Percoll Gradient-Centrifuged Capacitated Mouse Sperm Have Increased Fertilizing Ability and Higher Contents of Sulfogalactosylglycerolipid and Docosahexaenoic Acid-Containing Phosphatidylcholine Compared to Washed Capacitated Mouse Sperm1
Bibliographic record
Abstract
Although Percoll gradient centrifugation has been used routinely to prepare motile human sperm, its use in preparing motile mouse sperm has been limited. Here, we showed that Percoll gradient-centrifuged (PGC) capacitated mouse sperm had markedly higher fertilizing ability (sperm-zona pellucida [ZP] binding and in vitro fertilization) than washed capacitated mouse sperm. We also showed that the lipid profiles of PGC capacitated sperm and washed capacitated sperm differed significantly. The PGC sperm had much lower contents of cholesterol and phospholipids. This resulted in relative enrichment of male germ cell-specific sulfogalactosylglycerolipid (SGG), a ZP-binding ligand, in PGC capacitated sperm, and this would explain, in part, their increased ZP-binding ability compared with that of washed capacitated sperm. Analyses of phospholipid fatty acyl chains revealed that PGC capacitated sperm were enriched in phosphatidylcholine (PC) molecular species containing highly unsaturated fatty acids (HUFAs), with docosahexaenoic acid (DHA; C22: 6n-3) being the predominant HUFA (42% of total hydrocarbon chains of PC). In contrast, the level of PC-HUFAs comprising arachidonic acid (20:4n-6), docosapentaenoic acid (C22:5n-6), and DHA in washed capacitated sperm was only 27%. Having the highest unsaturation degree among all HUFAs in PC, DHA would enhance membrane fluidity to the uppermost. Therefore, membranes of PGC capacitated sperm would undergo fertilization-related fusion events at higher rates than washed capacitated sperm. These results suggested that PGC mouse sperm should be used in fertilization experiments and that SGG and DHA should be considered to be important biomarkers for sperm fertilizing ability.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".