Biology subjects

Blenkiron, C.

Publications and source records attributed to Blenkiron, C..

5 recordsLinked to original sources

bioRxiv2025

Evaluating sequencing strategies for endometrial microbiome profiling in endometrial cancer: a comparative study of short- and long-read 16S rRNA approaches

BackgroundEndometrial cancer (EC) is the most common gynaecological malignancy globally, with rising incidence and notable disparities in outcomes. In New Zealand, EC rates have increased significantly, particularly among M[a]ori and Pacific women, who face higher risks of advanced disease and poorer outcomes. Microbial dysbiosis has been implicated in EC pathogenesis, but characterising the uterine microbiome is challenging due to low microbial biomass and high contamination risk. AimsThis study aimed to pilot a protocol that could inform the preparation of a larger cohort trial. Short-read Illumina MiSeq and long-read Oxford Nanopore Technologies (ONT) 16S rRNA gene sequencing were investigated to profile the uterine microbiome in people with EC. Methods and ResultsUterine and vaginal swabs were analysed to assess platform performance in terms of DNA recovery, sequencing success, diversity metrics, and taxonomic resolution. The impact of sample freezing or immediate lysis prior to DNA extraction was also evaluated. ONT sequencing provided enhanced species-level resolution and improved detection of low-abundance taxa but showed variable performance in low-yield samples. Freezing prior to cell DNA extraction modestly increased bacterial 16S copy numbers and improved community consistency. Contamination was a problem across both platforms, particularly in low-biomass samples, but can be minimised during data analysis. ConclusionThis study provides practical guidance for sequencing platform selection and sample handling in uterine microbiome research. Our findings support future efforts to elucidate microbial contributions to EC pathogenesis and highlight the importance of rigorous contamination control. Importantly, this is the first presentation of a New Zealand cohort and contributes valuable data from an underrepresented population and informs future research in diverse clinical settings.

cancer biology↗

bioRxiv2023

Manifold Learning Enables Interpretable Analysis of Raman Spectra from Extracellular Vesicle and Other Mixtures

Extracellular vesicles (EVs) have emerged as promising diagnostic and therapeutic candidates in many biomedical applications. However, EV research continues to rely heavily on in vitro cell cultures for EV production, where the exogenous EVs present in fetal bovine (FBS) or other required serum supplementation can be difficult to remove entirely. Despite this and other potential applications involving EV mixtures, there are currently no rapid, robust, inexpensive, and label-free methods for determining the relative concentrations of different EV subpopulations within a sample. In this study, we demonstrate that surface-enhanced Raman spectroscopy (SERS) can biochemically fingerprint fetal bovine serum-derived and bioreactor-produced EVs, and after applying a novel manifold learning technique to the acquired spectra, enables the quantitative detection of the relative amounts of different EV populations within an unknown sample. We first developed this method using known ratios of Rhodamine B to Rhodamine 6G, then using known ratios of FBS EVs to breast cancer EVs from a bioreactor culture. In addition to quantifying EV mixtures, the proposed deep learning architecture provides some knowledge discovery capabilities which we demonstrate by applying it to dynamic Raman spectra of a chemical milling process. This label-free characterization and analytical approach should translate well to other EV SERS applications, such as monitoring the integrity of semipermeable membranes within EV bioreactors, ensuring the quality or potency of diagnostic or therapeutic EVs, determining relative amounts of EVs produced in complex co-culture systems, as well as many Raman spectroscopy applications.

bioengineering↗

bioRxiv2022

Investigating the Consistency of Extracellular Vesicle Production from Breast Cancer Subtypes Using CELLine Adherent Bioreactors

Extracellular vesicle (EV) research has grown rapidly in recent years, largely due to the potential use of EVs as liquid biopsy biomarkers or therapeutics. However, in-depth characterisation and validation of EVs produced using conventional in vitro cultures can be challenging due to the large area of cell monolayers and volumes of culture media required. To overcome this obstacle, multiple bioreactor designs have been tested for EV production with varying success, but the consistency of EVs produced over time in these systems has not been reported previously. In this study, we demonstrate that several breast cancer cell lines of different subtypes can be cultured simultaneously in space, resource, and time efficient manner using CELLine AD 1000 systems, allowing the consistent production of vast amounts of EVs for downstream experimentation. We report an improved workflow used for inoculating, maintaining, and monitoring the bioreactors, their EV production, and the characterisation of the EVs produced. Lastly, our proteomic analyses of the EVs produced throughout the lifetime of the bioreactors show that core EV-associated proteins are relatively consistent, with few minor variations over time, and that tracking the production of EVs may be a convenient method for indirectly monitoring the bioreactors health. These findings will aid future studies requiring the simultaneous production of large amounts of EVs from several cell lines of different subtypes of a disease and other EV biomanufacturing applications.

cancer biology↗

bioRxiv2021

Automated Parallel Pattern Search Optimisation of Microfluidic Geometry for Extracellular Vesicle Liquid Biopsies

Microfluidic liquid biopsies using affinity-based capture of extracellular vesicles (EVs) have demonstrated great potential for providing rapid disease diagnosis and monitoring. However, little effort has been devoted to optimising the geometry of the microfluidic channels for maximum EV capture due to the inherent challenges of physically testing many geometric designs. To address this, we developed an automated parallel pattern search (PPS) optimiser by combining a Python optimiser, COMSOL Multiphysics, and high performance computing. This unique approach was applied to a triangular micropillar array geometry by parameterising repeating unit cells, making several assumptions, and optimising for maximum particle capture efficiency. We successfully optimised the triangular pillar arrays and surprisingly found that simply maximising the total number of pillars and effective surface area did not result in maximum EV capture, as devices with slightly larger pillars and more spacing between pillars allowed contact with slower moving EVs that followed the pillar contours more closely. We then experimentally validated this finding using bioreactor-produced EVs in the best and worst channel designs that were functionalised with an antibody against CD63. Captured EVs were quantified using a fluorescent plate reader, followed by an established elution method and nanoparticle tracking analysis. These results demonstrate the power of automated microfluidic geometry optimisations for EV liquid biopsies and will support further development of this rapidly growing field.

bioengineering↗

bioRxiv2017

Recurrent loss of heterozygosity correlates with clinical outcome in pancreatic neuroendocrine cancer

Pancreatic neuroendocrine tumors (pNETs) are uncommon cancers arising from pancreatic islet cells. Analysis of gene mutation, copy number and RNA expression of 57 sporadic pNETs showed that pNET genomes are dominated by aneuploidy. Remarkably, ~25% of pNETs had genomes characterized by recurrent loss of heterozygosity (LoH) of the same 10 chromosomes, accompanied by bi-allelic MEN1 inactivation, and these cases had generally poor clinical outcome. Another ~25% of all pNETs had chromosome 11 LoH and bi-allelic MEN1 inactivation, lacking the recurrent LoH pattern - these had universally good clinical outcome. Some level of aneuploidy was common, and overall ~80% of pNETs had LoH of [≥]1 chromosome. This aneuploidy led to changes in RNA expression at the level of whole chromosomes and allowed pathogenic germline variants (e.g. ATM) to be expressed unopposed, inactivating downstream tumor suppressor pathways. Some pNETs appear to utilize VHL gene methylation or mutation to activate pseudo-hypoxia. Contrary to expectation neither tumor morphology within well-differentiated pNETs nor single gene mutation had significant associations with clinical outcome, nor did expression of RNAs reflecting the activity of immune, differentiation, proliferative or tumor suppressor pathways. MEN1 was the only statistically significant recurrently mutated driver gene in pNETs. Only one pNET had clearly oncogenic and actionable SNVs (in PTEN and FLCN) confirmed by corroborating RNA expression changes. The two distinct patterns of aneuploidy described here, associated with markedly poor and good clinical outcome respectively, define a novel oncogenic mechanism and the first route to genomic precision oncology for this tumor type.

cancer biology↗