Biology subjects

Hensman, J.

Publications and source records attributed to Hensman, J..

4 recordsLinked to original sources

bioRxiv2026

Usherin in the pineal gland: altered sleep in zebrafish models of Usher syndrome type 2a

Usher syndrome type 2A (USH2a), the most common form of hereditary deaf-blindness, is frequently accompanied by fatigue and poor sleep quality. As these sleep problems occur independently of visual decline, it is hypothesized that the USH2A-encoded protein usherin regulates sleep and circadian rhythmicity via an extra-retinal mechanism. Ush2a knockout zebrafish models were utilized to investigate this hypothesis. Immunohistochemical analysis demonstrated usherin localisation in pineal gland photoreceptor cells in wild-type larvae, alongside the USH2 complex proteins Adgrv1 and Whrna. Cross-species transcriptomic and proteomic analyses confirmed USH2A expression in all mammalian pineal gland tissues studied. Circadian clock gene expression was measured over 24 h and showed preserved oscillatory patterns in wild-type and mutant zebrafish. Ex vivo superfusion of pineal glands revealed sustained circadian melatonin release with comparable phase and period to controls, although potential differences in absolute melatonin levels could not be excluded. Despite intact clock gene expression and melatonin release in ush2a mutants, behavioural classification over 24-h recordings revealed altered sleep-wake behaviour: ush2a mutants displayed elevated daytime sleep and significantly prolonged and more variable sleep latency. The dissociation between intact molecular rhythms and abnormal sleep behaviour likely implicates that usherin plays a role in sleep-wake regulation independent of the circadian pacemaker and melatonin synthesis. These findings suggest a novel role of usherin in the pineal gland and establish a mechanistic link between usherin dysfunction and sleep disturbances, providing a biological basis for the fatigue and sleep problems reported in USH2a patients.

animal behavior and cognition↗

bioRxiv2020

Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSMotivationC_ST_ABSThe negative binomial distribution has been shown to be a good model for counts data from both bulk and single-cell RNA-sequencing (RNA-seq). Gaussian process (GP) regression provides a useful non-parametric approach for modeling temporal or spatial changes in gene expression. However, currently available GP regression methods that implement negative binomial likelihood models do not scale to the increasingly large datasets being produced by single-cell and spatial transcriptomics. ResultsThe GPcounts package implements GP regression methods for modelling counts data using a negative binomial likelihood function. Computational efficiency is achieved through the use of variational Bayesian inference. The GP function models changes in the mean of the negative binomial likelihood through a logarithmic link function and the dispersion parameter is fitted by maximum likelihood. We validate the method on simulated time course data, showing that it is better able to identify changes in over-dispersed counts data than methods based on Gaussian or Poisson likelihoods. To demonstrate temporal inference, we apply GPcounts to single-cell RNA-seq datasets after pseudotime and branching inference. To demonstrate spatial inference, we apply GPcounts to data from the mouse olfactory bulb to identify spatially variable genes and compare to two published GP methods. We also provide the option of modelling additional dropout using a zero-inflated negative binomial. Our results show that GPcounts can be used to model temporal and spatial counts data in cases where simpler Gaussian and Poisson likelihoods are unrealistic. AvailabilityGPcounts is implemented using the GPflow library in Python and is available at https://github.com/ManchesterBioinference/GPcounts along with the data, code and notebooks required to reproduce the results presented here. Contactnuha.bintayyash@manchester.ac.uk or magnus.rattray@manchester.ac.uk

bioinformatics↗

bioRxiv2020

A statistical framework for assessing pharmacological response and biomarkers with confidence

Drug high-throughput screenings across large molecular-characterised cancer cell line panels enable the discovery of biomarkers, and thereby, cancer precision medicine. The ability to experimentally generate drug response data has accelerated. However, this data is typically quantified by a summary statistic from a best-fit dose response curve, whilst neglecting the uncertainty of the curve fit and the potential variability in the raw readouts. Here, we model the experimental variance using Gaussian Processes, and subsequently, leverage this uncertainty for identifying associated biomarkers with a new statistical framework based on Bayesian testing. Applied to the Genomics of Drug Sensitivity in Cancer, in vitro screening data on 265 compounds across 1,074 cell lines, our uncertainty models identified 24 clinically established drug response biomarkers, and in addition provided evidence for 6 novel biomarkers. We validated our uncertainty estimates with an additional drug screen of 26 drugs, 10 cell lines with 8 to 9 replicates. Our method is applicable to drug high-throughput screens without replicates, and enables robust biomarker discovery for new cancer therapies.

cancer biology↗

bioRxiv2017

BGP: Branched Gaussian processes for identifying gene-specific branching dynamics in single cell data

High-throughput single-cell gene expression experiments can be used to uncover branching dynamics in cell populations undergoing differentiation through use of pseudotime methods. We develop the branching Gaussian process (BGP), a non-parametric model that is able to identify branching dynamics for individual genes and provides an estimate of branching times for each gene with an associated credible region. We demonstrate the effectiveness of our method on both synthetic data and a published single-cell gene expression hematopoiesis study. The method requires prior information about pseudotime and global cellular branching for each cell but the probabilistic nature of the method means that it is robust to errors in these global branch labels and can be used to discover early branching genes which diverge before the inferred global cell branching. The code is open-source and available at https://github.com/ManchesterBioinference/BranchedGP.

bioinformatics↗