Pillay, K. | Search bioRxiv

bioRxiv2024

High-content high-resolution microscopy and deep learning assisted analysis reveals host and bacterial heterogeneity during Shigella infection

Shigella flexneri is a Gram-negative bacterial pathogen and causative agent of bacillary dysentery. S. flexneri is closely related to Escherichia coli but harbors a virulence plasmid that encodes a Type III Secretion System (T3SS) required for host cell invasion. Widely recognized as a paradigm for research in cellular microbiology, S. flexneri has emerged as important to study mechanisms of cell-autonomous immunity, including septin cage entrapment. Here we use high-content high-resolution microscopy to monitor the dynamic and heterogeneous S. flexneri infection process by assessing multiple host and bacterial parameters (DNA replication, protein translation, T3SS activity). In the case of infected host cells, we report a reduction in DNA and protein synthesis together with morphological changes that suggest S. flexneri can induce cell-cycle arrest. We developed an artificial intelligence image analysis approach using Convolutional Neural Networks to reliably quantify, in an automated and unbiased manner, the recruitment of SEPT7 to intracellular bacteria. We discover that heterogeneous SEPT7 assemblies are recruited to bacteria with increased T3SS activation. Our automated microscopy workflow is useful to illuminate diverse host and bacterial interactions at the single-cell and population level, and to fully characterise the intracellular microenvironment controlling the S. flexneri infection process.

cell biology↗

bioRxiv2023

Brain age as an estimator of neurodevelopmental outcome: A deep learning approach for neonatal cot-side monitoring

The preterm neonate can experience stressors that affect the rate of brain maturation and lead to long-term neurodevelopmental deficits. However, some neonates who are born early follow normal developmental trajectories. Extraction of data from electroencephalography (EEG) signals can be used to calculate the neonates brain age which can be compared to their true age. Discrepancies between true age and brain age (the brain age delta) can then be used to quantify maturational deviation, which has been shown to correlate with long-term abnormal neurodevelopmental outcomes. Nevertheless, current brain age models that are based on traditional analytical techniques are less suited to clinical cot-side monitoring due to their dependency on long-duration EEG recordings, the need to record activity across multiple EEG channels, and the manual calculation of predefined EEG features which is time-consuming and may not fully capture the wealth of information in the EEG signal. In this study, we propose an alternative deep-learning approach to determine brain age, which operates directly on the EEG, using a Convolutional Neural Network (CNN) block based on the Inception architecture (called Sinc). Using this deep-learning approach on a dataset of preterm infants with normal neurodevelopmental outcomes (where we assume brain age = postmenstrual age), we can calculate infant brain age with a Mean Absolute Error (MAE) of 0.78 weeks (equivalent to a brain age estimation error for the infant within +/- 5.5 days of their true age). Importantly, this level of accuracy can be achieved by recording only 20 minutes of EEG activity from a single channel. This compares favourably to the degree of accuracy that can be achieved using traditional methods that require long duration recordings (typically >2 hours of EEG activity) recorded from a higher density 8-electrode montage (MAE = 0.73 weeks). Importantly, the deep learning models brain age deltas also distinguish between neonates with normal and severely abnormal outcomes (Normal MAE = 0.71 weeks, severely abnormal MAE = 1.27 weeks, p=0.02, one-way ANOVA), making it highly suited for potential clinical applications. Lastly, in an independent dataset collected at an independent site, we demonstrate the models generalisability in age prediction, as accurate age predictions were also observed (MAE of 0.97 weeks). HighlightsO_LIPreterm stress exposure leads to long-term neurodevelopmental deficits C_LIO_LIDeficits are quantifiable using EEG-based brain age prediction errors C_LIO_LIOur deep-learning solution for brain age prediction outperforms previous approaches C_LIO_LIPredictions are achieved with only 20 mins EEG and a single bipolar channel C_LIO_LIPrediction errors correlate with long-term Bayley scale neurodevelopmental outcomes C_LI

neuroscience↗