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Author Spotlight: Evaluation of Protein-Condensate Dynamics in Live Human Cells

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

Shahin Mohammadi¹, Jose Davila-Velderrain¹, Manolis Kellis¹

¹MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.

|December 2, 2019

Summary

SCINET reconstructs cell-type-specific interactomes by integrating global protein interaction data with single-cell gene expression. This reveals how diseases impact specific cell types in the brain and immune system.

Keywords:

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

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Mapping Dysfunctional Protein-Protein Interactions in Disease

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Published on: October 24, 2025

Area of Science:

Computational Biology
Systems Biology
Genomics

Background:

The human interactome is crucial for understanding cellular processes and diseases.
Existing interactomes lack cell-type specificity, limiting contextual disease analysis.
Protein interactions and modules function within specific cellular contexts.

Purpose of the Study:

To develop a computational framework, SCINET, for reconstructing cell-type-specific interactomes.
To integrate global interactome data with single-cell gene expression profiles.
To analyze cell-type specificity in disease perturbations.

Main Methods:

SCINET integrates a global, context-independent interactome with single-cell gene-expression data.
The framework robustly imputes, transforms, and normalizes noisy, sparse single-cell expression data.
It infers cell-level gene interaction probabilities and group-level interaction strengths.

Main Results:

SCINET successfully reconstructed cell-type-specific interactomes for major human brain and immune cell types.
The analysis revealed cell-type specificity and modularity in disease-associated perturbations.
Specific interactomes for brain and immune cells were generated.

Conclusions:

SCINET provides a powerful approach to generate context-specific interactomes.
This framework enhances understanding of disease mechanisms at a cell-type level.
The SCINET package is available for broader research application.