Highly efficient engineering of human immune cells and hematopoietic stem and progenitor cells using microfluidic transfection 2651
Bibliographic record
Abstract
Abstract Description We developed a novel mechanoporation-based system that enables efficient intracellular cargo delivery with minimal cellular perturbations. Using this CellPore™ Transfection System, we optimized workflows to genetically engineer unactivated pan T cells, T cell subsets, NK cells, and hematopoietic stem and progenitor cells (HSPCs). We achieved high knockout efficiency of surface MHC-I (74%) or TCRαβ (94%) by delivering CRISPR/Cas9 ribonucleoproteins (RNP) to unactivated T cells using our workflow. T cells transfected by CellPore™ remained unactivated, while electroporation caused T cell activation and secretion of pro-inflammatory cytokines. Similarly, we optimized a workflow to deliver mRNA constructs and RNPs to freshly isolated NK cells. Transfection with RNPs resulted in significant knockout of TIGIT (85%) or NKp46 (87%) surface markers. Importantly, NK cells transfected with CellPore™ maintained their cytotoxic and expansion capacity. Using CellPore™ to deliver RNPs to CD34+ HSPCs, we achieved high knockout efficiencies of either MHC-I (92%) or CD45 (84%) with a shortened process timeline. The bulk HSPC and primitive hematopoietic stem cell populations were preserved, with minimal impact on their differentiation, expansion, and clonogenic potential. In summary, the CellPore™ Transfection System enables robust and efficient cargo delivery to mature immune cells and HSPCs without impacting cell function, in a simple workflow that can be easily integrated into research protocols. Funding Sources N/A Topic Categories Technological Innovations in Immunology (TECH)
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".