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Data-driven microscopy allows for automated context-specific acquisition of high-fidelity image data

André, Oscar LU ; Kumra Ahnlide, Johannes LU orcid ; Norlin, Nils LU ; Swaminathan, Vinay LU and Nordenfelt, Pontus LU orcid (2023) In Cell reports methods 3(3).
Abstract
Light microscopy is a powerful single-cell technique that allows for quantitative spatial information at subcellular resolution. However, unlike flow cytometry and single-cell sequencing techniques, microscopy has issues achieving high-quality population-wide sample characterization while maintaining high resolution. Here, we present a general framework, data-driven microscopy (DDM) that uses real-time population-wide object characterization to enable data-driven high-fidelity imaging of relevant phenotypes based on the population context. DDM combines data-independent and data-dependent steps to synergistically enhance data acquired using different imaging modalities. As a proof of concept, we develop and apply DDM with plugins for... (More)
Light microscopy is a powerful single-cell technique that allows for quantitative spatial information at subcellular resolution. However, unlike flow cytometry and single-cell sequencing techniques, microscopy has issues achieving high-quality population-wide sample characterization while maintaining high resolution. Here, we present a general framework, data-driven microscopy (DDM) that uses real-time population-wide object characterization to enable data-driven high-fidelity imaging of relevant phenotypes based on the population context. DDM combines data-independent and data-dependent steps to synergistically enhance data acquired using different imaging modalities. As a proof of concept, we develop and apply DDM with plugins for improved high-content screening and live adaptive microscopy for cell migration and infection studies that capture events of interest, rare or common, with high precision and resolution. We propose that DDM can reduce human bias, increase reproducibility, and place single-cell characteristics in the context of the sample population when interpreting microscopy data, leading to an increase in overall data fidelity. (Less)
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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Cell reports methods
volume
3
issue
3
article number
100419
publisher
Cell Press
external identifiers
  • scopus:85150801245
  • pmid:37056378
ISSN
2667-2375
DOI
10.1016/j.crmeth.2023.100419
language
English
LU publication?
yes
id
ceeb5680-2c3e-489f-8e08-fe9b6aa41155
date added to LUP
2023-03-16 10:09:36
date last changed
2024-05-15 10:18:48
@article{ceeb5680-2c3e-489f-8e08-fe9b6aa41155,
  abstract     = {{Light microscopy is a powerful single-cell technique that allows for quantitative spatial information at subcellular resolution. However, unlike flow cytometry and single-cell sequencing techniques, microscopy has issues achieving high-quality population-wide sample characterization while maintaining high resolution. Here, we present a general framework, data-driven microscopy (DDM) that uses real-time population-wide object characterization to enable data-driven high-fidelity imaging of relevant phenotypes based on the population context. DDM combines data-independent and data-dependent steps to synergistically enhance data acquired using different imaging modalities. As a proof of concept, we develop and apply DDM with plugins for improved high-content screening and live adaptive microscopy for cell migration and infection studies that capture events of interest, rare or common, with high precision and resolution. We propose that DDM can reduce human bias, increase reproducibility, and place single-cell characteristics in the context of the sample population when interpreting microscopy data, leading to an increase in overall data fidelity.}},
  author       = {{André, Oscar and Kumra Ahnlide, Johannes and Norlin, Nils and Swaminathan, Vinay and Nordenfelt, Pontus}},
  issn         = {{2667-2375}},
  language     = {{eng}},
  number       = {{3}},
  publisher    = {{Cell Press}},
  series       = {{Cell reports methods}},
  title        = {{Data-driven microscopy allows for automated context-specific acquisition of high-fidelity image data}},
  url          = {{http://dx.doi.org/10.1016/j.crmeth.2023.100419}},
  doi          = {{10.1016/j.crmeth.2023.100419}},
  volume       = {{3}},
  year         = {{2023}},
}