Single-cell genomics unveils critical regulators of Th17 cell pathogenicity
Open accessAbstract
Extensive cellular heterogeneity exists within specific immune-cell subtypes classified as a single lineage, but its molecular underpinnings are rarely characterized at a genomic scale. Here, we use single-cell RNA-seq to investigate the molecular mechanisms governing heterogeneity and pathogenicity of Th17 cells isolated from the central nervous system (CNS) and lymph nodes (LN) at the peak of autoimmune encephalomyelitis (EAE) or differentiated in vitro under either pathogenic or non-pathogenic polarization conditions. Computational analysis relates a spectrum of cellular states in vivo to in-vitro-differentiated Th17 cells and unveils genes governing pathogenicity and disease susceptibility. Using knockout mice, we validate four new genes: Gpr65, Plzp, Toso, and Cd5l (in a companion paper). Cellular heterogeneity thus informs Th17 function in autoimmunity and can identify targets for selective suppression of pathogenic Th17 cells while potentially sparing non-pathogenic tissue-protective ones.
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scRNA-seq has been widely used to reveal the characteristics of immunity in different fields.
“It is notable that scRNA-seq has been widely used to reveal the characteristics of immunity in different fields. 6-10”
From Single-Cell RNA Sequencing to Dissect the Immunological Network of Autoimmune Myocarditis. (2020) · cites this paper as [6]
The Results section (page not available in our copy) shows that the paper uses single-cell RNA-seq to characterize Th17 immune cells, and the Introduction describes its value for interrogating cell states. This supports an example of the method revealing immune-cell characteristics, but the paper does not establish that it has been widely used across different fields. (LLM, conf=0.84)
Experimental — accuracy not yet measuredRecent studies have successfully applied co-expression-like approaches to single-cell data to identify pathogenicity regulators that co-vary with pro-inflammatory genes.
“by Gaublomme et al. [37] to identify pathogenicity regulators that co-vary with pro-inflammatory genes”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [37]
Even cells of the same type can exhibit substantial heterogeneity, reflecting finer sub-types, regulated functional variation, or inherent stochasticity.
“even cells of the same ‘type’ can exhibit substantial heterogeneity, reflecting finer sub-types, regulated functional variation, or inherent stochasticity1–6”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [1]
The number of undetected transcripts is correlated with batch effects and other metrics of library quality, and weakly expressed transcripts are more frequently undetected.
“Indeed, the number of undetected transcripts is correlated to batch effects60 and other metrics of library quality, and transcripts that are weakly expressed when detected are also more frequently undetected1.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [1]
The main axes of variation in scRNA-seq data are often dominated by technical factors such as library complexity, proportion of reads aligned to the genome, and proportion of detected transcripts.
“The main axes of variation (i.e., first few principal components) in scRNA-seq data are often dominated by additional technical factors1,60, such as library complexity64, proportion of reads that were successfully aligned to the genome, and proportion of detected transcripts”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [1]
Factors defining cell identity may be discrete categories, continuous spectra, or combinations of discrete and continuous categories.
“These factors may not only be the discrete categories that are often assumed when classifying cells into major types4,30,94 but may also represent a continuous spectrum1, or a combination of discrete and continuous categories36,95–97,227.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [1]
In in vitro differentiated TH17 lymphocytes, scRNA-seq did not reveal distinct subtypes, but the first principal component correlated with the cells' ability to trigger an autoimmune response.
“scRNA-seq profiles obtained from T helper 17 (TH17) lymphocytes differentiated in vitro did not reveal distinct subtypes, but their first principal component was highly correlated with a cell’s ability to trigger an autoimmune response (N.Y., A.R. and colleagues1).”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [1]
Even within a cell type, cells may span a continuous range of functional phenotypes.
“Moreover, even within a type, cells may span a continuous range of functional phenotypes (e.g., T cells of a single type, but with a range of inflammatory versus regulatory phenotypes, see N.Y., A.R. and colleagues1).”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [1]
Several studies have analyzed the covariation structure between transcripts or proteins across single cells to infer regulatory relations.
“Leveraging this insight, several studies have analyzed the covariation structure between transcripts or proteins across single cells to infer regulatory relations1–3,41,135,137,168–172.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [1]
Some methods adapt well-established tools previously used in bulk genomics to the particular challenges of noise and scale in single-cell data, whereas others are designed specifically for single-cell data.
“Some of these methods adapt well-established tools previously used in bulk genomics to the particular challenges of noise and scale in single-cell data1,44,89,90,105, whereas others are designed specifically for single-cell data71,93,135,137.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [1]
The statistically strongest correlations are often indicative of underlying regulatory circuits.
“While the statistically strongest correlations are often indicative of underlying regulatory circuits (N.Y., A.R. and colleagues1)”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [1]
Similar approaches have been used in other biological settings, and enhanced resolution, for example from pseudotemporal ordering, has helped recover such dependencies.
“Similar approaches have been used in other biological settings1, and enhanced resolution, for example from pseudotemporal ordering, has helped recover such dependencies135,137.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [1]
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Fundamental limits on dynamic inference from single-cell snapshots
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Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference
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The Intestine Harbors Functionally Distinct Homeostatic Tissue-Resident and Inflammatory Th17 Cells
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TH17 cell heterogeneity and its role in tissue inflammation
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Self-citationHotspot identifies informative gene modules across modalities of single-cell genomics
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Self-citationT cell responses in the central nervous system
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Stem-like intestinal Th17 cells give rise to pathogenic effector T cells during autoimmunity
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Mapping gene regulatory networks from single-cell omics data
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SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data
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Single-Cell RNA Sequencing to Dissect the Immunological Network of Autoimmune Myocarditis.
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Metabolic heterogeneity underlies reciprocal fates of TH17 cell stemness and plasticity
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“With progression of EAE, the CD27 + subset in YFP + T H 17 cells was diminished in proportion initially and then stabilized (Extended Data Fig.”
InfluentialT cell factor 1: A master regulator of the T cell response in disease
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“Further supporting a role for TCF1 in TH17 stemness, single-cell analysis of TH17 cells during EAE revealed high expression of Tcf7 and presence of a stem-like transcriptional signature in IL-17A–producing cells harvested from the central nervous system (CNS)–draining lymph nodes (41).”
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Self-citationSingle-Cell Genomics: A Stepping Stone for Future Immunology Discoveries
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Transcript-indexed ATAC-seq for precision immune profiling
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Chromosome contacts in activated T cells identify autoimmune disease candidate genes
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Self-citationInduction of a colitogenic phenotype in Th1-like cells depends on interleukin-23 receptor signaling
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The role of the adaptive immune system in the initiation and persistence of multiple sclerosis
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- Authors
- Gaublomme, J. T., Yosef, N., Lee, Y., Gertner, R. S., Yang, L. V., Wu, C., Pandolfi, P. P., Mak, T., Satija, R., Shalek, A. K., Kuchroo, V. K., Park, H., & Regev, A.
- Affiliations
- Broad Institute (US)
- Harvard University (US)
- East Carolina University (US)
- Brigham and Women's Hospital (US)
- Beth Israel Deaconess Medical Center (US)
- Princess Margaret Cancer Centre (CA)
- Corresponding author
- Park, Hongkun
- Regev, Aviv
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2015-12
- Volume
- 163
- Issue
- 6
- Pages
- 1400-1412
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC4671824
- OpenAlex
- W2209349785
- Cited by
- 575
- References
- 58
- Citations by year
- 2026: 16 · 2025: 32 · 2024: 44 · 2023: 54 · 2022: 66 · 2021: 60 · 2020: 69 · 2019: 51 · 2018: 70 · 2017: 65 · 2016: 44 · 2015: 3
- FWCI
- 29.80
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 368 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-nd)
- PMC
- Funders
- National Human Genome Research Institute (P50HG006193)
- National Cancer Institute (P30CA014051)
- National Institute of Allergy and Infectious Diseases (R00AI110649, AI 056299, AI039671, P01AI056299, P01AI039671)
- National Institute of Neurological Disorders and Stroke (R01NS030843, R29NS030843, NS030843, NS076410, P01NS076410, R37NS030843)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (F32HD075541)
- Topics
- Life Sciences › Immunology and Microbiology › Immunology › Psoriasis: Treatment and Pathogenesis
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Health Sciences › Medicine › Pathology and Forensic Medicine › Multiple Sclerosis Research Studies
- Keywords
- Th17 cells, cellular heterogeneity, autoimmune encephalomyelitis, multiple sclerosis, central nervous system, lymph nodes, susceptibility genes, GPR65, TOSO, CD5L, single-cell transcriptomics
- MeSH terms
- Promyelocytic Leukemia Zinc Finger Protein, Animals, Carrier Proteins, Central Nervous System, Encephalomyelitis, Autoimmune, Experimental, Humans, Lymph Nodes, Membrane Proteins, Mice, Inbred C57BL, Peptide Fragments, Receptors, Immunologic, Sequence Analysis, RNA, Mice, Knockout, Gene Expression Profiling, Receptors, G-Protein-Coupled, Apoptosis Regulatory Proteins, Receptors, Scavenger, Mice, Kruppel-Like Transcription Factors, Th17 Cells, Single-Cell Analysis, Myelin-Oligodendrocyte Glycoprotein
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