Multiplexed fluorescence and scatter detection with single cell resolution using on-chip fiber optics for droplet microfluidic applications
Bibliographic record
Abstract
ABSTRACT Droplet microfluidics has emerged as a critical component of several high-throughput single cell analysis techniques in biomedical research and diagnostics. However, while there has been significant progress in the individual assays being developed, multi-parametric optical sensing of droplets and their encapsulated contents remains challenging. The current common approach of microscopy based high-speed imaging of droplets is technically complex and requires expensive instrumentation limiting their large-scale adoption. Here we have adapted the principles of the widely established technique of flow cytometry to a novel optofluidic setup – the OptiDrop platform, with on-chip detection of scatter and multiple fluorescence signals from microfluidic droplets and their contents using optical fibers. The highly customizable on-chip optical fiber-based signal detection system enables simplified, miniaturized, low-cost, multi-parametric sensing of optical signals with high sensitivity and single cell resolution within each droplet. Our work on differential expression analysis of the Major Histocompatibility Complex (MHC) protein in response to IFNγ stimulation further demonstrates the OptiDrop platforms capabilities in sensitively detecting cell surface biomarkers using fluorescently labeled antibodies. The OptiDrop platform thus combines the versatility of flow cytometry with the power of droplet microfluidics to provide wide-ranging optical sensing solutions for research and diagnostics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".