Cargando…

Characterization of cell fate probabilities in single-cell data with Palantir

Single-cell RNA sequencing (scRNA-seq) studies of differentiating systems have raised fundamental questions regarding the discrete versus continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells—treating cell fa...

Descripción completa

Detalles Bibliográficos
Autores principales: Setty, Manu, Kiseliovas, Vaidotas, Levine, Jacob, Gayoso, Adam, Mazutis, Linas, Pe’er, Dana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7549125/
https://www.ncbi.nlm.nih.gov/pubmed/30899105
http://dx.doi.org/10.1038/s41587-019-0068-4
_version_ 1783592741610979328
author Setty, Manu
Kiseliovas, Vaidotas
Levine, Jacob
Gayoso, Adam
Mazutis, Linas
Pe’er, Dana
author_facet Setty, Manu
Kiseliovas, Vaidotas
Levine, Jacob
Gayoso, Adam
Mazutis, Linas
Pe’er, Dana
author_sort Setty, Manu
collection PubMed
description Single-cell RNA sequencing (scRNA-seq) studies of differentiating systems have raised fundamental questions regarding the discrete versus continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells—treating cell fate as a probabilistic process—and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudotime ordering of cells and, for each cell state, assigns a probability of differentiating into each terminal state. We apply our algorithm to human bone marrow scRNA-seq data and detect important landmarks of hematopoietic differentiation. Palantir’s resolution enables the identification of key transcription factors that drive lineage fate choice and closely track when cells lose plasticity. We show that Palantir outperforms existing algorithms in identifying cell lineages and recapitulating gene expression trends during differentiation generalizable to diverse tissue types and well-suited to resolve less-studied differentiating systems.
format Online
Article
Text
id pubmed-7549125
institution National Center for Biotechnology Information
language English
publishDate 2019
record_format MEDLINE/PubMed
spelling pubmed-75491252020-10-12 Characterization of cell fate probabilities in single-cell data with Palantir Setty, Manu Kiseliovas, Vaidotas Levine, Jacob Gayoso, Adam Mazutis, Linas Pe’er, Dana Nat Biotechnol Article Single-cell RNA sequencing (scRNA-seq) studies of differentiating systems have raised fundamental questions regarding the discrete versus continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells—treating cell fate as a probabilistic process—and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudotime ordering of cells and, for each cell state, assigns a probability of differentiating into each terminal state. We apply our algorithm to human bone marrow scRNA-seq data and detect important landmarks of hematopoietic differentiation. Palantir’s resolution enables the identification of key transcription factors that drive lineage fate choice and closely track when cells lose plasticity. We show that Palantir outperforms existing algorithms in identifying cell lineages and recapitulating gene expression trends during differentiation generalizable to diverse tissue types and well-suited to resolve less-studied differentiating systems. 2019-03-21 2019-04 /pmc/articles/PMC7549125/ /pubmed/30899105 http://dx.doi.org/10.1038/s41587-019-0068-4 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Setty, Manu
Kiseliovas, Vaidotas
Levine, Jacob
Gayoso, Adam
Mazutis, Linas
Pe’er, Dana
Characterization of cell fate probabilities in single-cell data with Palantir
title Characterization of cell fate probabilities in single-cell data with Palantir
title_full Characterization of cell fate probabilities in single-cell data with Palantir
title_fullStr Characterization of cell fate probabilities in single-cell data with Palantir
title_full_unstemmed Characterization of cell fate probabilities in single-cell data with Palantir
title_short Characterization of cell fate probabilities in single-cell data with Palantir
title_sort characterization of cell fate probabilities in single-cell data with palantir
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7549125/
https://www.ncbi.nlm.nih.gov/pubmed/30899105
http://dx.doi.org/10.1038/s41587-019-0068-4
work_keys_str_mv AT settymanu characterizationofcellfateprobabilitiesinsinglecelldatawithpalantir
AT kiseliovasvaidotas characterizationofcellfateprobabilitiesinsinglecelldatawithpalantir
AT levinejacob characterizationofcellfateprobabilitiesinsinglecelldatawithpalantir
AT gayosoadam characterizationofcellfateprobabilitiesinsinglecelldatawithpalantir
AT mazutislinas characterizationofcellfateprobabilitiesinsinglecelldatawithpalantir
AT peerdana characterizationofcellfateprobabilitiesinsinglecelldatawithpalantir