Scaling single-cell genomics from phenomenology to mechanism
Open accessAbstract
Three of the most fundamental questions in biology are how individual cells differentiate to form tissues, how tissues function in a coordinated and flexible fashion and which gene regulatory mechanisms support these processes. Single-cell genomics is opening up new ways to tackle these questions by combining the comprehensive nature of genomics with the microscopic resolution that is required to describe complex multicellular systems. Initial single-cell genomic studies provided a remarkably rich phenomenology of heterogeneous cellular states, but transforming observational studies into models of dynamics and causal mechanisms in tissues poses fresh challenges and requires stronger integration of theoretical, computational and experimental frameworks.
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For each cell type, variance across individuals is higher than variance across synthetic replicates (Lin's concordance = 0.007-0.20).
“for each cell type, the variance across individuals22, 30 is also higher than the variance across synthetic replicates (Lin’s concordance = 0.007–0.20)”
From Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2018) · cites this paper as [30]
The section “The mechanism axis: modeling gene regulation” discusses variation between cells, not variance across individuals within each cell type. Searches for synthetic replicates and Lin’s concordance found no comparison or reported range of 0.007–0.20 in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredCurrent single-cell RNA-seq protocols need fresh tissue to make single-cell suspensions, which limits clinical, archived, and hard-to-dissociate samples.
“However, current protocols require preparation of single cell suspension from fresh tissue, a major roadblock in many applications, including handling of clinical samples, archived materials, and tissues that cannot be readily dissociated.”
From Massively parallel single-nucleus RNA-seq with DroNc-seq (2017) · cites this paper as [2]
Cell type and state compositions differ between experimental conditions.
“As cell type and state compositions differ between experimental conditions (Segerstolpe et al, 2016; Tanay & Regev, 2017)”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Tanay & Regev, 2017
The biological processes that drive the development of the observed heterogeneity are continuous processes.
“The biological processes that drive the development of the observed heterogeneity are continuous processes (Tanay & Regev, 2017).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Tanay & Regev, 2017
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- Authors
- Tanay, A., & Regev, A.
- Affiliations
- Weizmann Institute of Science (IL)
- Broad Institute (US)
- Corresponding author
- Tanay, Amos
- Regev, Aviv
- Venue
- Nature
- Published
- 2017-01
- Volume
- 541
- Issue
- 7637
- Pages
- 331-338
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC5438464
- OpenAlex
- W2572662684
- Cited by
- 792
- References
- 114
- Citations by year
- 2026: 56 · 2025: 74 · 2024: 83 · 2023: 74 · 2022: 95 · 2021: 104 · 2020: 91 · 2019: 90 · 2018: 86 · 2017: 39
- FWCI
- 7.82
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- PMC
- Funders
- Howard Hughes Medical Institute
- Flight Attendant Medical Research Institute
- National Institutes of Health (5rm1hg006193-10)
- National Human Genome Research Institute (RM1‐HG006193)
- National Cancer Institute (U24CA180922)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- single-cell genomics, cellular heterogeneity, cell differentiation, tissue function, gene regulatory mechanisms, multicellular systems, cell dynamics, causal mechanisms
- MeSH terms
- Animals, Gene Expression Regulation, Humans, Models, Genetic, Genomics, Single-Cell Analysis, Spatio-Temporal Analysis
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