Journal article 831 views 58 downloads
Label-free cell segmentation of diverse lymphoid tissues in 2D and 3D
,
Jack Robertson,
Pani Tourlomousis,
Clare M.C. Gillis,
Claire Barnes ,
Michelle Miniter ,
Rachel E. Hewitt,
Clare E. Bryant,
Huw Summers ,
Jonathan J. Powell,
Paul Rees
Cell Reports Methods, Volume: 3, Issue: 2, Start page: 100398
Swansea University Authors:
Claire Barnes , Huw Summers , Paul Rees
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DOI (Published version): 10.1016/j.crmeth.2023.100398
Abstract
Unlocking and quantifying fundamental biological processes through tissue microscopy requires accurate, in situ segmentation of all cells imaged. Currently, achieving this is complex and requires exogenous fluorescent labels that occupy significant spectral bandwidth, increasing the duration and com...
| Published in: | Cell Reports Methods |
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| ISSN: | 2667-2375 |
| Published: |
Elsevier BV
2023
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa62581 |
| Abstract: |
Unlocking and quantifying fundamental biological processes through tissue microscopy requires accurate, in situ segmentation of all cells imaged. Currently, achieving this is complex and requires exogenous fluorescent labels that occupy significant spectral bandwidth, increasing the duration and complexity of imaging experiments while limiting the number of channels remaining to address the study’s objectives. We demonstrate that the excitation light reflected during routine confocal microscopy contains sufficient information to achieve accurate, label-free cell segmentation in 2D and 3D. This is achieved using a simple convolutional neural network trained to predict the probability that reflected light pixels belong to either nucleus, cytoskeleton, or background classifications. We demonstrate the approach across diverse lymphoid tissues and provide video tutorials demonstrating deployment in Python and MATLAB or via standalone software for Windows. |
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| Keywords: |
label free; cell segmentation; tissue; confocal microscopy; immunofluorescence; single cell; quantitative; 2D; 3D; digital pathology |
| College: |
Faculty of Science and Engineering |
| Funders: |
EPSRC, BBSRC, MRC, Wellcome Trust
EP/N013506/1, BB/P026818/1, MR/R005699/1, 108045/Z/15/Z |
| Issue: |
2 |
| Start Page: |
100398 |