In one paragraphArticle in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from itWhat it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
11 authors.
Jose Liñares-BlancoEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, U.K.ORCID 0000-0002-5454-1297 Philipp Sven Lars SchäferHeidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany.ORCID 0009-0008-4403-8592 Leoni ZimmermannHeidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany.ORCID 0009-0004-9338-4978 Ricardo Melo FerreiraDepartment of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA.ORCID 0000-0003-2063-9744 Daniel Toro DominguezUnit of Inflammatory Diseases, Department of Environmental Medicine, Karolinska Institute, Nobel väg 13, 171 67, Solna, Sweden.ORCID 0000-0001-8440-312X Pedro Carmona SaezBioinformatics and Health Data Science. Pfizer-University of Granada-Andalusian Regional Government Centre for Genomics and Oncological Research (GENYO). Granada, Spain.ORCID 0000-0002-6173-7255 Jovan TanevskiHeidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany.ORCID 0000-0001-7177-1003 Marta E Alarcon RiquelmeUnit of Inflammatory Diseases, Department of Environmental Medicine, Karolinska Institute, Nobel väg 13, 171 67, Solna, Sweden.ORCID 0000-0002-7632-4154 Michael T EadonDepartment of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA.ORCID 0000-0003-3066-2876 Ricardo O Ramirez FloresEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, U.K.ORCID 0000-0003-0087-371X Julio Saez-RodriguezEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, U.K.ORCID 0000-0002-8552-8976 Funding
Central Hub for Kidney Precision MedicineU24DK114886 · NIDDK · UNIVERSITY OF WASHINGTON · PI Jonathan Himmelfarb, Matthias Kretzler · 2022 to 2026
$21.1MKPMP Kidney Mapping and Atlas Project (KMAP)U01DK133090 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Jonathan Himmelfarb, Matthias Kretzler · 2022 to 2026
$10.4MIntegrated spatial interrogation of cellular and molecular signatures of human kidney diseaseU01DK114923 · NIDDK · INDIANA UNIVERSITY INDIANAPOLIS · PI Tarek Maurice Ashkar, Pierre C Dagher · 2022 to 2026
$5.4MSingle cell multiomic and spatial atlas of acute and chronic kidney injuryU01DK114933 · NIDDK · WASHINGTON UNIVERSITY · PI Sanjay Jain · 2022 to 2026
$5.0MSpatial Multi-Omics to Profile Metabolic Pathways for Kidney DiseaseU01DK114920 · NIDDK · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI Christopher R Anderton, Kumar Sharma · 2022 to 2026
$3.9MBoston Chronic Kidney Disease Research Biopsy CenterU01DK133092 · NIDDK · BOSTON MEDICAL CENTER · PI Sylvia E Rosas, Sushrut S. Waikar · 2022 to 2026
$3.5MMultimodal Imaging Mass Spectrometry and Spatial Omics for the Human KidneyU01DK133766 · NIDDK · VANDERBILT UNIVERSITY · PI Jeffrey M Spraggins · 2022 to 2026
$3.4MPREcision Medicine through IntErrogation of Rna in the kidnEy (PREMIERE)U01DK114907 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Nir Hacohen, Jeffrey Benton Hodgin · 2022 to 2026
$3.2MUniversity of Illinois at Chicago KPMP CKD Recruitment SiteU01DK133081 · NIDDK · UNIVERSITY OF ILLINOIS AT CHICAGO · PI JAMES P. LASH, Ana Catherine Ricardo · 2022 to 2026
$2.7MAKI Matched Phenotype Linked Evaluation with Tissue (AMPLE-Tissue)U01DK114866 · NIDDK · JOHNS HOPKINS UNIVERSITY · PI Chirag R Parikh · 2022 to 2026
$2.6MCleveland Precision Medicine Chronic Kidney Disease CohortU01DK114908 · NIDDK · CLEVELAND CLINIC LERNER COM-CWRU · PI JOHN F. O'TOOLE, EMILIO DANIEL POGGIO · 2022 to 2026
$2.2MGeographic and Environmental Representation in Kidney Precision MedicineU01DK133095 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Frank C Brosius, Amy Mottl · 2022 to 2026
$2.1MNIDDK NIH HHS U01 DK114866NIDDK NIH HHS U01 DK114907NIDDK NIH HHS U01 DK114908NIDDK NIH HHS U01 DK114920NIDDK NIH HHS U01 DK114923NIDDK NIH HHS U01 DK114933NIDDK NIH HHS U01 DK133081NIDDK NIH HHS U01 DK133090NIDDK NIH HHS U01 DK133091NIDDK NIH HHS U01 DK133092NIDDK NIH HHS U01 DK133093NIDDK NIH HHS U01 DK133095NIDDK NIH HHS U01 DK133097NIDDK NIH HHS U01 DK133113NIDDK NIH HHS U01 DK133766NIDDK NIH HHS U01 DK133768NIDDK NIH HHS U24 DK114886NIDDK NIH HHS UH3 DK114861NIDDK NIH HHS UH3 DK114915NIDDK NIH HHS UH3 DK114926NIDDK NIH HHS UH3 DK114937
6 · The paper itselfAbstract
Systemic lupus erythematosus (SLE) shows marked clinical and molecular heterogeneity, yet patient stratification often relies on gene expression signatures lacking multicellular context. Here we construct a transcriptional patient map of SLE by analyzing 1,167 total samples (783 SLE, 384 healthy controls) across different resolutions, including single-cell and bulk blood as well as spatially resolved kidney tissue transcriptomes. Using an unsupervised approach we inferred patient-level transcriptomic immune programs from two independent single-cell RNA sequencing cohorts of peripheral blood mononuclear cells (PBMCs), capturing both differences between SLE and health as well as within-SLE heterogeneity. Specifically, we identified four conserved programs comprising two multicellular inflammatory programs driven by interferon and TNF/NFkB activity across immune cells, and two cell type-specific programs reflecting CD8 T cell cytotoxicity and a CD4 T cell naive-to-effector state. Functional analysis of these programs revealed a rewiring of both cell-to-cell interactions and task allocation across cell types during disease activation. In addition, mapping these programs onto an external longitudinal blood transcriptomic cohort predicted flare risk and identified candidate blood protein biomarkers detectable by proteomics. Finally, we showed that these blood programs were enriched in immune-infiltrated glomerular regions from kidney biopsies of individuals with lupus nephritis using spatially resolved transcriptomic data, thereby linking systemic immune programs to local tissue pathology.
Indexed as
multicellular coordinationsingle-cell transcriptomicsSystemic Lupus Erythematosus
Identifiers
PMID42094356
PMCPMC13142532
What OpenQuestion holds
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LicenceCC BY
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