CellRank 2: unified fate mapping in multiview single-cell data.
Journal
Nature methods
ISSN: 1548-7105
Titre abrégé: Nat Methods
Pays: United States
ID NLM: 101215604
Informations de publication
Date de publication:
13 Jun 2024
13 Jun 2024
Historique:
received:
18
07
2023
accepted:
09
05
2024
medline:
14
6
2024
pubmed:
14
6
2024
entrez:
13
6
2024
Statut:
aheadofprint
Résumé
Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or RNA velocity to reconstruct state-change trajectories; however, trajectory inference does not incorporate valuable time point information or utilize additional modalities, whereas methods that address these different data views cannot be combined or do not scale. Here we present CellRank 2, a versatile and scalable framework to study cellular fate using multiview single-cell data of up to millions of cells in a unified fashion. CellRank 2 consistently recovers terminal states and fate probabilities across data modalities in human hematopoiesis and endodermal development. Our framework also allows combining transitions within and across experimental time points, a feature we use to recover genes promoting medullary thymic epithelial cell formation during pharyngeal endoderm development. Moreover, we enable estimating cell-specific transcription and degradation rates from metabolic-labeling data, which we apply to an intestinal organoid system to delineate differentiation trajectories and pinpoint regulatory strategies.
Identifiants
pubmed: 38871986
doi: 10.1038/s41592-024-02303-9
pii: 10.1038/s41592-024-02303-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s).
Références
Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016).
pubmed: 27571553
doi: 10.1038/nmeth.3971
Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).
pubmed: 28825705
pmcid: 5764547
doi: 10.1038/nmeth.4402
Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).
Wolf, F. A. et al. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 20, 59 (2019).
Setty, M. et al. Characterization of cell fate probabilities in single-cell data with Palantir. Nat. Biotechnol. 37, 451–460 (2019).
pubmed: 30899105
pmcid: 7549125
doi: 10.1038/s41587-019-0068-4
Nowotschin, S. et al. The emergent landscape of the mouse gut endoderm at single-cell resolution. Nature 569, 361–367 (2019).
pubmed: 30959515
pmcid: 6724221
doi: 10.1038/s41586-019-1127-1
La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).
pubmed: 30089906
pmcid: 6130801
doi: 10.1038/s41586-018-0414-6
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).
pubmed: 32747759
doi: 10.1038/s41587-020-0591-3
Battich, N. et al. Sequencing metabolically labeled transcripts in single cells reveals mRNA turnover strategies. Science 367, 1151–1156 (2020).
pubmed: 32139547
doi: 10.1126/science.aax3072
Cao, J., Zhou, W., Steemers, F., Trapnell, C. & Shendure, J. Sci-fate characterizes the dynamics of gene expression in single cells. Nat. Biotechnol. 38, 980–988 (2020).
pubmed: 32284584
pmcid: 7416490
doi: 10.1038/s41587-020-0480-9
Qiu, Q. et al. Massively parallel and time-resolved RNA sequencing in single cells with scNT-seq. Nat. Meth. 17, 991–1001 (2020).
doi: 10.1038/s41592-020-0935-4
Erhard, F. et al. scslam-seq reveals core features of transcription dynamics in single cells. Nature 571, 419–423 (2019).
pubmed: 31292545
doi: 10.1038/s41586-019-1369-y
Ren, J. et al. Spatiotemporally resolved transcriptomics reveals the subcellular RNA kinetic landscape. Nat. Meth. 20, 695–705 (2023).
doi: 10.1038/s41592-023-01829-8
Lange, M. et al. Cellrank for directed single-cell fate mapping. Nat. Meth. 19, 159–170 (2022).
doi: 10.1038/s41592-021-01346-6
Schiebinger, G. et al. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell 176, 928–943 (2019).
doi: 10.1016/j.cell.2019.01.006
Qiu, X. et al. Mapping transcriptomic vector fields of single cells. Cell 185, 690–711 (2022).
doi: 10.1016/j.cell.2021.12.045
Barile, M. et al. Coordinated changes in gene expression kinetics underlie both mouse and human erythroid maturation. Genome Biol. 22, 197 (2021).
Gulati, G. S. et al. Single-cell transcriptional diversity is a hallmark of developmental potential. Science 367, 405–411 (2020).
pubmed: 31974247
pmcid: 7694873
doi: 10.1126/science.aax0249
Reuter, B., Weber, M., Fackeldey, K., Röblitz, S. & Garcia, M. E. Generalized Markov state modeling method for nonequilibrium biomolecular dynamics: exemplified on amyloid β conformational dynamics driven by an oscillating electric field. J. Chem. Theory Comput. 14, 3579–3594 (2018).
pubmed: 29812922
doi: 10.1021/acs.jctc.8b00079
Reuter, B., Fackeldey, K. & Weber, M. Generalized Markov modeling of nonreversible molecular kinetics. J. Chem. Phys. 150, 174103 (2019).
Forrow, A. & Schiebinger, G. LineageOT is a unified framework for lineage tracing and trajectory inference. Nat. Commun. 12, 4940 (2021).
Wang, S.-W., Herriges, M. J., Hurley, K., Kotton, D. N. & Klein, A. M. Cospar identifies early cell fate biases from single-cell transcriptomic and lineage information. Nat. Biotechnol. 40, 1066–1074 (2022).
pubmed: 35190690
doi: 10.1038/s41587-022-01209-1
Lange, M. et al. Mapping lineage-traced cells across time points with moslin. Preprint at bioRxiv https://doi.org/10.1101/2023.04.14.536867 (2023).
Klein, D. et al. Mapping cells through time and space with moscot. Preprint at bioRxiv https://doi.org/10.1101/2023.05.11.540374 (2023).
Stassen, S. V., Yip, G. G. K., Wong, K. K. Y., Ho, J. W. K. & Tsia, K. K. Generalized and scalable trajectory inference in single-cell omics data with via. Nat.Commun. https://doi.org/10.1038/s41467-021-25773-3 (2021).
Lance, C. et al. in (eds Kiela, D., Ciccone, M. & Caputo, B.) Proc. NeurIPS 2021 Competitions and Demonstrations Track, Vol. 176 Proc. Machine Learning Research 162–176 (PMLR, 2022).
Sawai, C. M. et al. Transcription factor runx2 controls the development and migration of plasmacytoid dendritic cells. J. Exp. Med. 210, 2151–2159 (2013).
pubmed: 24101375
pmcid: 3804932
doi: 10.1084/jem.20130443
Ceribelli, M. et al. A druggable tcf4- and brd4-dependent transcriptional network sustains malignancy in blastic plasmacytoid dendritic cell neoplasm. Cancer Cell 30, 764–778 (2016).
pubmed: 27846392
pmcid: 5175469
doi: 10.1016/j.ccell.2016.10.002
Gao, M., Qiao, C. & Huang, Y. UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference. Nat. Commun. 13, 6586 (2022).
Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37, 547–554 (2019).
pubmed: 30936559
doi: 10.1038/s41587-019-0071-9
Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).
pubmed: 30787437
pmcid: 6434952
doi: 10.1038/s41586-019-0969-x
Moon, K. R. et al. Visualizing structure and transitions in high-dimensional biological data. Nat. Biotechnol. 37, 1482–1492 (2019).
pubmed: 31796933
pmcid: 7073148
doi: 10.1038/s41587-019-0336-3
Chen, Y.-F. et al. Control of matrix stiffness promotes endodermal lineage specification by regulating SMAD2/3 via lncRNA LINC00458. Sci. Adv. https://doi.org/10.1126/sciadv.aay0264 (2020).
Takenaga, M., Fukumoto, M. & Hori, Y. Regulated nodal signaling promotes differentiation of the definitive endoderm and mesoderm from ES cells. J. Cell Sci. 120, 2078–2090 (2007).
pubmed: 17535850
doi: 10.1242/jcs.004127
Fischer, D. S. et al. Inferring population dynamics from single-cell RNA-sequencing time series data. Nat. Biotechnol. 37, 461–468 (2019).
pubmed: 30936567
pmcid: 7397487
doi: 10.1038/s41587-019-0088-0
Cuturi, M. Sinkhorn distances: lightspeed computation of optimal transportation distances. Preprint at arXiv https://doi.org/10.48550/arXiv.1306.0895 (2013).
Magaletta, M. E. et al. Integration of single-cell transcriptomes and chromatin landscapes reveals regulatory programs driving pharyngeal organ development. Nat. Commun. 13, 457 (2022).
Choe, C. P. & Crump, J. G. Dynamic epithelia of the developing vertebrate face. Curr. Opin. Genet. Dev. 32, 66–72 (2015).
pubmed: 25748249
pmcid: 4470803
doi: 10.1016/j.gde.2015.02.003
Grevellec, A. & Tucker, A. S. The pharyngeal pouches and clefts: development, evolution, structure and derivatives. Semin. Cell Dev. Biol. 21, 325–332 (2010).
pubmed: 20144910
doi: 10.1016/j.semcdb.2010.01.022
Fagman, H., Andersson, L. & Nilsson, M. The developing mouse thyroid: embryonic vessel contacts and parenchymal growth pattern during specification, budding, migration, and lobulation. Dev. Dyn. 235, 444–455 (2005).
doi: 10.1002/dvdy.20653
Liu, Z., Yu, S. & Manley, N. R. Gcm2 is required for the differentiation and survival of parathyroid precursor cells in the parathyroid/thymus primordia. Dev. Biol. 305, 333–346 (2007).
pubmed: 17382312
pmcid: 1931567
doi: 10.1016/j.ydbio.2007.02.014
Posabella, A. et al. Derivation of thyroid follicular cells from pluripotent stem cells: insights from development and implications for regenerative medicine. Front. Endocrinol. https://doi.org/10.3389/fendo.2021.666565 (2021).
Ohigashi, I. et al. Aire-expressing thymic medullary epithelial cells originate from β5t-expressing progenitor cells. Proc. Natl Acad. Sci. USA 110, 9885–9890 (2013).
pubmed: 23720310
pmcid: 3683726
doi: 10.1073/pnas.1301799110
Rodrigues, P. M. et al. Thymic epithelial cells require p53 to support their long-term function in thymopoiesis in mice. Blood 130, 478–488 (2017).
pubmed: 28559356
doi: 10.1182/blood-2016-12-758961
Bautista, J. L. et al. Single-cell transcriptional profiling of human thymic stroma uncovers novel cellular heterogeneity in the thymic medulla. Nat. Commun. 12, 1096 (2021).
Dooley, J., Erickson, M. & Farr, A. G. Alterations of the medullary epithelial compartment in the aire-deficient thymus: Implications for programs of thymic epithelial differentiation. J. Immunol. 181, 5225–5232 (2008).
pubmed: 18832676
doi: 10.4049/jimmunol.181.8.5225
Stefanski, H. E. et al. P63 targeted deletion under the foxn1 promoter disrupts pre-and post-natal thymus development, function and maintenance as well as induces severe hair loss. PLoS ONE 17, e0261770 (2022).
pubmed: 35077450
pmcid: 8789144
doi: 10.1371/journal.pone.0261770
Lucas, B. et al. Embryonic keratin19
Haunerdinger, V. et al. Novel combination of surface markers for the reliable and comprehensive identification of human thymic epithelial cells by flow cytometry: quantitation and transcriptional characterization of thymic stroma in a pediatric cohort. Front. Immunol. https://doi.org/10.3389/fimmu.2021.740047 (2021).
Carter, J. A. et al. Transcriptomic diversity in human medullary thymic epithelial cells. Nat. Commun. 13, 4296 (2022).
Gotter, J., Brors, B., Hergenhahn, M. & Kyewski, B. Medullary epithelial cells of the human thymus express a highly diverse selection of tissue-specific genes colocalized in chromosomal clusters. J. Exp. Med. 199, 155–166 (2004).
pubmed: 14734521
pmcid: 2211762
doi: 10.1084/jem.20031677
Skogberg, G. et al. Human thymic epithelial primary cells produce exosomes carrying tissue-restricted antigens. Immunol. Cell Biol. 93, 727–734 (2015).
pubmed: 25776846
pmcid: 4575951
doi: 10.1038/icb.2015.33
Nusser, A. et al. Developmental dynamics of two bipotent thymic epithelial progenitor types. Nature 606, 165–171 (2022).
pubmed: 35614226
pmcid: 9159946
doi: 10.1038/s41586-022-04752-8
Haber, A. L. et al. A single-cell survey of the small intestinal epithelium. Nature 551, 333–339 (2017).
pubmed: 29144463
pmcid: 6022292
doi: 10.1038/nature24489
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).
pubmed: 29590089
doi: 10.1038/nature25969
Raj, B. et al. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nat. Biotechnol. 36, 442–450 (2018).
pubmed: 29608178
pmcid: 5938111
doi: 10.1038/nbt.4103
Spanjaard, B. et al. Simultaneous lineage tracing and cell-type identification using crispr–cas9-induced genetic scars. Nat. Biotechnol. 36, 469–473 (2018).
pubmed: 29644996
pmcid: 5942543
doi: 10.1038/nbt.4124
Chan, M. M. et al. Molecular recording of mammalian embryogenesis. Nature 570, 77–82 (2019).
pubmed: 31086336
pmcid: 7229772
doi: 10.1038/s41586-019-1184-5
Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777–1792 (2022).
doi: 10.1016/j.cell.2022.04.003
Liu, C. et al. Spatiotemporal mapping of gene expression landscapes and developmental trajectories during zebrafish embryogenesis. Dev. Cell 57, 1284–1298 (2022).
doi: 10.1016/j.devcel.2022.04.009
Wang, M. et al. High-resolution 3D spatiotemporal transcriptomic maps of developing Drosophila embryos and larvae. Dev. Cell 57, 1271–1283 (2022).
doi: 10.1016/j.devcel.2022.04.006
Otto, D., Jordan, C., Dury, B., Dien, C. & Setty, M. Quantifying cell-state densities in single-cell phenotypic landscapes using mellon. Preprint at bioRxiv https://doi.org/10.1101/2023.07.09.548272 (2023).
Maizels, R. J., Snell, D. M. & Briscoe, J. Deep dynamical modelling of developmental trajectories with temporal transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2023.07.06.547989 (2023).
Peng, Q., Qiu, X. & Li, T. Storm: Incorporating transient stochastic dynamics to infer the rna velocity with metabolic labeling information. Preprint at bioRxiv https://doi.org/10.1101/2023.06.21.545990 (2023).
De Jonghe, J. et al. spinDrop: a droplet microfluidic platform to maximise single-cell sequencing information content. Nat. Commun. 14, 4788 (2023).
Van’t Sant, L. J., White, J. J., Hoeijmakers, J. H. J., Vermeij, W. P. & Jaarsma, D. In vivo 5-ethynyluridine (EU) labelling detects reduced transcription in purkinje cell degeneration mouse mutants, but can itself induce neurodegeneration. Acta Neuropathol Commun. https://doi.org/10.1186/s40478-021-01200-y (2021).
Bergen, V., Soldatov, R. A., Kharchenko, P. V. & Theis, F. J. RNA velocity–current challenges and future perspectives. Mol. Sys. Biol. https://doi.org/10.15252/msb.202110282 (2021).
Weiler, P., Van den Berge, K., Street, K. & Tiberi, S. A Guide to Trajectory Inference and RNA Velocity, 269–292 (Springer, 2022).
Meier, A. B. et al. Epicardioid single-cell genomics uncovers principles of human epicardium biology in heart development and disease. Nat. Biotechnol. 41, 1787–1800 (2023).
pubmed: 37012447
pmcid: 10713454
doi: 10.1038/s41587-023-01718-7
Xiao, Y. et al. Tracking single-cell evolution using clock-like chromatin accessibility loci. Nat. Biotechnol. https://doi.org/10.1038/s41587-024-02241-z (2024).
Schölkopf, B. Causality for Machine Learning, 765–804 (ACM, 2022).
Gorin, G., Fang, M., Chari, T. & Pachter, L. RNA velocity unraveled. PLOS Comput. Biol. 18, e1010492 (2022).
pubmed: 36094956
pmcid: 9499228
doi: 10.1371/journal.pcbi.1010492
Zheng, S. C., Stein-O’Brien, G., Boukas, L., Goff, L. A. & Hansen, K. D. Pumping the brakes on RNA velocity by understanding and interpreting RNA velocity estimates. Genome Biol. 24, 246 (2023).
Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).
pubmed: 24531970
pmcid: 4412462
doi: 10.1126/science.1247651
Saad, Y. & Schultz, M. H. Gmres: a generalized minimal residual algorithm for solving nonsymmetric linear systems. SIAM J. Sci. Stat. Comput. 7, 856–869 (1986).
doi: 10.1137/0907058
van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729 (2018).
The Tabla Muris Consortium et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).
Packer, J. S. et al. A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution. Science https://doi.org/10.1126/science.aax1971 (2019).
Farrell, J. A. et al. Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis. Science https://doi.org/10.1126/science.aar3131 (2018).
Erhard, F. et al. Time-resolved single-cell RNA-seq using metabolic RNA labelling. Nat. Rev. Methods Primers https://doi.org/10.1038/s43586-022-00157-z (2022).
Hendriks, G.-J. et al. NASC-seq monitors RNA synthesis in single cells. Nat. Commun. 10, 3138 (2019).
Lin, S. et al. Well-TEMP-seq as a microwell-based strategy for massively parallel profiling of single-cell temporal RNA dynamics. Nat. Commun. 14, 1272 (2023).
Holler, K. et al. Spatio-temporal mRNA tracking in the early zebrafish embryo. Nat. Commun. 12, 3358 (2021).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Virshup, I., Rybakov, S., Theis, F. J., Angerer, P. & Wolf, F. A. anndata: annotated data. Preprint at bioRxiv https://doi.org/10.1101/2021.12.16.473007 (2021).
McInnes, L., Healy, J., Saul, N. & Großberger, L. UMAP: uniform manifold approximation and projection. J. Open Source Softw. 3, 861 (2018).
doi: 10.21105/joss.00861
Ashuach, T. et al. MultiVI: deep generative model for the integration of multimodal data. Nat. Meth. 20, 1222–1231 (2023).
doi: 10.1038/s41592-023-01909-9
Haghverdi, L., Buettner, F. & Theis, F. J. Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 31, 2989–2998 (2015).
pubmed: 26002886
doi: 10.1093/bioinformatics/btv325
Heumos, L. et al. Best practices for single-cell analysis across modalities. Nat. Rev. Genet. 24, 550–572 (2023).
pubmed: 37002403
doi: 10.1038/s41576-023-00586-w
Weiler, P. et al. Unified fate mapping in multiview single-cell data. figshare https://doi.org/10.6084/m9.figshare.c.6843633.v1 (2023).
Klein, M., Weiler, P. & Lange, M. theislab/cellrank: v.2.0.3. Zenodo https://doi.org/10.5281/zenodo.10210197 (2023).
Weiler, P. theislab/cellrank2_reproducibility: v.1.0.0. Zenodo https://doi.org/10.5281/zenodo.10827945 (2024).
Li, S. et al. A relay velocity model infers cell-dependent RNA velocity. Nat. Biotechnol. 42, 99–108 (2023).
pubmed: 37012448
pmcid: 10545816
doi: 10.1038/s41587-023-01728-5
Biddy, B. A. et al. Single-cell mapping of lineage and identity in direct reprogramming. Nature 564, 219–224 (2018).
pubmed: 30518857
pmcid: 6635140
doi: 10.1038/s41586-018-0744-4