Standardized Isolation of Human Mesenchymal Stromal Cells with Red Blood Cell Lysis
Bibliographic record
Abstract
Human mesenchymal stromal cells (MSC) raise high hopes for tissue engineering and therapeutic -applications. So far, it is not possible to isolate pure fractions from bone marrow and therefore MSC cell preparations notoriously represent heterogeneous mixtures of different cell types. The composition of -subpopulations can already be affected by the initial steps of cell preparation. Usually, isolation of MSC involves density fractionation to separate the mononuclear cells (MNCs) from erythrocytes and -granulocytes. However, this method is difficult to standardize especially under GMP conditions. Here, we describe an alternative approach for isolation of human MSC based on red blood cell (RBC) lysis with ammonium chloride. This results in a slightly higher number of fibroblastic colony forming units (CFU-F), whereas morphological analysis of the CFU-F reveals the same heterogeneous composition of MSC cultures indicating that the proportion of subpopulations is not affected by RBC lysis. Immunophenotype (CD73+, CD90+, CD105+, CD31-, CD34-, CD45-), adipogenic, and osteogenic differentiation potential of MSC were also similar with both methods. In conclusion, RBC lysis comprises an efficient method for the isolation of human MSC from bone marrow aspirate. This technique is faster and can be standardized more easily for clinical application of MSC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".