Single-Cell Transcriptomic Profile of Innate Cell Populations in Mesenteric Lymph Nodes of Inflammatory Bowel Disease Patients
Bibliographic record
Abstract
BACKGROUND AND AIMS: Innate immune cells, including dendritic cells (DCs), monocytes (Mono), macrophages (Mac), natural killer (NK), and innate lymphoid cells (ILC), contribute to chronic inflammation in lymphoid tissues. Here, we characterized the innate immune cell landscape in inflamed mesenteric lymph nodes (MLNs) of patients with inflammatory bowel diseases (IBD) at the single-cell level. METHODS: Surgically resected colonic MLNs were obtained from patients with Crohn's disease (CD; n = 3), ulcerative colitis (UC; n = 3), non-inflamed UC (n = 1), and non-IBD (n = 2). CD45+CD3-CD19- non-T/non-B cells were FACS-sorted to capture rare innate immune cells. Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) was performed on the BD Rhapsody platform alongside multiparameter flow cytometry staining. RESULTS: CITE-seq analysis unveiled the molecular signature of 11 Mono/Mac/DC (MMDC) and 7 NK/ILC enriched clusters in human MLNs. DC clusters included 3 newly characterized DC clusters such as CD1c/CD163/VCAN/CD64-expressing DC3; AXL-expressing DCs; and a CD103+ DC subset, expressing LTB, S100B, and IL22RA2 (encoding IL22BP). Mono/Mac clusters comprised inflammatory monocytes, which accumulated in IBD compared to non-IBD MLNs. Among NK/ILC clusters, we identified a cytotoxic ILC subset (IL7R, KLRD1, GNLY), previously not reported in MLNs, reminiscent of cytotoxic ILC1-like cells found in inflamed gut mucosa. CONCLUSION: CITE-seq and flow-cytometry analyses of colonic MLNs from patients with active IBD reveal the molecular signature and cell distribution of previously uncharacterized DC and ILC subpopulations in human MLNs. These findings expand our understanding of immune responses during chronic inflammation in IBD.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".