Researchers have revealed the potential usage of cytometric fingerprints of gut microbiota as a diagnostic tool to predict Crohn’s disease state.
Characterising gut microbiota
Evidence has linked variations in the gut microbiome to changes in health state. This includes obesity, inflammatory bowel disease and diabetes. Typically, researchers analyse the 16S rRNA gene in order to characterise the microbiome. Sequence analysis is becoming the mainstay in diagnosis and treatment-led management, with microbiome analysis showing great promise in contributing to precision medicine. Nevertheless, sequencing-based methods are still time consuming and budget limited.
Flow cytometry is a single-cell technology which researchers often apply to microbial communities to record the morphological and physiological characteristics of every cell. The aggregation of these characteristics describes the status of a microbial community. This can then be summarised by generating a cytometric fingerprint. A cytometric fingerprint can quantify community dynamics and has potential usage as a diagnostic tool to rapidly identify microbiome-associated diseases.
Flow cytometry vs. 16S rRNA
In a study, published in The ISME Journal, researchers reanalysed data of a disease cohort containing samples from patients diagnosed with Crohn’s disease and a healthy control group. The team analysed all the samples independently using both flow cytometry and 16S rRNA gene amplicon sequencing. The original study demonstrated, using 16S rRNA gene sequencing, a clear difference in microbial communities between the two groups. Therefore, in this recent study, researchers sought to demonstrate that they could also reflect these differences in the cytometry data. In addition, they wanted to compare the prediction power of both these technologies. The researchers used an adaptive cytometric fingerprinting strategy to cluster individual cells into operational groups.
The team found both richness and evenness of gut microbiota were significantly lower for Crohn’s disease patients than healthy control samples. They also assessed which cytometric groups captured significant changes according to the disease state. They found that 132 contained significantly more cell counts for Crohn’s than healthy controls, while 103 groups contained significantly more cell counts for healthy controls than Crohn’s. The location of these groups revealed a clear structure.
Cytometric fingerprints captured the structural differences in microbial community composition between the two groups. The team observed a consistent outcome for both approaches. However, they do emphasise that flow cytometry is becoming a vital part of clinical diagnostics. Therefore, its application to the human microbiome is readily available. Although flow cytometry does not provide genetic information about the microbial community, the team believe it can offer rapid and affordable screening for microbiome-associated diseases.
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