Unsupervised Trajectory Analysis of Single-Cell RNA-Seq and Imaging Data Reveals Alternative Tuft Cell Origins in the Gut.

Herring CA, Banerjee A, McKinley ET, Simmons AJ, Ping J, Roland JT, Franklin JL, Liu Q, Gerdes MJ, Coffey RJ, Lau KS
Cell Syst. 2018 6 (1): 37-51.e9

PMID: 29153838 · PMCID: PMC5799016 · DOI:10.1016/j.cels.2017.10.012

Modern single-cell technologies allow multiplexed sampling of cellular states within a tissue. However, computational tools that can infer developmental cell-state transitions reproducibly from such single-cell data are lacking. Here, we introduce p-Creode, an unsupervised algorithm that produces multi-branching graphs from single-cell data, compares graphs with differing topologies, and infers a statistically robust hierarchy of cell-state transitions that define developmental trajectories. We have applied p-Creode to mass cytometry, multiplex immunofluorescence, and single-cell RNA-seq data. As a test case, we validate cell-state-transition trajectories predicted by p-Creode for intestinal tuft cells, a rare, chemosensory cell type. We clarify that tuft cells are specified outside of the Atoh1-dependent secretory lineage in the small intestine. However, p-Creode also predicts, and we confirm, that tuft cells arise from an alternative, Atoh1-driven developmental program in the colon. These studies introduce p-Creode as a reliable method for analyzing large datasets that depict branching transition trajectories.

Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

MeSH Terms (15)

Algorithms Animals Basic Helix-Loop-Helix Transcription Factors Cell Differentiation Cell Lineage Humans Image Cytometry Intestinal Mucosa Intestine, Small K562 Cells Mice Mice, Inbred C57BL RNA Sequence Analysis, RNA Single-Cell Analysis

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