scTOP: physics-inspired order parameters for cellular identification and visualization.
Cell types
Computational method
Developmental trajectory
Differentiation
Single-cell RNA sequencing
Journal
Development (Cambridge, England)
ISSN: 1477-9129
Titre abrégé: Development
Pays: England
ID NLM: 8701744
Informations de publication
Date de publication:
01 11 2023
01 11 2023
Historique:
received:
11
04
2023
accepted:
11
09
2023
pmc-release:
03
11
2024
medline:
6
11
2023
pubmed:
27
9
2023
entrez:
27
9
2023
Statut:
ppublish
Résumé
Advances in single-cell RNA sequencing provide an unprecedented window into cellular identity. The abundance of data requires new theoretical and computational frameworks to analyze the dynamics of differentiation and integrate knowledge from cell atlases. We present 'single-cell Type Order Parameters' (scTOP): a statistical, physics-inspired approach for quantifying cell identity given a reference basis of cell types. scTOP can accurately classify cells, visualize developmental trajectories and assess the fidelity of engineered cells. Importantly, scTOP does this without feature selection, statistical fitting or dimensional reduction (e.g. uniform manifold approximation and projection, principle components analysis, etc.). We illustrate the power of scTOP using human and mouse datasets. By reanalyzing mouse lung data, we characterize a transient hybrid alveolar type 1/alveolar type 2 cell population. Visualizations of lineage tracing hematopoiesis data using scTOP confirm that a single clone can give rise to multiple mature cell types. We assess the transcriptional similarity between endogenous and donor-derived cells in the context of murine pulmonary cell transplantation. Our results suggest that physics-inspired order parameters can be an important tool for understanding differentiation and characterizing engineered cells. scTOP is available as an easy-to-use Python package.
Identifiants
pubmed: 37756586
pii: 329502
doi: 10.1242/dev.201873
pmc: PMC10629677
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NHLBI NIH HHS
ID : R01 HL158965
Pays : United States
Organisme : NIGMS NIH HHS
ID : R35 GM119461
Pays : United States
Organisme : NIGMS NIH HHS
ID : 1R35GM119461
Pays : United States
Organisme : NIH HHS
ID : R01 HL158965-01
Pays : United States
Commentaires et corrections
Type : UpdateOf
Informations de copyright
© 2023. Published by The Company of Biologists Ltd.
Déclaration de conflit d'intérêts
Competing interests The authors declare no competing or financial interests.
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