Biology subjects

Brumley, D. R.

Publications and source records attributed to Brumley, D. R..

3 recordsLinked to original sources

bioRxiv2026

Unlocking subcellular imaging of a cnidarian photosymbiont Breviolum minutum , through expansion microscopy

A cnidarian photosymbiont alga, Breviolum minutum, is an emerging model to study symbiosis due its ability to colonise host in absence of light, and amenability to genetic and physiological manipulations. This alga undergoes subcellular reorganisation in response to stress conditions such as elevated temperature and nutrient deprivation. However, subcellular visualisation of this alga is challenging because of its broad spectrum autofluorescence (400-700 nm) and relatively small size (6-8 m). We developed a super resolution imaging, Expansion Microscopy (ExM) workflow - a hydrogel-based technique for mechanical enlargement of cells, that reveals previously inaccessible subcellular features in B. minutum. This ExM workflow presents a set of thermic and enzymatic conditions which enables 4-fold expansion of B. minutum, optical clearing of autofluorescence as well as the removal of its thick cellulose rich cell wall. We implemented a recently described platinum (II)-based tri-functional linker 1, to retain in situ hybridised oligonucleotides targeted to 18S rRNA within ExM hydrogel and exploited its azide reactive group for post-ExM fluorophore labelling (DBCO modification). We observed actin patches (a cytoskeletal feature) and calmodulin (a calcium binding signalling protein) that are not previously visualised in B. minutum. This approach overcomes some of the long-standing problems in visualisation of B. minutum using commonly available reagents and commercially available low-cost ExM compatible chemistries. The broader uptake of this tool for the visualisation of diverse species of photosymbionts will pave the way for fundamental discoveries underpinning cellular reorganisation in formation and breakdown of symbiosis.

microbiology↗

bioRxiv2024

Machine learning driven image segmentation and shape clustering of algal microscopic images obtained from various water types

Algae and cyanobacteria are microorganisms found in almost all fresh and marine waters, where they can pose environmental and public health risks when they grow excessively and produce blooms. Accurate identification and quantification of these microorganisms are vital for ecological research, water quality monitoring, and public health safety. However, traditional methods of manually counting and morphologically identifying these microorganisms are time-consuming and prone to human error. Application of the machine learning-driven Fast Segment Anything Model (FastSAM), an image segmentation model, automates and potentially enhances the accuracy and efficiency of cell identification and enumeration from microscopic images. We assessed FastSAM for algal cell image segmentation, and three clustering evaluation metrics. Segmentation of microscopic images of algal and cyanobacterial cells in water and treated wastewater samples using the Convolutional Neural Network based FastSAM algorithm demonstrated benefits and challenges of this machine learning-driven image processing. Notably, the pre-trained algorithm segmented entire elements in all microscopic images used in this study. Depending on the shape, 50-100% similarity was observed between machine-based segmentation and manual validation of all segmented elements, with 100% of single cells being correctly segmented by FastSAM. The performance of clustering metrics varied between 57-94% with the Spectral Angle Mapper achieving the most accurate performance, 84-94%, compared to the manually chosen clustering benchmarks. Cyanobacterial and algal communities are biologically diverse and have ecological significance. The application of image clustering techniques in studying their cell shapes marks an important advancement in microbial ecology and environmental monitoring. As technology progresses, these methods will become increasingly utilised to decipher the complex roles that algae and cyanobacteria play in our ecosystems supporting mitigation and public health protection measures.

microbiology↗

bioRxiv2024

Cutting through host autofluorescence: fluorescence lifetime imaging microscopy for visualising intracellular bacteria in Symbiodiniaceae

O_LIPhotoperiodicity is key to the synchronization of life stages in Symbiodiniaceae, Breviolum minutum which harbors taxonomically diverse epi- and endosymbiotic bacteria. We examined influence of a light dark regime on the spatial association between B. minutum and bacteria. C_LIO_LIWe employed a novel approach using combination of fluorescence lifetime imaging microscopy with fluorescence in situ hybridisation approach to clearly distinguish labelled intracellular bacteria from broad spectrum (450-800 nm) background autofluorescence of B. minutum. C_LIO_LIBacteria were observed inside, tethered to and burrowing into the cell exterior, and at the furrow of dividing cells in B. minutum. Significant changes in the abundance of intracellular bacteria relative to autofluorescence in B. minutum cells were observed at initiation of light and dark conditions. C_LIO_LIWe suggest that the onset of bacterial endosymbiosis is linked to the photoperiod driven changes in B. minutum life stages. The re-organisation of thecal plates during cell division of B.minutum in dark is likely to result in internalisation of bacteria. C_LI

microbiology↗