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Data-driven microscopy allows for automated targeted acquisition of relevant data with higher fidelity

André, Oscar LU ; Kumra Ahnlide, Johannes LU orcid ; Norlin, Nils LU ; Swaminathan, Vinay LU and Nordenfelt, Pontus LU orcid (2022)
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 population-wide cell characterization to enable data-driven high-fidelity imaging of relevant phenotypes. DDM combines data-independent and data-dependent steps to synergistically enhance data acquired using different imaging modalities. As proof-of-concept, we apply DDM with plugins for improved high-content screening and live adaptive microscopy.... (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 population-wide cell characterization to enable data-driven high-fidelity imaging of relevant phenotypes. DDM combines data-independent and data-dependent steps to synergistically enhance data acquired using different imaging modalities. As proof-of-concept, we apply DDM with plugins for improved high-content screening and live adaptive microscopy. DDM also allows for easy correlative imaging in other systems with a plugin that uses the spatial relationship of the sample population for automated registration. We believe DDM will be a valuable approach for reducing human bias, increasing reproducibility, and placing singlecell characteristics in the context of the sample population when interpreting microscopy data, leading to an overall increase in data fidelity. (Less)
Abstract (Swedish)
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 population-wide cell characterization to enable data-driven high-fidelity imaging of relevant phenotypes. DDM combines data-independent and data-dependent steps to synergistically enhance data acquired using different imaging modalities. As proof-of-concept, we apply DDM with plugins for improved high-content screening and live adaptive microscopy.... (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 population-wide cell characterization to enable data-driven high-fidelity imaging of relevant phenotypes. DDM combines data-independent and data-dependent steps to synergistically enhance data acquired using different imaging modalities. As proof-of-concept, we apply DDM with plugins for improved high-content screening and live adaptive microscopy. DDM also allows for easy correlative imaging in other systems with a plugin that uses the spatial relationship of the sample population for automated registration. We believe DDM will be a valuable approach for reducing human bias, increasing reproducibility, and placing singlecell characteristics in the context of the sample population when interpreting microscopy data, leading to an overall increase in data fidelity. (Less)
Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Working paper/Preprint
publication status
published
subject
keywords
mikroskopi
publisher
bioRxiv
DOI
10.1101/2022.05.09.491153
language
English
LU publication?
yes
additional info
https://doi.org/10.1101/2022.05.09.491153
id
f3eb74b7-e56f-46ab-a544-93bd83ed0f93
date added to LUP
2022-12-10 16:32:16
date last changed
2023-05-15 09:29:34
@misc{f3eb74b7-e56f-46ab-a544-93bd83ed0f93,
  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 population-wide cell characterization to enable data-driven high-fidelity imaging of relevant phenotypes. DDM combines data-independent and data-dependent steps to synergistically enhance data acquired using different imaging modalities. As proof-of-concept, we apply DDM with plugins for improved high-content screening and live adaptive microscopy. DDM also allows for easy correlative imaging in other systems with a plugin that uses the spatial relationship of the sample population for automated registration. We believe DDM will be a valuable approach for reducing human bias, increasing reproducibility, and placing singlecell characteristics in the context of the sample population when interpreting microscopy data, leading to an overall increase in data fidelity.}},
  author       = {{André, Oscar and Kumra Ahnlide, Johannes and Norlin, Nils and Swaminathan, Vinay and Nordenfelt, Pontus}},
  keywords     = {{mikroskopi}},
  language     = {{eng}},
  month        = {{06}},
  note         = {{Preprint}},
  publisher    = {{bioRxiv}},
  title        = {{Data-driven microscopy allows for automated targeted acquisition of relevant data with higher fidelity}},
  url          = {{http://dx.doi.org/10.1101/2022.05.09.491153}},
  doi          = {{10.1101/2022.05.09.491153}},
  year         = {{2022}},
}