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Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome

ABSTRACT: Single-cell proteomics by mass spectrometry (MS) is emerging as a powerful and unbiased method for the characterization of biological heterogeneity. So far, it has been limited to cultured cells, whereas an expansion of the method to complex tissues would greatly enhance biological insights. Here we describe single-cell Deep Visual Proteomics (scDVP), a technology that integrates high-content imaging, laser microdissection and multiplexed MS. scDVP resolves the context-dependent, spatial proteome of murine hepatocytes at a current depth of 1,700 proteins from a slice of a cell. Half of the proteome was differentially regulated in a spatial manner, with protein levels changing dramatically in proximity to the central vein. We applied machine learning to proteome classes and images, which subsequently inferred the spatial proteome from imaging data alone. scDVP is applicable to healthy and diseased tissues and complements other spatial proteomics or spatial omics technologies.

INSTRUMENT(S): Bruker Daltonics timsTOF series, timsTOF SCP

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Hepatocyte, Liver

SUBMITTER: Mario Oroshi

LAB HEAD: Matthias Mann

PROVIDER: PXD038699 | Pride | 2023-10-02

REPOSITORIES: Pride

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Project description:Although current mass spectrometry (MS)-based proteomics identifies and quantifies thousands of proteins and (modified) peptides, only a minority of them are subjected to in-depth downstream analysis. With the advent of automated processing workflows, biologically or clinically important results within a study are rarely validated by visualization of the underlying raw information. Although several tools for this are in principle available, they are often not integrated into the overall analysis nor readily extendable with new approaches. To remedy this, we developed AlphaViz, an open-source Python package to superimpose output from common analysis workflows on the raw data for quick and easy visualization and validation of protein and peptide identifications. AlphaViz takes advantage of recent breakthroughs in the deep learning-assisted prediction of experimental peptide properties to allow manual assessment of the expected and measured peptide result deviation. We focused on the visualization of the 4-dimensional data cuboid provided by Bruker TimsTOF instruments, where the ion mobility dimension, besides intensity and retention time, can be predicted and used for verification. We illustrate how AlphaViz can quickly validate or invalidate peptide identifications regardless of the score given to them by automated workflows. Furthermore, we provide a â€˜predict modeâ€™ that can locate peptides present in the raw data but not reported by the search engine. This is illustrated with dilution series and the recovery of missing values from experimental replicates. Applied to phosphoproteomics of the EGF-signaling pathway, we show how key signaling nodes can be validated to enhance confidence for downstream interpretation or follow-up experiments. AlphaViz follows accepted standards for open-source software development, including extensive documentation, testing and continuous integration. It features an easy-to-install graphical user interface for end-users and a modular Python package for bioinformaticians. We hope that AlphaViz can help to make validation of critical proteomics results a standard feature in MS-based proteomics.

			Action	DRS
	SpatialDVP_visuallibmales.zip	Other
	m1A.zip	Other
	m3B.zip	Other
	m4A.zip	Other
	m5C.zip	Other

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Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome

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