Reconstruction of Cell-type-Specific Interactomes at Single-Cell Resolution
Open accessAbstract
The human interactome is instrumental in the systems-level study of the cell and the contextualization of disease-associated gene perturbations. However, reference organismal interactomes do not capture the cell-type-specific context in which proteins and modules preferentially act. Here, we introduce SCINET, a computational framework that reconstructs an ensemble of cell-type-specific interactomes by integrating a global, context-independent reference interactome with a single-cell gene-expression profile. SCINET addresses technical challenges of single-cell data by robustly imputing, transforming, and normalizing the initially noisy and sparse expression of data. Inferred cell-level gene interaction probabilities and group-level interaction strengths define cell-type-specific interactomes. We use SCINET to reconstruct and analyze interactomes of the major human brain and immune cell types, revealing specificity and modularity of perturbations associated with neurodegenerative, neuropsychiatric, and autoimmune disorders. We report cell-type interactomes for brain and immune cell types, together with the SCINET package.
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Similar cell-type specificity was reported by the interactome study, demonstrating robustness of the result using an orthogonal approach.
“Similar cell-type specificity was reported by the interactome study, demonstrating the robustness of the result using an orthogonal approach49.”
From A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles (2020) · cites this paper as [49]
Some approaches have used machine learning to integrate single cell data with context-independent data to create cell-type specific interactomes.
“or to integrate single cell data with context-independent data to create cell-type specific interactomes (Mohammadi et al., 2019).”
From Mapping the multiscale structure of biological systems (2021) · cites this paper as Mohammadi et al., 2019
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Coordinated neural progenitor adaptations drive primate neocortical downscalingPreprint
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Coordinated neural progenitor adaptations contribute to primate neocortical downscaling
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- Authors
- Mohammadi, S., Davila-Velderrain, J., & Kellis, M.
- Affiliations
- Broad Institute (US)
- Corresponding author
- Mohammadi, Shahin
- Venue
- Cell Systems
- Publisher
- Elsevier BV
- Published
- 2019-12
- Volume
- 9
- Issue
- 6
- Pages
- 559-568.e4
- ISSN
- 2405-4712 (ISSN-L)
- PMC
- PMC6943823
- OpenAlex
- W2990131011
- Cited by
- 73
- References
- 40
- Citations by year
- 2026: 10 · 2025: 10 · 2024: 8 · 2023: 8 · 2022: 10 · 2021: 16 · 2020: 11
- FWCI
- 3.06
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cell Systems (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- National Institutes of Health
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- 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
- Keywords
- human interactome, single-cell transcriptomics, SCINet, interaction strength, brain cell types, immune cell types, neurodegenerative diseases, neuropsychiatric disorders, autoimmune disease
- MeSH terms
- Algorithms, Female, Frontal Lobe, Humans, Male, Proteins, Computational Biology, Protein Interaction Mapping, Databases, Genetic, Systems Biology, Single-Cell Analysis
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