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Considerations and Software for Successful Immune Cell Deconvolution Using Proteomics Data

Zamore, Måns LU orcid ; Junior, Sergio Mosquim LU orcid ; Andree, Sebastian L LU ; Altunbulakli, Can LU orcid ; Lindstedt, Malin LU orcid and Levander, Fredrik LU orcid (2025) In Journal of Proteome Research 24(8). p.3751-3761
Abstract

Inferring the cell-type composition of bulk samples can provide biological insight. While bulk transcriptomics data has been extensively used for this purpose, the use of proteomics data has remained unexplored until recently. This study evaluates computational approaches for estimating immune cell composition using bulk sample proteomics data. Leveraging defined immune cell populations and simulated mixtures, we assess the impact of preprocessing methods and software tools on cell deconvolution outcomes. Our findings demonstrate the feasibility of using proteomics data for cell-type deconvolution, with Pearson correlations for estimated proportions in simulated sample mixtures above 0.9 when employing optimal missing value imputation... (More)

Inferring the cell-type composition of bulk samples can provide biological insight. While bulk transcriptomics data has been extensively used for this purpose, the use of proteomics data has remained unexplored until recently. This study evaluates computational approaches for estimating immune cell composition using bulk sample proteomics data. Leveraging defined immune cell populations and simulated mixtures, we assess the impact of preprocessing methods and software tools on cell deconvolution outcomes. Our findings demonstrate the feasibility of using proteomics data for cell-type deconvolution, with Pearson correlations for estimated proportions in simulated sample mixtures above 0.9 when employing optimal missing value imputation and reference matrix generation parameters. We further provide an R package, proteoDeconv, to facilitate the preprocessing of proteomics data for deconvolution and parsing of results. This study highlights the feasibility of using proteomics for analyzing cell-type composition in biological samples.

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author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Proteomics/methods, Software, Humans, Computational Biology/methods
in
Journal of Proteome Research
volume
24
issue
8
pages
11 pages
publisher
The American Chemical Society (ACS)
external identifiers
  • scopus:105012872448
  • pmid:40657748
ISSN
1535-3893
DOI
10.1021/acs.jproteome.4c00868
language
English
LU publication?
yes
id
fd9d5f0b-9cf4-4564-8570-e40647a5dd06
date added to LUP
2025-10-31 12:07:28
date last changed
2025-11-15 05:05:27
@article{fd9d5f0b-9cf4-4564-8570-e40647a5dd06,
  abstract     = {{<p>Inferring the cell-type composition of bulk samples can provide biological insight. While bulk transcriptomics data has been extensively used for this purpose, the use of proteomics data has remained unexplored until recently. This study evaluates computational approaches for estimating immune cell composition using bulk sample proteomics data. Leveraging defined immune cell populations and simulated mixtures, we assess the impact of preprocessing methods and software tools on cell deconvolution outcomes. Our findings demonstrate the feasibility of using proteomics data for cell-type deconvolution, with Pearson correlations for estimated proportions in simulated sample mixtures above 0.9 when employing optimal missing value imputation and reference matrix generation parameters. We further provide an R package, proteoDeconv, to facilitate the preprocessing of proteomics data for deconvolution and parsing of results. This study highlights the feasibility of using proteomics for analyzing cell-type composition in biological samples.</p>}},
  author       = {{Zamore, Måns and Junior, Sergio Mosquim and Andree, Sebastian L and Altunbulakli, Can and Lindstedt, Malin and Levander, Fredrik}},
  issn         = {{1535-3893}},
  keywords     = {{Proteomics/methods; Software; Humans; Computational Biology/methods}},
  language     = {{eng}},
  month        = {{08}},
  number       = {{8}},
  pages        = {{3751--3761}},
  publisher    = {{The American Chemical Society (ACS)}},
  series       = {{Journal of Proteome Research}},
  title        = {{Considerations and Software for Successful Immune Cell Deconvolution Using Proteomics Data}},
  url          = {{http://dx.doi.org/10.1021/acs.jproteome.4c00868}},
  doi          = {{10.1021/acs.jproteome.4c00868}},
  volume       = {{24}},
  year         = {{2025}},
}