Conditional density-based analysis of T cell signaling in single-cell data
Open accessAbstract
Cellular circuits sense the environment, process signals, and compute decisions using networks of interacting proteins. To model such a system, the abundance of each activated protein species can be described as a stochastic function of the abundance of other proteins. High-dimensional single-cell technologies, such as mass cytometry, offer an opportunity to characterize signaling circuit-wide. However, the challenge of developing and applying computational approaches to interpret such complex data remains. Here, we developed computational methods, based on established statistical concepts, to characterize signaling network relationships by quantifying the strengths of network edges and deriving signaling response functions. In comparing signaling between naïve and antigen-exposed CD4 + T lymphocytes, we find that although these two cell subtypes had similarly wired networks, naïve cells transmitted more information along a key signaling cascade than did antigen-exposed cells. We validated our characterization on mice lacking the extracellular-regulated mitogen-activated protein kinase (MAPK) ERK2, which showed stronger influence of pERK on pS6 (phosphorylated-ribosomal protein S6), in naïve cells as compared with antigen-exposed cells, as predicted. We demonstrate that by using cell-to-cell variation inherent in single-cell data, we can derive response functions underlying molecular circuits and drive the understanding of how cells process signals.
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The scale and precision of single-cell studies has continued to increase, reaching tens of thousands of cells for massively parallel scRNA-seq and millions of cells for mass cytometry protein measurements.
“The scale and precision of such studies has continued to increase, reaching tens of thousands of cells for massively parallel scRNA-seq30,31 and millions of cells for mass cytometry signature protein measurements32–34.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [32]
A simple analysis of the joint density f(X, Y) is dominated by the most common cell types, but misses parts of the dynamic range that transpire only in rare subsets of cells but are key to uncovering the biologically meaningful signal of the relationship between proteins.
“Considering a given pair of proteins (say X and Y), it has been observed that a simple analysis of their joint density f(X, Y) is dominated by the most common cell types, but misses parts of the dynamic range that transpire only in rare subsets of cells but are key to uncovering the biologically meaningful signal of the relationship between proteins.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [32]
Two complementary methods, DREVI and DREMI, tackled these challenges through visualization and quantification, respectively, in the context of single-cell mass cytometry (CyTOF) data for dozens of phosphoproteins across hundreds of thousands of cells.
“Two complementary methods, DREVI and DREMI32, tackled these challenges through visualization and quantification, respectively, in the context of single-cell mass cytometry (CyTOF) data for dozens of phosphoproteins across hundreds of thousands of cells.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [32]
Using the latest protocols for massively parallel scRNA-seq and mass cytometry, a single laboratory can now readily collect tens of thousands to hundreds of thousands of single-cell RNA-seq profiles and tens of millions of single-cell protein profiles.
“Using the latest protocols for massively parallel scRNA-seq30,31 and mass cytometry32–34, a single laboratory can now readily collect tens of thousands to hundreds of thousands of single-cell RNA-seq profiles and tens of millions of single-cell protein profiles.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [32]
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Self-citation
- Authors
- Krishnaswamy, S., Spitzer, M. H., Mingueneau, M., Bendall, S. C., Litvin, O., Stone, E., Pe’er, D., & Nolan, G. P.
- Affiliations
- Columbia University (US)
- Stanford University (US)
- Division of Immunology, Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA, USA.
- University of California San Diego (US)
- Corresponding author
- Pe’er, Dana
- Nolan, Garry P.
- Venue
- Science
- Published
- 2014-11-28
- Volume
- 346
- Issue
- 6213
- Pages
- 1250689-1250689
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC4334155
- OpenAlex
- W2079296583
- Cited by
- 225
- References
- 44
- Citations by year
- 2026: 8 · 2025: 14 · 2024: 8 · 2023: 18 · 2022: 16 · 2021: 15 · 2020: 29 · 2019: 17 · 2018: 25 · 2017: 34 · 2016: 25 · 2015: 14 · 2014: 1 · 2013: 1
- FWCI
- 10.12
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Heart, Lung, and Blood Institute (HV-10-05_, HHSN268201000034C)
- National Cancer Institute (U54CA121852, U54CA149145, R01CA130826, P01CA034233, CA 09011)
- National Institute of General Medical Sciences (R00GM104148)
- National Institute of Allergy and Infectious Diseases (U19AI100627, HHSN272200700038C, U19AI057229)
- National Institute of Diabetes and Digestive and Kidney Diseases (K01DK095008)
- National Center for Research Resources (S10RR027582)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- T cell signaling, signaling networks, conditional probability density, mass cytometry, single-cell data, naive CD4+ T cells, antigen-experienced T cells, ERK2, MAPK signaling, ribosomal protein S6, cell-to-cell variability
- MeSH terms
- Animals, Computer Simulation, Male, Mice, Mutant Strains, Receptors, Antigen, T-Cell, Signal Transduction, CD4-Positive T-Lymphocytes, Image Cytometry, eIF-2 Kinase, Mitogen-Activated Protein Kinase 1, Ribosomal Protein S6, Systems Biology, Mice, Single-Cell Analysis
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