Organelle-specific phase contrast microscopy (OS-PCM) enables facile correlation study of organelles and proteins
© 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement..
Current methods for studying organelle and protein interactions and correlations depend on multiplex fluorescent labeling, which is experimentally complex and harmful to cells. Here we propose to solve this challenge via OS-PCM, where organelles are imaged and segmented without labels, and combined with standard fluorescence microscopy of protein distributions. In this work, we develop new neural networks to obtain unlabeled organelle, nucleus and membrane predictions from a single 2D image. Automated analysis is also implemented to obtain quantitative information regarding the spatial distribution and co-localization of both protein and organelle, as well as their relationship to the landmark structures of nucleus and membrane. Using mitochondria and DRP1 protein as a proof-of-concept, we conducted a correlation study where only DRP1 is labeled, with results consistent with prior reports utilizing multiplex labeling. Thus our work demonstrates that OS-PCM simplifies the correlation study of organelles and proteins.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:15 |
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| Enthalten in: |
Biomedical optics express - 15(2024), 1 vom: 01. Jan., Seite 199-211 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Chen, Chen [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 16.01.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1364/BOE.510243 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Current methods for studying organelle and protein interactions and correlations depend on multiplex fluorescent labeling, which is experimentally complex and harmful to cells. Here we propose to solve this challenge via OS-PCM, where organelles are imaged and segmented without labels, and combined with standard fluorescence microscopy of protein distributions. In this work, we develop new neural networks to obtain unlabeled organelle, nucleus and membrane predictions from a single 2D image. Automated analysis is also implemented to obtain quantitative information regarding the spatial distribution and co-localization of both protein and organelle, as well as their relationship to the landmark structures of nucleus and membrane. Using mitochondria and DRP1 protein as a proof-of-concept, we conducted a correlation study where only DRP1 is labeled, with results consistent with prior reports utilizing multiplex labeling. Thus our work demonstrates that OS-PCM simplifies the correlation study of organelles and proteins | ||
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