Dataset Information

Use of high-content analysis and machine learning to characterize complex microbial samples via morphological analysis.

ABSTRACT: High Content Analysis (HCA) has become a cornerstone of cellular analysis within the drug discovery industry. To expand the capabilities of HCA, we have applied the same analysis methods, validated in numerous mammalian cell models, to microbiology methodology. Image acquisition and analysis of various microbial samples, ranging from pure cultures to culture mixtures containing up to three different bacterial species, were quantified and identified using various machine learning processes. These HCA techniques allow for faster cell enumeration than standard agar-plating methods, identification of "viable but not plate culturable" microbe phenotype, classification of antibiotic treatment effects, and identification of individual microbial strains in mixed cultures. These methods greatly expand the utility of HCA methods and automate tedious and low-throughput standard microbiological methods.

SUBMITTER: Petitte J

PROVIDER: S-EPMC6756541 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Use of high-content analysis and machine learning to characterize complex microbial samples via morphological analysis.

Petitte Jennifer J Doherty Michael M Ladd Jacob J Marin Cassandra L CL Siles Samuel S Michelou Vanessa V Damon Amanda A Quattrini Eckert Erin E Huang Xiang X Rice John W JW

PloS one 20190923 9

High Content Analysis (HCA) has become a cornerstone of cellular analysis within the drug discovery industry. To expand the capabilities of HCA, we have applied the same analysis methods, validated in numerous mammalian cell models, to microbiology methodology. Image acquisition and analysis of various microbial samples, ranging from pure cultures to culture mixtures containing up to three different bacterial species, were quantified and identified using various machine learning processes. These ...[more]

PMID: 31545814

Similar Datasets

Project description:Understanding the processes of mitochondrial dynamics (fission, fusion, biogenesis, and mitophagy) has been hampered by the lack of automated, deterministic methods to measure mitochondrial morphology from microscopic images. A method to quantify mitochondrial morphology and function is presented here using a commercially available automated high-content wide-field fluorescent microscopy platform and R programming-language-based semi-automated data analysis to achieve high throughput morphological categorization (puncta, rod, network, and large & round) and quantification of mitochondrial membrane potential. In conjunction with cellular respirometry to measure mitochondrial respiratory capacity, this method detected that increasing concentrations of toxicants known to directly or indirectly affect mitochondria (t-butyl hydroperoxide [TBHP], rotenone, antimycin A, oligomycin, ouabain, and carbonyl cyanide-p-trifluoromethoxyphenylhydrazone [FCCP]), decreased mitochondrial networked areas in cultured 661w cells to 0.60-0.80 at concentrations that inhibited respiratory capacity to 0.20-0.70 (fold change compared to vehicle). Concomitantly, mitochondrial swelling was increased from 1.4- to 2.3-fold of vehicle as indicated by changes in large & round areas in response to TBHP, oligomycin, or ouabain. Finally, the automated identification of mitochondrial location enabled accurate quantification of mitochondrial membrane potential by measuring intramitochondrial tetramethylrhodamine methyl ester (TMRM) fluorescence intensity. Administration of FCCP depolarized and administration of oligomycin hyperpolarized mitochondria, as evidenced by changes in intramitochondrial TMRM fluorescence intensities to 0.33- or 5.25-fold of vehicle control values, respectively. In summary, this high-content imaging method accurately quantified mitochondrial morphology and membrane potential in hundreds of thousands of cells on a per-cell basis, with sufficient throughput for pharmacological or toxicological evaluation.

Project description:Microbial community structure is influenced by the environment and in turn exerts control on many environmental parameters. We applied this concept in a bioreactor study to test whether microbial community structure contains information sufficient to predict the concentration of H2S as the product of sulfate reduction. Microbial sulfate reduction is a major source of H2S in many industrial and environmental systems and is often influenced by the existing physicochemical conditions. Production of H2S in industrial systems leads to occupational hazards and adversely affects the quality of products. A long-term (148 days) experiment was conducted in upflow bioreactors to mimic sulfidogenesis, followed by inhibition with nitrate salts and a resumption of H2S generation when inhibition was released. We determined microbial community structure in 731 samples across 20 bioreactors using 16S rRNA gene sequencing and applied a random forest algorithm to successfully predict different phases of sulfidogenesis and mitigation (accuracy = 93.17%) and sessile and effluent microbial communities (accuracy = 100%). Similarly derived regression models that also included cell abundances were able to predict H2S concentration with remarkably high fidelity (R2 > 0.82). Metabolic profiles based on microbial community structure were also found to be reliable predictors for H2S concentration (R2 = 0.78). These results suggest that microbial community structure contains information sufficient to predict sulfidogenesis in a closed system, with anticipated applications to microbially driven processes in open environments. IMPORTANCE Microbial communities control many biogeochemical processes. Many of these processes are impractical or expensive to measure directly. Because the taxonomic structure of the microbial community is indicative of its function, it encodes information that can be used to predict biogeochemistry. Here, we demonstrate how a machine learning technique can be used to predict sulfidogenesis, a key biogeochemical process in a model system. A distinction of this research was the ability to predict H2S production in a bioreactor from the effluent bacterial community structure without direct observations of the sessile community or other environmental conditions. This study establishes the ability to use machine learning approaches in predicting sulfide concentrations in a closed system, which can be further developed as a valuable tool for predicting biogeochemical processes in open environments. As machine learning algorithms continue to improve, we anticipate increased applications of microbial community structure to predict key environmental and industrial processes.

Project description:The incidence of type 2 diabetes (T2D) has been increasing globally, and a growing body of evidence links type 2 diabetes with altered microbiota composition. Type 2 diabetes is preceded by a long prediabetic state characterized by changes in various metabolic parameters. We tested whether the gut microbiome could have predictive potential for T2D development during the healthy and prediabetic disease stages. We used prospective data of 608 well-phenotyped Finnish men collected from the population-based Metabolic Syndrome in Men (METSIM) study to build machine learning models for predicting continuous glucose and insulin measures in a shorter (1.5 year) and longer (4 year) period. Our results show that the inclusion of the gut microbiome improves prediction accuracy for modeling T2D-associated parameters such as glycosylated hemoglobin and insulin measures. We identified novel microbial biomarkers and described their effects on the predictions using interpretable machine learning techniques, which revealed complex linear and nonlinear associations. Additionally, the modeling strategy carried out allowed us to compare the stability of model performance and biomarker selection, also revealing differences in short-term and long-term predictions. The identified microbiome biomarkers provide a predictive measure for various metabolic traits related to T2D, thus providing an additional parameter for personal risk assessment. Our work also highlights the need for robust modeling strategies and the value of interpretable machine learning.IMPORTANCE Recent studies have shown a clear link between gut microbiota and type 2 diabetes. However, current results are based on cross-sectional studies that aim to determine the microbial dysbiosis when the disease is already prevalent. In order to consider the microbiome as a factor in disease risk assessment, prospective studies are needed. Our study is the first study that assesses the gut microbiome as a predictive measure for several type 2 diabetes-associated parameters in a longitudinal study setting. Our results revealed a number of novel microbial biomarkers that can improve the prediction accuracy for continuous insulin measures and glycosylated hemoglobin levels. These results make the prospect of using the microbiome in personalized medicine promising.

Project description:Spontaneous activity is a common feature of immature neuronal networks throughout the central nervous system and plays an important role in network development and consolidation. In postnatal rodents, spontaneous activity in the spinal cord exhibits complex, stochastic patterns that have historically proven challenging to characterize. We developed a software tool for quickly and automatically characterizing and classifying episodes of spontaneous activity generated from developing spinal networks. We recorded spontaneous activity from in vitro lumbar ventral roots of 16 neonatal [postnatal day (P)0-P3] mice. Recordings were DC coupled and detrended, and episodes were separated for analysis. Amplitude-, duration-, and frequency-related features were extracted from each episode and organized into five classes. Paired classes and features were used to train and test supervised machine learning algorithms. Multilayer perceptrons were used to classify episodes as rhythmic or multiburst. We increased network excitability with potassium chloride and tested the utility of the tool to detect changes in features and episode class. We also demonstrate usability by having a novel experimenter use the program to classify episodes collected at a later time point (P5). Supervised machine learning-based classification of episodes accounted for changes that traditional approaches cannot detect. Our tool, named SpontaneousClassification, advances the detail in which we can study not only developing spinal networks, but also spontaneous networks in other areas of the nervous system. NEW & NOTEWORTHY Spontaneous activity is important for nervous system network development and consolidation. Our software uses machine learning to automatically and quickly characterize and classify episodes of spontaneous activity in the spinal cord of newborn mice. It detected changes in network activity following KCl-enhanced excitation. Using our software to classify spontaneous activity throughout development, in pathological models, or with neuromodulation, may offer insight into the development and organization of spinal circuits.

Dataset Information

Use of high-content analysis and machine learning to characterize complex microbial samples via morphological analysis.

Publications

Use of high-content analysis and machine learning to characterize complex microbial samples via morphological analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets