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Article: Quantitative phase imaging flow cytometry for ultra-large-scale single-cell biophysical phenotyping

TitleQuantitative phase imaging flow cytometry for ultra-large-scale single-cell biophysical phenotyping
Authors
Keywordsimaging flow cytometry
label-free biophysical phenotyping
quantitative phase imaging
ultrafast single cell imaging
Issue Date2019
PublisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0196-4763/
Citation
Cytometry Part A, 2019, v. 95 n. 5, p. 510-520 How to Cite?
AbstractCellular biophysical properties are the effective label-free phenotypes indicative of differences in cell types, states, and functions. However, current biophysical phenotyping methods largely lack the throughput and specificity required in the majority of cell-based assays that involve large-scale single-cell characterization for inquiring the inherently complex heterogeneity in many biological systems. Further confounded by the lack of reported robust reproducibility and quality control, widespread adoption of single-cell biophysical phenotyping in mainstream cytometry remains elusive. To address this challenge, here we present a label-free imaging flow cytometer built upon a recently developed ultrafast quantitative phase imaging (QPI) technique, coined multi-ATOM, that enables label-free single-cell QPI, from which a multitude of subcellularly resolvable biophysical phenotypes can be parametrized, at an experimentally recorded throughput of >10,000 cells/s—a capability that is otherwise inaccessible in current QPI. With the aim to translate multi-ATOM into mainstream cytometry, we report robust system calibration and validation (from image acquisition to phenotyping reproducibility) and thus demonstrate its ability to establish high-dimensional single-cell biophysical phenotypic profiles at ultra-large-scale (>1,000,000 cells). Such a combination of throughput and content offers sufficiently high label-free statistical power to classify multiple human leukemic cell types at high accuracy (~92–97%). This system could substantiate the significance of high-throughput QPI flow cytometry in enabling next frontier in large-scale image-derived single-cell analysis applied in biological discovery and cost-effective clinical diagnostics. © 2019 International Society for Advancement of Cytometry. © 2019 International Society for Advancement of Cytometry
Persistent Identifierhttp://hdl.handle.net/10722/276219
ISSN
2017 Impact Factor: 3.26
2015 SCImago Journal Rankings: 1.630

 

DC FieldValueLanguage
dc.contributor.authorLee, CM-
dc.contributor.authorWang, M-
dc.contributor.authorCheah, KSE-
dc.contributor.authorChan, GCF-
dc.contributor.authorSo, HKH-
dc.contributor.authorWong, KKY-
dc.contributor.authorTsia, KKM-
dc.date.accessioned2019-09-10T02:58:24Z-
dc.date.available2019-09-10T02:58:24Z-
dc.date.issued2019-
dc.identifier.citationCytometry Part A, 2019, v. 95 n. 5, p. 510-520-
dc.identifier.issn1552-4922-
dc.identifier.urihttp://hdl.handle.net/10722/276219-
dc.description.abstractCellular biophysical properties are the effective label-free phenotypes indicative of differences in cell types, states, and functions. However, current biophysical phenotyping methods largely lack the throughput and specificity required in the majority of cell-based assays that involve large-scale single-cell characterization for inquiring the inherently complex heterogeneity in many biological systems. Further confounded by the lack of reported robust reproducibility and quality control, widespread adoption of single-cell biophysical phenotyping in mainstream cytometry remains elusive. To address this challenge, here we present a label-free imaging flow cytometer built upon a recently developed ultrafast quantitative phase imaging (QPI) technique, coined multi-ATOM, that enables label-free single-cell QPI, from which a multitude of subcellularly resolvable biophysical phenotypes can be parametrized, at an experimentally recorded throughput of >10,000 cells/s—a capability that is otherwise inaccessible in current QPI. With the aim to translate multi-ATOM into mainstream cytometry, we report robust system calibration and validation (from image acquisition to phenotyping reproducibility) and thus demonstrate its ability to establish high-dimensional single-cell biophysical phenotypic profiles at ultra-large-scale (>1,000,000 cells). Such a combination of throughput and content offers sufficiently high label-free statistical power to classify multiple human leukemic cell types at high accuracy (~92–97%). This system could substantiate the significance of high-throughput QPI flow cytometry in enabling next frontier in large-scale image-derived single-cell analysis applied in biological discovery and cost-effective clinical diagnostics. © 2019 International Society for Advancement of Cytometry. © 2019 International Society for Advancement of Cytometry-
dc.languageeng-
dc.publisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0196-4763/-
dc.relation.ispartofCytometry Part A-
dc.rightsPreprint This is the pre-peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. Postprint This is the peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.-
dc.subjectimaging flow cytometry-
dc.subjectlabel-free biophysical phenotyping-
dc.subjectquantitative phase imaging-
dc.subjectultrafast single cell imaging-
dc.titleQuantitative phase imaging flow cytometry for ultra-large-scale single-cell biophysical phenotyping-
dc.typeArticle-
dc.identifier.emailCheah, KSE: hrmbdkc@hku.hk-
dc.identifier.emailChan, GCF: gcfchan@hku.hk-
dc.identifier.emailSo, HKH: hso@eee.hku.hk-
dc.identifier.emailWong, KKY: kywong@eee.hku.hk-
dc.identifier.emailTsia, KKM: tsia@hku.hk-
dc.identifier.authorityCheah, KSE=rp00342-
dc.identifier.authorityChan, GCF=rp00431-
dc.identifier.authoritySo, HKH=rp00169-
dc.identifier.authorityWong, KKY=rp00189-
dc.identifier.authorityTsia, KKM=rp01389-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/cyto.a.23765-
dc.identifier.pmid31012276-
dc.identifier.scopuseid_2-s2.0-85064716554-
dc.identifier.hkuros303379-
dc.identifier.volume95-
dc.identifier.issue5-
dc.identifier.spage510-
dc.identifier.epage520-
dc.publisher.placeUnited States-

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