MALDI Guided SpatialOMx uncovers proteomic profiles in tumor subpopulations of breast cancer

Significance of the topic

Spatial heterogeneity in breast cancer critically influences treatment response and prognosis. SpatialOMx leverages MALDI Imaging to guide microproteomic analysis, retaining positional context while delivering deep proteomic coverage. This integrated approach addresses limitations of conventional OMICs techniques that lack spatial resolution and standard imaging modalities that lack proteome depth.

Objectives and overview

The study introduces the SpatialOMx workflow on the timsTOF fleX platform to dissect proteomic profiles of distinct tumor subpopulations in breast cancer tissue. By combining unsupervised segmentation of lipid MALDI Imaging data with laser capture microdissection (LMD) and PASEF-enabled proteomics, the method aims to characterize region-specific molecular processes and identify potential biomarkers.

Methodology

The workflow comprises:

• Fresh-frozen breast tumor sections (12 µm) mounted on PEN slides.
• Lipid MALDI Imaging at 50 µm resolution after norharmane matrix application via TM-sprayer.
• Post-imaging H&E staining and pathological annotation to define tumor regions.
• Unsupervised k-means clustering in SCiLS Lab to segment intratumoral subpopulations.
• Image processing in MATLAB to refine ROI boundaries, remove artifacts, smooth shapes, and upscale to match optical images.
• Coregistration of processed ROIs onto a consecutive section for LMD (approx. 2000 cells per ROI).
• Microextraction and overnight tryptic digestion at 37 °C, followed by desalting.
• NanoLC separation and PASEF-enabled MS/MS acquisition on timsTOF fleX.
• Protein identification via PEAKS X against the SwissProt human database and functional annotation with PANTHER gene ontology.

Used instrumentation

• timsTOF fleX system (MALDI Imaging source, dual TIMS, PASEF).
• TM-sprayer (HTX Technologies) for matrix deposition.
• Leica LMD7000 for laser capture microdissection.
• Bruker nanoElute LC with Aurora C18 column (25 cm × 75 µm, 1.7 µm).
• CaptiveSpray nanoESI source.
• SCiLS Lab 2019c and MATLAB R2018a for data processing.

Main results and discussion

SpatialOMx enabled identification of ~3500 proteins per ROI from an estimated 160 ng of digested material. Venn analysis revealed distinct proteomic signatures across three tumor subpopulations. Gene ontology profiling highlighted differential representation of:

• “Biological regulation” underrepresented in subpopulation 1.
• “Developmental processes” underrepresented in subpopulation 2.
• “Cellular component organization” overrepresented in subpopulation 3.

These findings demonstrate the workflow’s capacity to resolve region-specific molecular processes and potential biomarkers within heterogeneous tumors.

Benefits and practical applications

• In situ guidance from lipid imaging ensures precise microdissection of defined regions.
• PASEF on timsTOF fleX achieves high sensitivity and scan rates (>100 Hz) for low-input samples.
• Single-instrument solution reduces sample transfer errors and enhances throughput.
• Applicable to biomarker discovery, tumor microenvironment studies, and QA/QC in pharmaceutical research.

Future trends and opportunities for use

Advancements may include integration with single-cell proteomics, multimodal imaging (e.g., spatial metabolomics, transcriptomics), and automated segmentation algorithms. Clinical translation could leverage SpatialOMx for patient stratification and real-time intraoperative diagnostics.

Conclusion

SpatialOMx on the timsTOF fleX platform unites spatial lipid imaging and deep proteomics to profile heterogeneous tumor subpopulations with high sensitivity. The method offers a robust, single-instrument workflow for region-specific biomarker discovery and mechanistic studies in cancer research.

References

• Meier F et al. (2015) J. Proteome Res. 14:5378–5387.
• Longuespée R et al. (2016) Methods 104:154–162.
• Dewez F et al. (2019) Anal. Bioanal. Chem. 411:5647–5633.