Single Cell Lipid Analysis using the Bruker ultrafleXtreme TOF/TOF and the 7T solariX MRMS Mass Spectrometers

Significance of the Topic

The ability to profile lipids at the single-cell level offers unprecedented insight into cellular heterogeneity and molecular composition within complex tissues such as the brain. Conventional MALDI-MS imaging methods often waste acquisition time on empty regions and provide limited spatial precision when cells are randomly dispersed. The development of targeted single-cell acquisition strategies enhances throughput, reduces background acquisitions, and allows multimodal correlation with other analytical techniques, advancing applications in neuroscience, cell biology, and clinical research.

Study Objectives and Overview

This work demonstrates a targeted MALDI-MS approach for analyzing lipids in individual rodent brain cells. By leveraging custom geometry files generated with the open-source microMS software, the authors aim to:

• Locate and interrogate thousands of dispersed cells efficiently on Bruker MALDI platforms.
• Achieve sufficient spatial accuracy to isolate single cells while minimizing spectra from empty matrix regions.
• Enable sequential measurements across different mass spectrometers for multimodal chemical profiling.
• Correlate lipid signatures with immunocytochemical cell type markers on the same cells.

Methodology and Instrumentation

Primary steps included enzymatic dissociation of rodent cerebellar tissue and deposition on ITO slides with fiducial marks. Brightfield and fluorescence imaging on a Zeiss Axio M2 microscope identified nuclear dye-stained cells. microMS processed these images to generate coordinates filtered by size, shape, and intercellular distance. Matrix application employed an automatic sprayer distributing dihydroxybenzoic acid (0.1–0.2 mg/cm²). Single-cell spectra were acquired on:

• Bruker ultrafleXtreme TOF/TOF: m/z 500–3000, ~100 µm laser footprint, 300 shots at 1000 Hz and 60 % energy (~1 cell/s, resolution ~11,000).
• Bruker 7 T solariX XR MRMS: m/z 150–3000, transient 2.94 s, 20 shots at 1000 Hz and 60 % energy (~1 cell/30 s, resolution ~180,000–250,000).

Immunocytochemical staining with anti-GFAP and anti-neurofilament-light antibodies enabled cell type validation.

Main Results and Discussion

Using custom geometries, thousands of cells were profiled with spatial targeting accuracy of ~30 µm. The high-resolution MRMS data resolved dozens of lipid species per cell (m/z 700–925) with signal-to-noise sufficient to detect on average 40 lipid features per cell. Phospholipid classes such as phosphatidylcholines, phosphatidylethanolamines, and sphingomyelins dominated spectra. Sequential TOF prescreening followed by MRMS deep profiling maximized throughput by narrowing down targets for high-resolution analysis. Correlated MALDI-MS and ICC data revealed modest but reproducible lipid differences between neurons (higher phosphatidylcholine) and astrocytes (higher phosphatidylethanolamine).

Benefits and Practical Applications

This targeted single-cell MALDI-MS approach delivers:

• High-throughput lipidomics of dispersed cells without oversampling empty areas.
• Compatibility across multiple Bruker MS platforms for multimodal workflows.
• Accurate cell type discrimination when combined with immunocytochemistry.
• Adaptability to diverse sample types including microbial colonies and tissue dissociates.

Future Trends and Potential Applications

Advancements may include increased spatial resolution via refined fiducial training, integration with other omics modalities (metabolomics, proteomics), and automated data analysis pipelines. Application areas extend to single-cell disease profiling, drug response monitoring, and engineered cell screening. Continued hardware and software enhancements will further shorten acquisition times and broaden chemical coverage.

Conclusion

The microMS-guided targeted MALDI-MS workflow provides an efficient, high-resolution platform for single-cell lipid analysis. By focusing acquisition on true cellular targets and enabling multimodal correlations, this methodology expands the analytical toolkit for studying cellular heterogeneity and molecular phenotyping.

Reference

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