Biology subjects

Parthasarathy, R.

Publications and source records attributed to Parthasarathy, R..

6 recordsLinked to original sources

bioRxiv2018

Bacterial cohesion predicts spatial distribution in the larval zebrafish intestine

Are there general biophysical relationships governing the spatial organization of the gut microbiome? Despite growing realization that spatial structure is important for population stability, inter-bacterial competition, and host functions, it is unclear in any animal gut whether such structure is subject to predictive, unifying rules, or if it results from contextual, species-specific behaviors. To explore this, we used light sheet fluorescence microscopy to conduct a high-resolution comparative study of bacterial distribution patterns throughout the entire intestinal volume of live, larval zebrafish. Fluorescently tagged strains of seven bacterial symbionts, representing six different species native to zebrafish, were each separately mono-associated with animals that had been raised initially germ-free. The strains showed large differences in both cohesion--the degree to which they auto-aggregate--and spatial distribution. We uncovered a striking correlation between each strains mean position and its cohesion, whether quantified as the fraction of cells existing as planktonic individuals, the average aggregate size, or the total number of aggregates. Moreover, these correlations held within species as well; aggregates of different sizes localized as predicted from the pan-species observations. Together, our findings indicate that bacteria within the zebrafish intestine are subject to generic processes that organize populations by their cohesive properties. The likely drivers of this relationship, peristaltic fluid flow, tubular anatomy, and bacterial growth and aggregation kinetics, are common throughout animals. We therefore suggest that the framework introduced here, of biophysical links between bacterial cohesion and spatial organization, should be useful for directing explorations in other host-microbe systems, formulating detailed models that can quantitatively map onto experimental data, and developing new tools that manipulate cohesion to engineer microbiome function.

microbiology↗

bioRxiv2018

Automated High-Throughput Light-Sheet Fluorescence Microscopy of Larval Zebrafish

AO_SCPCAPBSTRACTC_SCPCAPLight sheet fluorescence microscopy enables fast, minimally phototoxic, three-dimensional imaging of live specimens, but is currently limited by low throughput and tedious sample preparation. Here, we describe an automated high-throughput light sheet fluorescence microscope in which specimens are positioned by and imaged within a fluidic system integrated with the sheet excitation and detection optics. We demonstrate the ability of the instrument to rapidly examine live specimens with minimal manual intervention by imaging fluorescent neutrophils over a nearly 0.3 mm3 volume in dozens of larval zebrafish. In addition to revealing considerable inter-individual variability in neutrophil number, known previously from labor-intensive methods, three-dimensional imaging allows assessment of the correlation between the bulk measure of total cellular fluorescence and the spatially resolved measure of actual neutrophil number per animal. We suggest that our simple experimental design should considerably expand the scope and impact of light sheet imaging in the life sciences.

bioengineering↗

bioRxiv2018

Performance of convolutional neural networks for identification of bacteria in 3D microscopy datasets

Three-dimensional microscopy is increasingly prevalent in biology due to the development of techniques such as multiphoton, spinning disk confocal, and light sheet fluorescence microscopies. These methods enable unprecedented studies of life at the microscale, but bring with them larger and more complex datasets. New image processing techniques are therefore called for to analyze the resulting images in an accurate and efficient manner. Convolutional neural networks are becoming the standard for classification of objects within images due to their accuracy and generalizability compared to traditional techniques. Their application to data derived from 3D imaging, however, is relatively new and has mostly been in areas of magnetic resonance imaging and computer tomography. It remains unclear, for images of discrete cells in variable backgrounds as are commonly encountered in fluorescence microscopy, whether convolutional neural networks provide sufficient performance to warrant their adoption, especially given the challenges of human comprehension of their classification criteria and their requirements of large training datasets. We therefore applied a 3D convolutional neural network to distinguish bacteria and non-bacterial objects in 3D light sheet fluorescence microscopy images of larval zebrafish intestines. We find that the neural network is as accurate as human experts, outperforms random forest and support vector machine classifiers, and generalizes well to a different bacterial species through the use of transfer learning. We also discuss network design considerations, and describe the dependence of accuracy on dataset size and data augmentation. We provide source code, labeled data, and descriptions of our analysis pipeline to facilitate adoption of convolutional neural network analysis for three-dimensional microscopy data.\n\nAuthor summaryThe abundance of complex, three dimensional image datasets in biology calls for new image processing techniques that are both accurate and fast. Deep learning techniques, in particular convolutional neural networks, have achieved unprecedented accuracies and speeds across a large variety of image classification tasks. However, it is unclear whether or not their use is warranted in noisy, heterogeneous 3D microscopy datasets, especially considering their requirements of large, labeled datasets and their lack of comprehensible features. To asses this, we provide a case study, applying convolutional neural networks as well as feature-based methods to light sheet fluorescence microscopy datasets of bacteria in the intestines of larval zebrafish. We find that the neural network is as accurate as human experts, outperforms the feature-based methods, and generalizes well to a different bacterial species through the use of transfer learning.

microbiology↗

bioRxiv2017

The Vibrio cholerae Type VI Secretion System Can Modulate Host Intestinal Mechanics to Displace Commensal Gut Bacteria

Host-associated microbiota help defend against bacterial pathogens; the mechanisms that pathogens possess to overcome this defense, however, remain largely unknown. We developed a zebrafish model and used live imaging to directly study how the human pathogen Vibrio cholerae invades the intestine. The gut microbiota of fish mono-colonized by commensal strain Aeromonas veronii was displaced by V. cholerae expressing its Type VI Secretion System (T6SS), a syringe-like apparatus that deploys effector proteins into target cells. Surprisingly, displacement was independent of T6SS-mediated killing of Aeromonas, driven instead by T6SS-induced enhancement of zebrafish intestinal movements that led to expulsion of the resident commensal by the host. Deleting an actin crosslinking domain from the T6SS apparatus returned intestinal motility to normal and thwarted expulsion, without weakening V. cholerae's ability to kill Aeromonas in vitro. Our finding that bacteria can manipulate host physiology to influence inter-microbial competition has implications for both pathogenesis and microbiome engineering.

microbiology↗

bioRxiv2017

Modernized tools for streamlined genetic manipulation of wild and diverse symbiotic bacteria

The capacity to associate symbiotic bacteria with vital aspects of plant and animal biology is outpacing our understanding of the mechanisms shaping these interactions. A major barrier to mechanistic studies is the paucity of tools for genetically manipulating wild and diverse bacterial isolates. Solving this problem is crucial to elucidating the cellular and molecular rules that govern symbiotic relationships and ultimately harnessing them for agricultural and biomedical applications. Therefore, we constructed a series of vectors that expedite genetic knock-in and knock-out procedures across a range of bacterial lineages. This was accomplished by developing strategies for domestication-free bacterial conjugation, designing plasmids with customizable features, and streamlining allelic exchange using visual markers of homologous recombination. These tools enabled a comparative study based on live imaging of diverse bacterial symbionts native to the zebrafish intestine, with which we discovered heterogeneous colonization patterns and a striking correlation between bacterial population biogeography and cellular behavior.

genetics↗

bioRxiv2017

Image velocimetry and spectral analysis enable quantitative characterization of larval zebrafish gut motility

Summary StatementWe present a new image analysis technique using image velocimetry and spectral analysis that returns quantitative measures of gut contraction strength, frequency, and wave speed that can be used to study gut motility and other cellular movements.\n\nAbstractNormal gut function requires rhythmic and coordinated movements that are affected by developmental processes, physical and chemical stimuli, and many debilitating diseases. The imaging and characterization of gut motility, especially regarding periodic, propagative contractions driving material transport, are therefore critical goals. Whereas previous image analysis approaches have successfully extracted properties related to temporal frequency of motility modes, robust measures of contraction magnitude remain elusive. We developed a new image analysis method based on image velocimetry and spectral analysis that reveals temporal characteristics such as frequency and wave propagation speed, while also providing quantitative measures of the amplitude of gut motions. We validate this approach using several challenges to larval zebrafish, imaged with differential interference contrast microscopy. Both acetylcholine exposure and feeding increase frequency and amplitude of motility. Larvae lacking enteric nervous system gut innervation show the same average motility frequency, but reduced and less variable amplitude compared to wild-types. Our image analysis approach enables insights into gut dynamics in a wide variety of developmental and physiological contexts and can also be extended to analyze other types of cell movements.

developmental biology↗