Identifying the genes impacted by cell proliferation in proteomics and transcriptomics studies
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Identifying the genes impacted by cell proliferation in proteomics and transcriptomics studies. / Locard-Paulet, Marie; Palasca, Oana; Jensen, Lars Juhl.
In: PLOS Computational Biology, Vol. 18, No. 10, e1010604, 2022.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Identifying the genes impacted by cell proliferation in proteomics and transcriptomics studies
AU - Locard-Paulet, Marie
AU - Palasca, Oana
AU - Jensen, Lars Juhl
PY - 2022
Y1 - 2022
N2 - Hypothesis-free high-throughput profiling allows relative quantification of thousands of proteins or transcripts across samples and thereby identification of differentially expressed genes. It is used in many biological contexts to characterize differences between cell lines and tissues, identify drug mode of action or drivers of drug resistance, among others. Changes in gene expression can also be due to confounding factors that were not accounted for in the experimental plan, such as change in cell proliferation. We combined the analysis of 1,076 and 1,040 cell lines in five proteomics and three transcriptomics data sets to identify 157 genes that correlate with cell proliferation rates. These include actors in DNA replication and mitosis, and genes periodically expressed during the cell cycle. This signature of cell proliferation is a valuable resource when analyzing high-throughput data showing changes in proliferation across conditions. We show how to use this resource to help in interpretation of in vitro drug screens and tumor samples. It informs on differences of cell proliferation rates between conditions where such information is not directly available. The signature genes also highlight which hits in a screen may be due to proliferation changes; this can either contribute to biological interpretation or help focus on experiment-specific regulation events otherwise buried in the statistical analysis.
AB - Hypothesis-free high-throughput profiling allows relative quantification of thousands of proteins or transcripts across samples and thereby identification of differentially expressed genes. It is used in many biological contexts to characterize differences between cell lines and tissues, identify drug mode of action or drivers of drug resistance, among others. Changes in gene expression can also be due to confounding factors that were not accounted for in the experimental plan, such as change in cell proliferation. We combined the analysis of 1,076 and 1,040 cell lines in five proteomics and three transcriptomics data sets to identify 157 genes that correlate with cell proliferation rates. These include actors in DNA replication and mitosis, and genes periodically expressed during the cell cycle. This signature of cell proliferation is a valuable resource when analyzing high-throughput data showing changes in proliferation across conditions. We show how to use this resource to help in interpretation of in vitro drug screens and tumor samples. It informs on differences of cell proliferation rates between conditions where such information is not directly available. The signature genes also highlight which hits in a screen may be due to proliferation changes; this can either contribute to biological interpretation or help focus on experiment-specific regulation events otherwise buried in the statistical analysis.
U2 - 10.1371/journal.pcbi.1010604
DO - 10.1371/journal.pcbi.1010604
M3 - Journal article
C2 - 36201535
VL - 18
JO - P L o S Computational Biology (Online)
JF - P L o S Computational Biology (Online)
SN - 1553-734X
IS - 10
M1 - e1010604
ER -
ID: 322119721