High-dimensional single-cell technologies present new opportunities for biological discovery, but the complex nature of the resulting datasets makes it challenging to perform comprehensive analysis. One particular challenge is the analysis of single-cell time course datasets: how to identify unique cell populations and track how they change across time points. To facilitate this analysis, we developed FLOW-MAP, a graphical user interface (GUI)-based software tool that uses graph layout analysis with sequential time ordering to visualize cellular trajectories in high-dimensional single-cell datasets obtained from flow cytometry, mass cytometry or single-cell RNA sequencing (scRNAseq) experiments. Here we provide a detailed description of the FLOW-MAP algorithm and how to use the open-source R package FLOWMAPR via its GUI or with text-based commands. This approach can be applied to many dynamic processes, including in vitro stem cell differentiation, in vivo development, oncogenesis, the emergence of drug resistance and cell signaling dynamics. To demonstrate our approach, we perform a step-by-step analysis of a single-cell mass cytometry time course dataset from mouse embryonic stem cells differentiating into the three germ layers: endoderm, mesoderm and ectoderm. In addition, we demonstrate FLOW-MAP analysis of a previously published scRNAseq dataset. Using both synthetic and experimental datasets for comparison, we perform FLOW-MAP analysis side by side with other single-cell analysis methods, to illustrate when it is advantageous to use the FLOW-MAP approach. The protocol takes between 30 min and 1.5 h to complete.
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Mass cytometry datasets have been placed on Cytobank for the stem cell differentiation time course (http://community.cytobank.org/cytobank/experiments/71954) and synthetic 2D single-cell data (http://community.cytobank.org/cytobank/experiments/71953). Original scRNAseq data from Nestorowa et al.42 and Kee et al.65 can be found on NCBI GEO (accession numbers GSE81782 and GSE87069, respectively).
The code to run FLOW-MAP has been shared on Github (https://github.com/zunderlab/FLOWMAP/).
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We thank G.-C. Yuan for his assistance in performing SCUBA analysis. We are grateful to N. Kee (formerly of the Perlmann lab) for helpful discussions and advice. We thank P. Fabris for sharing synthetic datasets for comparison of dimensionality-reduction techniques. M.E.K. was supported by the National Science Foundation Graduate Research Fellowship under grant no. DGE-4747, the National Cancer Institute and the NIH under Award Number F99CA21223, and Stanford University’s Diversifying Academia, Recruiting Excellence Fellowship. C.M.W. was supported by NIH grant CVTG 5T32HL007284. K.I.F. was supported by NIGMS training grant 5T32GM008715. S.M.G. was supported by the BDS training grant (NIH 5T32LM012416). G.K.F. was supported by the CMB training grant (NIH T32GM007276). E.R.Z. was supported by NIH NRSA F32 (GM093508-01), AHA/Allen Frontiers Group Distinguished Investigator Program and the Simons Foundation SFARI Pilot Grant program. This work was further supported by grants to G.P.N.: U19 AI057229, 1U19AI100627, Department of Defense (CDMRP), Northrop-Grumman Corporation, R01CA184968, 1R33CA183654-01, R33CA183692, 1R01GM10983601, 1R21CA183660, 1R01NS08953304, OPP1113682, 5UH2AR067676, 1R01CA19665701, R01HL120724 and CIRM (RB2–01592). G.P.N. is supported by the Rachford & Carlotta A. Harris Endowed Chair.
G.P.N. is a paid consultant for Fluidigm, the manufacturer that produced some of the reagents and instrumentation used in this study. The remaining authors declare no competing interests.
Peer review information Nature Protocols thanks Evan Newell and the other anonymous reviewer(s) 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.
Key reference using this protocol
Zunder, E. R., Lujan, E., Goltsev, Y., Wernig, M. & Nolan, G. P. Cell Stem Cell 16, 323–337 (2015): https://www.cell.com/cell-stem-cell/fulltext/S1934-5909(15)00016-8
Supplementary Table 1, Supplementary Methods and Supplementary Figs. 1–18.
Parameter optimization graphs
mESC differentiation dataset
Nestorowa et al.42: scRNAseq data and code
Kee et al.65: scRNAseq data and code
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Ko, M.E., Williams, C.M., Fread, K.I. et al. FLOW-MAP: a graph-based, force-directed layout algorithm for trajectory mapping in single-cell time course datasets. Nat Protoc (2020). https://doi.org/10.1038/s41596-019-0246-3