Fluorescence-based spectrometric and imaging methods and machine learning analyses for microbiota analysis

Most microbiota determination (skin, gut, soil, etc.) are currently conducted in a laboratory using expensive equipment and lengthy procedures, including culture-dependent methods, nucleic acid amplifications (including quantitative PCR), DNA microarray, immunoassays, 16S rRNA sequencing, shotgun metagenomics, and sophisticated mass spectrometric methods. In situ and rapid analysis methods are desirable for fast turnaround time and low assay cost. Fluorescence identification of bacteria and their mixtures is emerging to meet this demand, thanks to the recent development in various machine learning methods. High-dimensional spectroscopic or microscopic imaging data can be obtained to identify the bacterial makeup and its implications for human health and the environment. For example, we can classify healthy versus non-healthy skin microbiome, inflammatory versus non-inflammatory gut microbiome, degraded versus non-degraded soil microbiome, etc. This tutorial summarizes the various machine-learning algorithms used in bacteria identification and microbiota determinations. It also summarizes the various fluorescence spectroscopic methods used to identify bacteria and their mixtures, including fluorescence lifetime spectroscopy, fluorescence resonance energy transfer (FRET), and synchronous fluorescence (SF) spectroscopy. Finally, various fluorescence microscopic imaging methods were summarized that have been used to identify bacteria and their mixtures, including epi-fluorescence microscopy, confocal microscopy, two-photon/multi-photon microscopy, and super-resolution imaging methods (STED, SIM, PALM, and STORM). Finally, it discusses how these methods can be applied to microbiota determinations, what can be demonstrated in the future, opportunities and challenges, and future directions.