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Peer-reviewedCC BY 4.0Science Advances

Image-based phenotypic sorting of synthetic cells

  • Marijn van den Brink
  • Marlena Stam
  • Nico Claassens
  • Christophe Danelon
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Abstract

Understanding the relationships between genotype and phenotype is key to many areas of biological research and to the development of synthetic cells. We describe an image-based screening and sorting workflow that explores the phenotypes of gene-expressing vesicles within nonclonal populations and selects the desired variants. Using automated confocal microscopy and real-time, neural network-assisted image analysis, we demonstrate that liposomes can be selected for fluorescence intensity, protein localization, membrane morphology, and dynamic behaviors, and their phenotype can be linked to genetic content. This approach could substantially accelerate the evolution of cellular functions in a minimal synthetic context.

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Cite

BibTeXRIS
BibTeX
@article{brink2026imagebased,
  title = {{Image-based phenotypic sorting of synthetic cells}},
  author = {Marijn van den Brink and Marlena Stam and Nico Claassens and Christophe Danelon},
  journal = {Science Advances},
  year = {2026},
  month = jun,
  doi = {10.1126/sciadv.aed0860},
  url = {https://hal.inrae.fr/hal-05781647v1},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Image-based phenotypic sorting of synthetic cells
AU  - Marijn van den Brink
AU  - Marlena Stam
AU  - Nico Claassens
AU  - Christophe Danelon
PY  - 2026
DA  - 2026/06/05
JO  - Science Advances
DO  - 10.1126/sciadv.aed0860
UR  - https://hal.inrae.fr/hal-05781647v1
AB  - Understanding the relationships between genotype and phenotype is key to many areas of biological research and to the development of synthetic cells. We describe an image-based screening and sorting workflow that explores the phenotypes of gene-expressing vesicles within nonclonal populations and selects the desired variants. Using automated confocal microscopy and real-time, neural network-assisted image analysis, we demonstrate that liposomes can be selected for fluorescence intensity, protein localization, membrane morphology, and dynamic behaviors, and their phenotype can be linked to genetic content. This approach could substantially accelerate the evolution of cellular functions in a minimal synthetic context.
SN  - 2375-2548
ER  -