Identification of Intestinal Lamina Propria Plasma Cells by Surface Transmembrane Activator and CAML Interactor Expression
Bibliographic record
Abstract
Plasma cells secrete an abundance of Abs and are a crucial component of our immune system. The intestinal lamina propria harbors the largest population of plasma cells, most of which produce IgA. These Abs can bind to beneficial gut bacteria to reinforce intestinal homeostasis and provide protection against enteric pathogens. Plasma cells downregulate many cell-surface proteins commonly used to identify B cells. In mice, expression of the surface marker CD138 has been widely used to identify plasma cells in lymph nodes, bone marrow, and spleen. Intestinal plasma cells require liberation via extensive tissue processing involving treatment with collagenase. We report that detection of CD138 surface expression is reduced following collagenase treatment. Using a mouse in which yellow fluorescent protein expression is controlled by the plasma cell requisite transcription factor Blimp-1, we show that surface detection of transmembrane activator and CAML interactor captures a significant proportion of Ab-secreting plasma cells in the intestinal lamina propria and gut-draining mesenteric lymph nodes. Additionally, we describe a flow cytometry panel based on the detection of surface markers to identify murine B cell subsets in the intestinal lamina propria and, as a proof of concept, combine it with a cutting-edge fate-tracking system to characterize the fate of germinal center B cells activated in early life. By identifying plasma cells and other key intestinal B subsets in a manner compatible with several downstream applications, including sorting and culturing and in vitro manipulations, this efficient and powerful approach can enhance studies of mucosal immunity.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".