors. Cell Stem Cell 26, 593–608 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. Yao, Z. et al. A single-cell roadmap of lineage bifurcation in human ESC models of embryonic brain development. Cell Stem Cell 20, 120–134 (2017).

    CAS  PubMed  Article  Google Scholar 

  24. Prasad, N., Yang, K. & Uhler, C. Optimal transport using GANs for lineage tracing. Preprint at https://arxiv.org/abs/2007.12098 (2020).

  25. Forrow, A. & Schiebinger, G. LineageOT is a unified framework for lineage tracing and trajectory inference. Nat. Commun. 12, 4940 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. Tibshirani, R. Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. Ser. B Stat. Methodol. 58, 267–288 (1996).

    Google Scholar 

  27. Tibshirani, R., Saunders, M., Rosset, S., Zhu, J. & Knight, K. Sparsity and smoothness via the fused lasso. J. R. Stat. Soc. Ser. B Stat. Methodol. 67, 91–108 (2005).

    Article  Google Scholar 

  28. Ferreira, R., Ohneda, K., Yamamoto, M. & Philipsen, S. GATA1 function, a paradigm for transcription factors in hematopoiesis. Mol. Cell. Biol. 25, 1215–1227 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. Orkin, S. H. & Zon, L. I. Hematopoiesis: an evolving paradigm for stem cell biology. Cell 132, 631–644 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. Lu, Y.-C. et al. The molecular signature of megakaryocyte-erythroid progenitors reveals a role for the cell cycle in fate specification. Cell Rep. 25, 2083–2093 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. Arinobu, Y. et al. Developmental checkpoints of the basophil/mast cell lineages in adult murine hematopoiesis. Proc. Natl Acad. Sci. USA 102, 18105–18110 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. Jacob, A. et al. Differentiation of human pluripotent stem cells into functional lung alveolar epithelial cells. Cell Stem Cell 21, 472–488 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. Perl, A.-K. T., Kist, R., Shan, Z., Scherer, G. & Whitsett, J. A. Normal lung development and function after Sox9 inactivation in the respiratory epithelium. Genesis 41, 23–32 (2005).

    CAS  PubMed  Article  Google Scholar 

  34. Rockich, B. E. et al. Sox9 plays multiple roles in the lung epithelium during branching morphogenesis. Proc. Natl Acad. Sci. USA 110, E4456–E4464 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. Quinton, L. J. et al. Leukemia inhibitory factor signaling is required for lung protection during pneumonia. J. Immunol. 188, 6300–6308 (2012).

    CAS  PubMed  Article  Google Scholar 

  37. Nogueira-Silva, C. et al. Leukemia inhibitory factor in rat fetal lung development: expression and functional studies. PLoS ONE 7, e30517 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. Frieda, K. L. et al. Synthetic recording and in situ readout of lineage information in single cells. Nature 541, 107–111 (2017).

    CAS  PubMed  Article  Google Scholar 

  39. Ludwig, L. S. et al. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell 176, 1325–1339 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. McKenna, A. et al. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  41. Nitzan, M., Karaiskos, N., Friedman, N. & Rajewsky, N. Gene expression cartography. Nature 576, 132–137 (2019).

    CAS  PubMed  Article  Google Scholar 

  42. Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. 20, 257–272 (2019).

    CAS  PubMed  Article  Google Scholar 

  43. Cleary, B. et al. Compressed sensing for highly efficient imaging transcriptomics. Nat. Biotechnol. 39, 936–942 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. Jaitin, D. A. et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167, 1883–1896 (2016).

    CAS  PubMed  Article  Google Scholar 

  46. Nitzan, M., Casadiego, J. & Timme, M. Revealing physical interaction networks from statistics of collective dynamics. Sci. Adv. 3, e1600396 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  47. Aggarwal, C. C. Recommender Systems: The Textbook (Springer, 2016).

  48. Coifman, R. R. & Lafon, S. Diffusion maps. Appl. Comput. Harmon. Anal. 21, 5–30 (2006).

    Article  Google Scholar 

  49. Shuman, D. I., Narang, S. K., Frossard, P., Ortega, A. & Vandergheynst, P. The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Process. Mag. 30, 83–98 (2013).

    Article  Google Scholar 

  50. McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).

  51. van Laarhoven, P. J. M. & Aarts, E. H. L. in Simulated Annealing: Theory and Applications (eds van Laarhoven, P. J. M. & Aarts, E. H. L.) 7–15 (Springer Netherlands, 1987).

  52. Weinreb, C., Wolock, S. & Klein, A. M. SPRING: a kinetic interface for visualizing high dimensional single-cell expression data. Bioinformatics 34, 1246–1248 (2018).

    CAS  PubMed  Article  Google Scholar 

  53. Peyré, G. & Cuturi, M. Computational optimal transport: with applications to data science. Found. Trends Mach. Learn. 11, 355–607 (2019).

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

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.

Corresponding authors

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

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

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

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–11 and Notes 1–7.

Reporting Summary

Supplementary Table

Supplementary Tables 1–3.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41587-022-01209-1

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing