A single-cell transcriptomics atlas for the parasitic nematode <i>Heligmosomoides bakeri</i> : Extrapolating model organism information to non-model systems
Bibliographic record
Abstract
Abstract Single-cell atlases aim to collect the gene expression information for every cell type in an organism but can be challenging to perform in non-model organisms. To try to circumvent the problem of having no verified cell type markers in the parasitic nematode Heligmosomoides bakeri to use for an atlas, we attempted to use orthologs of verified markers from the closely related model organism Caenorhabditis elegans . This resulted in a useful comparison between the two worms for each of the cell types recovered in preliminary H. bakeri single-cell RNA-sequencing. For H. bakeri males and females, robustly recovered cell types include the gametes, embryos, and male intestine, while hypodermis, neurons, muscles, and pharyngeal cells were under-represented cell types. The two worms appear to have a similar hypodermis, cuticle, eggshell, and spermatogenesis process. On the other hand, putative cell identities and cell cycle scores suggest the intestine and muscle cells in H. bakeri may still be cycling and dividing, unlike in C. elegans . Additionally, embryogenesis and early development appear to be quite different between the two worms, with only eight out of 94 confirmed paternal contributions to the embryo in C. elegans (with an ortholog) predicted to also be paternal contributions in H. bakeri . Overall, this new dataset allowed me to move beyond the presence or absence of orthologs to include their tissue specificity and expression level similarities and differences when comparing these two worms to better identify biological processes and traits in a parasitic nematode that are modelled well by C. elegans .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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) | 0.003 | 0.002 |
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".