Single-Cell Proteomic Characterization of Drug-Resistant Prostate Cancer Cells Reveals Molecular Signatures Associated with Morphological Changes

This study delves into the proteomic intricacies of drug-resistant cells (DRCs) within prostate cancer, which are known for their pivotal roles in therapeutic resistance, relapse, and metastasis. Utilizing single-cell proteomics (SCP) with an optimized high-throughput data-independent acquisition (DIA) approach with the throughput of 60 sample per day, we characterized the proteomic landscape of DRCs in comparison to parental PC3 cells. This DIA method allowed for robust and reproducible protein quantification at the single-cell level, enabling the identification and quantification of over 1300 proteins per cell on average. Distinct proteomic sub-clusters within the DRC population were identified, closely linked to variations in cell size. The study uncovered novel protein signatures, including the regulation of proteins critical for cell adhesion and metabolic processes, as well as the upregulation of surface proteins and transcription factors pivotal for cancer progression. Furthermore, by conducting single-cell RNA-seq (scRNA-seq) analysis, we identified six upregulated and 10 downregulated genes consistently altered in drug-treated cells across both SCP and scRNA-seq platforms. These findings underscore the heterogeneity of DRCs and their unique molecular signatures, providing valuable insights into their biological behavior and potential therapeutic targets.

Authors

Institutions

Publication Details

Journal
Molecular & Cellular Proteomics
Published
2025-03-14
DOI
https://doi.org/10.1016/j.mcpro.2025.100949
Citations
9
Primary Topic
Advanced Proteomics Techniques and Applications
Type
article
Field-Weighted Citation Impact
6.81

Funders

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article

Single-Cell Proteomic Characterization of Drug-Resistant Prostate Cancer Cells Reveals Molecular Signatures Associated with Morphological Changes

Sarah R. Amend, Jongmin Jacob Woo, Michael A. Loycano, Kenneth J. Pienta et al.
9 citations
Molecular & Cellular Proteomics
Advanced Proteomics Techniques and Applications
6.81
article

Single-Cell Proteomic Characterization of Drug-Resistant Prostate Cancer Cells Reveals Molecular Signatures Associated with Morphological Changes

Sarah R. Amend, Jongmin Jacob Woo, Michael A. Loycano, Kenneth J. Pienta, Hui Zhang, Qian Jiang, Md Amanullah
article en
9 citations

Abstract

This study delves into the proteomic intricacies of drug-resistant cells (DRCs) within prostate cancer, which are known for their pivotal roles in therapeutic resistance, relapse, and metastasis. Utilizing single-cell proteomics (SCP) with an optimized high-throughput data-independent acquisition (DIA) approach with the throughput of 60 sample per day, we characterized the proteomic landscape of DRCs in comparison to parental PC3 cells. This DIA method allowed for robust and reproducible protein quantification at the single-cell level, enabling the identification and quantification of over 1300 proteins per cell on average. Distinct proteomic sub-clusters within the DRC population were identified, closely linked to variations in cell size. The study uncovered novel protein signatures, including the regulation of proteins critical for cell adhesion and metabolic processes, as well as the upregulation of surface proteins and transcription factors pivotal for cancer progression. Furthermore, by conducting single-cell RNA-seq (scRNA-seq) analysis, we identified six upregulated and 10 downregulated genes consistently altered in drug-treated cells across both SCP and scRNA-seq platforms. These findings underscore the heterogeneity of DRCs and their unique molecular signatures, providing valuable insights into their biological behavior and potential therapeutic targets.

Molecular & Cellular ProteomicsVol. 24(4)
Johns Hopkins University (US), Johns Hopkins Medicine (US)
U.S. Department of Defense, National Institutes of Health, National Cancer Institute, Congressionally Directed Medical Research Programs, DOD Prostate Cancer Research Program
Good health and well-being
Openalex Percentile: Top 3%
Advanced Proteomics Techniques and Applications
6.81
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