Biology subjects

Bauer, A.

Publications and source records attributed to Bauer, A..

23 records · Page 2Linked to original sources

bioRxiv2020

pyTFM: A tool for Traction Force and Monolayer Stress Microscopy

Cellular force generation and force transduction are of fundamental importance for numerous biological processes and can be studied with the methods of Traction Force Microscopy (TFM) and Monolayer Stress Microscopy. Traction Force Microscopy and Monolayer Stress Microscopy solve the inverse problem of reconstructing cell-matrix tractions and inter- and intra-cellular stresses from the measured cell force-induced deformations of an adhesive substrate with known elasticity. Although several laboratories have developed software for Traction Force Microscopy and Monolayer Stress Microscopy computations, there is currently no software package available that allows non-expert users to perform a full evaluation of such experiments. Here we present pyTFM, a tool to perform Traction Force Microscopy and Monolayer Stress Microscopy on single cells, cell patches and cell layers grown in a 2-dimensional environment. pyTFM was optimized for ease-of-use; it is open-source and well documented (hosted at https://pytfm.readthedocs.io/) including usage examples and explanations of the theoretical background. pyTFM can be used as a standalone Python package or as an add-on to the image annotation tool ClickPoints. In combination with the ClickPoints environment, pyTFM allows the user to set all necessary analysis parameters, select regions of interest, examine the input data and intermediary results, and calculate a wide range of parameters describing forces, stresses, and their distribution. The Monolayer Stress Microscopy implementation in pyTFM allows for the analysis of small cell patches and single cells; we analyze the accuracy and performance of Traction Force Microscopy and Monolayer Stress Microscopy algorithms using synthetic and experimental data from epithelial cell patches.

biophysics↗

bioRxiv2019

Transient uterine hypercontractility causes oxidative stress in the fetal brain, sex-dependent mitochondrial abnormalities, and altered social behavior of adolescent male rat offspring

The impact of transient ischemic-hypoxemic insults on the developing fetal brain is poorly understood despite evidence suggesting an association with neurodevelopmental disorders such as schizophrenia and autism. To address this, we designed an aberrant uterine hypercontractility paradigm with oxytocin to better assess the consequences of acute, but transient, placental ischemia-hypoxemia in term pregnant rats. Using MRI imaging, we confirmed that oxytocin-induced aberrant uterine hypercontractility significantly compromised uteroplacental perfusion. This was supported by the observation of oxidative stress and increased lactate concentration in the fetal brain. Genes related to oxidative stress pathways were significantly upregulated in male, but not female, offspring 1 h after oxytocin-induced placental ischemia-hypoxemia. Persistent upregulation of select mitochondrial electron transport chain complex proteins in the anterior cingulate cortex of adolescent male offspring suggested that this sex-specific effect was enduring. Functionally, offspring exposed to oxytocin-induced uterine hypercontractility showed male-specific abnormalities in social behavior with associated region-specific changes in gene expression and functional cortical connectivity. Our findings, therefore, indicate that even transient but severe placental ischemia-hypoxemia could be detrimental to the developing brain and point to a possible mitochondrial link between intrauterine asphyxia and neurodevelopmental disorders.

neuroscience↗

bioRxiv2019

Identification of two independent COL5A1 variants in dogs with Ehlers Danlos syndrome

BACKGROUNDThe Ehlers Danlos syndromes (EDS) are a heterogeneous group of heritable disorders affecting connective tissues. The mutations causing the various forms of EDS in humans are well characterized, but the genetic mutations causing EDS-like clinical pathology in dogs are not known, thus hampering accurate clinical diagnosis.\n\nRESULTSClinical analysis of two independent cases of skin hyperextensibility and fragility, one with pronounced joint hypermobility was suggestive of EDS. Whole genome sequencing revealed de novo mutations of COL5A1 in both cases, confirming the diagnosis of the classical form of EDS. The heterozygous COL5A1 p.Gly1013ValfsTer260 mutation characterized in case 1 introduced a premature termination codon and would be expected to result in 1(V) mRNA nonsense-mediated mRNA decay and collagen V haploinsufficiency. While mRNA was not available from this dog, biochemical analysis of the dermis suggested reduced collagen V in the dermis and ultrastructural analysis demonstrated variability in collagen fibril diameter and the presence of collagen aggregates, termed collagen cauliflowers, consistent with COL5A1 mutations underlying classical EDS. In the second case DNA sequencing demonstrated a p.Gly1571Arg missense variant in the COL5A1 gene. While samples were not available for further analysis, such a glycine substitution would be expected to destabilize the strict molecular structure of the collagen V triple helix and thus affect protein stability and/or integration of the mutant collagen into the collagen V/collagen I heterotypic dermal fibrils.\n\nCONCLUSIONSThis is the first report of genetic variants in the COL5A1 gene causing the clinical presentation of EDS in dogs. These data provide further evidence of the important role of collagen V in dermal collagen fibrillogenesis. Importantly from the clinical perspective we show the utility of DNA sequencing, combined with the established clinical criteria, in the accurate diagnosis of EDS in dogs.

genetics↗

bioRxiv2019

AirSurf-Lettuce: an aerial image analysis platform for ultra-scale field phenotyping and precision agriculture using computer vision and deep learning

Aerial imagery is regularly used by farmers and growers to monitor crops during the growing season. To extract meaningful phenotypic information from large-scale aerial images collected regularly from the field, high-throughput analytic solutions are required, which not only produce high-quality measures of key crop traits, but also support agricultural practitioners to make reliable management decisions of their crops. Here, we report AirSurf-Lettuce, an automated and open-source aerial image analysis platform that combines modern computer vision, up-to-date machine learning, and modular software engineering to measure yield-related phenotypes of millions of lettuces across the field. Utilising ultra-large normalized difference vegetation index (NDVI) images acquired by fixed-wing light aircrafts together with a deep-learning classifier trained with over 100,000 labelled lettuce signals, the platform is capable of scoring and categorising iceberg lettuces with high accuracy (>98%). Furthermore, novel analysis functions have been developed to map lettuce size distribution in the field, based on which global positioning system (GPS) tagged harvest regions can be derived to enable growers and farmers precise harvest strategies and marketability estimates before the harvest.

bioinformatics↗

bioRxiv2018

CropMonitor: a scalable open-source experiment management system for distributed plant phenotyping and IoT-based crop management

BackgroundHigh-quality plant phenotyping and climate data lay the foundation of phenotypic analysis as well as genotype-by-environment interactions, which is important biological evidence not only to understand the dynamics between crop performance, genotypes, and environmental factors, but also for agronomists and farmers to monitor crops in fluctuating agricultural conditions. With the rise of Internet of Things technologies in recent years, many IoT-based remote sensing devices have been applied to phenotyping and crop monitoring that generate big plant-environment datasets every day; however, it is still technically challenging to calibrate, annotate, and aggregate big data effectively, especially when they were generated in multiple locations, and often at different scales.\n\nFindingsCropSurveyor is a PHP and SQL based server platform, which provides automated data collation, storage, device and experiment management through IoT-based sensors and distributed plant phenotyping workstations. It provides a two-component solution for monitoring biological experiments and networked devices, with interfaces specifically designed for distributed IoT devices and centralised data servers. Data transfer is performed automatically though an HTTP accessible RESTful API installed on both device-side and server-side of the CropSurveyor system, which synchronise daily representative crop growth images for quick and visual-based crop assessment, as well as detailed microclimate readings for GxE studies. CropSurveyor also supports the comparison of historical and ongoing crop performance whilst different experiments are being conducted.\n\nConclusionsAs an open-source experiment and data management system, CropSurveyor can be used to maintain and collate important crop performance and microclimate datasets captured by IoT sensors and distributed phenotyping installations. It provides near real-time environmental and crop growth monitoring in addition to historical and current data comparison through a single cloud-ready server system. Accessible both locally in the field through smart devices and remotely in an office using a PC, CropSurveyor has been used in wheat field experiments for prebreeding since 2016 and has the potential to enable scalable crop management and IoT-style agricultural practices in the near future.

plant biology↗