Data-driven microscopy allows for automated context-specific acquisition of high-fidelity image data
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/ceeb5680-2c3e-489f-8e08-fe9b6aa41155
- author
- André, Oscar LU ; Kumra Ahnlide, Johannes LU ; Norlin, Nils LU ; Swaminathan, Vinay LU and Nordenfelt, Pontus LU
- organization
-
- Quantitative immunobiology (research group)
- LUCC: Lund University Cancer Centre
- LTH Profile Area: Engineering Health
- Lund University Bioimaging Center
- Molecular Neuromodulation (research group)
- MultiPark: Multidisciplinary research focused on Parkinson´s disease
- LU Profile Area: Light and Materials
- LTH Profile Area: Photon Science and Technology
- NanoLund: Centre for Nanoscience
- Cell mechanobiology
- WCMM-Wallenberg Centre for Molecular Medicine
- LTH Profile Area: Nanoscience and Semiconductor Technology
- epIgG (research group)
- SEBRA Sepsis and Bacterial Resistance Alliance (research group)
- publishing date
- 2023
- 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}}, }