Academic Journal
View in EDS
Linked Full Text

Mitochondrial segmentation and function prediction in live-cell images with deep learning

Bibliographic Details
Title: Mitochondrial segmentation and function prediction in live-cell images with deep learning
Authors: Yang Ding, Jintao Li, Jiaxin Zhang, Panpan Li, Hua Bai, Bin Fang, Haixiao Fang, Kai Huang, Guangyu Wang, Cameron J. Nowell, Nicolas H. Voelcker, Bo Peng, Lin Li, Wei Huang
Source: Nature Communications, Vol 16, Iss 1, Pp 1-15 (2025)
Publisher Information: Nature Portfolio, 2025.
Publication Year: 2025
Collection: LCC:Science
Subject Terms: Science
More Details: Abstract Mitochondrial morphology and function are intrinsically linked, indicating the opportunity to predict functions by analyzing morphological features in live-cell imaging. Herein, we introduce MoDL, a deep learning algorithm for mitochondrial image segmentation and function prediction. Trained on a dataset of 20,000 manually labeled mitochondria from super-resolution (SR) images, MoDL achieves superior segmentation accuracy, enabling comprehensive morphological analysis. Furthermore, MoDL predicts mitochondrial functions by employing an ensemble learning strategy, powered by an extended training dataset of over 100,000 SR images, each annotated with functional data from biochemical assays. By leveraging this large dataset alongside data fine-tuning and retraining, MoDL demonstrates the ability to precisely predict functions of heterogeneous mitochondria from unseen cell types through small sample size training. Our results highlight the MoDL’s potential to significantly impact mitochondrial research and drug discovery, illustrating its utility in exploring the complex relationship between mitochondrial form and function within a wide range of biological contexts.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2041-1723
Relation: https://doaj.org/toc/2041-1723
DOI: 10.1038/s41467-025-55825-x
Access URL: https://doaj.org/article/fda03b6a1f6f430a89259753f46a1f30
Accession Number: edsdoj.fda03b6a1f6f430a89259753f46a1f30
Database: Directory of Open Access Journals
FullText Links:
  – Type: other
    Url: https://resolver.ebsco.com:443/public/rma-ftfapi/ejs/direct?AccessToken=447E9C3E238936CAAE6C&Show=Object
Text:
  Availability: 0
CustomLinks:
  – Url: https://login.libproxy.scu.edu/login?url=http://www.nature.com/openurl?genre=article&title=Nature%20Communications&volume=16&issue=1&spage=1
    Name: Nature Publishing
    Category: fullText
    Text: Full Text from Nature Publishing
    MouseOverText: Full Text from Nature Publishing
  – Url: https://resolver.ebsco.com/c/xy5jbn/result?sid=EBSCO:edsdoj&genre=article&issn=20411723&ISBN=&volume=16&issue=1&date=20250101&spage=1&pages=1-15&title=Nature Communications&atitle=Mitochondrial%20segmentation%20and%20function%20prediction%20in%20live-cell%20images%20with%20deep%20learning&aulast=Yang%20Ding&id=DOI:10.1038/s41467-025-55825-x
    Name: Full Text Finder (for New FTF UI) (s8985755)
    Category: fullText
    Text: Find It @ SCU Libraries
    MouseOverText: Find It @ SCU Libraries
  – Url: https://doaj.org/article/fda03b6a1f6f430a89259753f46a1f30
    Name: EDS - DOAJ (s8985755)
    Category: fullText
    Text: View record from DOAJ
    MouseOverText: View record from DOAJ
Header DbId: edsdoj
DbLabel: Directory of Open Access Journals
An: edsdoj.fda03b6a1f6f430a89259753f46a1f30
RelevancyScore: 1082
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1081.5498046875
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Mitochondrial segmentation and function prediction in live-cell images with deep learning
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Yang+Ding%22">Yang Ding</searchLink><br /><searchLink fieldCode="AR" term="%22Jintao+Li%22">Jintao Li</searchLink><br /><searchLink fieldCode="AR" term="%22Jiaxin+Zhang%22">Jiaxin Zhang</searchLink><br /><searchLink fieldCode="AR" term="%22Panpan+Li%22">Panpan Li</searchLink><br /><searchLink fieldCode="AR" term="%22Hua+Bai%22">Hua Bai</searchLink><br /><searchLink fieldCode="AR" term="%22Bin+Fang%22">Bin Fang</searchLink><br /><searchLink fieldCode="AR" term="%22Haixiao+Fang%22">Haixiao Fang</searchLink><br /><searchLink fieldCode="AR" term="%22Kai+Huang%22">Kai Huang</searchLink><br /><searchLink fieldCode="AR" term="%22Guangyu+Wang%22">Guangyu Wang</searchLink><br /><searchLink fieldCode="AR" term="%22Cameron+J%2E+Nowell%22">Cameron J. Nowell</searchLink><br /><searchLink fieldCode="AR" term="%22Nicolas+H%2E+Voelcker%22">Nicolas H. Voelcker</searchLink><br /><searchLink fieldCode="AR" term="%22Bo+Peng%22">Bo Peng</searchLink><br /><searchLink fieldCode="AR" term="%22Lin+Li%22">Lin Li</searchLink><br /><searchLink fieldCode="AR" term="%22Wei+Huang%22">Wei Huang</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: Nature Communications, Vol 16, Iss 1, Pp 1-15 (2025)
– Name: Publisher
  Label: Publisher Information
  Group: PubInfo
  Data: Nature Portfolio, 2025.
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2025
– Name: Subset
  Label: Collection
  Group: HoldingsInfo
  Data: LCC:Science
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Science%22">Science</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Abstract Mitochondrial morphology and function are intrinsically linked, indicating the opportunity to predict functions by analyzing morphological features in live-cell imaging. Herein, we introduce MoDL, a deep learning algorithm for mitochondrial image segmentation and function prediction. Trained on a dataset of 20,000 manually labeled mitochondria from super-resolution (SR) images, MoDL achieves superior segmentation accuracy, enabling comprehensive morphological analysis. Furthermore, MoDL predicts mitochondrial functions by employing an ensemble learning strategy, powered by an extended training dataset of over 100,000 SR images, each annotated with functional data from biochemical assays. By leveraging this large dataset alongside data fine-tuning and retraining, MoDL demonstrates the ability to precisely predict functions of heterogeneous mitochondria from unseen cell types through small sample size training. Our results highlight the MoDL’s potential to significantly impact mitochondrial research and drug discovery, illustrating its utility in exploring the complex relationship between mitochondrial form and function within a wide range of biological contexts.
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: article
– Name: Format
  Label: File Description
  Group: SrcInfo
  Data: electronic resource
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 2041-1723
– Name: NoteTitleSource
  Label: Relation
  Group: SrcInfo
  Data: https://doaj.org/toc/2041-1723
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1038/s41467-025-55825-x
– Name: URL
  Label: Access URL
  Group: URL
  Data: <link linkTarget="URL" linkTerm="https://doaj.org/article/fda03b6a1f6f430a89259753f46a1f30" linkWindow="_blank">https://doaj.org/article/fda03b6a1f6f430a89259753f46a1f30</link>
– Name: AN
  Label: Accession Number
  Group: ID
  Data: edsdoj.fda03b6a1f6f430a89259753f46a1f30
PLink https://login.libproxy.scu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsdoj&AN=edsdoj.fda03b6a1f6f430a89259753f46a1f30
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1038/s41467-025-55825-x
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 1
    Subjects:
      – SubjectFull: Science
        Type: general
    Titles:
      – TitleFull: Mitochondrial segmentation and function prediction in live-cell images with deep learning
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Yang Ding
      – PersonEntity:
          Name:
            NameFull: Jintao Li
      – PersonEntity:
          Name:
            NameFull: Jiaxin Zhang
      – PersonEntity:
          Name:
            NameFull: Panpan Li
      – PersonEntity:
          Name:
            NameFull: Hua Bai
      – PersonEntity:
          Name:
            NameFull: Bin Fang
      – PersonEntity:
          Name:
            NameFull: Haixiao Fang
      – PersonEntity:
          Name:
            NameFull: Kai Huang
      – PersonEntity:
          Name:
            NameFull: Guangyu Wang
      – PersonEntity:
          Name:
            NameFull: Cameron J. Nowell
      – PersonEntity:
          Name:
            NameFull: Nicolas H. Voelcker
      – PersonEntity:
          Name:
            NameFull: Bo Peng
      – PersonEntity:
          Name:
            NameFull: Lin Li
      – PersonEntity:
          Name:
            NameFull: Wei Huang
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 20411723
          Numbering:
            – Type: volume
              Value: 16
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Nature Communications
              Type: main
ResultId 1