A new benchtop system for simple and versatile introduction of macromolecules into human lymphocytes and CD34+ cells by microfluidic squeezing
Bibliographic record
Abstract
Abstract Intracellular delivery of molecules is a key step in biological research that enables the development of new methods of cell and gene-based therapies. Traditional methods for intracellular delivery are limited in a) delivering a wide variety of cargos, b) efficient delivery to a broad range of cell types, and c) preventing unwanted genetic dysregulation. To address these challenges, we have developed the CellPore™ system, a benchtop device for gentle, yet efficient delivery of cargos to a range of primary cell types. The CellPore™ system is a microfluidic platform that creates transient disruptions in the plasma membrane via mechanical deformation by applying pressure, thereby allowing cargo entry to the cytosol. We developed a simple and straightforward workflow to determine optimal delivery conditions. This workflow consists of cell isolation, cargo preparation for delivery across a range of system pressures, followed by viability and delivery efficiency measurements. Optimized parameters were subsequently applied to deliver eGFP and mCherry mRNA as well as B2M-targeting Cas9 ribonucleoproteins to human unactivated T and CD34+ cells. Importantly, edited cells were functionally active after manipulation by the CellPore™ system, and subsequent studies in T cells revealed minimal transcriptional perturbations compared to electroporated samples. These results demonstrate the importance of having a comprehensive understanding of the impact a delivery modality can have in a biological system. The CellPore™ system enables efficient delivery of cargos to target cells, while safeguarding key functional attributes. This simple workflow allows the CellPore™ system to be easily integrated into cell therapy research protocols.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".