Single cell transcriptomics and development of gametocyte-specific molecular markers for avian malaria parasites
Bibliographic record
Abstract
Avian malaria, caused by Plasmodium parasites, poses a significant threat to bird populations worldwide, particularly in vulnerable island ecosystems. Yet, progress in understanding avian malaria transmission dynamics has been hampered by the lack of molecular tools to quantify and sex the transmissible stages of the parasite: the male and female gametocytes. This challenge is compounded by the nucleated erythrocytes of avian hosts and the absence of an in vitro culture system, which have historically hindered the advancement of molecular approaches. Here, we develop and validate the first molecular markers to discriminate between asexual, male and female gametocytes in the widespread avian malaria parasite Plasmodium relictum. Using single-cell RNA sequencing and orthology-guided stage mapping, we identified conserved, stage-specific transcripts and leveraged this information to develop molecular markers capable of quantifying and distinguishing male and female gametocytes in two parasite cyt-b lineages, pSGS1 and pDELURB4. These markers outperformed microscopy in sensitivity, detected gametocytes earlier and for longer and revealed consistently higher male-to-female gametocyte ratios than estimated by blood smears. The high degree of conservation of these markers across Plasmodium species suggests broad applicability of these markers to other avian lineages.
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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) | 0.000 | 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".