The Brain Protein Atlas: A conglomerate of proteomics datasets of human neural tissue

Abstract

The human brain represents one of the most complex biological structures with significant spatiotemporal molecular plasticity occurring through early development, learning, aging, and disease. While much progress has been made in mapping its transcriptional architecture, more downstream phenotypic readouts are relatively scarce due to limitations with tissue heterogeneity and accessibility, as well as an inability to amplify protein species prior to global -OMICS analysis. To address some of these barriers, our group has recently focused on using mass-spectrometry workflows compatible with small amounts of formalin-fixed paraffin-embedded tissue samples. This has enabled exploration into spatiotemporal proteomic signatures of the brain and disease across otherwise inaccessible neurodevelopmental timepoints and anatomical niches.Given the similar theme and approaches, we introduce an integrated online portal, "The Brain Protein Atlas (BPA)" (www.brainproteinatlas.org), representing a public resource that allows users to access and explore these amalgamated datasets. Specifically, this portal contains a growing set of peer-reviewed mass-spectrometry-based proteomic datasets, including spatiotemporal profiles of human cerebral development, diffuse gliomas, clinically aggressive meningiomas, and a detailed anatomic atlas of glioblastoma. One barrier to entry in mass spectrometry-based proteomics data analysis is the steep learning curve required to extract biologically relevant data. BPA, therefore, includes several built-in analytical tools to generate relevant plots (e.g., volcano plots, heatmaps, boxplots, and scatter plots) and evaluate the spatiotemporal patterns of proteins of interest. Future iterations aim to expand available datasets, including those generated by the community at large, and analytical tools for exploration. Ultimately, BPA aims to improve knowledge dissemination of proteomic information across the neuroscience community in hopes of accelerating the biological understanding of the brain and various maladies.

Standardizing analysis of intra‐tumoral heterogeneity with computational pathology

“…38 We carried out a similar analysis using mass spectrometry (MS)-based proteomics and uncovered that there is spatial separation of tumor regions that are programmed for proliferation, migration, and hypoxic response. 8,42 Using these expression-based profiles as reference sets, niche deconvolution of the tumor microenvironment of independent datasets also supports that specific transcriptional subtypes and single cell phenotypes of glioblastoma may be driven by microenvironmental factors. 43,44 For example, the mesenchymal cell state and RNA signature is enriched in tumors regions showing higher levels of microvascular proliferation and hypoxia.…”

“…By using laser‐capture microdissection to spatially recover glioblastoma tissue in areas of high tumor cellularity, infiltration, and/or tumor cells palisading around necrosis, the Ivy Glioblastoma Atlas Project (Ivy GAP) defined niche‐specific transcriptomes 38 . We carried out a similar analysis using mass spectrometry (MS)‐based proteomics and uncovered that there is spatial separation of tumor regions that are programmed for proliferation, migration, and hypoxic response 8,42 . Using these expression‐based profiles as reference sets, niche deconvolution of the tumor microenvironment of independent datasets also supports that specific transcriptional subtypes and single cell phenotypes of glioblastoma may be driven by microenvironmental factors 43,44 .…”

HAVOC: Small-scale histomic mapping of biodiversity across entire tumor specimens using deep neural networks

“…proliferation, hypoxia, immune response). We therefore make this unique morphology-driven spatial analysis of the glioma proteome available for further exploration through an inter-active data portal (Brain Protein Atlas 29 ; https://www.brainproteinatlas.org/dash/apps/ad).…”

“…The mass spectrometry proteomics data of all HAVOC derived regions presented in this manuscript have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD037548 (username: reviewer_pxd037548@ebi.ac.uk; password: fy4uPFAi). The data can also be examined directly through our inter-active data portal (Brain Protein Atlas 29 ; https://www.brainproteinatlas.org/dash/apps/ad). Labeling legend for mass spectrometry proteomics data can be found at https://bitbucket.org/diamandislabii/havoc.…”

“…This regional analysis also specifically revealed that tumor subpopulations with varying degrees of phenotypic differentiation and proliferative capacity can often co-exist and be geographically separated in high grade gliomas; underscoring the need for more intelligent sampling strategies. This unique spatially-defined glioma proteomic dataset generated in this study also showed additional patient-specific regional differences that can be further explored in real-time in the Brain Protein Atlas 29 (https://www.brainproteinatlas.org/dash/apps/ad). Importantly, we further highlight the generalizability of HAVOC to other tumor types and cancer models and show it can be deployed without the need for any additional context-specific training.…”