Machine Learning in Single-Molecule Tracking Analysis of Superresolution Optical Microscopy Data

Autores
Saavedra, Lucas A.; Barrantes, Francisco José
Año de publicación
2026
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Fil: Saavedra, Lucas A. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas; Argentina
Fil: Barrantes, Francisco J. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas; Argentina
Machine learning (ML) is transforming the analysis of biomolecular data, holding significant promise for improving the efficiency and accuracy of microscopy image analysis and for studying the dynamics of molecules in live cells. As data-driven approaches continue to evolve, they may eventually replace traditional statistical methods that rely on conventional analytical methods. This review examines and critically analyses the state of the art of ML techniques as applied to various levels of data supervision in the analysis of dynamic single-molecule datasets obtained using superresolution optical microscopy. Collectively encompassed under the umbrella of “nanoscopy”, these methods currently comprise targeted techniques such as stimulated emission depletion (STED) microscopy and stochastic techniques like single-molecule localization microscopies (SMLMs), comprising photoactivated localization microscopy (PALM), DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) microscopy, and minimal fluorescence photon flux (MINFLUX) microscopy. These techniques all enable the imaging of subcellular components and molecules beyond the diffraction limit, and some are additionally capable of studying their dynamics in real time, as reviewed here, using several ML techniques that facilitate motion analysis in two or three dimensions with qualitative and quantitative characterisation in the live cell. It is expected that the growing use of learning-based approaches in biological microscopy data processing will dramatically increase throughput and accelerate progress in this rapidly developing field.
Fuente
Cells 2026, 15(8), 686
Materia
MACHINE LEARNING
MOLECULAS
FISICA
CELULAS
MICROSCOPIA
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
Repositorio Institucional (UCA)
Institución
Pontificia Universidad Católica Argentina
OAI Identificador
oai:ucacris:123456789/21919
Acceder
id RIUCA_14e11e95e6e35e4922bfd3da31343a04
oai_identifier_str oai:ucacris:123456789/21919
network_acronym_str RIUCA
repository_id_str 2585
network_name_str Repositorio Institucional (UCA)
spelling Machine Learning in Single-Molecule Tracking Analysis of Superresolution Optical Microscopy DataSaavedra, Lucas A.Barrantes, Francisco JoséMACHINE LEARNINGMOLECULASFISICACELULASMICROSCOPIAFil: Saavedra, Lucas A. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas; ArgentinaFil: Barrantes, Francisco J. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas; ArgentinaMachine learning (ML) is transforming the analysis of biomolecular data, holding significant promise for improving the efficiency and accuracy of microscopy image analysis and for studying the dynamics of molecules in live cells. As data-driven approaches continue to evolve, they may eventually replace traditional statistical methods that rely on conventional analytical methods. This review examines and critically analyses the state of the art of ML techniques as applied to various levels of data supervision in the analysis of dynamic single-molecule datasets obtained using superresolution optical microscopy. Collectively encompassed under the umbrella of “nanoscopy”, these methods currently comprise targeted techniques such as stimulated emission depletion (STED) microscopy and stochastic techniques like single-molecule localization microscopies (SMLMs), comprising photoactivated localization microscopy (PALM), DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) microscopy, and minimal fluorescence photon flux (MINFLUX) microscopy. These techniques all enable the imaging of subcellular components and molecules beyond the diffraction limit, and some are additionally capable of studying their dynamics in real time, as reviewed here, using several ML techniques that facilitate motion analysis in two or three dimensions with qualitative and quantitative characterisation in the live cell. It is expected that the growing use of learning-based approaches in biological microscopy data processing will dramatically increase throughput and accelerate progress in this rapidly developing field.MDPI2026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttps://repositorio.uca.edu.ar/handle/123456789/219192073-440910.3390/cells15080686Cells 2026, 15(8), 686reponame:Repositorio Institucional (UCA)instname:Pontificia Universidad Católica Argentinaenginfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/4.0/2026-11-05T12:34:21Zoai:ucacris:123456789/21919instacron:UCAInstitucionalhttps://repositorio.uca.edu.ar/Universidad privadaNo correspondehttps://repositorio.uca.edu.ar/oaiclaudia_fernandez@uca.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:25852026-11-05 12:34:21.421Repositorio Institucional (UCA) - Pontificia Universidad Católica Argentinafalse
dc.title.none.fl_str_mv Machine Learning in Single-Molecule Tracking Analysis of Superresolution Optical Microscopy Data
title Machine Learning in Single-Molecule Tracking Analysis of Superresolution Optical Microscopy Data
spellingShingle Machine Learning in Single-Molecule Tracking Analysis of Superresolution Optical Microscopy Data
Saavedra, Lucas A.
MACHINE LEARNING
MOLECULAS
FISICA
CELULAS
MICROSCOPIA
title_short Machine Learning in Single-Molecule Tracking Analysis of Superresolution Optical Microscopy Data
title_full Machine Learning in Single-Molecule Tracking Analysis of Superresolution Optical Microscopy Data
title_fullStr Machine Learning in Single-Molecule Tracking Analysis of Superresolution Optical Microscopy Data
title_full_unstemmed Machine Learning in Single-Molecule Tracking Analysis of Superresolution Optical Microscopy Data
title_sort Machine Learning in Single-Molecule Tracking Analysis of Superresolution Optical Microscopy Data
dc.creator.none.fl_str_mv Saavedra, Lucas A.
Barrantes, Francisco José
author Saavedra, Lucas A.
author_facet Saavedra, Lucas A.
Barrantes, Francisco José
author_role author
author2 Barrantes, Francisco José
author2_role author
dc.subject.none.fl_str_mv MACHINE LEARNING
MOLECULAS
FISICA
CELULAS
MICROSCOPIA
topic MACHINE LEARNING
MOLECULAS
FISICA
CELULAS
MICROSCOPIA
dc.description.none.fl_txt_mv Fil: Saavedra, Lucas A. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas; Argentina
Fil: Barrantes, Francisco J. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas; Argentina
Machine learning (ML) is transforming the analysis of biomolecular data, holding significant promise for improving the efficiency and accuracy of microscopy image analysis and for studying the dynamics of molecules in live cells. As data-driven approaches continue to evolve, they may eventually replace traditional statistical methods that rely on conventional analytical methods. This review examines and critically analyses the state of the art of ML techniques as applied to various levels of data supervision in the analysis of dynamic single-molecule datasets obtained using superresolution optical microscopy. Collectively encompassed under the umbrella of “nanoscopy”, these methods currently comprise targeted techniques such as stimulated emission depletion (STED) microscopy and stochastic techniques like single-molecule localization microscopies (SMLMs), comprising photoactivated localization microscopy (PALM), DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) microscopy, and minimal fluorescence photon flux (MINFLUX) microscopy. These techniques all enable the imaging of subcellular components and molecules beyond the diffraction limit, and some are additionally capable of studying their dynamics in real time, as reviewed here, using several ML techniques that facilitate motion analysis in two or three dimensions with qualitative and quantitative characterisation in the live cell. It is expected that the growing use of learning-based approaches in biological microscopy data processing will dramatically increase throughput and accelerate progress in this rapidly developing field.
description Fil: Saavedra, Lucas A. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas; Argentina
publishDate 2026
dc.date.none.fl_str_mv 2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://repositorio.uca.edu.ar/handle/123456789/21919
2073-4409
10.3390/cells15080686
url https://repositorio.uca.edu.ar/handle/123456789/21919
identifier_str_mv 2073-4409
10.3390/cells15080686
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv Cells 2026, 15(8), 686
reponame:Repositorio Institucional (UCA)
instname:Pontificia Universidad Católica Argentina
reponame_str Repositorio Institucional (UCA)
collection Repositorio Institucional (UCA)
instname_str Pontificia Universidad Católica Argentina
repository.name.fl_str_mv Repositorio Institucional (UCA) - Pontificia Universidad Católica Argentina
repository.mail.fl_str_mv claudia_fernandez@uca.edu.ar
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score 12.823554

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