Spatial transcriptomics landscape of lesions from non-communicable inflammatory skin diseases
Spatial transcriptomics landscape of lesions from non-communicable inflammatory skin diseases is a dataset published in Nature Communications (2022). On theSindex it has a DataRank of 2.2, placing it in the top 34.6% of the data-sharing corpus. It has been cited 85 times, with 74 citing works in its 1-hop citation network. Its calibrated FAIR score is 50/100.
Abstract
Abundant heterogeneous immune cells infiltrate lesions in chronic inflammatory diseases and characterization of these cells is needed to distinguish disease-promoting from bystander immune cells. Here, we investigate the landscape of non-communicable inflammatory skin diseases (ncISD) by spatial transcriptomics resulting in a large repository of 62,000 spatially defined human cutaneous transcriptomes from 31 patients. Despite the expected immune cell infiltration, we observe rather low numbers of pathogenic disease promoting cytokine transcripts (IFNG, IL13 and IL17A), i.e. >125 times less compared to the mean expression of all other genes over lesional skin sections. Nevertheless, cytokine expression is limited to lesional skin and presented in a disease-specific pattern. Leveraging a density-based spatial clustering method, we identify specific responder gene signatures in direct proximity of cytokines, and confirm that detected cytokine transcripts initiate amplification cascades of up to thousands of specific responder transcripts forming localized epidermal clusters. Thus, within the abundant and heterogeneous infiltrates of ncISD, only a low number of cytokine transcripts and their translated proteins promote disease by initiating an inflammatory amplification cascade in their local microenvironment.
›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
0.644
From this paper's citation signal
Citation Network Contribution
1.6
From 55 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- Multidimensional binary search trees used for associative searchingCommunications of the ACM19757,402 citationsDataRank 1.3
- Visualization and analysis of gene expression in tissue sections by spatial transcriptomicsScience20163,731 citationsDataRank 1.2
- Pan-cancer analysis of whole genomesNature20203,267 citationsDataRank 5.9Top 27%
- Deep learning and alignment of spatially resolved single-cell transcriptomes with TangramNature Methods2021856 citationsDataRank 1.0
- A novel molecular disease classifier for psoriasis and eczemaExperimental Dermatology201671 citationsDataRank 0.641
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 29% comes from its base citations and 71% from the citation network (55 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.
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