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Peer-reviewedCC BY 4.0Journal of cell science

Removing barriers to advanced imaging and machine learning-based analysis

  • Jodie R Malcolm1,2
  • Stuart Lacy3
  • Andrea Papaleo4
  • Le Liu4
  • Suleiman H Kwairanga5,6
  • Graeme Park7
  • Joanne Marrison7
  • Olympia Physouni2
  • Richard Kasprowicz8
  • Kerry Hallbrook9
  • Richard Williams10
  • Kildare Miranda11
10 more authors
  • Teng-Leong Chew12
  • Caron A Jacobs13
  • Mahmoud Bukar Maina5,6
  • Ana Paula C A Lima11
  • Jeremy C Mottram2
  • Beth A Cimini4
  • Ben Powell14
  • Peter O’Toole7
  • Laura Wiggins15
  • William J Brackenbury1,2
15 affiliations
  1. 1Jack Birch Cancer Research Unit, Department of Biology, University of York, Heslington, York, UK
  2. 2York Biomedical Research Institute, Department of Biology, University of York, Heslington, York, UK
  3. 3IT Services, University of York, Heslington, York, UK
  4. 4Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
  5. 5Biomedical Science Research and Training Centre, Yobe State University, Damaturu, Nigeria
  6. 6Sussex Neuroscience, School of Life Sciences, University of Sussex, Brighton, UK
  7. 7Bioscience Technology Facility, Department of Biology, University of York, Heslington, York, YO10 5DD, UK
  8. 8GlaxoSmithKline Medicines Research Centre, Stevenage, UK
  9. 9UK Cell Culture and Banking, Discovery Sciences, AstraZeneca, Cambridge, UK
  10. 10ioLight, Whitchurch, Hampshire, UK
  11. 11Instituto de Biofisica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
  12. 12Advanced Imaging Center and Integrative Imaging, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA, USA
  13. 13Department of Pathology and Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
  14. 14Department of Mathematics, University of York, Heslington, York, UK
  15. 15School of Chemical, Materials and Biological Engineering, University of Sheffield, Sheffield, UK
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Abstract

Global and community-driven initiatives have recently achieved considerable success in overcoming key challenges that hinder the widespread adoption of advanced microscopy and bioimage analysis tools in under-resourced settings. To build upon this progress, we held a workshop in May 2025 at the University of York, UK to address the needs and barriers associated with implementing time-lapse imaging and machine learning-based phenotyping in low-resource research environments. We focussed on identifying the specific challenges faced by the existing networks represented at the meeting, emphasising how integrating combined imaging hardware and machine learning-based approaches can solve these problems. This article summarises the key observations and actionable strategies made at the workshop. These proposed steps aim to significantly increase the dissemination and uptake of these powerful technologies to advance biological research in low-resource settings globally.

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Cite

BibTeXRIS
BibTeX
@article{malcolm2026removing,
  title = {{Removing barriers to advanced imaging and machine learning-based analysis}},
  author = {Jodie R Malcolm and Stuart Lacy and Andrea Papaleo and Le Liu and Suleiman H Kwairanga and Graeme Park and Joanne Marrison and Olympia Physouni and Richard Kasprowicz and Kerry Hallbrook and Richard Williams and Kildare Miranda and Teng-Leong Chew and Caron A Jacobs and Mahmoud Bukar Maina and Ana Paula C A Lima and Jeremy C Mottram and Beth A Cimini and Ben Powell and Peter O’Toole and Laura Wiggins and William J Brackenbury},
  journal = {Journal of cell science},
  year = {2026},
  month = aug,
  doi = {10.1242/jcs.264705},
  url = {https://doi.org/10.1242/jcs.264705},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Removing barriers to advanced imaging and machine learning-based analysis
AU  - Jodie R Malcolm
AU  - Stuart Lacy
AU  - Andrea Papaleo
AU  - Le Liu
AU  - Suleiman H Kwairanga
AU  - Graeme Park
AU  - Joanne Marrison
AU  - Olympia Physouni
AU  - Richard Kasprowicz
AU  - Kerry Hallbrook
AU  - Richard Williams
AU  - Kildare Miranda
AU  - Teng-Leong Chew
AU  - Caron A Jacobs
AU  - Mahmoud Bukar Maina
AU  - Ana Paula C A Lima
AU  - Jeremy C Mottram
AU  - Beth A Cimini
AU  - Ben Powell
AU  - Peter O’Toole
AU  - Laura Wiggins
AU  - William J Brackenbury
PY  - 2026
DA  - 2026/08/12
JO  - Journal of cell science
DO  - 10.1242/jcs.264705
UR  - https://doi.org/10.1242/jcs.264705
AB  - Global and community-driven initiatives have recently achieved considerable success in overcoming key challenges that hinder the widespread adoption of advanced microscopy and bioimage analysis tools in under-resourced settings. To build upon this progress, we held a workshop in May 2025 at the University of York, UK to address the needs and barriers associated with implementing time-lapse imaging and machine learning-based phenotyping in low-resource research environments. We focussed on identifying the specific challenges faced by the existing networks represented at the meeting, emphasising how integrating combined imaging hardware and machine learning-based approaches can solve these problems. This article summarises the key observations and actionable strategies made at the workshop. These proposed steps aim to significantly increase the dissemination and uptake of these powerful technologies to advance biological research in low-resource settings globally.
SN  - 0021-9533
SN  - 1477-9137
ER  -