Mapping transcriptomic vector fields of single cells

Abstract

Single-cell (sc)RNA-seq, together with RNA velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires kinetic models capable of unveiling governing regulatory functions. Here, we introduce an analytical framework dynamo (https://github.com/aristoteleo/dynamo-release), which infers absolute RNA velocity, reconstructs continuous vector fields that predict cell fates, employs differential geometry to extract underlying regulations, and ultimately predicts optimal reprogramming paths and perturbation outcomes. We highlight dynamo's power to overcome fundamental limitations of conventional splicing-based RNA velocity analyses to enable accurate velocity estimations on a metabolically labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal mechanisms driving early megakaryocyte appearance and elucidate asymmetrical regulation within the PU.1-GATA1 circuit. Leveraging the least-action-path method, dynamo accurately predicts drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo, thus, represents an important step in advancing quantitative and predictive theories of cell-state transitions.

References 106

1. 1.

   Adamson B, Norman TM, Jost M, Cho MY, Nuñez JK, Chen Y, Villalta JE, Gilbert LA, Horlbeck MA, Hein MY, (2016 ). A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response. Cell 167, 1867–1882.e21.

   Not in our library10.1016/j.cell.2016.11.048

2. 2.

   Alon U (2006 ). An Introduction to Systems Biology.

   Not in our library

3. 3.

   Aurell E, and Sneppen K (2002 ). Epigenetics as a first exit problem. Phys. Rev. Lett. 88, 048101.

   Not in our library10.1103/physrevlett.88.048101

4. 4.

   Baker M (2010 ). Taking a long, hard look. Nature 466, 1137–1138.

   Not in our library10.1038/4661137a

5. 5.
   Coordinated changes in gene expression kinetics underlie both mouse and human erythroid maturation · 2021

   Not checked
6. 6.

   Battich N, Beumer J, de Barbanson B, Krenning L, Baron CS, Tanenbaum ME, Clevers H, and van Oudenaarden A (2020 ). Sequencing metabolically labeled transcripts in single cells reveals mRNA turnover strategies. Science 367, 1151–1156.

   Not in our library10.1126/science.aax3072

7. 7.

   Bendall SC, Davis KL, el-AD A, Tadmor, Simonds EF, Chen TJ, Shenfeld DK, Nolan GP, and Pe’er D (2014 ). Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157.

   Not in our library10.1016/j.cell.2014.04.005

8. 8.

   Bergen V, Lange M, Peidli S, Wolf FA, and Theis FJ (2020 ). Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408–1414.

   Not in our library10.1038/s41587-020-0591-3

9. 9.
   RNA velocity—current challenges and future perspectives · 2021

   No notice
10. 10.

    Brauer F, and Kribs C (2015 ). Dynamical Systems for Biological Modeling: An Introduction (CRC Press).

    Not in our library

11. 11.

    Buenrostro JD, Wu B, Chang HY, and Greenleaf WJ (2015 ). ATAC-seq: A Method for Assaying Chromatin Accessibility Genome-Wide. Curr. Protoc. Mol. Biol. 109, 21.29.1–21.29.9.

    Not in our library10.1002/0471142727.mb2129s109

12. 12.

    Cahan P, Li H, Morris SA, Lummertz da Rocha E, Daley GQ, and Collins JJ (2014 ). CellNet: network biology applied to stem cell engineering. Cell 158, 903–915.

    Not in our library10.1016/j.cell.2014.07.020

13. 13.

    Cao J (2020 ). Tracking development at the cellular level. Science 370, 924–925.

    Not in our library10.1126/science.abf1686

14. 14.

    Cao J, O’Day DR, Pliner HA, Kingsley PD, Deng M, Daza RM, Zager MA, Aldinger KA, Blecher-Gonen R, Zhang F, (2020a ). A human cell atlas of fetal gene expression. Science 370.

    Not in our library10.1126/science.aba7721

15. 15.

    Cao J, Zhou W, Steemers F, Trapnell C, and Shendure J (2020b ). Sci-fate characterizes the dynamics of gene expression in single cells. Nat. Biotechnol. 38, 980–988.

    Not in our library10.1038/s41587-020-0480-9

16. 16.

    Capellera-Garcia S, Pulecio J, Dhulipala K, Siva K, Rayon-Estrada V, Singbrant S, Sommarin MNE, Walkley CR, Soneji S, Karlsson G, (2016 ). Defining the Minimal Factors Required for Erythropoiesis through Direct Lineage Conversion. Cell Rep. 15, 2550–2562.

    Not in our library10.1016/j.celrep.2016.05.027

17. 17.

    Chan MM, Smith ZD, Grosswendt S, Kretzmer H, Norman TM, Adamson B, Jost M, Quinn JJ, Yang D, Jones MG, (2019 ). Molecular recording of mammalian embryogenesis. Nature 570, 77–82.

    Not in our library10.1038/s41586-019-1184-5

18. 18.

    Chen A, Liao S, Cheng M, Ma K, Wu L, Lai Y, Yang J, Li W, Xu J, Hao S, (2021 ). Large field of view-spatially resolved transcriptomics at nanoscale resolution.

    Not in our library

19. 19.

    Dixit A, Parnas O, Li B, Chen J, Fulco CP, Jerby-Arnon L, Marjanovic ND, Dionne D, Burks T, Raychowdhury R, (2016 ). Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens. Cell 167, 1853–1866.e17.

    Not in our library10.1016/j.cell.2016.11.038

20. 20.

    Drissen R, Buza-Vidas N, Woll P, Thongjuea S, Gambardella A, Giustacchini A, Mancini E, Zriwil A, Lutteropp M, Grover A, (2016 ). Distinct myeloid progenitor–differentiation pathways identified through single-cell RNA sequencing. Nature Immunology 17, 666–676.

    Not in our library10.1038/ni.3412

21. 21.

    Drissen R, Thongjuea S, Theilgaard-Mönch K, and Nerlov C (2019 ). Identification of two distinct pathways of human myelopoiesis. Sci Immunol 4.

    Not in our library10.1126/sciimmunol.aau7148

22. 22.

    Erhard F, Baptista MAP, Krammer T, Hennig T, Lange M, Arampatzi P, Jürges CS, Theis FJ, Saliba A-E, and Dölken L (2019 ). scSLAM-seq reveals core features of transcription dynamics in single cells. Nature 571, 419–423.

    Not in our library10.1038/s41586-019-1369-y

23. 23.

    Feynman RP, Hibbs AR, and Styer DF (2010 ). Quantum Mechanics and Path Integrals (Courier Corporation).

    Not in our library

24. 24.

    Freidlin MI, and Wentzell AD (2012 ). Random Perturbations of Dynamical Systems (Springer Berlin Heidelberg).

    Not in our library

25. 25.

    Frieda KL, Linton JM, Hormoz S, Choi J, Chow K-HK, Singer ZS, Budde MW, Elowitz MB, and Cai L (2017 ). Synthetic recording and in situ readout of lineage information in single cells. Nature 541, 107–111.

    Not in our library10.1038/nature20777

26. 26.

    Golding I, Paulsson J, Zawilski SM, and Cox EC (2005 ). Real-time kinetics of gene activity in individual bacteria. Cell 123, 1025–1036.

    Not in our library10.1016/j.cell.2005.09.031

27. 27.

    Gorin G, Svensson V, and Pachter L (2020 ). Protein velocity and acceleration from single-cell multiomics experiments. Genome Biol. 21, 39.

    Not in our library10.1186/s13059-020-1945-3

28. 28.

    Graf T, and Enver T (2009 ). Forcing cells to change lineages. Nature 462, 587–594.

    Not in our library10.1038/nature08533

29. 29.

    Grün D, Kester L, and van Oudenaarden A (2014 ). Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637–640.

    Not in our library10.1038/nmeth.2930

30. 30.

    Guo MH, Nandakumar SK, Ulirsch JC, Zekavat SM, Buenrostro JD, Natarajan P, Salem RM, Chiarle R, Mitt M, Kals M, (2017 ). Comprehensive population-based genome sequencing provides insight into hematopoietic regulatory mechanisms. Proc. Natl. Acad. Sci. U. S. A. 114, E327–E336.

    Not in our library10.1073/pnas.1619052114

31. 31.

    Haghverdi L, Büttner M, Wolf FA, Buettner F, and Theis FJ (2016 ). Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848.

    Not in our library10.1038/nmeth.3971

32. 32.

    Hansen LP (1982 ). Large Sample Properties of Generalized Method of Moments Estimators. Econometrica 50, 1029–1054.

    Not in our library

33. 33.

    Hendriks G-J, Jung LA, Larsson AJM, Lidschreiber M, Andersson Forsman O, Lidschreiber K, Cramer P, and Sandberg R (2019 ). NASC-seq monitors RNA synthesis in single cells. Nat. Commun. 10, 3138.

    Not in our library10.1038/s41467-019-11028-9

34. 34.

    Holler K, Neuschulz A, Drewe-Boß P, Mintcheva J, Spanjaard B, Arsiè R, Ohler U, Landthaler M, and Junker JP (2021 ). Spatio-temporal mRNA tracking in the early zebrafish embryo. Nat. Commun. 12, 3358.

    Not in our library10.1038/s41467-021-23834-1

35. 35.

    Huang S, Eichler G, Bar-Yam Y, and Ingber DE (2005 ). Cell fates as high-dimensional attractor states of a complex gene regulatory network. Phys. Rev. Lett. 94, 128701.

    Not in our library10.1103/physrevlett.94.128701

36. 36.

    Huang S, Guo Y-P, May G, and Enver T (2007 ). Bifurcation dynamics in lineage-commitment in bipotent progenitor cells. Dev. Biol. 305, 695–713.

    Not in our library10.1016/j.ydbio.2007.02.036

37. 37.

    Iman RL, Helton JC, and Campbell JE (1981 ). An Approach to Sensitivity Analysis of Computer Models: Part I—Introduction, Input Variable Selection and Preliminary Variable Assessment. Journal of Quality Technology 13, 174–183.

    Not in our library

38. 38.

    Iwasaki H, Mizuno S-I, Arinobu Y, Ozawa H, Mori Y, Shigematsu H, Takatsu K, Tenen DG, and Akashi K (2006 ). The order of expression of transcription factors directs hierarchical specification of hematopoietic lineages. Genes Dev. 20, 3010–3021.

    Not in our library10.1101/gad.1493506

39. 39.

    Jürges C, Dölken L, and Erhard F (2018 ). Dissecting newly transcribed and old RNA using GRAND-SLAM. Bioinformatics 34, i218–i226.

    Not in our library10.1093/bioinformatics/bty256

40. 40.

    Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM, Bolival B Jr, Assad-Garcia N, Glass JI, and Covert MW (2012 ). A whole-cell computational model predicts phenotype from genotype. Cell 150, 389–401.

    Not in our library10.1016/j.cell.2012.05.044

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