Sonoporation of Immune Cells: Heterogeneous Impact on Lymphocytes, Monocytes and Granulocytes
Bibliographic record
Abstract
Microbubble-mediated ultrasound (MB-US) can be used to realize sonoporation and, in turn, facilitate the transfection of leukocytes in the immune system. Nevertheless, the bio-effects that can be induced by MB-US exposure on leukocytes have not been adequately studied, particularly for different leukocyte lineage subsets with distinct cytological characteristics. Here, we describe how that same set of MB-US exposure conditions would induce heterogeneous bio-effects on the three main leukocyte subsets: lymphocytes, monocytes and granulocytes. MB-US exposure was delivered by applying 1-MHz pulsed ultrasound (0.50-MPa peak negative pressure, 10% duty cycle, 30-s exposure period) in the presence of microbubbles (1:1 cell-to-bubble ratio); sonoporated and non-viable leukocytes were respectively labeled using calcein and propidium iodide. Flow cytometry was then performed to classify leukocytes into their corresponding subsets and to analyze each subset's post-exposure viability, sonoporation rate, uptake characteristics and morphology. Results revealed that, when subjected to MB-US exposure, granulocytes experienced the highest loss of viability (64.0 ± 11.0%) and the lowest sonoporation rate (6.3 ± 2.5%), despite maintaining their size and granularity. In contrast, lymphocytes exhibited the lowest loss of viability (20.9 ± 7.0%), while monocytes had the highest sonoporation rate (24.1 ± 13.6%). For these two sonoporated leukocyte subsets, their cell size and granularity were found to be reduced. Also, they exhibited graded levels of calcein uptake, whereas sonoporated granulocytes achieved only mild calcein uptake. These heterogeneous bio-effects should be accounted for when using MB-US and sonoporation in immunomodulation applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".