Evidence map›Paper›PMID 42445523›Full record

ArticleComputational and structural biotechnology journal2026

SLECA: A Single-Cell Atlas of Systemic Lupus Erythematosus Enabling Rare-Cell Discovery Using Graph Transformer.

Maoteng Duan, Yao Shi, Hao Tian, Qiuqin Wu, Xiaoying Wang, Bingqiang Liu

Abstract read
In one paragraph

Article in Computational and structural biotechnology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What 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 registry

The 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 literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Maoteng DuanSchool of Mathematics, Shandong University, Jinan, Shandong 250100, China.ORCID https://orcid.org/0009-0000-2622-9848
Yao ShiSchool of Mathematics, Shandong University, Jinan, Shandong 250100, China.
Hao TianSchool of Mathematics, Shandong University, Jinan, Shandong 250100, China.
Qiuqin WuSchool of Mathematics, Shandong University, Jinan, Shandong 250100, China.
Xiaoying WangSchool of Mathematics, Shandong University, Jinan, Shandong 250100, China.
Bingqiang LiuSchool of Mathematics, Shandong University, Jinan, Shandong 250100, China.ORCID https://orcid.org/0000-0002-5734-1135

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Systemic lupus erythematosus (SLE) is a complex autoimmune disease with multisystem involvement and marked interpatient heterogeneity. This heterogeneity has hindered precise characterization of the immune dysregulatory mechanisms underlying the disease. While rare immune cell populations are increasingly recognized as critical drivers of disease pathogenesis and progression, the lack of sufficiently powered, comprehensive single-cell transcriptomic resources has limited their systematic identification and characterization. To address this gap, we present SLECA, the first large-scale single-cell RNA sequencing atlas of SLE, together with a novel graph-transformer framework for the interpretable discovery and analysis of disease-relevant rare-cell populations. SLECA integrates 366 samples with standardized clinical and biological metadata, providing an atlas of SLE with a unified analytical framework. Through scalable integration and systematic analysis, SLECA resolves 54 distinct cell types, including rare populations of potential disease relevance. Notably, we identify double-negative T cells (DNTs) as a disease-expanded population whose abundance correlates with clinical severity. In silico perturbation analyses predict shifts in DNT-cell states following perturbation of JUN and EGR1, suggesting potential regulatory roles in DNT-cell transcriptional programs.

Identifiers

PMID42445523
PMCPMC13358165

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.