determination-microbial-biomass-soil-using-digital-droplet-pcr

Determination of Microbial Biomass in Soil using Digital Droplet PCR (ddPCR)

Soil represents the largest terrestrial pool of organic carbon, yet more than 133 Gt of carbon has been lost from soils over the past two centuries. ...

University of Exeter

Bacteria modify their morphology in response to various factors including growth stage, nutrient availability, predation, motility and long-term survival strategies. Morphological changes may also be associated with specific physiological phenotypes such as the formation of dormant or persister cells in a "viable but non-culturable" (VBNC) state which frequently display different shapes and size compared to their active counterparts. Such dormancy phenotypes can display various degrees of tolerance to antibiotics and therefore a detailed understanding of these phenotypes is crucial for combatting chronic infections and associated diseases. Cell shape and size are therefore more than simple phenotypic characteristics; they are important physiological properties for understanding bacterial life-strategies and pathologies. However, quantitative studies on the changes to cell morphologies during bacterial growth, persister cell formation and the VBNC state are few and severely constrained by current limitations in the most used investigative techniques of flow cytometry (FC) and light or electron microscopy. In this study, we applied high-throughput Imaging Flow Cytometry (IFC) to characterise and quantify, at single-cell level and over time, the phenotypic heterogeneity and morphological changes in cultured populations of four bacterial species,  and  Morphologies in relation to growth stage and stress responses, cell integrity and metabolic activity were analysed. Additionally, we were able to identify and morphologically classify dormant cell phenotypes such as VBNC cells and monitor the resuscitation of persister cells in  following antibiotic treatment. We therefore demonstrate that IFC, with its high-throughput data collection and image capture capabilities, provides a platform by which a detailed understanding of changes in bacterial phenotypes and their physiological implications may be accurately monitored and quantified, leading to a better understanding of the role of phenotypic heterogeneity in the dynamic microbiome.

The Application of Imaging Flow Cytometry for Characterisation and Quantification of Bacterial Phenotypes.

Anaerobic digestion is an established method for the biological conversion of waste feedstocks to biogas and biomethane. While anaerobic digestion is an excellent waste management technique, it can be susceptible to toxins and pollutants from contaminated feedstocks, which may have a detrimental impact on a digester's efficiency and productivity. Ethylene glycol (EG) is readily used in the heat-transfer loops of anaerobic digestion facilities to maintain reactor temperature. Failure of the structural integrity of these heat transfer loops can cause EG to leak into the digester, potentially causing a decrease in the resultant gas yields. Batch fermentations were incubated with 0, 10, 100 and 500 ppm (parts per million) of EG, and analysis showed that the EG was completely metabolised by the digester microbiome. The concentrations of EG tested showed significant increases in gas yields, however there were no significant changes to the digester microbiome.

Metagenomic analysis of ethylene glycol contamination in anaerobic digestion.

Pollen and tracheophyte spores are ubiquitous environmental indicators at local and global scales. Palynology is typically performed manually by microscopic analysis; a specialised and time-consuming task limited in taxonomical precision and sampling frequency, therefore restricting data quality used to inform climate change and pollen forecasting models. We build on the growing work using AI (artificial intelligence) for automated pollen classification to design a flexible network that can deal with the uncertainty of broad-scale environmental applications. We combined imaging flow cytometry with Guided Deep Learning to identify and accurately categorise pollen in environmental samples; here, pollen grains captured within c. 5500 Cal yr BP old lake sediments. Our network discriminates not only pollen included in training libraries to the species level but, depending on the sample, can classify previously unseen pollen to the likely phylogenetic order, family and even genus. Our approach offers valuable insights into the development of a widely transferable, rapid and accurate exploratory tool for pollen classification in 'real-world' environmental samples with improved accuracy over pure deep learning techniques. This work has the potential to revolutionise many aspects of palynology, allowing a more detailed spatial and temporal understanding of pollen in the environment with improved taxonomical resolution.

Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry.

Direct sequencing of DNA from environmental samples (eDNA) is increasingly utilized to provide a census of natural and industrial habitats. The methodology required to perform metagenomics can be divided into three distinct stages: DNA Purification, Library Preparation and Sequencing, and Bioinformatic Analysis. Here we demonstrate an end-to-end protocol that can be utilized either in the field or laboratory for metagenomic analysis of environmental samples utilizing the Oxford Nanopore Technologies MinION sequencing platform.

Article	Year
The Application of Imaging Flow Cytometry for Characterisation and Quantification of Bacterial Phenotypes. Frontiers in cellular and infection microbiology\| PubMed ID: 34368019	2021
Metagenomic analysis of ethylene glycol contamination in anaerobic digestion. Bioresource technology\| PubMed ID: 37597572	2023
Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry. The New phytologist\| PubMed ID: 37678361	2023
Laboratory and In-Field Metagenomics for Environmental Monitoring. Methods in molecular biology (Clifton, N.J.)\| PubMed ID: 40736894	2025

Daniel G. Barber

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