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A Rapid Automated Method for the Sequential Isolation of CD19, CD3 and Myeloid Cells from One Tube of Whole Blood.

2007· article· en· W2593597398 on OpenAlexaff
Karina L. McQueen, Maureen Fairhurst, Melany Nauer, Jenna L. Warren, Allen Eaves, Terry E. Thomas

Bibliographic record

VenueBlood · 2007
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsTerry Fox Research InstituteStemcell Technologies
Fundersnot available
KeywordsMyeloidCD19BiologyImmunomagnetic separationImmunologyTransplantationHaematopoiesisImmune systemLeukapheresisT cellMolecular biologyCD34Stem cellMedicineCell biologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Immune ablation followed by allogeneic hematopoietic cell transplantation in humans necessitates hematopoietic cell reconstitution and immune re-education. All blood cell lineages are affected and post-transplantation immune restoration depends upon the graft’s ability to generate both lymphoid and myeloid lineage cells. Decisions regarding immunomodulation treatment post-transplantation are often made on the basis of chimerism testing. Chimerism analysis is typically performed on small blood samples, especially with pediatric patients. Since lymphoid and myeloid engraftment is asynchronous the determination of lineage-specific chimerism is needed. Analysis of purified cell subsets requires techniques which can isolate >1 cell type from a single small starting sample. This avoids dividing the sample. Performing flow cytometry as well as isolation of DNA from the purified subsets means that high cell recovery is essential. Preparation of the sample using a ficoll-based method often results in cell loss of 50% while certain lysis and wash steps can affect granulocyte content. We describe a method of sequential selections to isolate B cells, then T cells and finally myeloid lineages (CD33+ and/or CD66b+) using a fully automated pipetting robot called RoboSep®. RoboSep® can process sample sizes that range from 0.5 to 4.25 ml of human whole blood. CD19 (B cell) positive and CD3 (T cell) positive and myeloid cell fractions are isolated using immunomagnetic, column-free positive selection (EasySep®). Briefly, cells are first labeled with antibody targeting CD19 positive cells. These are then coupled to magnetic nanoparticles and the sample is placed in a magnet. The supernatant with unlabeled cells is removed to a new tube, leaving isolated CD19 positive cells in the magnet. The supernatant is then labeled with anti-CD3 antibody, magnetic nanoparticles, placed in a magnet and the supernatant is removed to a new tube leaving isolated CD3 positive cells. Finally, a cocktail of antibodies (anti-CD33, anti-CD66b) is used to label and select the myeloid cells from the supernatant. The resultant positive cells are collected in the magnet. Assessment by flow cytometry yields average purities over 90% for all cell types. Cell isolation using this method produces highly purified cells in quantities sufficient to generate genomic DNA for chimerism testing, even from very small amounts of starting sample. For example, 2.0 ml of whole blood yields on average 1.3ug of B cell genomic DNA, 10.2ug of T cell genomic DNA and 6.1ug of myeloid cell genomic DNA. In conclusion, we have developed a rapid, fully automated RoboSep® method to sequentially isolate highly purified B cells, then T cells and finally myeloid cells from a single starting sample of whole blood. The number of cells (x106) and amount of total genomic DNA (range) obtained from 2.0 ml of whole blood starting sample (n=3). No. Enriched Cells Total DNA (ug) CD19+ 0.12 – 0.34 1.1 – 1.6 CD3+ 1.8 – 3.2 7.9 – 11.9 Myeloid 2.2 – 2.9 4 – 7.2

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.023

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.

Opus teacher head0.023
GPT teacher head0.290
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations1
Published2007
Admission routes1
Has abstractyes

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