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.

  • Review Article
  • Published:

Temporal modelling using single-cell transcriptomics

Nature Reviews Genetics volume 23, pages 355–368 (2022)Cite this article

Subjects

Abstract

Methods for profiling genes at the single-cell level have revolutionized our ability to study several biological processes and systems including development, differentiation, response programmes and disease progression. In many of these studies, cells are profiled over time in order to infer dynamic changes in cell states and types, sets of expressed genes, active pathways and key regulators. However, time-series single-cell RNA sequencing (scRNA-seq) also raises several new analysis and modelling issues. These issues range from determining when and how deep to profile cells, linking cells within and between time points, learning continuous trajectories, and integrating bulk and single-cell data for reconstructing models of dynamic networks. In this Review, we discuss several approaches for the analysis and modelling of time-series scRNA-seq, highlighting their steps, key assumptions, and the types of data and biological questions they are most appropriate for.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of time-series scRNA-seq data analysis.
Fig. 2: Selecting time points to sample in scRNA-seq experiments.
Fig. 3: Trajectory inference methods for time-series scRNA-seq data.

Similar content being viewed by others

Code availability

The code to reproduce the plots shown in Fig. 3 is available at http://dinglab.rimuhc.ca:8080/nrg/.

Data availability

The primary data for the analyses shown in Fig. 3 are from Treutlein et al.69, and the processed data presented in the figure are available at http://dinglab.rimuhc.ca:8080/nrg/.

References

  1. Gasch, A. P. et al. Single-cell RNA sequencing reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stress. PLoS Biol. 15, e2004050 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Zou, Z. et al. A single-cell transcriptomic atlas of human skin aging. Dev. Cell 56, 383–397.e8 (2021).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  4. Alavi, A., Ruffalo, M., Parvangada, A., Huang, Z. & Bar-Joseph, Z. A web server for comparative analysis of single-cell RNA-seq data. Nat. Commun. 9, 4768 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Dumitrascu, B., Villar, S., Mixon, D. G. & Engelhardt, B. E. Optimal marker gene selection for cell type discrimination in single cell analyses. Nat. Commun. 12, 1186 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc. 15, 1484–1506 (2020).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Bar-Joseph, Z., Gitter, A. & Simon, I. Studying and modelling dynamic biological processes using time-series gene expression data. Nat. Rev. Genet. 13, 552–564 (2012).

    Article  CAS  PubMed  Google Scholar 

  9. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018). This paper introduces the idea of RNA velocity and presents the first method to infer velocity from scRNA-seq data.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Herzog, V. A. et al. Thiol-linked alkylation of RNA to assess expression dynamics. Nat. Methods 14, 1198–1204 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Erhard, F. et al. scSLAM-seq reveals core features of transcription dynamics in single cells. Nature 571, 419–423 (2019).

    Article  CAS  PubMed  Google Scholar 

  12. Hendriks, G.-J. et al. NASC-seq monitors RNA synthesis in single cells. Nat. Commun. 10, 3138 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Qiu, Q. et al. Massively parallel and time-resolved RNA sequencing in single cells with scNT-seq. Nat. Methods 17, 991–1001 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Schofield, J. A., Duffy, E. E., Kiefer, L., Sullivan, M. C. & Simon, M. D. TimeLapse-seq: adding a temporal dimension to RNA sequencing through nucleoside recoding. Nat. Methods 15, 221 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Kiefer, L., Schofield, J. A. & Simon, M. D. Expanding the nucleoside recoding toolkit: revealing RNA population dynamics with 6-thioguanosine. J. Am. Chem. Soc. 140, 14567–14570 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Matsushima, W. et al. SLAM-ITseq: sequencing cell type-specific transcriptomes without cell sorting. Development 145, dev164640 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Byrnes, L. E. et al. Lineage dynamics of murine pancreatic development at single-cell resolution. Nat. Commun. 9, 3922 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Gehart, H. et al. Identification of enteroendocrine regulators by real-time single-cell differentiation mapping. Cell 176, 1158–1173.e16 (2019). This study, by determining the change in the ratios between two fluorescent proteins with different half-lives over time, establishes a real-time scale bar for virtual pseudo-time maps.

    Article  CAS  PubMed  Google Scholar 

  20. Reizel, Y. et al. Colon stem cell and crypt dynamics exposed by cell lineage reconstruction. PLoS Genet. 7, e1002192 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. McKenna, A. et al. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016). This study uses CRISPR–Cas9 to introduce mutations at specific loci on the genome, which remain as ‘scars’ that register the cell lineage.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. 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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 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.e27 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Quinn, J. J. et al. Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts. Science 371, eabc1944 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Pearson, K. L. III. On lines and planes of closest fit to systems of points in space. Lond. Edinb. Dublin Philos. Mag. J. Sci. 2, 559–572 (1901).

    Article  Google Scholar 

  29. Maaten, Lvd & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  30. Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2019).

    Article  CAS  Google Scholar 

  31. Lin, C., Jain, S., Kim, H. & Bar-Joseph, Z. Using neural networks for reducing the dimensions of single-cell RNA-seq data. Nucleic Acids Res. 45, e156 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Ji, Z. & Ji, H. TSCAN: pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res. 44, e117 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019). This study introduces and uses Monocle 3, one of the most successful and popular methods for pseudo-time inference.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ding, J. et al. Reconstructing differentiation networks and their regulation from time series single-cell expression data. Genome Res. 28, 383–395 (2018).

    Article  CAS  PubMed Central  Google Scholar 

  39. Guo, M., Bao, E. L., Wagner, M., Whitsett, J. A. & Xu, Y. SLICE: determining cell differentiation and lineage based on single cell entropy. Nucleic Acids Res. 45, e54 (2017).

    Article  PubMed  CAS  Google Scholar 

  40. Halbritter, F. et al. Epigenomics and single-cell sequencing define a developmental hierarchy in Langerhans cell histiocytosis. Cancer Discov. 9, 1406–1421 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Schiebinger, G. et al. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell 176, 928–943.e22 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Yang, K. D. et al. Predicting cell lineages using autoencoders and optimal transport. PLoS Comput. Biol. 16, e1007828 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37, 547–554 (2019). This comprehensive trajectory inference comparison study systematically benchmarks several different pseudo-time and trajectory inference methods.

    Article  CAS  PubMed  Google Scholar 

  45. Chen, H. et al. Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM. Nat. Commun. 10, 1903 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Megill, C. et al. cellxgene: a performant, scalable exploration platform for high dimensional sparse matrices. Preprint at bioRxiv https://doi.org/10.1101/2021.04.05.438318 (2021).

    Article  Google Scholar 

  47. Rashid, S., Kotton, D. N. & Bar-Joseph, Z. TASIC: determining branching models from time series single cell data. Bioinformatics 33, 2504–2512 (2017).

    Article  PubMed  CAS  Google Scholar 

  48. Marco, E. et al. Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape. Proc. Natl Acad. Sci. USA 111, E5643–E5650 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Lin, C. & Bar-Joseph, Z. Continuous-state HMMs for modeling time-series single-cell RNA-seq data. Bioinformatics 35, 4707–4715 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Consortium, T. M. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).

    Article  CAS  Google Scholar 

  51. 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). This study generalizes RNA velocity-based trajectory inference to transient cell states.

    Article  CAS  PubMed  Google Scholar 

  52. Lange, M. et al. CellRank for directed single-cell fate mapping. Preprint at bioRxiv https://doi.org/10.1101/2020.10.19.345983 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019). This paper presents Seurat, one of the most comprehensive and popular packages for the analysis and visualization of scRNA-seq data.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. 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.e8 (2020). This study applies and validates methods to reconstruct dynamic regulatory networks by integrating time-series scRNA-seq data with interaction data.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Hwang, B. et al. Lineage tracing using a Cas9-deaminase barcoding system targeting endogenous L1 elements. Nat. Commun. 10, 1234 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Zafar, H., Lin, C. & Bar-Joseph, Z. Single-cell lineage tracing by integrating CRISPR–Cas9 mutations with transcriptomic data. Nat. Commun. 11, 3055 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Ding, J., Hagood, J. S., Ambalavanan, N., Kaminski, N. & Bar-Joseph, Z. iDREM: interactive visualization of dynamic regulatory networks. PLoS Comput. Biol. 14, e1006019 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Ding, J. et al. Integrating multiomics longitudinal data to reconstruct networks underlying lung development. Am. J. Physiol. Lung Cell. Mol. Physiol. 317, L556–L568 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Campbell, K. R. & Yau, C. Uncovering pseudotemporal trajectories with covariates from single cell and bulk expression data. Nat. Commun. 9, 2442 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Duren, Z., Chen, X., Xin, J., Wang, Y. & Wong, W. H. Time course regulatory analysis based on paired expression and chromatin accessibility data. Genome Res. 30, 622–634 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Sanchez-Castillo, M., Blanco, D., Tienda-Luna, I. M., Carrion, M. & Huang, Y. A Bayesian framework for the inference of gene regulatory networks from time and pseudo-time series data. Bioinformatics 34, 964–970 (2018).

    Article  CAS  PubMed  Google Scholar 

  63. Hamey, F. K. et al. Reconstructing blood stem cell regulatory network models from single-cell molecular profiles. Proc. Natl Acad. Sci. USA 114, 5822–5829 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Todorov, H., Cannoodt, R., Saelens, W. & Saeys, Y. in Gene Regulatory Networks Vol. 1883 (eds Sanguinetti, G. & Huynh-Thu, V.) 235–249 (Humana, 2019).

  65. Nguyen, H., Tran, D., Tran, B., Pehlivan, B. & Nguyen, T. A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data. Brief. Bioinform. 22, bbaa190 (2021).

    Article  PubMed  CAS  Google Scholar 

  66. Peng, J. et al. SimiC: a single cell gene regulatory network inference method with similarity constraints. Preprint at bioRxiv https://doi.org/10.1101/2020.04.03.023002 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Friedman, C. E. et al. Single-cell transcriptomic analysis of cardiac differentiation from human PSCs reveals HOPX-dependent cardiomyocyte maturation. Cell Stem Cell 23, 586–598.e8 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Lin, C., Ding, J. & Bar-Joseph, Z. Inferring TF activation order in time series scRNA-seq studies. PLoS Comput. Biol. 16, e1007644 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Treutlein, B. et al. Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq. Nature 534, 391–395 (2016). This paper is one of the first to use time-series scRNA-seq data to study and model development.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Zhang, M. J., Ntranos, V. & Tse, D. Determining sequencing depth in a single-cell RNA-seq experiment. Nat. Commun. 11, 774 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Kleyman, M. et al. Selecting the most appropriate time points to profile in high-throughput studies. eLife 6, e18541 (2017). This paper introduces one of the first methods for designing time-series RNA-seq experiments.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  74. Davis, A., Gao, R. & Navin, N. E. SCOPIT: sample size calculations for single-cell sequencing experiments. BMC Bioinformatics 20, 566 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Haque, A., Engel, J., Teichmann, S. A. & Lönnberg, T. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Med. 9, 75 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. Schwabe, D., Formichetti, S., Junker, J. P., Falcke, M. & Rajewsky, N. The transcriptome dynamics of single cells during the cell cycle. Mol. Syst. Biol. 16, e9946 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).

    Article  CAS  PubMed  Google Scholar 

  78. de Kok, J. B. et al. Normalization of gene expression measurements in tumor tissues: comparison of 13 endogenous control genes. Lab. Invest. 85, 154–159 (2005).

    Article  PubMed  CAS  Google Scholar 

  79. Lun, A. T., Calero-Nieto, F. J., Haim-Vilmovsky, L., Göttgens, B. & Marioni, J. C. Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data. Genome Res. 27, 1795–1806 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

    Article  PubMed  Google Scholar 

  81. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  82. Tran, H. T. N. et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 21, 12 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Moter, A. & Göbel, U. B. Fluorescence in situ hybridization (FISH) for direct visualization of microorganisms. J. Microbiol. Methods 41, 85–112 (2000).

    Article  CAS  PubMed  Google Scholar 

  84. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. Moffitt, J. R. et al. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization. Proc. Natl Acad. Sci. USA 113, 11046–11051 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Shah, S., Lubeck, E., Zhou, W. & Cai, L. In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus. Neuron 92, 342–357 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Eng, C.-H. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nature 568, 235–239 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Codeluppi, S. et al. Spatial organization of the somatosensory cortex revealed by osmFISH. Nat. Methods 15, 932–935 (2018).

    Article  CAS  PubMed  Google Scholar 

  89. Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  90. Bergenstråhle, J., Larsson, L. & Lundeberg, J. Seamless integration of image and molecular analysis for spatial transcriptomics workflows. BMC Genomics 21, 482 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  91. Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Schiller, H. B. et al. The Human Lung Cell Atlas: a high-resolution reference map of the human lung in health and disease. Am. J. Respir. Cell Mol. Biol. 61, 31–41 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Park, J., Liu, C. L., Kim, J. & Susztak, K. Understanding the kidney one cell at a time. Kidney Int. 96, 862–870 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Gitter, A., Carmi, M., Barkai, N. & Bar-Joseph, Z. Linking the signaling cascades and dynamic regulatory networks controlling stress responses. Genome Res. 23, 365–376 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Wagner, D. E. et al. Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo. Science 360, 981–987 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Su, Y. et al. Single-cell analysis resolves the cell state transition and signaling dynamics associated with melanoma drug-induced resistance. Proc. Natl Acad. Sci. USA 114, 13679–13684 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Russell, A. B., Elshina, E., Kowalsky, J. R., Te Velthuis, A. J. & Bloom, J. D. Single-cell virus sequencing of influenza infections that trigger innate immunity. J. Virol. 93, e00500-19 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  98. Rozenblatt-Rosen, O. et al. The Human Tumor Atlas Network: charting tumor transitions across space and time at single-cell resolution. Cell 181, 236–249 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Delile, J. et al. Single cell transcriptomics reveals spatial and temporal dynamics of gene expression in the developing mouse spinal cord. Development 146, dev173807 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Huisman, S. M. et al. BrainScope: interactive visual exploration of the spatial and temporal human brain transcriptome. Nucleic Acids Res. 45, e83 (2017).

    PubMed  PubMed Central  Google Scholar 

  101. Zhu, Y. et al. Spatiotemporal transcriptomic divergence across human and macaque brain development. Science 362, eaat8077 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Maniatis, S. et al. Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis. Science 364, 89–93 (2019). This paper is one of the first to use time-series spatial transcriptomics to study a dynamic biological process.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The work in this article is partially supported by National Institutes of Health (NIH) grants 1R01GM122096, OT2OD026682, 1U54AG075931 and 1U24CA268108 to Z.B.-J.

Author information

Authors and Affiliations

  1. Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, Montreal, Quebec, Canada

    Jun Ding

  2. Department of Biology, Technion — Israel Institute of Technology, Technion City, Haifa, Israel

    Nadav Sharon

  3. Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA

    Ziv Bar-Joseph

  4. Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA

    Ziv Bar-Joseph

Authors
  1. Jun Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nadav Sharon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ziv Bar-Joseph
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The authors contributed to all aspects of the article.

Corresponding author

Correspondence to Ziv Bar-Joseph.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information

Nature Reviews Genetics thanks Hongkai Ji, Yvan Saeys and Xiuwei Zhang for their contribution to the peer review of this work.

Publisher’s note

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

Related links

BioTuring: https://bioturing.com/

How Many Cells: https://satijalab.org/howmanycells/

Glossary

Trajectory

A graph (often tree) structure that represents the states and their order during the biological process being studied. Cells are assigned to points on this graph.

Pseudo-time

Partial ordering of cells in single-cell RNA sequencing (scRNA-seq) data that represents predecessor and descendent cell state information.

Unique molecular identifiers

Sequence indices (often randomly generated) that are added to sequencing libraries before PCR amplification and enable the identification of PCR duplicates.

Dimensionality

In single-cell analyses, typically refers to the high versus low number of dimensions of the data. When working with large samples where each is composed of tens of thousands of features (for example, cells and their gene expression levels), the high dimension corresponds to the original values whereas the low dimension is a compact, although lossy, way to represent the data with many fewer values. Several low-dimension representation methods have been developed and they differ in the function they attempt to optimize (such as minimizing reconstruction loss, or minimizing differences in distance between the high-dimensional and low-dimensional spaces).

Auto-encoders

Neural networks whose goal is to reconstruct the input values. These networks are used for dimensionality reduction as they compress all input values through a small intermediate layer and then reconstruct them from the information in that layer.

Graphical models

Computational methods that are used to represent joint probability distributions in a compact manner. These include Bayesian networks, hidden Markov models (HMMs) and more.

Expectation–maximization

A widely used computational method that can be used to fill in missing data while simultaneously learning model parameters. The method iterates between the expectation step which determines expected values for missing data and the maximization step which infers parameters using the values obtained by the expectation step.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ding, J., Sharon, N. & Bar-Joseph, Z. Temporal modelling using single-cell transcriptomics. Nat Rev Genet 23, 355–368 (2022). https://doi.org/10.1038/s41576-021-00444-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41576-021-00444-7

This article is cited by

Search

Advanced 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