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bioRxiv · 10.1101/2023.05.13.540426

A framework for ultra-low input spatial tissue proteomics

Makhmut, A.·Qin, D.·Fritzsche, S.·Nimo, J.·Koenig, J.·Coscia, F.
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Abstract

Spatial tissue proteomics combining microscopy-based cell phenotyping with ultra-sensitive mass spectrometry (MS)-based proteomics is an emerging and powerful concept for the study of cell function and heterogeneity in health and disease. However, optimized workflows that preserve morphological information for image-based phenotype discovery and maximize proteome coverage of few or even single cells from laser microdissected archival tissue, are currently lacking. Here, we report a robust and scalable workflow for the proteomic analysis of ultra-low input formalin-fixed, paraffin-embedded (FFPE) material. Benchmarking in the murine liver resulted in up to 2,000 quantified proteins from single hepatocyte contours and nearly 5,000 proteins from 50-cell regions with high quantitative reproducibility. Applied to human tonsil, we profiled 146 microregions including spatially defined T and B lymphocyte niches and quantified cell type specific markers, cytokines, immune cell regulators and transcription factors. These rich data also highlighted proteome dynamics in spatially defined zones of activated germinal centers, illuminating sites undergoing active B-cell proliferation and somatic hypermutation. Our results demonstrate the power of spatially-resolved proteomics for tissue phenotyping by integrating high-content imaging, laser microdissection, and ultra-sensitive mass spectrometry. This approach has broad implications for a wide range of biomedical applications, including early disease profiling, drug target discovery and biomarker research.

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Makhmut, A., Qin, D., Fritzsche, S., Nimo, J., Koenig, J., Coscia, F.. 2023-05-14. A framework for ultra-low input spatial tissue proteomics. https://doi.org/10.1101/2023.05.13.540426

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