Biology subjects

Dexter, A.

Publications and source records attributed to Dexter, A..

3 recordsLinked to original sources

bioRxiv2025

Reducing Foetal Bovine Serum Culture Conditions does not affect GPCR Signalling

Animal use in research extends beyond the use of animal models to study physiology and disease. Many aspects of in vitro research use reagents derived from animals, most prolifically the use of foetal bovine serum (FBS) in growth media for cellular models. With the aim to reduce animal use, we investigated the effect of reduced FBS culture conditions on cell growth, as well as different stages of G protein-coupled receptor (GPCR) signalling, a wide area of research which might therefore impact many groups. We identified little differences on cell growth or GPCR signalling when reducing culture FBS percentage from 10% to 5%, using assays ranging from receptor activation to downstream transcription factors stimulation. In addition to diminishing animal use, the reduction of FBS use will also have economic and environmental benefits, which we hope will be of benefit to the wider research community.

pharmacology and toxicology↗

bioRxiv2023

Metabolic profiling stratifies colorectal cancer and reveals adenosylhomocysteinase as a therapeutic target

With colorectal cancer (CRC) being the second most common cause of cancer-related deaths worldwide1, there is an urgent need for better diagnostic tools and new, more targeted therapies. Here we used genetically engineered mouse models (GEMMs), and multimodal mass spectrometry-based metabolomics to study the impact of common genetic drivers of CRC on the metabolic landscape of the intestine. We show that unsupervised metabolic profiling can stratify intestinal tissues according to underlying genetic alterations, and use mass spectrometry imaging (MSI) to identify tumour, stromal and normal adjacent tissues. By identifying ions that drive variation between normal and transformed tissues, we found dysregulation of the methionine cycle to be a hallmark of APC-mutant CRC, and propose one of its enzymes, i.e. adenosylhomocysteinase (AHCY), as a new therapeutic target. Collectively, we show that the profound genotype-dependent alterations in both lipid and small molecule metabolism in CRC may be exploited for tissue classification with no need for ion identification, and we applied further data analysis to expose a novel metabolic vulnerability of CRC.

cancer biology↗

bioRxiv2020

Training a neural network to learn other dimensionality reduction removes data size restrictions in bioinformatics and provides a new route to exploring data representations

High dimensionality omics and hyperspectral imaging datasets present difficult challenges for feature extraction and data mining due to huge numbers of features that cannot be simultaneously examined. The sample numbers and variables of these methods are constantly growing as new technologies are developed, and computational analysis needs to evolve to keep up with growing demand. Current state of the art algorithms can handle some routine datasets but struggle when datasets grow above a certain size. We present a training deep learning via neural networks on non-linear dimensionality reduction, in particular t-distributed stochastic neighbour embedding (t-SNE), to overcome prior limitations of these methods. One Sentence SummaryAnalysis of prohibitively large datasets by combining deep learning via neural networks with non-linear dimensionality reduction.

bioinformatics↗