Fully Automated Magnetic Labeling and Separation of Hematopoietic Cells from Multiple Samples.
Bibliographic record
Abstract
Abstract Laboratory process automation is an important requirement for streamlining and standardizing technical procedures. Despite the extensive use of magnetic cell separation, only the latter steps in these procedures have been automated. Currently magnetic cell labeling is done manually followed by automated magnetic separation (e.g. AutoMACS and Isolex). Additionally, current technology only allows for processing of a single sample at a time. Our objective was to develop a fully automated system to magnetically separate multiple blood and bone marrow samples. The major barrier to automation of cell labeling is that these procedures typically require a centrifugal wash step, which is relatively expensive to automate and requires bulky equipment. We had previously developed a magnetic cell labeling/separation system call EasySep® (Stemcell Technologies) which does not require a centrifugal wash step. We have now fully automated EasySep® and present the RoboSep™ instrument which magnetically labels and separates 4 samples at once, with up to 2×109 total cells per sample or 8×109 total cells. The instrument operates in a standard biosafety hood and uses sterile disposable pipette tips to ensure aseptic operation and avoid cross-contamination between samples. Standardized automation protocols have been developed for both positive and negative selection. With positive selection, the desired cells are magnetically labeled and then purified by a sequence of magnetic wash steps. With negative selection, unwanted cells are magnetically labeled and then depleted. To demonstrate the suitability of RoboSep™ for automated positive selection of hematopoietic progenitors and stem cells, we performed CD34+ cell selection from previously frozen cord blood (CB) and mobilized peripheral blood (MPB). For the CB separations, the CD34+ cell content was enriched from 1.2±0.4% to 96.6±3.1% with a recovery of 45±9% (n=9, mean ± 1 SD). For the MPB separations the CD34+ cell content was enriched from 0.7±0.1% to 96.7±3.1%, with a recovery of 45±13% (n=4). To test RoboSep in negative selection we used an EasySep® antibody cocktail depleting cells that express any of CD2, CD3, CD11b, CD11c, CD14, CD16, CD19, CD24, CD56, CD66b, and glycophorin A to isolate hematopoietic progenitors from bone marrow (BM) and MPB. CB separations required the addition of anti-CD41 to the antibody cocktail for depletion of platelets. The table below shows results for negative selection from BM, CB and MPB. Manual separations performed in parallel with the above automated separations showed comparable purity and recovery, indicating that we have succeeded in automating both positive and negative selection procedures. The RoboSep instrument processes up to 4 tissue samples at once and provides the opportunity to isolate multiple cell subsets from the same sample by combining positive and negative selection methods in a single automated procedure. Negative Selection Results (Mean± 1 SD) Sample % CD34+ in start % CD34+ in enriched % Recovery CD34+ cells Fold-enrichment of total BFU-E, CFU-GM, CFU-GEMM % recovery of total BFU-E, CFU-GM, CFU-GEMM N.A. Not Available CB (n=2) 1.5 67.4 50 36 38 MPB (n=2) 1.1 50.0 45 50 41 BM (n=4) 4.7±3.1 47.5±7.5 N.A. 47±10 71±13
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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) | 0.000 | 0.000 |
| 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) | 0.002 | 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".