Depletion of Dead Cells from Primary Tissue in 6 Minutes
Bibliographic record
Abstract
Abstract Tissue specific research often requires mechanical and/or enzymatic digestion to isolate and study certain cell types. The digestion can be a harsh process resulting in a significant number of dead cells in the final cell suspension. Subsequent analysis by flow cytometry is difficult to interpret due to non-specific binding of antibodies to dead cells and dead cell auto fluorescence. Factors released by dead cells can also interfere with downstream assays, complicating the study of primary tissues. During apoptosis, the cell membrane loses its phospholipid asymmetry resulting in exposure of negatively charged phospholipids on the cell surface. Relocation of phosphatidylserine (PS) to the outer leaflet of the cell membrane is a well-established marker of apoptosis. By targeting exposed PS with Annexin V we have developed a rapid method (EasySep™) to immunomagnetically remove dead cells from primary tissue samples. Performance of this kit was examined on various mouse and human tissue types. Using this method, we were able to improve viability of a single cell suspension of mouse lungs digested with collagenase/hyaluronidase from an initial viability of 39.6 ± 12.3% AnxV−/PI− to 70.9 ± 11.8% AnxV−/PI. From 1×10^8 total start cells, 1.43 ± 0.68 ×10^7 live cells were recovered (n=10). From human polymorphonuclear leukocytes cultured overnight, viability was improved from 23.7 ± 9.8% AnxV−/PI− to 67.7 ± 12.1% AnxV−/PI− with recovery of 1.27 ± 0.52 ×10^7 live cells from 1×10^8 total start cells (n=7). This equates to removal of 88.9 ± 8.3% and 91.8 ± 5.3% dead cells respectively. Since live cells are untouched, subsequent isolation of desired cell types can be performed, resulting in a more viable population of cells for downstream applications.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".