Profiling Cellular Ecosystems at Single-Cell Resolution and at Scale with EcoTyper

Chloé B Steen; Bogdan A Luca; Ash A Alizadeh; Andrew J Gentles; Aaron M Newman

doi:10.1007/978-1-0716-2986-4_4

Profiling Cellular Ecosystems at Single-Cell Resolution and at Scale with EcoTyper

Methods Mol Biol. 2023:2629:43-71. doi: 10.1007/978-1-0716-2986-4_4.

Authors

Chloé B Steen^{1

2

3}, Bogdan A Luca^{1

4}, Ash A Alizadeh^{3

5

6

7}, Andrew J Gentles^{1

4

7}, Aaron M Newman^{8

9

10}

Affiliations

¹ Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
² Department of Medical Genetics, Oslo University Hospital, Oslo, Norway.
³ Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA.
⁴ Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA.
⁵ Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.
⁶ Division of Hematology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
⁷ Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
⁸ Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. amnewman@stanford.edu.
⁹ Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA. amnewman@stanford.edu.
¹⁰ Stanford Cancer Institute, Stanford University, Stanford, CA, USA. amnewman@stanford.edu.

PMID: 36929073
DOI: 10.1007/978-1-0716-2986-4_4

Abstract

Tissues are composed of diverse cell types and cellular states that organize into distinct ecosystems with specialized functions. EcoTyper is a collection of machine learning tools for the large-scale delineation of cellular ecosystems and their constituent cell states from bulk, single-cell, and spatially resolved gene expression data. In this chapter, we provide a primer on EcoTyper and demonstrate its use for the discovery and recovery of cell states and ecosystems from healthy and diseased tissue specimens.

Keywords: Cell states; Ecosystems; Ecotypes; Single-cell RNA sequencing; Spatial transcriptomics; Tissue heterogeneity; Transcriptomics; Tumor microenvironment.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Ecosystem*
Gene Expression Profiling
Health Status*
Machine Learning
Single-Cell Analysis
Transcriptome

Abstract

Publication types

MeSH terms

Grants and funding