Single-cell topological RNA-seq analysis reveals insights into cellular differentiation and development
Open accessAbstract
Transcriptional programs control cellular lineage commitment and differentiation during development. Understanding of cell fate has been advanced by studying single-cell RNA-sequencing (RNA-seq) but is limited by the assumptions of current analytic methods regarding the structure of data. We present single-cell topological data analysis (scTDA), an algorithm for topology-based computational analyses to study temporal, unbiased transcriptional regulation. Unlike other methods, scTDA is a nonlinear, model-independent, unsupervised statistical framework that can characterize transient cellular states. We applied scTDA to the analysis of murine embryonic stem cell (mESC) differentiation in vitro in response to inducers of motor neuron differentiation. scTDA resolved asynchrony and continuity in cellular identity over time and identified four transient states (pluripotent, precursor, progenitor, and fully differentiated cells) based on changes in stage-dependent combinations of transcription factors, RNA-binding proteins, and long noncoding RNAs (lncRNAs). scTDA can be applied to study asynchronous cellular responses to either developmental cues or environmental perturbations.
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Single-cell RNA sequencing data are suitable for graph representation learning to model cellular differential processes and disease states.
“Single-cell RNA sequencing (scRNA-seq) data lend themselves to graph representation learning to model cellular differential processes [136, 155] and disease states [140].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [140]
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- Authors
- Rizvi, A. H., Camara, P. G., Kandror, E. K., Roberts, T. J., Schieren, I., Maniatis, T., & Rabadan, R.
- Affiliations
- Columbia University Irving Medical Center (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Maniatis, Tom
- Venue
- Nature Biotechnology
- Published
- 2017-05-01
- Volume
- 35
- Issue
- 6
- Pages
- 551-560
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC5569300
- OpenAlex
- W2610774430
- Cited by
- 248
- References
- 48
- Citations by year
- 2026: 13 · 2025: 17 · 2024: 17 · 2023: 23 · 2022: 36 · 2021: 13 · 2020: 36 · 2019: 33 · 2018: 43 · 2017: 17
- FWCI
- 14.45
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (U54CA193313)
- National Institute of General Medical Sciences (R01GM117591, T32GM008224)
- National Institute of Neurological Disorders and Stroke (R21NS088992, DP1NS082099)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- single-cell RNA sequencing, cell differentiation, motor neuron differentiation, mouse embryonic stem cells, transcriptional regulation, lineage commitment, long non-coding RNA, RNA-binding proteins, transcription factors
- MeSH terms
- Algorithms, Animals, Cell Differentiation, Cells, Cultured, Gene Expression Regulation, Motor Neurons, RNA, Transcription, Genetic, Transcriptional Activation, Sequence Analysis, RNA, Mice, Embryonic Stem Cells, Single-Cell Analysis
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