Inferring Viral Dynamics in Chronically HCV Infected Patients from the Spatial Distribution of Infected Hepatocytes

Frederik Graw, Ashwin Balagopal, Abraham Kandathil, Stuart Campbell Ray, David L Thomas, Ruy M. Ribeiro, Alan S. Perelson

Research output: Contribution to journalArticle

Abstract

Chronic liver infection by hepatitis C virus (HCV) is a major public health concern. Despite partly successful treatment options, several aspects of intrahepatic HCV infection dynamics are still poorly understood, including the preferred mode of viral propagation, as well as the proportion of infected hepatocytes. Answers to these questions have important implications for the development of therapeutic interventions. In this study, we present methods to analyze the spatial distribution of infected hepatocytes obtained by single cell laser capture microdissection from liver biopsy samples of patients chronically infected with HCV. By characterizing the internal structure of clusters of infected cells, we are able to evaluate hypotheses about intrahepatic infection dynamics. We found that individual clusters on biopsy samples range in size from (Formula presented.) infected cells. In addition, the HCV RNA content in a cluster declines from the cell that presumably founded the cluster to cells at the maximal cluster extension. These observations support the idea that HCV infection in the liver is seeded randomly (e.g. from the blood) and then spreads locally. Assuming that the amount of intracellular HCV RNA is a proxy for how long a cell has been infected, we estimate based on models of intracellular HCV RNA replication and accumulation that cells in clusters have been infected on average for less than a week. Further, we do not find a relationship between the cluster size and the estimated cluster expansion time. Our method represents a novel approach to make inferences about infection dynamics in solid tissues from static spatial data.

Original languageEnglish (US)
JournalPLoS Computational Biology
Volume10
Issue number11
DOIs
Publication statusPublished - Nov 1 2014

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ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Modeling and Simulation
  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Molecular Biology
  • Ecology
  • Cellular and Molecular Neuroscience

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