GliaMorph: A modular image analysis toolkit to quantify Müller glial cell morphology
Bibliographic record
Abstract
Abstract Cell morphology is critical for all cell functions. This is particularly true for glial cells as they rely on their complex shape to contact and support neurons. However, methods to quantify complex glial cell shape accurately and reproducibly are lacking. To address this gap in quantification approaches, we developed an analysis pipeline called “ GliaMorph ”. GliaMorph is a modular image analysis toolkit developed to perform (i) image pre-processing, (ii) semi-automatic region-of-interest (ROI) selection, (iii) apicobasal texture analysis, (iv) glia segmentation, and (v) cell feature quantification. Müller Glia (MG) are the principal retinal glial cell type with a stereotypic shape linked to their maturation and physiological status. We here characterized MG on three levels, including (a) global image-level, (b) apicobasal texture, and (c) apicobasal vertical-to-horizontal alignment. Using GliaMorph, we show structural changes occurring in the developing retina. Additionally, we study the loss of cadherin2 in the zebrafish retina, as well as a glaucoma mouse disease model. The GliaMorph toolkit enables an in-depth understanding of MG morphology in the developing and diseased retina. Graphical Abstract Highlights Glial morphology is complex, making it challenging to accurately quantify 3D cell shape. We developed the GliaMorph toolkit for image pre-processing, glial segmentation, and quantification of Müller glial cells. Müller glia elaborate their morphology and rearrange subcellular features during embryonic development. GliaMorph accurately identifies subcellular changes in models with disrupted glia cells, including zebrafish cadherin2 loss of function and a mouse glaucoma model.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".