ProMoIJ: A new tool for automatic three‐dimensional analysis of microglial process motility
Bibliographic record
Abstract
Microglia, the immune cells of the central nervous system, continuously survey the brain to detect alterations and maintain tissue homeostasis. The motility of microglial processes is indicative of their surveying capacity in normal and pathological conditions. The gold standard technique to study motility involves the use of two-photon microscopy to obtain time-lapse images from brain slices or the cortex of living animals. This technique generates four dimensionally-coded images which are analyzed manually using time-consuming, non-standardized protocols. Microglial process motility analysis is frequently performed using Z-stack projections with the consequent loss of three-dimensional (3D) information. To overcome these limitations, we developed ProMoIJ, a pack of ImageJ macros that perform automatic motility analysis of cellular processes in 3D. The main core of ProMoIJ is formed by two macros that assist the selection of processes, automatically reconstruct their 3D skeleton, and analyze their motility (process and tip velocity). Our results show that ProMoIJ presents several key advantages compared with conventional manual analysis: (1) reduces the time required for analysis, (2) is less sensitive to experimenter bias, and (3) is more robust to varying numbers of processes analyzed. In addition, we used ProMoIJ to demonstrate that commonly performed 2D analysis underestimates microglial process motility, to reveal that only cells adjacent to a laser injured area extend their processes toward the lesion site, and to demonstrate that systemic inflammation reduces microglial process motility. ProMoIJ is a novel, open-source, freely-available tool which standardizes and accelerates the time-consuming labor of 3D analysis of microglial process motility.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".