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Record W4284882189 · doi:10.3389/fimmu.2022.927125

Single-Cell RNA Sequencing Reveals the Pathogenic Relevance of Intracranial Atherosclerosis in Blood Blister-Like Aneurysms

2022· article· en· W4284882189 on OpenAlexfundno aff
Dingke Wen, Xing Wang, Ruiqi Chen, Hao Li, Jun Zheng, Wei Fu, Tianjie Zhang, Mu Yang, Chao You, Lu Ma

Bibliographic record

VenueFrontiers in Immunology · 2022
Typearticle
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaOutstanding Youth Science and Technology Talents Program of SichuanMcGill UniversitySichuan Province Science and Technology Support ProgramSichuan UniversityUniversity of Electronic Science and Technology of ChinaWest China Hospital, Sichuan UniversityNational Natural Science Foundation of China
KeywordsMural cellCellVascular smooth musclePathogenesisBiologyStainPathologyTranscriptomeEndothelial stem cellMedicineMolecular biologyGene expressionGeneStainingBiochemistryEndocrinologyIn vitro

Abstract

fetched live from OpenAlex

Background Intracranial non-branching site blood blister-like aneurysms (BBA) are extremely rare and vicious. Their etiology remains elusive, and no molecular study has been carried out to reveal its pathogenic relevance to intracranial atherosclerosis. To investigate its transcriptomic landscape and underlying potential pathogenesis, we performed single-cell RNA sequencing with extensive pathological validation. Methods In total, 12,245 cells were recovered for single-cell RNA sequencing analysis from 1 BBA and 2 saccular intracranial aneurysms (IAs). Unbiased clustering using Seurat-based pipeline was used for cellular landscape profiling. Cellchat was used to understand intracellular communications. Furthermore, 10 BBAs and 30 IAs were retrospectively collected for pathological validations like scanning electron microscopy, H&E stain, Masson stain, Verhoeff Van Gielson stain, and immunofluorescence. Results Single-cell transcriptome profiled 14 total subclusters in 6 major groups, namely, 6 monocyte/macrophage clusters, 2 T&NK clusters, 3 vascular smooth muscle cell (VSMC) clusters, 1 dendritic cell, 1 B cell, and 1 endothelial cell cluster. The only mural cell identified in BBAs was VSMC-2 cluster, while mural cells in IAs comprise most clusters of VSMCs and endothelial cells. Upregulated genes in BBA-derived VSMCs are related to arterial mineralization and atherosclerosis, such as PTX3 , SPP1 , LOX , etc. , whereas vasodilation and physiological regulatory genes such as MGP , ACTA2 , and MYL9 were conversely enriched in conventional IA-derived VSMCs. Immune cells in the BBA were predominantly macrophages, with a low fraction of T&NK cells, while conventional IAs had a higher percentage of T&NK. Gene enrichment analysis suggested that macrophages in BBA were highly enriched in lipid metabolism as well as atherosclerosis. Ligand–receptor interaction suggested that secretory phosphoprotein 1 (also known as osteopontin) played a major role in mediating the intracellular communication between VSMC and macrophages, especially in BBA. Pathological experiments corroborate with the bioinformatic findings and further characterized BBAs as a thin-walled thrombotic aneurysm with severe atherosclerotic lesions, where ApoE+ macrophages and OPN+ mural cells are intimately involved in the inflammation process. Conclusions The preexisting intracranial atherosclerosis might predispose the parent artery to the pathogenic occurrence of BBAs. These data shed light on the pathophysiology of intracranial aneurysms and might assist in the further resolution of the complexity in aneurysm pathogenesis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.221
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations30
Published2022
Admission routes1
Has abstractyes

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