A region‐resolved proteomic map of the human brain enabled by high‐throughput proteomics
A region‐resolved proteomic map of the human brain enabled by high‐throughput proteomics is a dataset published in The EMBO Journal (2023). On theSindex it has a DataRank of 0.846, placing it in the top 43.6% of the data-sharing corpus. It has been cited 35 times, with 32 citing works in its 1-hop citation network. Its calibrated FAIR score is 47/100.
Abstract
Substantial efforts are underway to deepen our understanding of human brain morphology, structure, and function using high-resolution imaging as well as high-content molecular profiling technologies. The current work adds to these approaches by providing a comprehensive and quantitative protein expression map of 13 anatomically distinct brain regions covering more than 11,000 proteins. This was enabled by the optimization, characterization, and implementation of a high-sensitivity and high-throughput microflow liquid chromatography timsTOF tandem mass spectrometry system (LC-MS/MS) capable of analyzing more than 2,000 consecutive samples prepared from formalin-fixed paraffin embedded (FFPE) material. Analysis of this proteomic resource highlighted brain region-enriched protein expression patterns and functional protein classes, protein localization differences between brain regions and individual markers for specific areas. To facilitate access to and ease further mining of the data by the scientific community, all data can be explored online in a purpose-built R Shiny app (https://brain-region-atlas.proteomics.ls.tum.de).
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
- Dataset classification
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
DataRank Breakdown
Base Score Contribution
0.520
From this paper's citation signal
Citation Network Contribution
0.326
From 17 citing papers with measurable signal
Top 3 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- Systematic and integrative analysis of large gene lists using DAVID bioinformatics resourcesNature Protocols200837,330 citationsDataRank 26.7Top 1%
- The PsychENCODE projectNature Neuroscience2015483 citationsDataRank 0.927
- Spatial proteomics in three-dimensional intact specimensCell202295 citationsDataRank 0.685
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 61% comes from its base citations and 39% from the citation network (17 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
Click a node to highlight its connections. Use scroll to zoom. Drag to pan.