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Exploring the Distribution and Orientation of Cell Proliferation as Drivers of Mouse Facial Development

2022· article· en· W4225424797 on OpenAlexaff
Andreas Dauter, Rebecca M. Green, Lucas Lo Vercio, Elizabeth C. Barretto, Samuel Robertson, Anandita Mahika, Marta Vidal‐García, Nils D. Forkert, Benedikt Hallgrímsson

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCell growthMorphogenesisCell biologyBiologyImmunostainingCell divisionOrientation (vector space)Cell typeCellImmunohistochemistryImmunologyGeneticsGeometryGene

Abstract

fetched live from OpenAlex

During the development of the face, tissues move, change shape, and fuse in tightly orchestrated patterns to create all the parts of a normal face. These shape changes are driven by factors such as cell signaling, migration, proliferation, and apoptosis. However, the contributions of each of these drivers to morphogenesis are poorly studied. Here, we explore differential cell proliferation as a driver of mouse facial morphogenesis. We quantify patterns in both the spatial distribution and orientation of proliferation in the developing face in 3D over a critical period of murine facial development (E9.5‐E11.5). We use immunostaining with light sheet microscopy (LSM) to capture total and proliferating nuclei. To compare proliferative density in facial tissues, we segment these images using a novel convolutional neural network. We then generate atlases of average proliferation at each half‐day age point within our range and use these to identify relationships between morphology and cell proliferation. We show that regions with more dense proliferation tend to undergo more intensive shape changes. We then simulate outgrowth of the maxillary process using a cell simulation engine, PhysiCell, to demonstrate that differential proliferation is necessary to maintain expected morphology in growing tissues. In addition to differential proliferation, localized orientation of cell division could also affect morphology. In plants and some animal tissues, including murine limb buds, preferentially oriented cell proliferation drives shape change by causing tissue elongation in specific directions. We explore the orientation and distribution of cell proliferation using LSM: we inject pregnant dams with a synthetic nucleotide, EdU, 5 minutes before harvest to mark the daughter cells of proliferative events occurring in the interim. We then compare the angles of the proliferative axes for each pair of daughters relative to the primary direction of tissue growth. Preliminary results suggest that cell proliferation in the maxillary and nasal processes is oriented preferentially towards the axis of tissue growth. These results suggest that both the distribution and orientation of cell proliferation play a role in murine facial morphogenesis. Understanding the mechanisms underlying morphogenesis is important to guide future research that could lead to earlier and more robust diagnosis and treatment of syndromes and facial abnormalities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.239
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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