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Updated: Jun 15, 2025

Gene Expression Profiling of Infecting Microbes Using a Digital Bar-coding Platform
Published on: January 13, 2016
An approach to analyze spatiotemporal patterns of gene expression at single-cell resolution in Candida
Elena Lindemann-Perez1, Diana L Rodríguez1, J Christian Pérez1
1Department of Microbiology and Molecular Genetics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Abstract:
Microbial gene expression measurements derived from infected organs are invaluable to understand pathogenesis. However, current methods are limited to "bulk" analyses that neglect microbial cell heterogeneity and the lesion's spatial architecture. Here, we report the use of hybridization chain reaction RNA fluorescence in situ hybridization (HCR RNA-FISH) to visualize and quantify Candida albicans transcripts at single-cell resolution in tongues of infected mice. The method is compatible with fixed-frozen and formalin-fixed paraffin-embedded tissues. We document cell-to-cell variation and intriguing spatiotemporal expression patterns for C. albicans mRNAs that encode products implicated in oral candidiasis. The approach provides a spatial dimension to gene expression analyses of host-Candida interactions.
Importance:
Candida albicans is a fungal pathobiont inhabiting multiple mucosal surfaces of the human body. Immunosuppression, antibiotic-induced microbial dysbiosis, or implanted medical devices can impair mucosal integrity enabling C. albicans to overgrow and disseminate, causing either mucosal diseases such as oropharyngeal candidiasis or life-threatening systemic infections. Profiling fungal genes that are expressed in the infected mucosa or in any other infected organ is paramount to understand pathogenesis. Ideally, these transcript profiling measurements should reveal the expression of any gene at the single-cell level. The resolution typically achieved with current approaches, however, limits most gene expression measurements to cell population averages. The approach described in this report provides a means to dissect fungal gene expression in infected tissues at single-cell resolution.
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