Quantitative Proteomics of the Cancer Cell Line Encyclopedia

Abstract

Highlights d Quantified the proteomes of 375 cell lines from diverse lineages in the CCLE d Correlated expression of proteins across many pathways d Downregulation of multiple protein complexes in microsatellite instability d Protein complexes associated with sensitivity to gene knockdown and mutation

A Guide to the Quantitative Proteomic Profiles of the Cancer Cell Line Encyclopedia

“…As described in the manuscript (Nusinow et al, 2020), the experiments were performed in a multiplex setup using a reagent named Tandem Mass Tags (TMT, Figure 1) that, at the start of the project, allowed 10 samples to be run in parallel on the mass spectrometer (McAlister et al, 2012(McAlister et al, , 2014Werner et al, 2012). Each sample in the data (the column names on supplemental table S2 of Nusinow et al, 2020) denotes which of the forty-two 10-plexes it was part of. In each 10-plex there were nine biological samples and one "bridge" sample that allowed normalization across plexes.…”

“…Details about the cell lines was provided as Supplementary Table S1 in Nusinow et al, 2020 and can also be found on the CCLE website. We used the internal CCLE nomenclature for cell line identification.…”

“…Our protein counts are defined by these Protein IDs, so one gene symbol will often be represented by multiple Protein IDs because peptides belonging to different variants were quantified. Our data processing pipeline (Huttlin et al, 2010;Nusinow et al, 2020) reduces the number of protein IDs down to the minimal number that explain all of the identified isoforms, so there should not be more proteins identified than are necessary. Common ways to analyze the data are per-protein or collapsed down to per-gene symbol.…”

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We recently reported the quantitative proteomics of 375 samples as part of the Cancer Cell Line Encyclopedia (Nusinow et al, 2020). Mass spectrometry-based proteomics data is broadly unfamiliar to most biologists in our experience, resulting in questions from analysts about how to use the data.

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Potential microenvironment of SARS-CoV-2 infection in airway epithelial cells revealed by Human Protein Atlas database analysis

“…In addition, mRNA data of all 64 cell lines in the HPA show low expression of ACE2 in human small-cell lung cancer cells (SCLC-21H, Figure S2). However, ACE2 was not detected at the protein level in airway or lung tissues, indicating that the correlation between protein and transcription levels were widely varied [21]. Thus, limitations arise when predicting protein expression based only on mRNA expression levels.…”