High-throughput Imaging for Everyone: New Designs for Parallel Multiwell Imaging
Bibliographic record
Abstract
Many life-science experiments require capturing images from different samples in multiwell plates. Most use robotics to move the microscope field of view to sequentially sample each well, generating a series of short measurements. Some assays, however, require continuous long-duration recordings: slowly evolving processes (metastasis, organoid or embryo development), unpredictable dynamical event (cardiac transitions to arrhythmia), and behavioral studies in model organisms (zebrafish, C Elegans) must be imaged in parallel if replicates are required. Continuous measurements in many samples have been achieved by using a wide-field lens or using many detectors[1-3], however these approaches greatly reduce spatial resolution or increase system cost. The RAP microscope[4,5] was designed for continuous, long-term recordings of dozens of wells at full resolution (Fig 1A). RAPs optical path allows for changes in the FOV through microsecond light source switching with a fast light-emitting diode (LED) array. Thus, RAP can collect images from different wells where the number of wells is limited only by the camera’s frame rate. A fast camera allows RAP to revisit each well quickly enough to effectively observe them in parallel. This presentation describes new modalities for the RAP system that allow parallel image capture without LED switching (Fig 1B). This new modality bypasses one of the limits intrinsic in the original design, where the number of samples depended on the camera frame rate. This enables the efficient use of high-resolution, lower-frame rate sensors for high throughput imaging while also simplifying the optical path and electronics of the original RAP scope. These designs are being incorporated into an open-source microscope platform for low-cost continuous high-throughput imaging. Different RAP configurations. A) LEDs illuminate samples in sequence, so each acquired frame (t1,t2…) is of a different sample; B) LEDS illuminate samples in parallel, and the optical path is altered so several samples are projected onto different locations of the sensor.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".