Rapid, simple, column-free isolation of CD11b+ cells from various mouse organs (INC6P.323)
Bibliographic record
Abstract
Abstract CD11b+ cells are distributed throughout the body and act as sentinels for the immune system. The frequency of CD11b+ myeloid cells varies in different organs, ranging from approximately 48 % of the brain myeloid (microglia) cells, 30% of bone marrow and 6% of total nucleated cells in gut and spleen. The integrin CD11b heterodimerizes with CD18 to form the mature complex Mac-1. Mac-1 facilitates a variety of immune cell responses including phagocytosis, adhesion, migration and chemotaxis, all functions that are crucial for normal tissue homeostasis and inflammatory responses. CD11b+ cells are difficult and time consuming to isolate, due to their variable frequency using traditional methods. Here, we describe EasySepTM, a simple, column-free immunomagnetic method for the positive selection of highly purified CD11b+ cells from brain, bone marrow, spleen and gut. Labelled CD11b+ cells are retained in a tube in an EasySepTM magnet while unwanted cells are poured off. Selection can be fully automated using RoboSepTM. Starting with a frequency of 7.6 ± 1.3 % CD11b+ cells in spleen, purities of 93.9 ± 4.5 % (n=41) were achieved. Fully competent cells, as shown by functional assays (phagocytosis, ROS), were isolated from the above tissues. This EasySepTM technology provides a fast and simple method to access highly purified functional CD11b+ cells from a variety of tissues, facilitating their study ex-vivo.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".