Solid state yellow and orange lasers for flow cytometry
Bibliographic record
Abstract
Diode and DPSS lasers emitting a variety of wavelengths are now commonly incorporated into flow cytometers, greatly increasing our capacity to excite a wide variety of fluorochromes. Until recently, however, virtually no practical technology existed for generating yellow or orange laser light for flow cytometry that was compatible with smaller instrumentation. In this study, we evaluate several new solid state laser systems that emit from the 570 to 600 nm as excitation sources for flow cytometry. DPSS 580, 589, and 592 nm sources were integrated into a cuvette-based flow cytometer (BD LSR II) and a stream-in-air cell sorter (FACSVantage DiVa), and used to excite a variety of yellow, orange, and red excited fluorochromes, including Texas Red, APC, and its tandem conjugates, and the genetically encoded red fluorescent protein HcRed and the more recently developed Katushka. All laser sources were successfully incorporated into the indicated flow cytometry platforms. The yellow and orange sources (particularly 592 nm) were ideal for exciting Texas Red, and provided excitation of APC and its tandems that was comparable to a traditional red laser source, albeit at higher power levels than red sources. Yellow and orange laser light was optimal for exciting HcRed and Katushka. Practical yellow and orange laser sources are now available for flow cytometry. This technology fills an important gap in the laser wavelengths available for flow, now almost any fluorochrome requiring visible light excitation can be accommodated.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.024 |
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".