Deciphering animal development through proteomics: requirements and prospects
Bibliographic record
Abstract
In recent years proteomic techniques have started to become very useful tools in a variety of model systems of developmental biology. Applications cover many different aspects of development, including the characterization of changes in the proteome during early embryonic stages. During early animal development the embryo becomes patterned through the temporally and spatially controlled activation of distinct sets of genes. Patterning information is then translated, from gastrulation onwards, into regional specific morphogenetic cell and tissue movements that give the embryo its characteristic shape. On the molecular level, patterning is the outcome of intercellular communication via signaling molecules and the local activation or repression of transcription factors. Genetic approaches have been used very successfully to elucidate the processes behind these events. Morphogenetic movements, on the other hand, have to be orchestrated through regional changes in the mechanical properties of cells. The molecular mechanisms that govern these changes have remained much more elusive, at least in part due to the fact that they are more under translational/posttranslational control than patterning events. However, recent studies indicate that proteomic approaches can provide the means to finally unravel the mechanisms that link patterning to the generation of embryonic form. To intensify research in this direction will require close collaboration between proteome scientists and developmental researchers. It is with this aim in mind that we first give an outline of the classical questions of patterning and morphogenesis. We then summarize the proteomic approaches that have been applied in developmental model systems and describe the pioneering studies that have been done to study morphogenesis. Finally we discuss current and future strategies that will allow characterizing the changes in the embryonic proteome and ultimately lead to a deeper understanding of the cellular mechanisms that govern the generation of embryonic form.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".