Biology subjects

Pollard, S.

Publications and source records attributed to Pollard, S..

9 recordsLinked to original sources

bioRxiv2024

A comprehensive pharmacological survey across heterogeneous patient-derived GBM stem cell models

Despite substantial drug discovery investments, the lack of any significant therapeutic advancement in the treatment of glioblastoma (GBM) over the past two decades calls for more innovation in the identification of effective treatments. The inter-and intra-patient heterogeneity of GBM presents significant obstacles to effective clinical progression of novel treatments by contributing to tumour plasticity and rapid drug resistance that confounds contemporary target directed drug discovery strategies. Phenotypic drug screening is ideally suited to heterogeneous diseases, where targeting specific oncogenic drivers have been broadly ineffective. Our hypothesis is that a modern phenotypic led approach using disease relevant patient-derived GBM stem cell systems will be the most productive approach to identifying new therapeutic targets, drug classes and future drug combinations that target the heterogeneity of GBM. In this study we incorporate a panel of patient-derived GBM stem cell lines into an automated and unbiased Cell Painting assay to quantify multiple GBM stem cell phenotypes. By screening several compound libraries at multiple concentrations across a panel of patient-derived GBM stem cells we provide the first comprehensive survey of distinct pharmacological classes and known druggable targets, including all clinically approved drug classes and oncology drug candidates upon multiple GBM stem cell phenotypes linked to cell proliferation, survival and differentiation. Our data set representing, 3866 compounds, 2.2million images and 64000 datapoints is the largest phenotypic screen carried out to date on a panel of patient-derived GBM stem cell models that we are aware of. We seek to identify agents and target classes which engender potent activity across heterogenous GBM genotypes and phenotypes, in this study we further characterize two validated target classes, histone deacetylase inhibitors and cyclin dependent kinases that exert broad and potent effects on the phenotypic and transcriptomic profiles of GBM stem cells. Here we present all validated hit compounds and their target assignments for the GBM community to explore.

cancer biology↗

bioRxiv2024

Machine learning recognises senescence in glioblastoma and discovers senescence-inducing compounds

Senescence is a cell-intrinsic tumour suppressive response. A one-two-punch cancer treatment strategy aims to induce senescence in cancerous cells before removing them with a senolytic. It is important to accurately recognise senescent cells to investigate the feasibility of such a treatment strategy and identify compounds that induce senescence in cancer. We focus specifically on the terminal brain cancer glioblastoma, firstly identifying senescent glioblastoma cells with conventional stains, before training a machine learning model to distinguish senescent cells using only a DAPI nuclear stain. To demonstrate how our method can aid drug discovery, we apply our pipeline to existing glioblastoma high-throughput phenotypic drug screening imaging data to identify compounds that induce senescence in glioblastoma and verify these predictions experimentally. Author SummaryDamaged cells can enter a senescent cell state, in which they do not divide, but continue to interact with the environment around them. A novel potential cancer treatment strategy is to make tumor cells senescent, before removing senescent cancer cells with a targeted drug. To investigate this treatment strategy in the brain cancer glioblastoma, it is important to be able to accurately recognise senescent glioblastoma cells. As identifying senescent cells is challenging, we create a machine learning pipeline which can detect senescent glioblastoma cells in imaging data. We show that by applying our method to existing data we can discover compounds that induce senescence in glioblastoma. We verify our predictions by testing the compounds experimentally.

cell biology↗

bioRxiv2022

Elevated FOXG1 supports exit from quiescence in neural stem cells through FoxO6

The molecular mechanisms controlling the balance of quiescence and proliferation in adult neural stem cells (NSCs) are often deregulated in brain cancers such as glioblastoma (GBM). Previously, we reported that FOXG1, a forebrain-restricted neurodevelopmental transcription factor, is frequently upregulated in glioblastoma stem cells (GSCs) and limits the effects of cytostatic pathways, in part by repression of the tumour suppressor Foxo3. Here, we show that increased FOXG1 upregulates FoxO6, a more recently discovered FoxO family member with potential oncogenic functions. Although genetic ablation of FoxO6 in proliferating NSCs has no effect on the cell cycle or entry into quiescence, we find that FoxO6-null NSCs can no longer efficiently exit quiescence following FOXG1 elevation. Increased FoxO6 results in the formation of large acidic vacuoles, reminiscent of Pak1-regulated macropinocytosis. Consistently, Pak1 expression is upregulated by FOXG1 overexpression and downregulated upon FoxO6 loss in proliferative NSCs. These data suggest a pro-oncogenic role for FoxO6 in controlling the exit from quiescence in NSCs, and shed light on the functions of this underexplored FoxO family member. Research highlightsO_LIFoxO6 is a downstream effector of elevated FOXG1 in mouse NSCs and GSCs. C_LIO_LIUpregulation of FoxO6 is necessary for FOXG1 to drive efficient quiescence exit of NSCs. C_LIO_LIFoxO6 overexpression stimulates macropinocytosis, a process regulated by the actin cytoskeleton regulator Pak1. C_LIO_LIPak1 is upregulated by FOXG1 overexpression and downregulated upon FoxO6 loss. C_LI

cancer biology↗

bioRxiv2022

Benchmarking brain organoid recapitulation of fetal corticogenesis

Brain organoids are becoming increasingly relevant to dissect the molecular mechanisms underlying psychiatric and neurological conditions. The in vitro recapitulation of key features of human brain development affords the unique opportunity of investigating the developmental antecedents of neuropsychiatric conditions in the context of the actual patients genetic backgrounds. Specifically, multiple strategies of brain organoid (BO) differentiation have enabled the investigation of human cerebral corticogenesis in vitro with increasing accuracy. However, the field lacks a systematic investigation of how closely the gene co-expression patterns seen in cultured BO from different protocols match those observed in fetal cortex, a paramount information for ensuring the sensitivity and accuracy of modeling disease trajectories. Here we benchmark BO against fetal corticogenesis by integrating transcriptomes from in-house differentiated cortical BO (CBO), other BO systems, human fetal brain samples processed in-house, and prenatal cortices from the BrainSpan Atlas. We identified co-expression patterns and prioritized hubs of human corticogenesis and CBO differentiation, highlighting both well-preserved and discordant trends across BO protocols, including different degrees of heterochronicity in differentiation across BO models compared to fetal cortex. Our approach provides a framework to directly compare the extent of in vivo/in vitro alignment of neurodevelopmentally relevant processes and their attending temporalities, structured as an accessible resource to query for modelling human corticogenesis and the neuropsychiatric outcomes of its alterations.

developmental biology↗

bioRxiv2022

Oncogene expression from extrachromosomal DNA is driven by copy number amplification and does not require spatial clustering

Extrachromosomal DNA (ecDNA) are frequently observed in human cancers and are responsible for high levels of oncogene expression. In glioblastoma (GBM), ecDNA copy number correlates with poor prognosis. It is hypothesized that their copy number, size and chromatin accessibility facilitate clustering of ecDNA and colocalization with transcriptional condensates, and that this underpins their elevated transcriptional activity. Here, we use super-resolution imaging and quantitative image analysis to evaluate GBM stem cells harboring distinct ecDNA species (EGFR, MYC, PDGFR). We found no evidence that ecDNA cluster with one another or closely interact with transcriptional condensates. Cells with EGFR-containing ecDNA have increased EGFR transcriptional output, but transcription per gene copy was similar in ecDNA compared to the endogenous chromosomal locus. These data suggest that is the increased copy number of oncogene-harbouring ecDNA that primarily drives high levels of oncogene transcription, rather than specific interactions of ecDNA with the cellular transcriptional machinery.

genetics↗

bioRxiv2018

CRISPR-Cas9 Screens Reveal Genes Regulating a G0-like State in Human Neural Progenitors

Single cell RNA-seq has emerged as a powerful tool for resolving cellular states associated with normal and maligned developmental processes. Here, we used scRNA-seq to examine the cell cycle states of expanding human neural stem cells (hNSCs). From this data, we created a cell cycle classifier, which, in addition to traditional cell cycle phases, also identifies a putative quiescent-like state in neuroepithelial-derived cell types during mammalian neurogenesis and in gliomas. This state, Neural G0, is enriched for expression of quiescent NSC genes and other neurodevelopmental markers found in non-dividing neural progenitors. For gliomas, Neural G0 cell populations and gene expression is significantly associated with less aggressive tumors and extended patient survival. Genetic screens to identify modulators of Neural G0 revealed that knockout of genes associated with the Hippo/Yap and p53 pathways diminished Neural G0 in vitro, resulting in faster G1 transit, down regulation of quiescence-associated markers, and loss of Neural G0 gene expression. Thus, Neural G0 represents a dynamic quiescent-like state found in neuro-epithelial derived cells and gliomas.

cell biology↗

bioRxiv2018

Markov katana: a novel method for Bayesian resampling of parameter space applied to phylogenetic trees

Phylogenetic inference requires a means to search phylogenetic tree space. This is usually achieved using progressive algorithms that propose and test small alterations in the current tree topology and branch lengths. Current programs search tree topology space using branch-swapping algorithms, but proposals do not discriminate well between swaps likely to succeed or fail. When applied to datasets with many taxa, the huge number of possible topologies slows these programs dramatically. To overcome this, we developed a statistical approach for proposal generation in Bayesian analysis, and evaluated its applicability for the problem of searching phylogenetic tree space. The general idea of the approach, which we call Markov katana, is to make proposals based on a heuristic algorithm using bootstrapped subsets of the data. Such proposals induce an unintended sampling distribution that must be determined and removed to generate posterior estimates, but the cost of this extra step can in principle be small compared to the added value of more efficient parameter exploration in Markov chain Monte Carlo analyses. Our prototype application uses the simple neighbor-joining distance heuristic on data subsets to propose new reasonably likely phylogenetic trees (including topologies and branch lengths). The evolutionary model used to generate distances in our prototype was far simpler than the more complex model used to evaluate the likelihood of phylogenies based on the full dataset. This prototype implementation indicates that the Markov katana approach could be easily incorporated into existing phylogenetic search programs and may prove a useful alternative in conjunction with existing methods. The general features of this statistical approach may also prove useful in disciplines other than phylogenetics. We demonstrate that this method can be used to efficiently estimate a Bayesian posterior.

evolutionary biology↗

bioRxiv2017

From Cohorts to Molecules: Adverse Impacts of Endocrine Disrupting Mixtures

Convergent evidence associates endocrine disrupting chemicals (EDCs) with major, increasingly-prevalent human disorders. Regulation requires elucidation of EDC-triggered molecular events causally linked to adverse health outcomes, but two factors limit their identification. First, experiments frequently use individual chemicals, whereas real life entails simultaneous exposure to multiple EDCs. Second, population-based and experimental studies are seldom integrated. This drawback was exacerbated until recently by lack of physiopathologically meaningful human experimental systems that link epidemiological data with results from model organisms.\n\nWe developed a novel approach, integrating epidemiological with experimental evidence. Starting from 1,874 mother-child pairs we identified mixtures of chemicals, measured during early pregnancy, associated with language delay or low-birth weight in offspring. These mixtures were then tested on multiple complementary in vitro and in vivo models. We demonstrate that each EDC mixture, at levels found in pregnant women, disrupts hormone-regulated and disease-relevant gene regulatory networks at both the cellular and organismal scale.

molecular biology↗

bioRxiv2017

Modelling glioblastoma tumour-host cell interactions using adult brain organotypic slice co-culture

Glioblastoma (GBM) is an aggressive incurable brain cancer. The cells that fuel the growth of tumours resemble neural stem cells found in the developing and adult mammalian forebrain. These are referred to as GBM stem cells (GSCs). Similar to neural stem cells, GSCs exhibit a variety of phenotypic states: dormant, quiescent, proliferative and differentiating. How environmental cues within the brain influence these distinct states is not well understood. Laboratory models of GBM tumours can be generated using either genetically engineered mouse models, or via intracranial transplantation of cultured tumour initiating cells (mouse or human). Unfortunately, these approaches are expensive, time-consuming, low-throughput and ill-suited for monitoring of live cell behaviours. Here we explored whole adult brain coronal organotypic slices as a complementary strategy to remove the experimental bottleneck. Mouse adult brain slices remain viable in a neural stem cell serum-free basal media for several weeks. GSCs can therefore be easily microinjected into specific anatomical sites ex vivo. We demonstrated distinct responses of engrafted GSCs to different microenvironments in the brain. Within the subependymal zone - one of the adult neural stem cell niches - a subset of injected tumour cells could effectively engraft and respond to endothelial niche signals. GSCs transplanted slices were treated with the anti-mitotic drug temozolomide as proof-of-principle of the utility in modelling responses to existing treatments. Thus, engraftment of mouse or human GSCs onto whole brain coronal organotypic brain slices provides a convenient experimental model for studies of GSC-host interactions and preclinical testing of candidate therapeutic agents.

cancer biology↗