Proteomic changes in tissue samples of mouse gastric carcinoma: Label-free quantitation on the timsTOF fleX with PASEF

Importance of the Topic

Quantitative proteomics is a cornerstone of modern analytical chemistry, enabling the detailed characterization of protein dynamics in complex biological systems. Label-free quantitation (LFQ) provides a cost-effective approach to deep proteome profiling without the need for isotopic labeling. This is particularly relevant for clinical and translational research where sample throughput, sensitivity and robustness are paramount. The timsTOF fleX mass spectrometer, employing Trapped Ion Mobility Spectrometry (TIMS) and Parallel Accumulation Serial Fragmentation (PASEF), addresses these challenges by delivering high sensitivity, rapid acquisition rates and enhanced reproducibility.

Study Objectives and Overview

This application study aimed to evaluate the performance of the timsTOF fleX equipped with PASEF for LFQ of proteins extracted from sectioned mouse gastric carcinoma tissues. The main goals were to:

• Assess proteome coverage and sensitivity using minimal sample input (240 ng).
• Demonstrate technical and biological reproducibility across 27 tissue samples.
• Compare tumor versus non-tumor regions in a well-characterized CEA424-SV40 Tag mouse model of early-onset invasive gastric carcinoma.
• Identify differentially regulated proteins and enriched pathways related to tumor biology.

Methodology and Used Instrumentation

Biological Material and Sample Preparation:

• Mouse model: Transgenic CEA424-SV40 Tag (TCEA-positive) and wild-type (WT) gastric tissues.
• Cryosectioning of stomachs into tumor (T), non-tumor (NT) and whole WT sections.
• Lysis and tryptic digestion of proteins; dilution to inject 240 ng in 1 µL (0.1% formic acid).

Chromatography and Mass Spectrometry:

• UHPLC: nanoElute system (Bruker Daltonics).
• Column: C18, 25 cm × 75 µm, 1.6 µm particles (IonOpticks).
• Gradient: 90-minute linear gradient from 6% to 35% acetonitrile (0.1% FA) at 400 nL/min; column temperature 50 °C.
• Ion source: CaptiveSpray nano-ESI.
• Mass spectrometer: timsTOF fleX with PASEF; mass range m/z 100–1700; TIMS cycle 1.1 s including 1 MS and 10 PASEF MS/MS scans (~109 Hz acquisition).

Data Processing and Statistics:

• Software: MaxQuant v1.6.4.0 with Andromeda search engine.
• Database: Reviewed Uniprot Mus musculus (16,996 entries).
• Search parameters: Trypsin specificity; carbamidomethylation (C) fixed; oxidation (M) and N-terminal acetylation variable; up to two missed cleavages.
• Match-between-runs enabled (four-dimensional matching including ion mobility).
• Differential expression: Limma moderated t-tests with Benjamini-Hochberg correction; significance thresholds: |fold-change|>1.5 and adjusted p-value<0.05.

Main Results and Discussion

Proteome Coverage and Completeness:

• Average of 3,652 protein groups quantified per sample (±225); total of 5,001 protein groups across all 27 samples.
• Approximately 2,330 proteins ( ≈ 50%) consistently quantified in every biological and technical replicate.
• High data completeness attributed to fast sequencing speed of PASEF and four-dimensional matching in MaxQuant.

Reproducibility:

• Technical replicate R² values >0.98 (HeLa QC samples), permitting emphasis on biological and process replicates.
• Combined reproducibility for tissue samples averaged R² of 0.91 across all replicates.
• Quantitative linearity spanned over 4.5 orders of magnitude in protein abundance.

Biological Insights:

• Principal component analysis distinguished WT, NT and T groups, with PC1 and PC2 explaining 29.2% and 10.3% of variance, respectively.
• 110 proteins significantly regulated between tumor and non-tumor tissues.
• Gene Ontology and KEGG enrichment highlighted DNA replication initiation and the minichromosome maintenance (MCM) complex as overrepresented in tumor samples.
• Upregulation of MCM proteins correlates with genomic instability and enhanced proliferation in various carcinomas, including gastric cancer.

Benefits and Practical Applications

The timsTOF fleX with PASEF offers:

• Deep proteome coverage from sub-microgram inputs, suitable for scarce clinical specimens.
• High-speed acquisition (>100 Hz) combined with industry-leading sensitivity.
• Excellent reproducibility, reducing the need for extensive technical replication.
• Capability to uncover biologically relevant pathways and potential biomarkers in tumor research.

Future Trends and Opportunities

Advances likely to emerge include:

• Extension to post-translational modification profiling and phosphoproteomics in tissue sections.
• Integration with mass spectrometry imaging (MSI) for spatial proteomics.
• Application to single-cell and low-input proteomics enabled by enhanced sensitivity.
• Combination with multi-omics workflows for comprehensive tumor microenvironment mapping.

Conclusion

This study demonstrates that label-free quantitation on the timsTOF fleX with PASEF provides robust, reproducible and in-depth proteome analysis of complex tissue samples. The platform identified over 5,000 protein groups from minimal input, distinguished tumor from non-tumor regions in a gastric carcinoma model and revealed critical pathway alterations such as MCM complex upregulation. These results underscore the utility of TIMS-PASEF technology in translational cancer research and biomarker discovery.

References

1. Meier F et al. Parallel Accumulation–Serial Fragmentation (PASEF): Multiplying sequencing speed and sensitivity in mass spectrometry. Journal of Proteome Research, 2015; PMID: 26538118.
2. Thompson J et al. Early onset invasive gastric carcinoma in CEA424-SV40 T antigen transgenic mice. International Journal of Cancer, 2000; PMID: 10842202.
3. Hinsenkamp I et al. Protease activity profiling in gastric cancer by MALDI imaging. Neoplasia, 2016; PMID: 27566106.
4. Erich K et al. Mass spectrometry imaging of cancer therapeutics in a gastric carcinoma model. Molecular & Cellular Proteomics, 2018; DOI: 10.1074/mcp.RA118.000980.
5. Ritchie M et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 2015; DOI: 10.1093/nar/gkv007.