Biology subjects

Alex Hewitt

Publications and source records attributed to Alex Hewitt.

4 recordsLinked to original sources

bioRxiv2016

Development of a modular automated system for maintenance and differentiation of adherent human pluripotent stem cells.

Patient-specific induced pluripotent stem cells (iPSCs) have tremendous potential for development of regenerative medicine, disease modelling and drug discovery. However, the processes of reprogramming, maintenance and differentiation are labour intensive and subject to inter-technician variability. To address these issues, we established and optimised protocols to allow for the automated maintenance of reprogrammed somatic cells into iPSCs to enable the large-scale culture and passaging of human pluripotent stem cells (PSCs) using a customized TECAN Freedom EVO. Generation of iPSCs was performed offline by nucleofection followed by selection of TRA-1-60 positive cells using a Miltenyi MultiMACS24 Separator. Pluripotency markers were assessed to confirm pluripotency of the generated iPSCs. Passaging was performed using an enzyme-free dissociation method. Proof of concept of differentiation was obtained by differentiating human PSCs into cells of the retinal lineage. Key advantages of this automated approach are the ability to increase sample size, reduce variability during reprogramming or differentiation, and enable medium to high-throughput analysis of human PSCs and derivatives. These techniques will become increasingly important with the emergence of clinical trials using stem cells.

Bioengineering↗

bioRxiv2016

DNA methylation landscape of ocular tissue relative to matched to peripheral blood.

BACKGROUNDEpigenetic variation is implicated in a range of non-communicable diseases, including those of the eye. However, investigating the role of epigenetic variation in ocular disease remains problematic as the degree of correlation in epigenetic profile between central (such as the brain or eye) and peripheral tissues (blood or saliva) within an individual remains largely unclear.\n\nMETHODSMatched whole blood from the subclavian vein, and whole eyes (N=8) were obtained post-mortem. DNA was isolated from blood, neurosensory retina, retinal pigment epithelium (RPE)/choroid and optic nerve tissue. DNA methylation profiling was performed using the Illumina Infinium HumanMethylation450 platform. Following standard quality control measures a total of 433,768 methylation values common to all samples were available for use in subsequent analysis.\n\nRESULTSUnsupervised hierarchical clustering and principal components analysis revealed tissue of origin as the main driver of variation within the dataset. Despite this, there was a strong correlation of methylation profiles between tissues within each individual. Over 255,000 CpG sites were found to have similar methylation levels (beta <0.2 or beta >0.8) across different tissues in the same individuals, with a further ~16,000 sites having similar methylation profiles across ocular tissues only. Only a small proportion of probes showing interindividual variation in blood, co-varied across blood and eye tissues within individuals.\n\nCONCLUSIONSAn improved understanding of the epigenetic landscape of the eye will have important ramifications for regenerative medicine and ongoing dissection of gene-environment interactions in eye disease. Despite a generally high correlation in methylation values irrespective of sample origin, tissue type is the major driver of methylation variation, with only limited covariation between blood and any specific ocular tissue. Caution is warranted when aiming to infer ocular tissue methylation status from blood samples.

Genomics↗

bioRxiv2016

AAV-mediated CRISPR/Cas gene editing of retinal cells in vivo.

PURPOSECRISPR/Cas has recently been adapted to enable efficient editing of the mammalian genome, opening novel avenues for therapeutic intervention of inherited diseases. In seeking to disrupt Yellow Fluorescent Protein (YFP) in a Thy1-YFP transgenic mouse, we assessed the feasibility of utilising the adeno-associated virus 2 (AAV2) to deliver CRISPR/Cas for genome modification of retinal cells in vivo.\n\nMETHODSsgRNA plasmids were designed to target YFP and after in vitro validation, selected guides were cloned into a dual AAV system. One AAV2 construct was used to deliver SpCas9 and the other delivered sgRNA against YFP or LacZ (control) in the presence of mCherry. Five weeks after intravitreal injection, retinal function was determined using electroretinography and CRISPR/Cas-mediated gene modifications were quantified in retinal flat mounts.\n\nRESULTSAAV2-mediated in vivo delivery of SpCas9 with sgRNA targeting YFP, significantly reduced the number of YFP fluorescent cells of the inner retina of our transgenic mouse model. Overall, we found an 84.0% (95% CI: 81.8-86.9) reduction of YFP-positive cells in YFP-sgRNA infected retinal cells compared to eyes treated with LacZ-sgRNA. Electroretinography profiling found no significant alteration in retinal function following AAV2-mediated delivery of CRISPR/Cas components compared to contralateral untreated eyes.\n\nCONCLUSIONSThy1-YFP transgenic mice were used as a rapid quantifiable means to assess the efficacy of CRISPR/Cas-based retinal gene modification in vivo. We demonstrate that genomic modification of cells in the adult retina can be readily achieved by viral mediated delivery of CRISPR/Cas.

Genomics↗

bioRxiv2014

Benchmarking undedicated cloud computing providers for analysis of genomic datasets.

A major bottleneck in biological discovery is now emerging at the computational level. Cloud computing offers a dynamic means whereby small and medium-sized laboratories can rapidly adjust their computational capacity. We benchmarked two established cloud computing services, Amazon Web Services Elastic MapReduce (EMR) on Amazon EC2 instances and Google Compute Engine (GCE), using publicly available genomic datasets (E.coli CC102 strain and a Han Chinese male genome) and a standard bioinformatic pipeline on a Hadoop-based platform. Wall-clock time for complete assembly differed by 52.9% (95%CI: 27.5-78.2) for E.coli and 53.5% (95%CI: 34.4-72.6) for human genome, with GCE being more efficient than EMR. The cost of running this experiment on EMR and GCE differed significantly, with the costs on EMR being 257.3% (95%CI: 211.5-303.1) and 173.9% (95%CI: 134.6-213.1) more expensive for E.coli and human assemblies respectively. Thus, GCE was found to outperform EMR both in terms of cost and wall-clock time. Our findings confirm that cloud computing is an efficient and potentially cost-effective alternative for analysis of large genomic datasets. In addition to releasing our cost-effectiveness comparison, we present available ready-to-use scripts for establishing Hadoop instances with Ganglia monitoring on EC2 or GCE.

Bioinformatics↗