A.S. Dhoble, K.D. Bhalerao
University of Illinois at Urbana-Champaign,
Keywords: Microbiome, Animal Systems
Summary:Perturbations in the structure and function of a microbiome are now identified as an important step in the etiology of infectious diseases. There is a recognized need to develop advanced characterization tools to understand the key players in our microbial ecosystems. Even though next generation sequencing (NGS) technologies have made genomic and metagenomic approaches more accessible, these approaches are not high throughput enough to resolve dynamic changes in the structure of the microbiome over time due to labor required in sample processing. Flow cytometry can provide this information in a high throughput manner since it is fast, inline, automated, permits sample labeling, requires small sample volumes and minimal sample preparation. Furthermore, it requires low capital investment, has low per-sample cost, and produces rich high-dimensional information and is capable of sorting and classifying a sample. A novel multidimensional flow cytometry based method has been demonstrated to monitor and rapidly characterize the dynamics of a model of animal gut microbiomes resulting from perturbations in external environmental factors. Autocorrelation analysis between diverging microbial communities, exploiting multiple measurable dimensions in flow cytometry such as cell size (FSC or forward scatter), cell granularity/morphology (SSC or side scatter) and autofluorescence (corresponding to the same excitation/emission wavelength as in AmCyan standard dye), can be used as a simple and rapid tool to monitor perturbations in complex anaerobic microbiome due to addition of various carbon sources and nanoparticles. Further, it is also possible to quantitatively discriminate between divergent microbiomes, in a manner analogous to community fingerprinting techniques using automated ribosomal intergenic spacer analysis (ARISA). Applications of the proposed methodology have been demonstrated in the high value threat to the US dairy industry i.e. bovine mastitis. Initial studies indicate that proposed multidimensional flow cytometry based method can potentially detect and classify (type) subclinical mastitis, somatic cell count and the differential inflammatory cell count along with the quantification of the portion of viable, apoptotic, and necrotic leukocytes isolated from the milk samples infected with different mastitis pathogens. Clear identification of the pathogen, and the number, function and viability of milk leukocytes will pave the way for high throughput microbiological milk quality evaluation and may be able to inform appropriate antibiotic intervention strategies in dairy farms.