Investigating Cell Fate Decisions with ICGS Analysis of Single Cells

Advances in single-cell transcriptome profiling have contributed to new insights into the cellular states and underlying regulatory networks that govern lineage commitment. Such cell states include multipotent progenitors that can manifest as mixed-lineage patterns of gene expression at a single-cell level. Multipotent and other self-renewing progenitors are often difficult to isolate and characterized by subtle transcriptional differences that are challenging to define. This chapter examines the application of newly developed analytical tools to define heterogeneity in diverse stem cell and multipotent progenitor populations from single-cell RNA-Seq data. In addition to the methodology and output of these approaches, we explore their application to diverse single-cell technologies (e.g., Fluidigm, Drop-Seq, 10× Genomics Chromium) and their usability by computational and non-computational biologists. We focus specifically on the use of one tool, called Iterative Clustering and Guide-gene Selection (ICGS), which has been shown to uncover novel committed, transitional, and metastable progenitor cell states. As a component of the AltAnalyze toolkit, ICGS provides advanced methods to evaluate cellular heterogeneity in combination with regulatory prediction, pathway, and alternative splicing analyses. We walk through the individual steps required to perform these analyses in hematopoietic and embryonic kidney progenitor datasets in both graphical user and command-line interfaces. By the end of this chapter, users should be able to analyze their own single-cell RNA-Seq data and obtain deeper insights into the regulatory biology of the discovered cell states.