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PreprintCited by 21Single-cell and spatial transcriptomicsAdvanced Proteomics Techniques and ApplicationsGene expression and cancer classification

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ProteoMixture: A Cell Type Deconvolution Tool for Bulk Tissue Proteomics Data

Open access
Teng, Pang-ning, Schaaf, Joshua P., Abulez, Tamara, Hood, Brian L., Wilson, Katlin N., Litzi, Tracy J., Mitchell, David, Conrads, Kelly A., Hunt, Allison L., Olowu, Victoria, Oliver, Julie, Park, Fred S., Edwards, Marshé, Chiang, AiChun, Raj-Kumar, Praveen-Kumar, Tarney, Christopher M., Darcy, Kathleen M., Phippen, Neil T., Maxwell, G. Larry, Conrads, Thomas P., Bateman, Nicholas W.·2023·iScience
DOI 10.1101/2023.07.27.550810PMC PMC10910246

Preprint — published as 10.1016/j.isci.2024.109198 · Paper page

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Abstract

Summary: Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome and transcriptome data generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture in two commonly used deconvolution algorithms, ESTIMATE and Consensus TME (r ≥ 0.63). Here we have developed and optimized protein-based signatures to estimate cell admixture proportions and benchmarked these using bulk tumor proteomics data from over 150 HGSOC patients. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomics data are available at ( https://lmdomics.org/ProteoMixture/ .

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Metadata

Authors
Teng, P.-N., Schaaf, J. P., Abulez, T., Hood, B. L., Wilson, K. N., Litzi, T. J., Mitchell, D., Conrads, K. A., Hunt, A. L., Olowu, V., Oliver, J., Park, F. S., Edwards, M., Chiang, A., Raj-Kumar, P.-K., Tarney, C. M., Darcy, K. M., Phippen, N. T., Maxwell, G. L., Conrads, T. P., & Bateman, N. W.
Affiliations
  • Henry M. Jackson Foundation (US)
  • Inova Health System (US)
  • Windber Research Institute (US)
  • Uniformed Services University of the Health Sciences (US)
Corresponding author
  • Conrads, Thomas P.
  • Bateman, Nicholas W.
ORCID
Venue
iScience
Publisher
openRxiv
Published
2023-07-29
Volume
27
Issue
3
Pages
109198-109198
ISSN
2589-0042 (ISSN-L)
DOI
10.1101/2023.07.27.550810
PMC
PMC10910246
OpenAlex
W4391755943
Cited by
21
References
1
Citations by year
2026: 1 · 2025: 10 · 2024: 10
FWCI
2.99
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • iScience (publishedVersion, cc-by-nc-nd)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • U.S. Department of Defense (HU00011620006)
  • Henry M. Jackson Foundation (HU0001-16-2-0006)
  • Uniformed Services University of the Health Sciences (HU0001-16-2-0006)
  • Defense Health Agency
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords
cell type deconvolution, high-grade serous ovarian cancer, tumor microenvironment, immune cell proportions, proteomic signatures, transcript-protein correlation, laser microdissection, estimates

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