Understanding health and disease with multidimensional single-cell methods

Autores
Candia, Julián Marcelo; Banavar, Jayanth R; Losert, Wolfgang
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Current efforts in the biomedical sciences and related interdisciplinary fields are focused ongaining a molecular understanding of health and disease, which is a problem of dauntingcomplexity that spans many orders of magnitude in characteristic length scales, from smallmolecules that regulate cell function to cell ensembles that form tissues and organs workingtogether as an organism. In order to uncover the molecular nature of the emergent properties of acell, it is essential to measure multiple cell components simultaneously in the same cell. In turn,cell heterogeneity requires multiple cells to be measured in order to understand health and diseasein the organism. This review summarizes current efforts towards a data-driven framework thatleverages single-cell technologies to build robust signatures of healthy and diseased phenotypes.While some approaches focus on multicolor flow cytometry data and other methods are designedto analyze high-content image-based screens, we emphasize the so-called Supercell/SVMparadigm (recently developed by the authors of this review and collaborators) as a unifiedframework that captures mesoscopic-scale emergence to build reliable phenotypes. Beyond theirspecific contributions to basic and translational biomedical research, these efforts illustrate, from alarger perspective, the powerful synergy that might be achieved from bringing together methodsand ideas from statistical physics, data mining, and mathematics to solve the most pressingproblems currently facing the life sciences.
Fil: Candia, Julián Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina. University of Maryland; Estados Unidos
Fil: Banavar, Jayanth R. University of Maryland; Estados Unidos
Fil: Losert, Wolfgang. University of Maryland; Estados Unidos
Materia
Single-Cell Biophysics
Complexity
Flow Cytometry
Cell Imaging
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/33172
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Understanding health and disease with multidimensional single-cell methodsCandia, Julián MarceloBanavar, Jayanth RLosert, WolfgangSingle-Cell BiophysicsComplexityFlow CytometryCell Imaginghttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Current efforts in the biomedical sciences and related interdisciplinary fields are focused ongaining a molecular understanding of health and disease, which is a problem of dauntingcomplexity that spans many orders of magnitude in characteristic length scales, from smallmolecules that regulate cell function to cell ensembles that form tissues and organs workingtogether as an organism. In order to uncover the molecular nature of the emergent properties of acell, it is essential to measure multiple cell components simultaneously in the same cell. In turn,cell heterogeneity requires multiple cells to be measured in order to understand health and diseasein the organism. This review summarizes current efforts towards a data-driven framework thatleverages single-cell technologies to build robust signatures of healthy and diseased phenotypes.While some approaches focus on multicolor flow cytometry data and other methods are designedto analyze high-content image-based screens, we emphasize the so-called Supercell/SVMparadigm (recently developed by the authors of this review and collaborators) as a unifiedframework that captures mesoscopic-scale emergence to build reliable phenotypes. Beyond theirspecific contributions to basic and translational biomedical research, these efforts illustrate, from alarger perspective, the powerful synergy that might be achieved from bringing together methodsand ideas from statistical physics, data mining, and mathematics to solve the most pressingproblems currently facing the life sciences.Fil: Candia, Julián Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina. University of Maryland; Estados UnidosFil: Banavar, Jayanth R. University of Maryland; Estados UnidosFil: Losert, Wolfgang. University of Maryland; Estados UnidosIOP Publishing2014-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/33172Candia, Julián Marcelo; Banavar, Jayanth R; Losert, Wolfgang; Understanding health and disease with multidimensional single-cell methods; IOP Publishing; Journal of Physics: Condensed Matter; 26; 7; 1-2014; 1-210953-8984CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1088/0953-8984/26/7/073102info:eu-repo/semantics/altIdentifier/url/http://iopscience.iop.org/article/10.1088/0953-8984/26/7/073102/metainfo:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4020281/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:57:42Zoai:ri.conicet.gov.ar:11336/33172instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:57:42.841CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Understanding health and disease with multidimensional single-cell methods
title Understanding health and disease with multidimensional single-cell methods
spellingShingle Understanding health and disease with multidimensional single-cell methods
Candia, Julián Marcelo
Single-Cell Biophysics
Complexity
Flow Cytometry
Cell Imaging
title_short Understanding health and disease with multidimensional single-cell methods
title_full Understanding health and disease with multidimensional single-cell methods
title_fullStr Understanding health and disease with multidimensional single-cell methods
title_full_unstemmed Understanding health and disease with multidimensional single-cell methods
title_sort Understanding health and disease with multidimensional single-cell methods
dc.creator.none.fl_str_mv Candia, Julián Marcelo
Banavar, Jayanth R
Losert, Wolfgang
author Candia, Julián Marcelo
author_facet Candia, Julián Marcelo
Banavar, Jayanth R
Losert, Wolfgang
author_role author
author2 Banavar, Jayanth R
Losert, Wolfgang
author2_role author
author
dc.subject.none.fl_str_mv Single-Cell Biophysics
Complexity
Flow Cytometry
Cell Imaging
topic Single-Cell Biophysics
Complexity
Flow Cytometry
Cell Imaging
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Current efforts in the biomedical sciences and related interdisciplinary fields are focused ongaining a molecular understanding of health and disease, which is a problem of dauntingcomplexity that spans many orders of magnitude in characteristic length scales, from smallmolecules that regulate cell function to cell ensembles that form tissues and organs workingtogether as an organism. In order to uncover the molecular nature of the emergent properties of acell, it is essential to measure multiple cell components simultaneously in the same cell. In turn,cell heterogeneity requires multiple cells to be measured in order to understand health and diseasein the organism. This review summarizes current efforts towards a data-driven framework thatleverages single-cell technologies to build robust signatures of healthy and diseased phenotypes.While some approaches focus on multicolor flow cytometry data and other methods are designedto analyze high-content image-based screens, we emphasize the so-called Supercell/SVMparadigm (recently developed by the authors of this review and collaborators) as a unifiedframework that captures mesoscopic-scale emergence to build reliable phenotypes. Beyond theirspecific contributions to basic and translational biomedical research, these efforts illustrate, from alarger perspective, the powerful synergy that might be achieved from bringing together methodsand ideas from statistical physics, data mining, and mathematics to solve the most pressingproblems currently facing the life sciences.
Fil: Candia, Julián Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina. University of Maryland; Estados Unidos
Fil: Banavar, Jayanth R. University of Maryland; Estados Unidos
Fil: Losert, Wolfgang. University of Maryland; Estados Unidos
description Current efforts in the biomedical sciences and related interdisciplinary fields are focused ongaining a molecular understanding of health and disease, which is a problem of dauntingcomplexity that spans many orders of magnitude in characteristic length scales, from smallmolecules that regulate cell function to cell ensembles that form tissues and organs workingtogether as an organism. In order to uncover the molecular nature of the emergent properties of acell, it is essential to measure multiple cell components simultaneously in the same cell. In turn,cell heterogeneity requires multiple cells to be measured in order to understand health and diseasein the organism. This review summarizes current efforts towards a data-driven framework thatleverages single-cell technologies to build robust signatures of healthy and diseased phenotypes.While some approaches focus on multicolor flow cytometry data and other methods are designedto analyze high-content image-based screens, we emphasize the so-called Supercell/SVMparadigm (recently developed by the authors of this review and collaborators) as a unifiedframework that captures mesoscopic-scale emergence to build reliable phenotypes. Beyond theirspecific contributions to basic and translational biomedical research, these efforts illustrate, from alarger perspective, the powerful synergy that might be achieved from bringing together methodsand ideas from statistical physics, data mining, and mathematics to solve the most pressingproblems currently facing the life sciences.
publishDate 2014
dc.date.none.fl_str_mv 2014-01
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 http://hdl.handle.net/11336/33172
Candia, Julián Marcelo; Banavar, Jayanth R; Losert, Wolfgang; Understanding health and disease with multidimensional single-cell methods; IOP Publishing; Journal of Physics: Condensed Matter; 26; 7; 1-2014; 1-21
0953-8984
CONICET Digital
CONICET
url http://hdl.handle.net/11336/33172
identifier_str_mv Candia, Julián Marcelo; Banavar, Jayanth R; Losert, Wolfgang; Understanding health and disease with multidimensional single-cell methods; IOP Publishing; Journal of Physics: Condensed Matter; 26; 7; 1-2014; 1-21
0953-8984
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1088/0953-8984/26/7/073102
info:eu-repo/semantics/altIdentifier/url/http://iopscience.iop.org/article/10.1088/0953-8984/26/7/073102/meta
info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4020281/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv IOP Publishing
publisher.none.fl_str_mv IOP Publishing
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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