Predicting characterization of microbiome taxonomy from imaging using machine learning approaches

AO_SCPLOWBSTRACTC_SCPLOWFor this study, a total of 47 mock human skin microbiome communities were created using microorganisms collected from human donors and grown in vitro for between eight and 32 days. Each mock community sample was split. Ten mL of each sample was used to determine the taxonomy of the community, using metatranscriptomics and Kraken2 to provide population-level taxonomic information; five mL of each sample was used for imaging. The resulting micrographs served as the basis for establishing a new analysis pipeline that sequentially used two different methods for machine learning and one statistical technique: (1) confocal microscopy images were segmented into individual cells using the generalist, deep learning, publicly available machine learning model Cellpose; (2) continuous probability density functions describing the joint distribution of the cell area and eccentricity were found using algorithms expressing the statistical technique of kernel density estimation; (3) these probability density functions were used as input for convolutional neural networks, that were trained to predict both the taxonomic diversity and the most common bacterial class, independently of metatranscriptomics. Specifically, models were made to predict the Shannon index (a quantitative measure of taxonomic diversity) and to predict the most common bacterial class, for each micrograph. Measured Shannon indices (based on metatranscriptomics) ranged from nearly 0 to 1.4. The model predictions of Shannon indices had a mean squared error of 0.0321 +/- 0.0035. The model predictions of the most common taxonomic class of bacteria had an accuracy of 94.0% +/- 0.7%. IO_SCPLOWMPORTANCEC_SCPLOWTaxonomic diversity is a useful metric for describing microbial communities and can be used as a measure of ecosystems health, resilience, and biological interactions. Characterization of microbial community diversity also has diagnostic applications. For the human skin microbiome in particular, microbial diversity directly impacts skin health, including resilience against pathogens and regulation of immune responses. Currently, microbial diversity can be determined either using traditional staining methods that are limited to pure cultures or using sequencing methods that require high investment in cost, time, and expertise. In this study, we demonstrate an innovative method that employs microscopy images of bacterial communities and machine learning to predict taxonomic diversity and the dominant bacterial classes of bacterial communities. The underlying framework of the pipeline for taxonomy prediction has the potential to be adapted and extended to other organisms and microbiomes and to make taxonomic analyses less expensive and more feasible in low-resource settings.