Fast and easy isolation of CD27-positive human memory B cells using EasySep&[trade] Releasable RapidSpheres&[trade]
Bibliographic record
Abstract
Abstract Over the course of a lifetime, memory B cells are key to maintaining antibody-mediated protection from viral and bacterial pathogens and they are one outcome of an effective vaccine. Since memory B cells are typically found at frequencies of less than 5% of normal human peripheral blood mononuclear cells (PBMC), research into their function would be aided by a fast and easy isolation method. To meet this need, we have developed an improved EasySep™ kit for the isolation of CD27-positive (CD27+) human memory B cells from fresh PBMC. First, CD27+ cells are labeled using an antibody cocktail and EasySep™ Releasable RapidSpheres™ and are positively selected using a hand-held EasySep™ magnet. Next, magnetic particles are released from the positively-selected cells, and then non-B cells within the CD27+ fraction are targeted for depletion using a second antibody cocktail and EasySep™ Dextran RapidSpheres™. Following an additional magnetic separation step, the particle-free, CD27+ memory B cells are poured off into a new tube and ready for use. With one additional step, CD27− naive B cells can be isolated from the same starting sample. The entire isolation protocol is completed in under 40 minutes without any centrifugation steps. Using this method, CD19+CD27+ memory B cells can be isolated to 97±2% purity and 37±14% recovery (average of n=19 ± S.D.). The CD19+CD27− naive B cells can be isolated to 93±5% purity and 28±11% recovery (n=9 ± S.D.). When stimulated with CpG and IL-15, the isolated memory and naive B cells are functional, as assessed by both proliferative responses and the secretion of IgG.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".