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Related Experiment Video

Updated: Nov 19, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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DeCompress: tissue compartment deconvolution of targeted mRNA expression panels using compressed sensing.

Arjun Bhattacharya1, Alina M Hamilton2, Melissa A Troester2,3

  • 1Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA 90095, USA.

Nucleic Acids Research
|February 1, 2021
PubMed
Summary

DeCompress is a new method for analyzing gene expression in bulk tissue samples. It accurately estimates cell types without needing pure cell references, improving biological discoveries in cancer research.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Targeted mRNA expression panels are cost-effective for analyzing archived samples but struggle with cell-type heterogeneity in bulk tissues.
  • Existing reference-free deconvolution methods have limitations with the reduced feature space of targeted panels.

Purpose of the Study:

  • To introduce DeCompress, a novel semi-reference-free deconvolution method designed for targeted mRNA expression panels.
  • To enhance the analysis of cell-type composition and gene signatures in complex biological samples.

Main Methods:

  • DeCompress expands the feature space of targeted panels using compressed sensing with external reference datasets.
  • It employs ensemble reference-free deconvolution on the expanded dataset to estimate cell-type proportions and gene signatures.
  • The method was validated using simulated mixtures, cell line mixtures, and a large breast cancer cohort.

Main Results:

  • DeCompress accurately recapitulates cell-type proportions with lower error compared to traditional reference-free methods.
  • The method identified biologically relevant cellular compartments within the breast cancer dataset.
  • Integration with cis-eQTL mapping revealed a tumor-specific cis-eQTL for CCR3 at a breast cancer risk locus.

Conclusions:

  • DeCompress offers an improved approach to cell-type deconvolution for targeted expression panels, overcoming limitations of existing reference-free methods.
  • The method does not require pure cell population expression profiles, making it broadly applicable.
  • DeCompress has significant potential for advancing genomic analyses and clinical applications, particularly in complex tissues.