Biology subjects

Ajaib, S.

Publications and source records attributed to Ajaib, S..

4 recordsLinked to original sources

bioRxiv2025

Spatial profiling of longitudinal glioblastoma reveals consistent changes in cellular architecture, post-treatment

Glioblastoma (GBM), the most aggressive adult brain cancer, comprises a complex tumour microenvironment (TME) with diverse cellular interactions driving progression and pathobiology. How these spatial patterns and interactions evolve with treatment remains unclear. Here, we apply imaging mass cytometry to analyse protein-level changes in paired pre- and post-treatment GBM samples from five patients. We find a significant post-treatment increase in normal brain cells alongside a reduction in vascular cells. Moreover, despite minimal overall change in cellular diversity, interactions among astrocytes, oligodendrocytes, and vascular cells increase post-treatment, suggesting reorganisation of the TME. The GBM TME cells form spatially organized layers driven by hypoxia pre-treatment, but this influence diminishes post-treatment, giving way to less organised layers with organisation driven by reactive astrocytes and lymphocytes. These findings provide insight into treatment-induced shifts in GBMs cellular landscape, highlighting aspects of the evolving TME that appear to facilitate recurrence and are, therefore, potential therapeutic targets. Key pointsO_LISpatial organisation in primary GBM consist of layers driven by the presence of hypoxia C_LIO_LIThe layers in recurrent GBM appear are driven more by the presence of reactive astrocytes C_LIO_LIIncreased cellular cross-talk in recurrent GBM presents novel therapeutic targets C_LI

cancer biology↗

bioRxiv2024

GBMPurity: A Machine Learning Tool for Estimating Glioblastoma Tumour Purity from Bulk RNA-seq Data

BackgroundGlioblastoma (GBM) presents a significant clinical challenge due to its aggressive nature and extensive heterogeneity. Tumour purity, the proportion of malignant cells within a tumour, is an important covariate for understanding the disease, having direct clinical relevance or obscuring signal of the malignant portion in molecular analyses of bulk samples. However, current methods for estimating tumour purity are non-specific, unreliable or technically demanding. Therefore, we aimed to build a reliable and accessible purity estimator for GBM. MethodsWe developed GBMPurity, a deep learning model specifically designed to estimate the purity of IDH-wildtype primary GBM from bulk RNA-seq data. The model was trained using simulated pseudobulk tumours of known purity from labelled single-cell data acquired from the GBmap resource. The performance of GBMPurity was evaluated and compared to several existing tools using independent datasets. ResultsGBMPurity outperformed existing tools, achieving a mean absolute error of 0.15 and a concordance correlation coefficient of 0.88 on validation datasets. We demonstrate the utility of GBMPurity through inference on bulk RNA-seq samples and reveal reduced purity of the Proneural molecular subtype attributed to increased presence of healthy brain cells. ConclusionsGBMPurity provides a reliable and accessible tool for estimating tumour purity from bulk RNA-seq data, enhancing the interpretation of bulk RNA-seq data and offering valuable insights into GBM biology. To facilitate the use of this tool by the wider research community, GBMPurity is available as a web-based tool at: https://gbmdeconvoluter.leeds.ac.uk/. Key PointsO_LIGBMPurity is a glioblastoma-specific purity estimation tool. C_LIO_LIThe model accurately estimates the purity of bulk RNA-seq data, outperforming existing tools. C_LIO_LIThe model is available online at: https://gbmdeconvoluter.leeds.ac.uk/. C_LI Importance of the StudyGlioblastoma (GBM) is a deadly brain tumour with a dismal prognosis. Research on this disease has lagged compared to other cancers, underscoring the need to streamline investigations. The cellular composition of the GBM tumour microenvironment significantly influences therapy resistance, prognosis, and the molecular state of neoplastic cells. Consequently, tumour purity (the proportion of malignant cells within a tumour) is a critical variable for understanding and contextualizing molecular and clinical analyses. We present GBMPurity (https://gbmdeconvoluter.leeds.ac.uk/), an accessible, GBM-specific tool that accurately predicts sample purity from bulk RNA-seq data. This tool can be used by the wider research community to support the interpretation of bulk omics data and accelerate the identification of more effective therapeutic strategies for treating GBM.

bioinformatics↗

bioRxiv2023

Single Cell Track and Trace: live cell labelling and temporal transcriptomics via nanobiopsy.

Single-cell RNA sequencing has revolutionised our understanding of cellular heterogeneity, but whether using isolated cells or more recent spatial transcriptomics approaches, these methods require isolation and lysis of the cell under investigation. This provides a snapshot of the cell transcriptome from which dynamic trajectories, such as those that trigger cell state transitions, can only be inferred. Here, we present cellular nanobiopsy: a platform that enables simultaneous labelling and sampling from a single cell without killing it. The technique is based on scanning ion conductance microscopy (SICM) and uses a double-barrel nanopipette to inject a fluorescent dye and to extract femtolitre-volumes of cytosol. We used the nanobiopsy to longitudinally profile the transcriptome of single glioblastoma (GBM) brain tumour cells in vitro over 72hrs with and without standard treatment. Our results suggest that treatment either induces or selects for more transcriptionally stable cells. We envision the nanobiopsy will transform standard single-cell transcriptomics from a static analysis into a dynamic and temporal assay.

genomics↗

bioRxiv2023

IDHwt glioblastomas can be stratified by their transcriptional response to standard treatment, with implications for targeted therapy

Glioblastoma (GBM) brain tumours lacking IDH1 mutations (IDHwt) have the worst prognosis of all brain neoplasms. Patients receive surgery and chemoradiotherapy but tumours almost always fatally recur. Using RNAseq data from 107 pairs of pre- and post-standard treatment locally recurrent IDHwt GBM tumours, we identified two responder subtypes based on therapy-driven changes in gene expression. In two thirds of patients a specific subset of genes is up-regulated from primary to recurrence (Up responders) and in one third the same genes are down-regulated (Down responders). Characterisation of the responder subtypes indicates subtype-specific adaptive treatment resistance mechanisms. In Up responders treatment enriches for quiescent proneural GBM stem cells and differentiated neoplastic cells with increased neurotransmitter signalling, whereas Down responders commonly undergo therapy-driven mesenchymal transition. Stratifying GBM tumours by response subtype may lead to more effective treatment. In support of this, modulators of gamma aminobutyric acid (GABA) neurotransmitter signalling differentially sensitise Up and Down responder GBM models to standard treatment in vitro.

cancer biology↗