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Zath GK, Thomas MM, Loveday EK, Bikos DA, Sanche S, Ke R, Brooke CB, Chang CB. Influenza A viral burst size from thousands of infected single cells using droplet quantitative PCR (dqPCR). PLoS Pathog 2024; 20:e1012257. [PMID: 38950082 PMCID: PMC11244780 DOI: 10.1371/journal.ppat.1012257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 07/12/2024] [Accepted: 05/13/2024] [Indexed: 07/03/2024] Open
Abstract
An important aspect of how viruses spread and infect is the viral burst size, or the number of new viruses produced by each infected cell. Surprisingly, this value remains poorly characterized for influenza A virus (IAV), commonly known as the flu. In this study, we screened tens of thousands of cells using a microfluidic method called droplet quantitative PCR (dqPCR). The high-throughput capability of dqPCR enabled the measurement of a large population of infected cells producing progeny virus. By measuring the fully assembled and successfully released viruses from these infected cells, we discover that the viral burst sizes for both the seasonal H3N2 and the 2009 pandemic H1N1 strains vary significantly, with H3N2 ranging from 101 to 104 viruses per cell, and H1N1 ranging from 101 to 103 viruses per cell. Some infected cells produce average numbers of new viruses, while others generate extensive number of viruses. In fact, we find that only 10% of the single-cell infections are responsible for creating a significant portion of all the viruses. This small fraction produced approximately 60% of new viruses for H3N2 and 40% for H1N1. On average, each infected cell of the H3N2 flu strain produced 709 new viruses, whereas for H1N1, each infected cell produced 358 viruses. This novel method reveals insights into the flu virus and can lead to improved strategies for managing and preventing the spread of viruses.
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Affiliation(s)
- Geoffrey K. Zath
- Center for Biofilm Engineering, Montana State University, Bozeman, Montana, United States of America
- Department of Chemical and Biological Engineering, Montana State University, Bozeman, Montana, United States of America
| | - Mallory M. Thomas
- Center for Biofilm Engineering, Montana State University, Bozeman, Montana, United States of America
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, United States of America
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, Montana, United States of America
| | - Emma K. Loveday
- Center for Biofilm Engineering, Montana State University, Bozeman, Montana, United States of America
- Department of Chemical and Biological Engineering, Montana State University, Bozeman, Montana, United States of America
| | - Dimitri A. Bikos
- Center for Biofilm Engineering, Montana State University, Bozeman, Montana, United States of America
- Department of Chemical and Biological Engineering, Montana State University, Bozeman, Montana, United States of America
| | - Steven Sanche
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Ruian Ke
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Christopher B. Brooke
- Department of Microbiology, University of Illinois, Urbana-Champaign, Urbana, Illinois, United States of America
| | - Connie B. Chang
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, United States of America
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Paremskaia AI, Volchkov PY, Deviatkin AA. IAVCP (Influenza A Virus Consensus and Phylogeny): Automatic Identification of the Genomic Sequence of the Influenza A Virus from High-Throughput Sequencing Data. Viruses 2024; 16:873. [PMID: 38932165 PMCID: PMC11209090 DOI: 10.3390/v16060873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 04/27/2024] [Accepted: 05/28/2024] [Indexed: 06/28/2024] Open
Abstract
Recently, high-throughput sequencing of influenza A viruses has become a routine test. It should be noted that the extremely high diversity of the influenza A virus complicates the task of determining the sequences of all eight genome segments. For a fast and accurate analysis, it is necessary to select the most suitable reference for each segment. At the same time, there is no standardized method in the field of decoding sequencing results that allows the user to update the sequence databases to which the reads obtained by virus sequencing are compared. The IAVCP (influenza A virus consensus and phylogeny) was developed with the goal of automatically analyzing high-throughput sequencing data of influenza A viruses. Its goals include the extraction of a consensus genome directly from paired raw reads. In addition, the pipeline enables the identification of potential reassortment events in the evolutionary history of the virus of interest by analyzing the topological structure of phylogenetic trees that are automatically reconstructed.
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Affiliation(s)
- Anastasiia Iu. Paremskaia
- Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies, 125315 Moscow, Russia;
| | - Pavel Yu. Volchkov
- Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies, 125315 Moscow, Russia;
- Department of Fundamental Medicine, Lomonosov Moscow State University, 119992 Moscow, Russia
- The MCSC Named after A. S. Loginov, 111123 Moscow, Russia
| | - Andrei A. Deviatkin
- Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies, 125315 Moscow, Russia;
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 119992 Moscow, Russia
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Nan L, Zhang H, Weitz DA, Shum HC. Development and future of droplet microfluidics. LAB ON A CHIP 2024; 24:1135-1153. [PMID: 38165829 DOI: 10.1039/d3lc00729d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2024]
Abstract
Over the past two decades, advances in droplet-based microfluidics have facilitated new approaches to process and analyze samples with unprecedented levels of precision and throughput. A wide variety of applications has been inspired across multiple disciplines ranging from materials science to biology. Understanding the dynamics of droplets enables optimization of microfluidic operations and design of new techniques tailored to emerging demands. In this review, we discuss the underlying physics behind high-throughput generation and manipulation of droplets. We also summarize the applications in droplet-derived materials and droplet-based lab-on-a-chip biotechnology. In addition, we offer perspectives on future directions to realize wider use of droplet microfluidics in industrial production and biomedical analyses.
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Affiliation(s)
- Lang Nan
- Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong, China
| | - Huidan Zhang
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA
| | - David A Weitz
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong, China
| | - Ho Cheung Shum
- Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong, China
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Swaminath S, Russell AB. The use of single-cell RNA-seq to study heterogeneity at varying levels of virus-host interactions. PLoS Pathog 2024; 20:e1011898. [PMID: 38236826 PMCID: PMC10796064 DOI: 10.1371/journal.ppat.1011898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2024] Open
Abstract
The outcome of viral infection depends on the diversity of the infecting viral population and the heterogeneity of the cell population that is infected. Until almost a decade ago, the study of these dynamic processes during viral infection was challenging and limited to certain targeted measurements. Presently, with the use of single-cell sequencing technology, the complex interface defined by the interactions of cells with infecting virus can now be studied across the breadth of the transcriptome in thousands of individual cells simultaneously. In this review, we will describe the use of single-cell RNA sequencing (scRNA-seq) to study the heterogeneity of viral infections, ranging from individual virions to the immune response between infected individuals. In addition, we highlight certain key experimental limitations and methodological decisions that are critical to analyzing scRNA-seq data at each scale.
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Affiliation(s)
- Sharmada Swaminath
- School of Biological Sciences, University of California, San Diego, La Jolla, California, United States of America
| | - Alistair B. Russell
- School of Biological Sciences, University of California, San Diego, La Jolla, California, United States of America
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