ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data

Pang ning Teng; Joshua P. Schaaf; Tamara Abulez; Brian L. Hood; Katlin N. Wilson; Tracy J. Litzi; David Mitchell; Kelly A. Conrads; Allison L. Hunt; Victoria Olowu; Julie Oliver; Fred S. Park; Marshé Edwards; Ai Chun Chiang; Matthew D. Wilkerson; Praveen Kumar Raj-Kumar; Christopher M. Tarney; Kathleen M. Darcy; Neil T. Phippen; G. Larry Maxwell; Thomas P. Conrads; Nicholas W. Bateman

doi:10.1016/j.isci.2024.109198

ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data

Pang ning Teng, Joshua P. Schaaf, Tamara Abulez, Brian L. Hood, Katlin N. Wilson, Tracy J. Litzi, David Mitchell, Kelly A. Conrads, Allison L. Hunt, Victoria Olowu, Julie Oliver, Fred S. Park, Marshé Edwards, Ai Chun Chiang, Matthew D. Wilkerson, Praveen Kumar Raj-Kumar, Christopher M. Tarney, Kathleen M. Darcy, Neil T. Phippen, G. Larry MaxwellThomas P. Conrads^*, Nicholas W. Bateman^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

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 (r ≥ 0.63) in two commonly used deconvolution algorithms, ESTIMATE or Consensus^TME. We further developed and optimized protein-based signatures estimating cell admixture proportions and benchmarked these using bulk tumor proteomic data from over 150 patients with HGSOC. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/.

Original language	English
Article number	109198
Journal	iScience
Volume	27
Issue number	3
DOIs	https://doi.org/10.1016/j.isci.2024.109198
State	Published - 15 Mar 2024
Externally published	Yes

Keywords

Computational bioinformatics
Proteomics
Transcriptomics

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10.1016/j.isci.2024.109198

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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. C., Wilkerson, M. D., Raj-Kumar, P. K., Tarney, C. M., Darcy, K. M., Phippen, N. T., ... Bateman, N. W. (2024). ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data. iScience, 27(3), Article 109198. https://doi.org/10.1016/j.isci.2024.109198

@article{f39cc7a8e796478a9fca9f1689bd9cc5,

title = "ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data",

abstract = "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 (r ≥ 0.63) in two commonly used deconvolution algorithms, ESTIMATE or ConsensusTME. We further developed and optimized protein-based signatures estimating cell admixture proportions and benchmarked these using bulk tumor proteomic data from over 150 patients with HGSOC. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/.",

keywords = "Computational bioinformatics, Proteomics, Transcriptomics",

author = "Teng, {Pang ning} and Schaaf, {Joshua P.} and Tamara Abulez and Hood, {Brian L.} and Wilson, {Katlin N.} and Litzi, {Tracy J.} and David Mitchell and Conrads, {Kelly A.} and Hunt, {Allison L.} and Victoria Olowu and Julie Oliver and Park, {Fred S.} and Marsh{\'e} Edwards and Chiang, {Ai Chun} and Wilkerson, {Matthew D.} and Raj-Kumar, {Praveen Kumar} and Tarney, {Christopher M.} and Darcy, {Kathleen M.} and Phippen, {Neil T.} and Maxwell, {G. Larry} and Conrads, {Thomas P.} and Bateman, {Nicholas W.}",

note = "Publisher Copyright: {\textcopyright} 2024 The Author(s)",

year = "2024",

month = mar,

day = "15",

doi = "10.1016/j.isci.2024.109198",

language = "English",

volume = "27",

journal = "iScience",

issn = "2589-0042",

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Teng, PN, Schaaf, JP, Abulez, T, Hood, BL, Wilson, KN, Litzi, TJ, Mitchell, D, Conrads, KA, Hunt, AL, Olowu, V, Oliver, J, Park, FS, Edwards, M, Chiang, AC, Wilkerson, MD, Raj-Kumar, PK, Tarney, CM, Darcy, KM, Phippen, NT, Maxwell, GL, Conrads, TP & Bateman, NW 2024, 'ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data', iScience, vol. 27, no. 3, 109198. https://doi.org/10.1016/j.isci.2024.109198

TY - JOUR

T1 - ProteoMixture

T2 - A cell type deconvolution tool for bulk tissue proteomic data

AU - Teng, Pang ning

AU - Schaaf, Joshua P.

AU - Abulez, Tamara

AU - Hood, Brian L.

AU - Wilson, Katlin N.

AU - Litzi, Tracy J.

AU - Mitchell, David

AU - Conrads, Kelly A.

AU - Hunt, Allison L.

AU - Olowu, Victoria

AU - Oliver, Julie

AU - Park, Fred S.

AU - Edwards, Marshé

AU - Chiang, Ai Chun

AU - Wilkerson, Matthew D.

AU - Raj-Kumar, Praveen Kumar

AU - Tarney, Christopher M.

AU - Darcy, Kathleen M.

AU - Phippen, Neil T.

AU - Maxwell, G. Larry

AU - Conrads, Thomas P.

AU - Bateman, Nicholas W.

PY - 2024/3/15

Y1 - 2024/3/15

N2 - 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 (r ≥ 0.63) in two commonly used deconvolution algorithms, ESTIMATE or ConsensusTME. We further developed and optimized protein-based signatures estimating cell admixture proportions and benchmarked these using bulk tumor proteomic data from over 150 patients with HGSOC. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/.

AB - 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 (r ≥ 0.63) in two commonly used deconvolution algorithms, ESTIMATE or ConsensusTME. We further developed and optimized protein-based signatures estimating cell admixture proportions and benchmarked these using bulk tumor proteomic data from over 150 patients with HGSOC. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/.

KW - Computational bioinformatics

KW - Proteomics

KW - Transcriptomics

UR - http://www.scopus.com/inward/record.url?scp=85186570013&partnerID=8YFLogxK

U2 - 10.1016/j.isci.2024.109198

DO - 10.1016/j.isci.2024.109198

M3 - Article

AN - SCOPUS:85186570013

SN - 2589-0042

VL - 27

JO - iScience

JF - iScience

IS - 3

M1 - 109198

ER -

ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data

Abstract

Keywords

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