AutoMorphoTrack: A modular framework for quantitative analysis of organelle morphology, motility, and interactions at single-cell resolution.
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| Title: | AutoMorphoTrack: A modular framework for quantitative analysis of organelle morphology, motility, and interactions at single-cell resolution. |
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| Authors: | Bayati A; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02129, USA.; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA., Schumacher JG; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02129, USA.; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA., Chen X; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02129, USA.; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA. |
| Source: | BioRxiv : the preprint server for biology [bioRxiv] 2026 Feb 02. Date of Electronic Publication: 2026 Feb 02. |
| Publication Type: | Journal Article; Preprint |
| Language: | English |
| Journal Info: | Country of Publication: United States NLM ID: 101680187 Publication Model: Electronic Cited Medium: Internet ISSN: 2692-8205 (Electronic) Linking ISSN: 26928205 NLM ISO Abbreviation: bioRxiv Subsets: PubMed not MEDLINE |
| Abstract: | Quantitative imaging of organelle dynamics provides crucial insights into cellular function, state, and organization; however, existing analysis workflows often require advanced coding expertise and multiple software tools. AutoMorphoTrack is an open-source Python toolkit that automates organelle detection, morphology classification, motility tracking, and colocalization from multichannel fluorescence microscopy image stacks. The platform includes adaptive segmentation, organelle trajectory reconstruction, and pixel-level overlap quantification within a unified, reproducible framework that can be executed as an interactive Jupyter notebook, a modular Python package, or through AI-assisted natural-language commands. Each analysis step outputs publication-ready images, time-lapse videos, and standardized quantitative data tables. To complement the main pipeline, an accompanying script-AMTComparison.py-is provided to demonstrate how AutoMorphoTrack's outputs can be extended for comparative analysis across individual neurons or experimental conditions. Together, these tools provide an accessible and framework for high-content, reproducible quantification of subcellular morphology, motility, and interactions at single-cell resolution. |
| References: | Mol Cell. 2022 Jan 20;82(2):241-247. (PMID: 35063094) Traffic. 2022 May;23(5):238-269. (PMID: 35343629) Sci Rep. 2019 Mar 7;9(1):3794. (PMID: 30846705) Sci Rep. 2022 Feb 17;12(1):2702. (PMID: 35177675) Cell Mol Life Sci. 2021 Apr;78(8):3969-3986. (PMID: 33576841) F1000Res. 2021 Apr 26;10:320. (PMID: 34136134) |
| Grant Information: | R01 NS102735 United States NS NINDS NIH HHS |
| Entry Date(s): | Date Created: 20260212 Date Completed: 20260224 Latest Revision: 20260226 |
| Update Code: | 20260226 |
| PubMed Central ID: | PMC12889450 |
| DOI: | 10.1101/2025.07.19.665650 |
| PMID: | 41676503 |
| Database: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstract: | Quantitative imaging of organelle dynamics provides crucial insights into cellular function, state, and organization; however, existing analysis workflows often require advanced coding expertise and multiple software tools. AutoMorphoTrack is an open-source Python toolkit that automates organelle detection, morphology classification, motility tracking, and colocalization from multichannel fluorescence microscopy image stacks. The platform includes adaptive segmentation, organelle trajectory reconstruction, and pixel-level overlap quantification within a unified, reproducible framework that can be executed as an interactive Jupyter notebook, a modular Python package, or through AI-assisted natural-language commands. Each analysis step outputs publication-ready images, time-lapse videos, and standardized quantitative data tables. To complement the main pipeline, an accompanying script-AMTComparison.py-is provided to demonstrate how AutoMorphoTrack's outputs can be extended for comparative analysis across individual neurons or experimental conditions. Together, these tools provide an accessible and framework for high-content, reproducible quantification of subcellular morphology, motility, and interactions at single-cell resolution. |
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| ISSN: | 2692-8205 |
| DOI: | 10.1101/2025.07.19.665650 |