Microfluidic Single-Cell Monitoring Versus Microplate Bulk-Cell Measurement
Bibliographic record
Abstract
This study compares two methods for measuring cell changes: a microfluidic chip single-cell monitoring and a microplate bulk-cell measurement. As intracellular calcium ion concentration ([Ca2+]i) plays a critical role in various cellular functions and biochemical processes, measurements of [Ca2+]i may be used to compare the two methods. The microfluidic approach allows real-time monitoring of individual cells, utilizing the fluorescence emitted from calcium-Fluo 4 chelate, while the microplate method offers bulk analysis of approximately 10,000 cells per well in a 96-well microplate. We have demonstrated that the single-cell method provides insights into [Ca2+]i dynamics with low reagent consumption and rapid analysis, whereas the microplate method enables comprehensive bulk measurements when isolation of single cells is difficult. By integrating both techniques, we aim to complement measurements on both single-cell and population levels, especially when cell availability is an issue. For the cellular process, we specifically investigated the increase in [Ca2+]i following histamine receptor activation, in ACE2-enriched A549 and wild-type A549 cells. In our findings, both approaches yielded consistent calcium-signaling patterns, that wild-type A549 cells exhibited stronger histamine-induced calcium responses than ACE2-enriched cells, and that the two methods complement each other—single-cell assays providing temporal and low-reagent analysis, while bulk assays provide high-throughput, population-level averages.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".