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Cell Syst. 2019 Dec 18;9(6):559-568.e4. doi: 10.1016/j.cels.2019.10.007. Epub 2019 Nov 27.

Reconstruction of Cell-type-Specific Interactomes at Single-Cell Resolution.

Author information

1
MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Electronic address: mohammadi@broadinstitute.org.
2
MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.

Abstract

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.

KEYWORDS:

ACTION; ACTIONet; PCNet; Protein-Protein Interactions; SCINET; differential network analysis; imputation; interactome; network biology; single cell

PMID:
31786210
PMCID:
PMC6943823
[Available on 2020-12-18]
DOI:
10.1016/j.cels.2019.10.007
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