Evidence map›Paper›PMID 42180183›Full record

ArticleBiochemistry and biophysics reports2026

Integrated single-cell transcriptomic analysis identifies

Shaoqi Chen, Yu Fan, Miaotong Su, Yuqing Lin, Shaoyu Zheng, Zexuan Zhou, Weijin Zhang, Jianqun Lin, Shijian Hu, Marco Matucci-Cerinic and 3 more

Abstract read
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In one paragraph

Article in Biochemistry and biophysics reports, 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 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

Shaoqi ChenDepartment of Ultrasound, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Yu FanDepartment of Pathology, Shantou University Medical College, Shantou, China.
Miaotong SuDepartment of Pathology, Shantou University Medical College, Shantou, China.
Yuqing LinDepartment of Ultrasound, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Shaoyu ZhengDepartment of Rheumatology and Immunology, Shantou Central Hospital, Shantou, China.
Zexuan ZhouDepartment of Rheumatology and Immunology, Shantou Central Hospital, Shantou, China.
Weijin ZhangDepartment of Rheumatology and Immunology, Shantou Central Hospital, Shantou, China.
Jianqun LinDepartment of Rheumatology and Immunology, Shantou Central Hospital, Shantou, China.
Shijian HuDepartment of Rheumatology and Immunology, Shantou Central Hospital, Shantou, China.
Marco Matucci-CerinicUnit of Immunology, Rheumatology, Allergy and Rare Diseases (UnIRAR), and Inflammation, Fibrosis and Ageing Initiative (INFLAGE), IRCCS San Raffaele Hospital, Milano, Italy.
Daniel E FurstUniversity of California Los Angeles, Los Angeles, USA.
Guohong ZhangDepartment of Pathology, Shantou University Medical College, Shantou, China.
Yukai WangDepartment of Rheumatology and Immunology, Shantou Central Hospital, Shantou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Megakaryocytes (MKs) and low-density granulocytes (LDGs) are implicated in immune dysregulation and vascular pathology in autoimmune diseases (ADs), yet their precise subsets and pathological interactions remain poorly defined. We aimed to characterize MK and LDG subpopulations and elucidate their potential intercellular communication in ADs using single-cell transcriptomic analysis. Methods: Single-cell RNA sequencing (scRNA-seq) was performed on peripheral blood mononuclear cells from 10 treatment-naive AD patients (4 pSS, 3 RA, and 3 SLE) and 3 healthy controls (HCs). MKs and LDGs were re-clustered to identify transcriptional subpopulations and interrogated for intercellular communication using CellChat. A distinct megakaryocyte-like granulocyte population was validated in an independent scRNA-seq dataset. Bulk RNA-seq (n = 139) and plasma ELISA assays were employed to support the associated molecular signatures. Crucially, flow cytometry of peripheral blood from AD patients (n = 5) and HCs (n = 4) was performed to provide protein-level validation of the identified megakaryocyte-like granulocytes. Results: MKs segregated into immune-active and platelet-generating subtypes, both exhibiting altered signaling in ADs. LDGs harbored a unique Conclusions: We identify a potentially

Indexed as

Autoimmune diseaseCell communicationLow-density granulocytesMegakaryocytesNET formation

Identifiers

PMID42180183
PMCPMC13191102

What Socratic holds

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LicenceCC BY
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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 Socratic graph.