Trajectory Algorithms to Infer Stem Cell Fate Decisions
Single-cell trajectory analysis is an active research area in single-cell genomics aiming at developing sophisticated algorithms to reconstruct complex cell-state transition trajectories. Here, we present a step-by-step protocol to use CellRouter, a multi
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Introduction Single-cell trajectory analysis aims to reconstruct the structure and dynamics of cell-state transitions [1–4]. This includes, for example, lineage diversification, when stem or progenitor cells differentiate to multiple lineages; lineage convergence, when distinct precursor cells give rise to a common cell type; and re-specification of cell identity by cellular reprogramming. Several algorithms have been developed to organize cells according to molecular divergence in unidimensional trajectories [5–7], when a stem or progenitor cell gives rise to only one cell type, and bifurcations, when two or more cell lineages are generated from precursor cells [8–14]. These algorithms were designed to reconstruct single-cell trajectories describing cell-state transitions by exploring asynchronous cellular
Patrick Cahan (ed.), Computational Stem Cell Biology: Methods and Protocols, Methods in Molecular Biology, vol. 1975, https://doi.org/10.1007/978-1-4939-9224-9_9, © Springer Science+Business Media, LLC, part of Springer Nature 2019
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Edroaldo Lummertz da Rocha and Mohan Malleshaiah
behaviors, stochastic and regulated cell-to-cell variation, or the intrinsic heterogeneity of single-cell multidimensional omics profiles. Application of these algorithms to a variety of experimental systems has provided important insights on cell fate decision processes [8–10, 15–18]. The existence of complex subpopulations with diverse cell types or states within single-cell datasets impose several analytical challenges to identify cell-state transition trajectories. Although singlecell transcriptional profiles allow in-depth characterization of complex cellular ecosystems in health and disease, sophisticated analytical strategies are required. Here we describe a step-by-step protocol for using CellRouter [8]—a multifaceted single-cell analysis platform to reconstruct complex cell-state transition trajectories with high resolution and flexibility. As an example or a case study, we apply CellRouter to analyze gene expression dynamics during hematopoietic stem and progenitor cell differentiation. Moreover, it can be easily applied to analyze any single-cell transcriptomic or proteomic datasets. CellRouter explores the subpopulation structure of single-cell multidimensional data to precisely identify transitions between cell states. Moreover, CellRouter integrates gene expression dynamics reconstructed along cell-state transition trajectories with gene regulatory networks (GRNs) to identify putative regulators of these fate transitions. The analysis pipeline consists of six main steps (Fig. 1): Starting from single-cell transcriptomes (1), GRN reconstruction and determination of subpopulation structure are performed (2). Then, this subpopulation structure is explored to reconstruct multistate transition trajectories (3) and (4). Finally, identification of dynamically regulated genes and putative regulators as well as characterization of the waves of transcriptional regulation during cell-state transitions are performed (5) a
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