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

Journal: Nature biomedical engineering
PMID: 37349390

Abstract

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 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.

Authors

  • Tairan Liu
    Department of Mechanical Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA.
  • Yuzhu Li
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Hatice Ceylan Koydemir
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Yijie Zhang
    Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, China; School of Psychology, South China Normal University, Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China. Electronic address: fanfandez@163.com.
  • Ethan Yang
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Merve Eryilmaz
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Hongda Wang
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Jingxi Li
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Bijie Bai
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Guangdong Ma
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Aydogan Ozcan
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA.

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