Live sperm trap microarray for high throughput imaging and analysis
Bibliographic record
Abstract
There is a growing appreciation and understanding of cell-to-cell variability in biological samples. However, research and clinical practice in male fertility has relied on population, or sample-based characteristics. Single-cell resolution is particularly important given the winner-takes-all nature of both natural and in vitro fertilization: it is the properties of a single cell, not the population, that are passed to the next generation. While there are a range of methods for single cell analysis, arraying a larger number of live sperm has not been possible due to the strong locomotion of the cells. Here we present a 103-trap microarray that traps, aligns and arrays individual live sperm. The method enables high-resolution imaging of the aligned cell head, the application of dye-based DNA and mitochondrial analyses, and the quantification of motility characteristics, such as tail beat. In testing, a 2400-post array trapped ∼400 sperm for individual analyses of tail beating frequency and amplitude, DNA integrity via acridine orange staining, and mitochondrial activity via staining. While literature results are mixed regarding a possible correlation between motility and DNA integrity of sperm at sample-level, results here find no statistical correlation between tail beat characteristics and DNA integrity at the cell-level. The trap array uniquely enables the high-throughput study of individual live sperm in semen samples - assessing the inherently single-cell selection process of fertilization, with single-cell resolution.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.021 | 0.011 |
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".