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Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning

A plaque assay—the gold-standard method for measuring the concentration of replication-competent lytic virions—requires staining and usually more than 48 h of runtime. Here we show that lens-free holographic imaging and deep learning can be combined to expedite and automate the assay. The compact im...

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Autores principales: Liu, Tairan, Li, Yuzhu, Koydemir, Hatice Ceylan, Zhang, Yijie, Yang, Ethan, Eryilmaz, Merve, Wang, Hongda, Li, Jingxi, Bai, Bijie, Ma, Guangdong, Ozcan, Aydogan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10427422/
https://www.ncbi.nlm.nih.gov/pubmed/37349390
http://dx.doi.org/10.1038/s41551-023-01057-7
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author Liu, Tairan
Li, Yuzhu
Koydemir, Hatice Ceylan
Zhang, Yijie
Yang, Ethan
Eryilmaz, Merve
Wang, Hongda
Li, Jingxi
Bai, Bijie
Ma, Guangdong
Ozcan, Aydogan
author_facet Liu, Tairan
Li, Yuzhu
Koydemir, Hatice Ceylan
Zhang, Yijie
Yang, Ethan
Eryilmaz, Merve
Wang, Hongda
Li, Jingxi
Bai, Bijie
Ma, Guangdong
Ozcan, Aydogan
author_sort Liu, Tairan
collection PubMed
description A plaque assay—the gold-standard method for measuring the concentration of replication-competent lytic virions—requires staining and usually more than 48 h of runtime. Here we show that lens-free holographic imaging and deep learning can be combined to expedite and automate the assay. The compact imaging device captures phase information label-free at a rate of approximately 0.32 gigapixels per hour per well, covers an area of about 30 × 30 mm(2) and a 10-fold larger dynamic range of virus concentration than standard assays, and quantifies the infected area and the number of plaque-forming units. For the vesicular stomatitis virus, the automated plaque assay detected the first cell-lysing events caused by viral replication as early as 5 h after incubation, and in less than 20 h it detected plaque-forming units at rates higher than 90% at 100% specificity. Furthermore, it reduced the incubation time of the herpes simplex virus type 1 by about 48 h and that of the encephalomyocarditis virus by about 20 h. The stain-free assay should be amenable for use in virology research, vaccine development and clinical diagnosis.
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spelling pubmed-104274222023-08-17 Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning Liu, Tairan Li, Yuzhu Koydemir, Hatice Ceylan Zhang, Yijie Yang, Ethan Eryilmaz, Merve Wang, Hongda Li, Jingxi Bai, Bijie Ma, Guangdong Ozcan, Aydogan Nat Biomed Eng Article A plaque assay—the gold-standard method for measuring the concentration of replication-competent lytic virions—requires staining and usually more than 48 h of runtime. Here we show that lens-free holographic imaging and deep learning can be combined to expedite and automate the assay. The compact imaging device captures phase information label-free at a rate of approximately 0.32 gigapixels per hour per well, covers an area of about 30 × 30 mm(2) and a 10-fold larger dynamic range of virus concentration than standard assays, and quantifies the infected area and the number of plaque-forming units. For the vesicular stomatitis virus, the automated plaque assay detected the first cell-lysing events caused by viral replication as early as 5 h after incubation, and in less than 20 h it detected plaque-forming units at rates higher than 90% at 100% specificity. Furthermore, it reduced the incubation time of the herpes simplex virus type 1 by about 48 h and that of the encephalomyocarditis virus by about 20 h. The stain-free assay should be amenable for use in virology research, vaccine development and clinical diagnosis. Nature Publishing Group UK 2023-06-22 2023 /pmc/articles/PMC10427422/ /pubmed/37349390 http://dx.doi.org/10.1038/s41551-023-01057-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Liu, Tairan
Li, Yuzhu
Koydemir, Hatice Ceylan
Zhang, Yijie
Yang, Ethan
Eryilmaz, Merve
Wang, Hongda
Li, Jingxi
Bai, Bijie
Ma, Guangdong
Ozcan, Aydogan
Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning
title Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning
title_full Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning
title_fullStr Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning
title_full_unstemmed Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning
title_short Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning
title_sort rapid and stain-free quantification of viral plaque via lens-free holography and deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10427422/
https://www.ncbi.nlm.nih.gov/pubmed/37349390
http://dx.doi.org/10.1038/s41551-023-01057-7
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