Immune Cell Profiling in Clinical Sepsis: Insights from scRNA-seq Meta-Analysis of publicly available datasets 4438
Bibliographic record
Abstract
Abstract Description Sepsis is a life-threatening condition caused by an extreme immune response to infection, often resulting in organ dysfunction and death. Clinical variability, influenced by pathogens, infection sites, and genetic differences in immune responses, complicates the identification of universal biomarkers and treatments. Additionally, the availability and quality of single-cell RNA sequencing (scRNA-seq) datasets for sepsis are limited, often lacking appropriate control groups. To address these gaps, this study analyzed publicly available scRNA-seq data from peripheral blood mononuclear cells of septic (S) patients and patients with cardiogenic shock (SC) as controls. A robust pipeline was developed, combining quality control in R with large-scale data integration in Python using Scanpy and Scanorama. Nine immune cell clusters (macrophages, monocytes, dendritic cells, B cells, NK cells, mucosa-associated T, naïve T, CD4+ T, and CD8+ T cells) were present in both groups. However, reduced proportions of T cells expressing CD4, ADRB2, CD8A, EOMES, CD28, CD69, and GATA3 were found in the S compared to CS, suggesting their role in immune paralysis. Pathway enrichment analysis identified unique pathways implicated in sepsis pathology, including macrophage migration inhibitory factor, CD99, annexins, resistin, and selectin P ligand. Targeting these pathways could provide a means to refine inflammatory responses and restore immune balance in sepsis. Funding Sources Author Marina Ninkov was supported by The American Association of Immunologists through an Intersect Fellowship for Computational Scientists and Immunologists. Topic Categories Computational and Systems Immunology (COMP)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".