LCM-Guided Spatial Proteomics Service | ROI LC-MS/MS Analysis

Introduction: Precision Proteomics for Defined Tissue Regions

By combining precise laser microdissection (LCM/LMD) with high-sensitivity, low-input LC–MS/MS, we help researchers profile proteins directly from pathology-defined regions of interest (ROIs). Unlike bulk analysis that averages signals across the whole tissue, masking critical spatial heterogeneity, our workflow isolates specific niches. Whether you are investigating the invasive tumor front, immune-excluded zones, fibrotic stroma, or specific neural layers, we ensure your data reflects the biology of the ROI, not the noise of the surrounding tissue.

This service is designed for cohort consistency, quantitative stability, and decision-ready outputs. It supports both fresh-frozen samples and archival FFPE tissue sections, making it a versatile tool for translational research and retrospective clinical studies. We bridge the gap between histology and deep proteomic profiling, transforming stained slides into quantitative molecular maps.

[Research Use Only (RUO). Not for use in diagnostic procedures.]

The Challenge: Why Spatial Proteomics is Hard (And How We De-risk It)

LCM-guided proteomics is widely recognized as a powerful tool for spatial biology, but it is technically unforgiving. Most failed spatial proteomics projects do not fail at the mass spectrometer; they fail earlier due to poor ROI planning, uncontrolled variability during dissection, or downstream sample loss. High-value projects succeed only when these risks are identified and managed upfront.

We have structured our service to systematically address the four critical failure modes in spatial analysis:

1. The Low-Input Challenge & Surface Adsorption

• Unlike bulk proteomics where milligrams of protein are available, LCM samples often yield nanograms. At this scale, physical laws change: proteins stick avidly to tube walls and pipette tips (adsorption), leading to catastrophic sample loss before the sample ever reaches the instrument.
• Our Solution: We utilize a specialized "one-pot" digestion workflow and ultra-low-adsorption consumables designed specifically for micro-scale samples. By minimizing liquid transfer steps and optimizing surfactant chemistry, we maximize recovery. This allows for robust quantification even from small ROI inputs that would be undetectable in standard workflows.

2. The ROI Consistency Challenge

• In comparative cohort studies, the definition of the ROI is as important as the measurement itself. If "ROI A" (e.g., "Tumor Core") in Patient 1 includes significant stroma, while "ROI A" in Patient 2 is purely epithelial, the resulting proteomic differences will reflect sampling error, not biology.
• Our Solution: We implement a Pathology-Guided ROI Standardization step. Before a single laser cut is made, we align ROI definitions (using H&E or IF guidance) across the entire cohort. We define exclusion criteria (e.g., "exclude necrosis," "maintain >50μm distance from margin") to ensure biological comparability.

3. The Contamination Risk

• In spatial proteomics, "spatial" implies separation. However, during microdissection, cross-contamination can occur if excised tissue fragments scatter or if the laser ablation plume settles on adjacent regions.
• Our Solution: We employ gravity-assisted or laser-pressure catapulting methods (LMD) that physically avoid contact between the collected ROI and the remaining tissue. Furthermore, we collect ROIs into separate cap-controlled environments and process them individually, significantly reducing the risk of cross-talk between distinct biological compartments.

4. The Context Challenge (Hidden Phenotypes)

• Sometimes visual selection based on morphology (H&E) isn't enough to define complex molecular phenotypes. You may need to excise a region defined by a metabolic gradient or a specific drug distribution.
• Our Solution: For projects requiring molecularly-defined ROIs prior to excision, we offer a MALDI-MSI Add-on. This allows you to guide microdissection based on metabolic or lipid distributions mapped first by imaging mass spectrometry, ensuring you capture the functional niche, not just the visible structure.

Applications: Who This Service Is Built For

This service is optimized for pharmaceutical teams, biotech researchers, and academic labs working on high-value, decision-driven projects. It serves those who have moved beyond "what is in the tissue?" to "what is happening in *this specific part* of the tissue?".

Tumor Heterogeneity and Clonal Evolution

• Tumors are rarely uniform. Sub-clones with distinct mutations and proteomic profiles often drive resistance and relapse.
• Application: We enable the separate isolation of primary tumor cores, metastatic seeds, and invasive margins. By comparing the proteomes of these distinct regions, researchers can reconstruct the trajectory of tumor evolution and identify proteins specific to the most aggressive sub-clones.
• Value: Identifies targets that are present in the dangerous invasive cells but diluted out in bulk analysis.

Tumor Microenvironment (TME) Dissection

• The interplay between cancer cells and their surroundings is critical for immunotherapy response. Bulk lysis conflates the tumor signal with the immune and stromal signal.
• Application: We precisely excise immune niches (e.g., Tertiary Lymphoid Structures), Cancer-Associated Fibroblasts (CAFs), and endothelial structures separately from the tumor cells.
• Value: Uncovers the signaling networks mediating immune exclusion or exhaustion, providing clear data on stromal biology that is often invisible in bulk datasets.

Biomarker Discovery & Validation

• Many potential biomarkers are expressed only in specific tissue structures. If a marker is highly expressed in a small duct but absent elsewhere, bulk analysis may report it as "low abundance" or miss it entirely due to dilution.
• Application: By enriching the sample for the relevant histological structure (e.g., glomeruli in kidney, islets in pancreas, amyloid plaques in brain), we dramatically increase the effective concentration of structure-specific proteins.
• Value: Improves the sensitivity of biomarker discovery and provides immediate spatial validation of the marker's location.

Mechanism of Action (MOA) & Pharmacodynamics

• Drugs often penetrate tissues unevenly. Understanding how a drug affects the proteome requires knowing *where* the drug is and *where* the effect is happening.
• Application: In conjunction with imaging modalities that locate the drug, we can excise regions of high vs. low drug exposure and perform differential proteomics to assess the local pharmacodynamic response.
• Value: Distinguishes between "lack of efficacy" and "lack of delivery," a critical distinction in pre-clinical drug development.

Neuroscience & Neurodegeneration

• The brain's complexity demands spatial resolution. A protein change in the hippocampus may be meaningless if averaged with the cortex.
• Application: We support the isolation of specific brain sub-regions, layers, or pathological features like plaques and tangles.
• Value: Connects proteomic changes to specific neural circuits or pathological aggregates.

End-to-End Workflow: From Tissue to Data

Our process ensures that every step, from sample intake to data delivery, contributes to a reproducible and interpretable result. We view the workflow as a chain of custody for biological information.

Quality Control & Methodological Rigor

We do not just "run samples"; we validate the process. Our QC framework is designed to detect issues early and ensure data integrity, especially given the inherent variability of clinical tissue.

Contamination Control

• Blanks: We run "dissection blanks" (caps left open in the machine but no tissue cut) and "process blanks" to detect environmental contaminants (keratins, dust).
• Carryover Monitoring: We monitor LC-MS wash runs between samples to ensure no peptides from a high-abundance sample bleed into the next low-abundance sample.

Quantitative Stability & Batch Effects

• Internal Standards: We can spike in heavy-isotope labeled peptides (iRT kit) to monitor retention time stability and instrument sensitivity across the entire project.
• Batch Correction: For large cohorts processed over multiple days or weeks, we employ statistical batch correction methods (like ComBat) if necessary, based on the performance of bridging samples (a pooled sample run repeatedly).

Cohort-Level Standardization

• Run Order: We randomize the run order of samples. We never run "all controls" then "all treatments," as this confounds biological signal with instrument drift.
• Exclusion Criteria: We pre-define QC failure criteria (e.g., "samples with<500 protein IDs are excluded from PCA"). This prevents low-quality outliers from skewing the statistical analysis.

Sample Requirements & Spec Sheet

To ensure the best possible outcome, please review the following requirements. We encourage a consultation to discuss specific project needs, as "one size fits all" rarely applies to spatial proteomics.

Note on Feasibility: Pricing and turnaround timelines depend on the number of cases, ROIs per case, and acquisition method. Please contact us for a customized estimate.

Case Study: Spatially Resolved Proteomics in Action

Frequently Asked Questions (FAQ)

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References

1. Springer Nature Primer. Laser capture microdissection. Nature Reviews Methods Primers, 2025. DOI: 10.1038/s43586-025-00453-4
2. Nature Protocols. Spatially resolved analysis of FFPE tissue proteomes by quantitative mass spectrometry. Nature Protocols, 2020. DOI: 10.1038/s41596-020-0356-y
3. Molecular & Cellular Proteomics. Spatially resolved proteome mapping of laser capture microdissected tissue by nanoPOTS-based proteomics. Mol Cell Proteomics, 2019. DOI: 10.1074/mcp.TIR118.000686
4. Cell Systems. A framework for ultra-low-input spatial tissue proteomics. Cell Systems, 2023. DOI: 10.1016/j.cels.2023.07.004
5. Critical Reviews in Analytical Chemistry. MALDI mass spectrometry imaging–guided spatial proteomics workflows. Crit Rev Anal Chem, 2025. DOI: 10.1080/14789450.2025.2537212