Automated Quantification of Subcellular Particles in Myogenic Progenitors
Bibliographic record
Abstract
Fluorescence microscopy is a powerful tool enabling the visualization of protein localization within cells. In this article, we outline an automated and non-biased way to detect and quantify subcellular particles using immunocytochemistry, fluorescence microscopy, and the program CellProfiler. We discuss the examination of two types of subcellular particles: messenger ribonucleoprotein (mRNP) granules, namely processing bodies and stress granules, and autophagosomes. Fluorescent microscopy Z-stacks are acquired and deconvolved, and maximum intensity images are generated. The number of subcellular particles per cell is then quantified using the described CellProfiler pipeline. We also explain how to isolate primary myoblast progenitor cells from mice, which were used to obtain the presented results. Last, we discuss the critical parameters to be considered for each of these techniques. Both mRNP granules and autophagosomes play important roles in sequestering intracellular cargo, such as messenger RNAs and RNA-binding proteins for mRNP granules and cytoplasmic waste for autophagosomes. The methods outlined in this article are widely applicable for studies relating to subcellular particle formation, localization, and flux during homeostasis, following stimuli, and during disease. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Immunofluorescence microscopy of messenger ribonucleoprotein granules in primary myoblasts Alternate Protocol: Immunofluorescence microscopy of autophagosomes in primary myoblasts Support Protocol: Isolation of primary myoblasts from mice Basic Protocol 2: Automated quantification of subcellular particles.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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".