Dataset Information

Spatial Proteomics of a Human Brain Tumour at 40 Âµm resolution

ABSTRACT: The spatial organisation of Cellular protein expression profiles within tissues determines cellular function and are key to understanding disease pathology. To define molecular phenotypes in the spatial context of tissue, there is a need for unbiased, quantitative technology capable of mapping proteomes within tissue structures. Here, we present a workflow for spatially resolved, quantitative proteomics of tissue that generates maps of protein expression across a tissue slice derived from a human atypical teratoid-rhabdoid tumour (AT/RT). We employ spatially-aware statistical methods that do not require prior knowledge of the fine tissue structure to detect proteins and pathways with varying spatial abundance patterns. We identified PYGL, ASPH and CD45 as spatial markers for tumour boundary and reveal immune response driven, spatially organized protein networks of the extracellular tumour matrix. Overall, this work informs on methods for spatially resolved deep proteo-phenotyping of tissue heterogeneity, which will push the boundaries of understanding tissue biology and pathology at the molecular level.

INSTRUMENT(S): timsTOF SCP

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Brain

DISEASE(S): Atypical Teratoid Rhabdoid Tumor

SUBMITTER: Simon Davis

LAB HEAD: Roman Fischer

PROVIDER: PXD044714 | Pride | 2023-10-10

REPOSITORIES: pride

ACCESS DATA

Similar Datasets

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.

Dataset Information

Spatial Proteomics of a Human Brain Tumour at 40 Âµm resolution

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure