Biology subjects

Curry, N.

Publications and source records attributed to Curry, N..

5 recordsLinked to original sources

bioRxiv2026

Multi-Site Reproducibility Study of 3D High-Content Analysis with Dual-View Oblique Plane Microscopy

High content imaging is being applied to achieve quantitative fluorescence readouts in increasingly complex 3-dimensional (3D) cell culture models such as spheroids and organoids. Compared to conventional 2D assays, 3D assays better represent biological heterogeneity but require more complex sample preparation, 3D imaging and 3D image analysis that can affect the accuracy and precision of such assays. We used spheroids formed from the NRAS-activated melanoma cell line 19161 modified to express an ERK kinase translocation reporter (KTR) as an exemplar 3D phenotypic assay carried out in 96-well plates. The spheroids were treated with the ERK activator TPA and a range of concentrations of the MEK inhibitor Binimetinib. 3D live-cell imaging with sub-cellular spatial resolution was performed using a dual-view oblique plane microscope (dOPM) - a form of single-objective light-sheet microscope - and the experiment was performed separately at 4 different institutes. The results were analysed using an identical 3D analysis pipeline and parameters. We assessed the variation in assay readout using a linear mixed effects model. Random variance at the well level was negligible (SD = 0.0048 relative to range of KTR biosensor readout at reference site of 0.17), indicating low technical noise. Treatment effects were dose-dependent and highly statistically significant compared to DMSO control across all sites (Dunnett-corrected p < 0.001). The range in KTR readout between the minimum (3.5 M Binimetinib) and maximum (100 nM TPA) treatments varied between 59 to 96% relative to the reference site. Measured bias in KTR readout between sites was between 6 and 12% of the range of the reference site. This study quantifies the reproducibility of a 3D live spheroid-based assay employing a fluorescence biosensor requiring readout out at the per-cell level using the dOPM platform and discusses areas where experimental protocol could be improved in the future to further improve reproducibility.

bioengineering↗

bioRxiv2022

3D single-cell shape analysis of cancer cells using geometric deep learning

AO_SCPLOWBSTRACTC_SCPLOWAberrations in 3D cell morphogenesis are linked to diseases such as cancer. Yet there is little systems-level understanding of cell shape determination in 3D, largely because there is a paucity of data-driven methods to quantify and describe 3D cell shapes. We have addressed this need using unsupervised geometric deep learning to learn shape representations of over 95,000 melanoma cells imaged by 3D high-throughput light-sheet microscopy. We used a dynamic graph convolutional foldingnet autoencoder with improved deep embedded clustering to simultaneously learn lower-dimensional representations and classes of 3D cell shapes. We describe a landscape of 3D cell morphology using deep learning-derived 3D quantitative morphological signatures (3DQMS) across different substrate geometries, following treatment by different clinically relevant small molecules and systematic gene depletion in high-throughput. By data integration, we predict modes of action for different small molecules providing mechanistic insights and blueprints for biological re-engineering. Finally, we provide explainability and interpretability for deep learning models.

bioinformatics↗

bioRxiv2021

Environmentally dependent and independent control of cell shape determination by Rho GTPase regulators in melanoma

In order to invade 3D tissues, cancer cells dynamically change cell morphology in response to geometric and mechanical cues in the environment. But how cells determine their shape in 3D versus 2D environments is poorly understood. Studying 2D versus 3D single cell shape determination has historically been technically difficult due to the lack of methodologies to directly compare the two environments. We developed an approach to study cell shape in 2D versus 3D by measuring cell shape at different depths in collagen using stage-scanning oblique plane microscopy (ssOPM). We find characteristic shape changes occur in melanoma cells depending on whether a cell is attached to a 2D surface or 3D environment, and that these changes can be modulated by Rho GTPase regulatory proteins. Our data suggest that regulation of cell protrusivity undergoes a switch of control between different Rho GTPase regulators depending on the physical microenvironment.

cell biology↗

bioRxiv2019

In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with Autism risk genes

The thousands of disease risk genes and loci identified through human genetic studies far outstrip our current capacity to systematically study their functions. New experimental approaches are needed for functional investigations of large panels of genes in a biologically relevant context. Here, we developed a scalable genetic screen approach, in vivo Perturb-Seq, and applied this method to the functional evaluation of 35 autism spectrum disorder (ASD) de novo loss-of-function risk genes. Using CRISPR-Cas9, we introduced frameshift mutations in these risk genes in pools, within the developing brain in utero, and then performed single-cell RNA-Seq in the postnatal brain. We identified cell type-specific gene signatures from both neuronal and glial cell classes that are affected by genetic perturbations, and pointed at elements of both convergent and divergent cellular effects across this cohort of ASD risk genes. In vivo Perturb-Seq pioneers a systems genetics approach to investigate at scale how diverse mutations affect cell types and states in the biologically relevant context of the developing organism.

genomics↗

bioRxiv2018

Fast fluorescence lifetime imaging reveals the maturation process of α-synuclein aggregates in ageing Caenorhabditis elegans

The nematode worm Caenorhabditis elegans has emerged as an important model organism to study the molecular mechanisms of protein misfolding diseases associated with amyloid formation because of its small size, ease of genetic manipulation and optical transparency. Obtaining a reliable and quantitative read-out of protein aggregation in this system, however, remains a challenge. To address this problem, we here present a fast time-gated fluorescence lifetime imaging (TG-FLIM) method and show that it provides functional insights into the process of protein aggregation in living animals by enabling the rapid characterisation of different types of aggregates. More specifically, in longitudinal studies of C. elegans models of Parkinsons and Huntingtons diseases, we observed marked differences in the aggregation kinetics and the nature of the protein inclusions formed by -synuclein and polyglutamine. In particular, we found that -synuclein inclusions do not display amyloid-like features until late in the life of the worms, whereas polyglutamine forms amyloid characteristics rapidly in early adulthood. Furthermore, we show that the TG-FLIM method is capable of imaging live and non-anaesthetised worms moving in specially designed agarose micro-chambers. Taken together, our results show that the TG-FLIM method enables high-throughput functional imaging of living C. elegans that can be used to study in vivo mechanisms of aggregation and that has the potential to aid the search for therapeutic modifiers of protein aggregation and toxicity.

biophysics↗