Proteomics has emerged as a critical complement to genomic and transcriptomic analyses, bridging the gap between genetic blueprints and functional phenotypes. While transcriptomics captures mRNA abundance, proteomics directly interrogates the effector molecules of cellular processes---proteins---including their post-translational modifications (PTMs), interactions, and turnover rates. This capability is particularly vital for understanding diseases like cancer, where dysregulated signaling pathways (e.g., MAPK, PI3K/AKT) and aberrant PTMs (e.g., phosphorylation, ubiquitination) drive malignancy. Technological advancements in mass spectrometry (MS), such as high-resolution Orbitrap platforms and data-independent acquisition (DIA), have propelled proteomics from a niche technique to a cornerstone of systems biology, enabling deep profiling of thousands of proteins across diverse cell line models.
By far, the most ambitious effort on cell line proteomics profiling was the pan-cancer proteomic mapping of 949 human cell lines by Goncalves et al. (2022). To make the proteomic workflow clinically applicable, the authors reduced preparation times, minimized peptide loads, and shortened LC/MS run times. This allows for efficient analysis of many small cancer samples, achieving high throughput with minimal instrument downtime. As a result, the dataset quantifies a total of 8,498 proteins across various cancer cell lines, with a median of 5,237 (min-max range: 2,523–6,251) proteins per cell line.
Figure 1. Number of quantifed proteins by tissue type for 949 cancer cell lines (Drawn by Meritudio based on data from Goncalves et al. 2022)
While their method offers significant advantages in terms of efficiency and applicability to small cancer samples, it does have a notable drawback: it quantifies too few proteins. This limitation can restrict the depth of biological insights. For cell lines, where it's crucial to check protein expression across different lines, missing data creates a significant problem. It also makes pathway-level analysis using member protein expression impractical.
Optimized experimental techniques coupled with longer run time can significantly increase the number of quantified proteins, especially with the recent introduction of the Astral mass spectrometer, which combines ultra-high sensitivity with rapid scan rates to achieve deep proteome coverage at unprecedented speeds (Thermo Fisher Scientific, 2023). The One Hour Human Proteome (2024) study reports:
"Here, in triplicate 7-min microflow active LC gradients on the Orbitrap Astral MS, we report 7852 protein groups from 94,267 peptides on average. When using 15-, 30-, and 60-min active, nano-LC gradients, triplicate experiments yield an average of 9,831, 10,411, and 10,645 unique protein groups from 195,612, 234,406, and 245,754 unique peptides, respectively… Our 30-min method delivered approximately 347 proteins per minute."
Conclusion
In lieu of the advancement, it is expected that new initiatives of cell line proteomics profiling projects will routinely quantify >8000 proteins per cell line.
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