Efficient enzyme-free method to assess the development and maturation of the innate and adaptive immune systems in the mouse colon
Bibliographic record
Abstract
Researchers who aim to globally analyze the gastrointestinal immune system via flow cytometry have many protocol options to choose from, with specifics generally tied to gut wall layers of interest. To get a clearer idea of the approach we should use on full-thickness colon samples from mice, we first undertook a systematic comparison of three tissue dissociation techniques: two based on enzymatic cocktails and the other one based on manual crushing. Using flow cytometry panels of general markers of lymphoid and myeloid cells, we found that the presence of cell-surface markers and relative cell population frequencies were more stable with the mechanical method. Both enzymatic approaches were associated with a marked decrease of several cell-surface markers. Using mechanical dissociation, we then developed two minimally overlapping panels, consisting of a total of 26 antibodies, for serial profiling of lymphoid and myeloid lineages from the mouse colon in greater detail. Here, we highlight how we accurately delineate these populations by manual gating, as well as the reproducibility of our panels on mouse spleen and whole blood. As a proof-of-principle of the usefulness of our general approach, we also report segment- and life stage-specific patterns of immune cell profiles in the colon. Overall, our data indicate that mechanical dissociation is more suitable and efficient than enzymatic methods for recovering immune cells from all colon layers at once. Additionally, our panels will provide researchers with a relatively simple tool for detailed immune cell profiling in the murine gastrointestinal tract, regardless of life stage or experimental conditions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".