Integrative analysis of cell state changes in lung fibrosis with peripheral protein biomarkers
Integrative analysis of cell state changes in lung fibrosis with peripheral protein biomarkers is a dataset published in EMBO Molecular Medicine (2021). On theSindex it has a DataRank of 2.8, placing it in the top 32.7% of the data-sharing corpus. It has been cited 102 times, with 75 citing works in its 1-hop citation network. Its calibrated FAIR score is 33/100.
Abstract
The correspondence of cell state changes in diseased organs to peripheral protein signatures is currently unknown. Here, we generated and integrated single-cell transcriptomic and proteomic data from multiple large pulmonary fibrosis patient cohorts. Integration of 233,638 single-cell transcriptomes (n = 61) across three independent cohorts enabled us to derive shifts in cell type proportions and a robust core set of genes altered in lung fibrosis for 45 cell types. Mass spectrometry analysis of lung lavage fluid (n = 124) and plasma (n = 141) proteomes identified distinct protein signatures correlated with diagnosis, lung function, and injury status. A novel SSTR2+ pericyte state correlated with disease severity and was reflected in lavage fluid by increased levels of the complement regulatory factor CFHR1. We further discovered CRTAC1 as a biomarker of alveolar type-2 epithelial cell health status in lavage fluid and plasma. Using cross-modal analysis and machine learning, we identified the cellular source of biomarkers and demonstrated that information transfer between modalities correctly predicts disease status, suggesting feasibility of clinical cell state monitoring through longitudinal sampling of body fluid proteomes.
›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
0.688
From this paper's citation signal
Citation Network Contribution
2.1
From 62 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.
- STAR: ultrafast universal RNA-seq alignerBioinformatics201355,202 citationsDataRank 1.6
- Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter DropletsCell20157,753 citationsDataRank 1.3
- Single-cell transcriptomics of 20 mouse organs creates a Tabula MurisNature20183,174 citationsDataRank 15.2Top 12%
- A cellular census of human lungs identifies novel cell states in health and in asthmaNature Medicine2019956 citationsDataRank 1.0
- An integrated cell atlas of the lung in health and diseaseNature Medicine2023736 citationsDataRank 5.9Top 27%
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 24% comes from its base citations and 76% from the citation network (62 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.