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Integrating Ultra-High Speed MALDI-TOF and MALDI MRMS Imaging For Spatial Proteomics

Applications | 2018 | BrukerInstrumentation
MALDI, MS Imaging, LC/MS, LC/Ultra-HRMS
Industries
Proteomics
Manufacturer
Bruker

Summary

Significance of the Topic


The integration of ultra-high speed MALDI-TOF and high-mass resolution MALDI MRMS imaging addresses key limitations in spatial proteomics. By combining rapid data acquisition with precise molecular specificity, these techniques enable detailed mapping of intact proteins in heterogeneous tissues, facilitating deeper insights into disease mechanisms and improving diagnostic precision.

Objectives and Study Overview


This study aims to demonstrate the complementary strengths of two next-generation MALDI platforms:
  • Ultra-high speed MALDI-TOF imaging for high spatial resolution and throughput
  • High-mass resolution MALDI MRMS imaging for precise protein identification

Applications are illustrated using cystic fibrosis (CF) lung tissue and human clear cell renal cell carcinoma (ccRCC) samples.

Methodology and Instrumentation


Sample Preparation:
  • 10 µm cryosections mounted on ITO-coated slides
  • Sequential washes: 70% EtOH, 100% EtOH, Carnoy fluid, 100% EtOH, 0.2% TFA, final 100% EtOH
  • Matrix deposition: four passes of DHA (15 mg/mL in 9:1 ACN:H2O) via HTX TM-Sprayer

Instrumentation:
  • rapifleX MALDI Tissue Imager (Bruker) in linear positive mode for TOF imaging
  • solariX 15 T FTICR (Bruker) in positive ion mode for MRMS imaging

Acquisition Parameters:
  • CF lung: ~5 µm laser spot, 30 µm pixel size, m/z 2,000–20,000, 141 000 pixels in ~1.5 h
  • ccRCC kidney: ~50 µm laser spot, 100 µm pixel size, m/z 1,100–25,000, ~1.5 s/pixel, total ~6 h

Main Results and Discussion


CF Lung Imaging (TOF):
  • Hemoglobin distributed across tissue, indicating vascular features
  • Histone H4 and S100A8 localized in infected airway regions, reflecting inflammatory response
  • Neutrophil Defensin-1 co-localized with these markers
  • Calcyclin observed in mechanically stressed cells, highlighting microenvironment heterogeneity

ccRCC Imaging (MRMS):
  • Hemoglobin enrichment near tumor margins, consistent with angiogenesis
  • Co-localization of histone H4 (acetylated/demethylated) with S100A6 (calcyclin subunit)
  • Additional tumor-associated protein distributions resolved by high mass accuracy

The combined data underscore the spatial complexity of diseased tissues and the value of multiplexed protein imaging.

Benefits and Practical Applications


The integrated approach delivers:
  • Enhanced throughput for large tissue areas without sacrificing resolution
  • High confidence in protein identification via accurate mass measurements
  • Capability to map post-translational modifications in situ
  • Improved margin assessment in surgical oncology and deeper pathological insights in chronic diseases

Future Trends and Applications


Emerging directions include:
  • Further acceleration of imaging speed through laser and detector optimizations
  • Integration with single-cell and multimodal imaging to correlate proteomic data with histology and transcriptomics
  • Development of targeted assays for low-abundance biomarkers in clinical diagnostics
  • Cloud-based data analysis pipelines leveraging AI for automated feature recognition

Conclusion


The fusion of ultra-high speed MALDI-TOF and high-mass resolution MALDI MRMS imaging represents a powerful platform for spatial proteomics. This dual-mode strategy overcomes traditional trade-offs between resolution, sensitivity, and specificity, offering new opportunities in biomedical research and clinical diagnostics.

References


  • Spraggins JM, Rizzo DG, Moore JL, Rose KL et al. J. Am. Soc. Mass Spectrom. 2015, 26, 974–985.
  • O’Sullivan BP, Freedman SD. Lancet 2009, 373, 1891–1904.
  • Rowe SM, Miller S, Sorscher EJ. N. Engl. J. Med. 2005, 352, 1992–2001.
  • Siegel RL, Miller KD, Jemal A. CA Cancer J. Clin. 2015, 65, 5–29.
  • Chow WH, Dong LM, Devesa SS. Nat Rev Urol 2010, 7, 245–257.
  • Oppenheimer SR et al. J. Proteome Res. 2010, 9, 2182–2190.
  • Spraggins JM et al. Proteomics 2016, 16, 1678–1689.

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