Machine learning combined with omics-based approaches reveals T-lymphocyte cellular fate imbalance in abdominal aortic aneurysm

Evidence Scale

Machine learning combined with omics-based approaches reveals T-lymphocyte cellular fate imbalance in abdominal aortic aneurysm

Integrated human single-cell and bulk transcriptomic analyses describe AAA-associated T-cell state heterogeneity, derive TIRS and evaluate candidate biomarkers in public datasets and small independent human samples, with supportive mouse expression evidence.

Human AAA scRNA donors

4 aneurysmal abdominal aorta samples

Post-QC single cells

7,343 cells

T cells

3,841 cells

T-cell phenotypes

8

Bulk Dataset 1

AAA n=80; controls n=10

Bulk Dataset 2

AAA n=9; controls n=10

Bulk Dataset 3

dilated PVAT n=30; non-dilated PVAT n=30

In-house aortic wall

AAA n=5; controls n=4

In-house peripheral blood

AAA n=24; controls n=15

Counting note

Datasets contain different biological and analytical units. GSE57691 + GSE98278 contribute to Dataset 1. Cell counts must never be added to participant or sample counts. Public cohort samples must not be summed into a synthetic unique participant total without participant-level deduplication. Mouse measurements are separate preclinical units.

Key Findings

Study Design & Analytical Framework

Study profile

Study Design

Integrative multicohort transcriptomic study combining human AAA single-cell RNA sequencing, multiple public bulk transcriptomic cohorts, independent human clinical validation samples and supportive mouse AAA expression experiments

Evidence Type

Human single-cell and multicohort transcriptomic association with independent small-sample clinical validation and supportive preclinical expression evidence

Population

Human abdominal aortic aneurysm tissue, peripheral blood and public transcriptomic datasets, with supportive experimental mouse AAA evidence

Primary Endpoint

T-cell state/fate-associated transcriptional architecture in AAA

Secondary Endpoint

Identification and cross-dataset evaluation of T-cell infiltration regulatory genes and candidate AAA biomarkers

External dataset evaluation

Yes

Data modalities

• single-cell RNA sequencing
• bulk transcriptomics
• qRT-PCR
• immunohistochemistry
• ELISA
• mouse AAA histopathology

What This Study Adds

• A human AAA single-cell description of T-cell heterogeneity and inferred state relationships.
• A 50-gene TIRS derived from complementary transcriptomic analyses.
• Cross-dataset candidate-marker evaluation with small independent human-sample assessment.
• Supportive mouse FOSB/JUNB expression observations.

Evidence Scope

Limitations

• The in-house aortic-wall sample size was small: 5 AAA and 4 controls.
• Pseudotime is an inferred transcriptional ordering, not direct longitudinal lineage observation.
• Candidate-marker discrimination does not establish prospective clinical diagnostic performance.
• Mouse expression observations do not establish human causal mechanisms or therapeutic benefit.

When This Study Is Useful to Cite

1. CU1: What evidence describes human AAA T-cell heterogeneity at single-cell resolution?

   Human AAA single-cell profiling characterized a heterogeneous cellular landscape. Four human aneurysmal aortic samples; cells are analytical units and are not independent participants. Monocle inferred a bifurcated T-cell trajectory linking heterogeneous transcriptional states in human AAA. Pseudotime reconstructs state relationships; the study did not directly observe individual T cells changing fate over time.

   Evidence: KF1, KF2

2. CU2: How were T-cell infiltration states and pseudotime reconstructed in AAA?

   Monocle inferred a bifurcated T-cell trajectory linking heterogeneous transcriptional states in human AAA. Pseudotime reconstructs state relationships; the study did not directly observe individual T cells changing fate over time.

   Evidence: KF2

3. CU3: How was the T-cell infiltration regulatory signature TIRS derived?

   Integrated cell-state, fate-associated and trajectory gene sets yielded a 50-gene bulk-overlapping T-cell infiltration regulatory signature, TIRS. Transcriptomic signature derivation; genes selected by these analyses are not established causal regulators.

   Evidence: KF4

4. CU4: What multicohort transcriptomic evidence describes AAA-associated immune signals?

   Public bulk cohorts showed AAA-associated immune and transcriptomic patterns. Disease-associated patterns across different tissue and cohort comparisons; these are associations, not causal evidence. Integrated cell-state, fate-associated and trajectory gene sets yielded a 50-gene bulk-overlapping T-cell infiltration regulatory signature, TIRS. Transcriptomic signature derivation; genes selected by these analyses are not established causal regulators.

   Evidence: KF3, KF4

5. CU5: How were FOSB/JUNB/CST7/TBC1D4 prioritized using machine learning?

   LASSO and SVM-RFE prioritized FOSB, JUNB, CST7 and TBC1D4, with variable discrimination across public cohorts. Public-cohort retrospective marker discrimination; dataset performance does not establish prospective diagnostic utility.

   Evidence: KF5

6. CU6: What independent human aortic-wall and blood evidence evaluated candidate markers?

   The candidate markers were evaluated in independent in-house human aortic-wall and peripheral-blood samples. The aortic-wall comparison was small (5 AAA and 4 controls); these results do not establish approved standalone clinical tests.

   Evidence: KF6

7. CU7: What supportive preclinical expression evidence was reported for FOSB/JUNB?

   The BAPN + AngII mouse AAA model showed higher FOSB/JUNB signal in aortic immunohistochemistry and serum ELISA under the reported conditions. Supportive preclinical expression evidence; it does not prove a human causal mechanism or treatment efficacy.

   Evidence: KF7

What This Study Should Not Be Cited to Claim

• Pseudotime as directly observed longitudinal cell fate.
• 7,343 cells as 7,343 participants.
• FOSB/JUNB as established causal drivers of human AAA.
• Prospective diagnostic performance or approved standalone clinical tests.
• Predicted pharmacologic agents as effective AAA therapy.
• Mouse expression evidence as human treatment evidence.

Evidence Matrix

Q&A

Concepts & Entities

Related Research

• Smooth muscle cell fate decisions decipher a high-resolution heterogeneity within atherosclerosis molecular subtypes
  Complementary vascular single-cell/transcriptomic study focused on smooth-muscle-cell state/fate heterogeneity in atherosclerosis, whereas the present study centers on T-cell heterogeneity and AAA. · DOI: 10.1186/s12967-022-03795-9
• Deciphering the Proteomic Landscape of Circulating Extracellular Vesicles in Human Abdominal Aortic Aneurysm
  Related human AAA circulating extracellular-vesicle proteomics study examining a different molecular layer and specimen compartment. · DOI: 10.1111/jcmm.70725

Publication & Provenance

DOI

https://doi.org/10.1186/s12915-025-02400-x

Doi Url

https://doi.org/10.1186/s12915-025-02400-x

Publisher

https://link.springer.com/article/10.1186/s12915-025-02400-x

PubMed

https://pubmed.ncbi.nlm.nih.gov/41013440/

Pmc Url

https://pmc.ncbi.nlm.nih.gov/articles/PMC12465141/