Biology subjects

Best, S. A.

Publications and source records attributed to Best, S. A..

11 recordsLinked to original sources

bioRxiv2025

A new genetically engineered transplant model of glioma recapitulates key phenotypes of low- and high-grade gliomas

Immune competent animal models are essential in preclinical glioma research. The ability to investigate tumor development with key tumor microenvironment components such as immune infiltration, stromal cells and extracellular matrix allows for the investigation of these complex tumors. However, the current range of syngeneic models of glioma possess intrinsic limitations which must be acknowledged when designing a preclinical study. To address this gap, we developed genetically engineered mouse cell line models (GEM-CLeMs) by introducing common glioma driver mutations into immortalized astrocytes. A high-grade glioma model was generated by combining Pten knockdown with RAS V12 overexpression, while a low-grade glioma model was produced through p53 knockdown with mutant IDH1R132H overexpression. The RAS/Pten GEM-CLeM tumors grew rapidly in vivo, displayed necrosis, multinucleated pleomorphisms, abundant vascularization, and showed strong enrichment for extracellular matrix remodelling and mesenchymal genes, features closely aligned with human glioblastoma. Importantly, the RAS/Pten GEM-CLeM tumors showed similar survival trends and immune infiltration patterns as a corresponding KrasG12D/PtencKO GEMM, but could be grown to larger sizes, facilitating better stromal and immune analyses. In contrast, the IDH1R132H/p53 GEM-CLeM formed slow-growing tumors with distinctive immune infiltration and vascular patterns, consistent with low-grade glioma phenotypes. Compared with the commonly used cell line GL261, the GEM-CLeM tumors had higher levels of stromal integration and immune suppression, making them a more faithful model of the glioma tumor microenvironment. This system enables rapid generation of transplantable glioma models with defined driver mutations in a low-mutational background, offering a flexible platform for dissecting glioma biology and evaluating immunotherapies. Importance of the studyWe have generated a set of customizable, modular Genetically Engineered Mouse Cell Line Models (GEM-CLeMs) of "high grade" and "low grade" glioma. They reliably form tumors when transplanted intracranially into immune-competent C57BL/6 mice, and they are cost- and time-effective at capturing the important characteristics of glioma, both mutant IDH1 low grade glioma and high grade glioblastoma. Critically these characteristics include the myeloid-rich immune suppressive tumor immune microenvironment, a key weakness of existing murine glioma cell lines like GL261. These GEM-CLeM models can be used in multiple ways. The cells are amenable to further manipulation, so therapeutic targets and drug mechanism of action can be assessed. The activity of candidate genes in tumour formation and phenotype can be determined. Most importantly the models can be used to develop effective immunotherapies, including strategies to target macrophage and myeloid cell immune suppression. Key pointsO_LICombinations of driver mutations were engineered into an immortalised mouse astrocyte. C_LIO_LIEngineered cells formed tumours on intracranial transplant into immune competent mice. C_LIO_LITumors had key histological and immune suppressive features of human glioma. C_LI

cancer biology↗

bioRxiv2025

γδ T cells modulate anti-tumor immunity in small cell lung cancer

Small cell lung cancer (SCLC) is a highly aggressive neoplasm with limited sensitivity to anti-PD-(L)1 blockade, likely due to the epigenetic silencing of MHC-I. Elucidating MHC-I-independent immune recognition mechanisms is therefore crucial for enhancing treatment responses and improving clinical outcomes in a greater number of patients. Leveraging single cell approaches, we discovered {gamma}{delta} T cell infiltration in biospecimens from patients with SCLC. Despite PD-1 expression, {gamma}{delta} T cells maintained a cytotoxic transcriptional profile, suggestive of an anti-tumor role. Indeed, high {gamma}{delta} T cell infiltration predicted improved response to anti-PD-L1 immunotherapy in patients with SCLC. Moreover, using pre-clinical models, we demonstrated that {gamma}{delta} T cells are effective at tarlatamab (DLL3-CD3 BiTE) redirected SCLC killing and that zoledronate, an FDA-approved compound, can sensitize SCLC cells to {gamma}{delta} T cell-mediated killing. Thus, our findings suggest that engaged {gamma}{delta} T cells are potentially valuable targets for SCLC therapy.

cancer biology↗

bioRxiv2025

Three-dimensional patient-derived models of glioblastoma retain intra-tumoral heterogeneity

The intra- and inter-tumoral heterogeneity of glioblastoma represents a significant therapeutic challenge, as well as difficulty in generating reliable models for in vitro studies. Historical 2D adherent cell lines do not recapitulate this complexity, whereas both patient-derived neurospheres (PDN) and organoids (PDO) demonstrate intra-tumoral heterogeneity. Here, we quantify the tumor cell composition from matched models established from the same primary tumor using a series of multi-omic interrogations. We find that both patient-derived models recapitulate the genomic, epigenomic and tumor cell heterogeneity of the primary tissue. Furthermore, single-nuclei RNA sequencing revealed a subset of organoids containing small numbers of non-malignant cells from neuron and immune cell lineages. Harnessing the intra-tumoral heterogeneity of PDN models, we reveal the impact of temozolomide chemotherapy on individual cell states, altering composition of tumors over time in response to treatment. Our data confirms that both patient-derived models recapitulate patient intra-tumoral heterogeneity providing a platform for tumor cell state refined therapeutic studies. Key PointsO_LIGeneration of matched patient-derived neurosphere and organoid models from resected GBM tissue C_LIO_LINeurosphere models exhibit greater proliferative signatures C_LIO_LIBoth patient-derived models recapitulate genomic and epigenomic features of the primary tissue C_LIO_LISingle-nuclei RNA sequencing reveals both models recapitulate intra-tumoral heterogeneity of the primary tissue C_LIO_LINeurosphere models enable interrogation of therapeutic responses in the context of heterogeneity C_LI Importance of studyPatient derived models can be powerful tools when they faithfully recapitulate the tumor tissue from which they are derived. In glioblastoma, patient derived neurospheres (PDN) and organoids (PDO) have both been used in studies, however the differences between these models and the recapitulation of patient heterogeneity remain to be fully characterized. To address this, we performed multi-omic profiling of PDN and PDO models generated from the same tumor tissue. We find that that across a range of data modalities, both model systems exhibit a high level of resemblance to tissue, and critically, maintain heterogeneity and tumor cell composition. The importance of modeling heterogeneity was demonstrated in PDN models, where temozolomide treatment specifically alters the abundance of MES-like and AC-like tumor cells. Our findings demonstrate that neurosphere and organoid models effectively preserve cellular heterogeneity, genomic alterations, methylation signatures and transcriptomic features, both highly suitable to model glioblastomas complex cellular landscape.

cancer biology↗

bioRxiv2024

TIRE-seq: an Integrated Sample Extraction and Transcriptomics Workflow for High Throughput Perturbation Studies

RNA sequencing (RNA-seq) is widely used in biomedical research, advancing our understanding of gene expression across biological systems. Traditional methods require upstream RNA extraction from biological inputs, adding time and expense to workflows. We developed TIRE-seq (Turbocapture Integrated RNA Expression Sequencing) to address these challenges. TIRE-seq integrates mRNA purification directly into library preparation, eliminating the need for a separate extraction step. This streamlined approach reduces turnaround time, minimizes sample loss, and improves data quality. A comparative study with the widely used Prime-seq protocol demonstrates TIRE-seqs superior sequencing efficiency with crude cell lysates as inputs. TIRE-seqs utility was demonstrated across three biological applications. It captured transcriptional changes in stimulated human T cells, revealing activation-associated gene expression profiles. It also identified key genes driving murine dendritic cell differentiation, providing insights into lineage commitment. Lastly, TIRE-seq analyzed the dose-response and time-course effects of temozolomide on patient-derived neurospheres, identifying differentially expressed genes and enriched pathways linked to the drugs mechanism of action. With its simplified workflow and high sequencing efficiency, TIRE-seq offers a cost-effective solution for large-scale gene expression studies across diverse biological systems.

genomics↗

bioRxiv2024

SpaMTP: Integrative Statistical Analysis and Visualisation of Spatial Metabolomics and Transcriptomics data.

The ability to spatially measure multi-modal data provides an unprecedented opportunity to comprehensively explore molecular regulation at transcriptional, translational and metabolic levels to acquire insights on cellular activities underpinning health and disease. However, there is currently a lack of analytical tools to integrate complementary information across different spatial-omics data modalities, particularly with respect to spatial metabolomics data, which is becoming increasingly invaluable. We introduce SpaMTP, a versatile software that implements an end-to-end integrative analysis of spatial metabolomics and transcriptomics data. Based in R, SpaMTP bridges processing functionalities for metabolomics data from Cardinal with user-friendly cell-centric analyses implemented in Seurat. Furthermore, SpaMTPs comprehensive analysis pipeline covers (1) automated mass-to-charge ratio (m/z) metabolite annotation; (2) a wide range of metabolite-gene based downstream statistical analyses including differential expression, pathway analysis, and correlation analysis; (3) integrative spatial-omics analysis; and (4) a suite of visualisation functions. For flexibility and interoperability, SpaMTP includes various functions for data import/export and object conversion, enabling seamless integration with other R and Python packages. We demonstrated the utility of SpaMTP to draw new biological understandings through analysing two biological system. We believe this software and implemented methods will be broadly utilised in spatial multi-omics and spatial metabolomics analyses.

bioinformatics↗

bioRxiv2024

An integrative spatial multi-omic workflow for unified analysis of tumor tissue

Combining molecular profiling with imaging techniques has advanced the field of spatial biology, offering new insights into complex biological processes. Focusing on diffuse IDH-mutated low-grade glioma, this study presents a workflow for Spatial Multi-omics Integration, SMINT, specifically combining spatial transcriptomics and spatial metabolomics. Our workflow incorporates both existing and custom-developed computational tools to enable cell segmentation and registration of spatial coordinates from both modalities to a common coordinate framework. During our investigation of cell segmentation strategies, we found that nuclei-only segmentation, while containing only 40% of segmented cell transcripts, enables accurate cell type annotation, but does not account for multinucleated cells. Our integrative workflow including cell-morphology segmentation identified distinct cellular neighborhoods at the infiltrating edge of gliomas, which were enriched in multinucleated and oligodendrocyte-lineage tumor cells, that may drive tumor invasion into the normal cortical layers of the brain. HighlightsO_LIAlignment and integrated analysis of spatial transcriptomic and metabolomic data C_LIO_LINuclei-only and cell-morphology segmentations are concordant for cell annotation C_LIO_LISpatially distinct regions are conserved in transcriptomic and metabolomic datasets C_LIO_LIMulti-omic exploration of glioma leading edge identifies novel biological features C_LI

bioinformatics↗

bioRxiv2023

Tumor cell-derived spermidine promotes a pro-tumorigenic immune microenvironment in glioblastoma via CD8+ T cell inhibition

The glioblastoma microenvironment is enriched in immunosuppressive factors that potently interfere with the function of cytotoxic T lymphocytes. Cancer cells can directly impact the immune system, but the mechanisms driving these interactions are not completely clear. Here we demonstrate that the polyamine metabolite spermidine is elevated in the glioblastoma tumor microenvironment. Exogenous administration of spermidine drives tumor aggressiveness in an immune-dependent manner in pre-clinical mouse models via reduction of CD8+ T cell frequency and phenotype. Knockdown of ornithine decarboxylase, the rate-limiting enzyme in spermidine synthesis, did not impact cancer cell growth in vitro but did result in extended survival. Furthermore, glioblastoma patients with a more favorable outcome had a significant reduction in spermidine compared to patients with a poor prognosis. Our results demonstrate that spermidine functions as a cancer cell-derived metabolite that drives tumor progression by reducing CD8+T cell number and function.

cancer biology↗

bioRxiv2023

Matrix selection for the visualization of small molecules and lipids in brain tumors using untargeted MALDI-TOF mass spectrometry imaging

Matrix-assisted laser desorption/ionization mass spectrometry imaging allows the study of metabolic activity in the tumor microenvironment of brain cancers. The detectable metabolites within these tumors are contingent upon the choice of matrix, deposition technique, and polarity setting. In this study, we compared the performance of three different matrices, two deposition techniques, and use of positive and negative polarity in two different brain cancer types and across two species. Optimal combinations were confirmed by comparative analysis of lipid and small molecule abundance using liquid chromatography-mass spectrometry and RNA-sequencing assessing differential metabolites between normal and tumor regions. Our findings indicate that the recrystallized cyano-4-hydroxycinnamic acid matrix in positive polarity offered superior performance for both detected metabolites and consistency with other techniques. Beyond these implications for brain cancer, our work establishes a workflow to identify optimal matrices for spatial metabolomics studies.

molecular biology↗

bioRxiv2023

A Risk-reward Examination of Sample Multiplexing Reagents for Single Cell RNA-Seq

Single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for understanding cellular heterogeneity and function. However the choice of sample multiplexing reagents can impact data quality and experimental outcomes. In this study, we compared various multiplexing reagents, including MULTI-Seq, Hashtag antibody, and CellPlex, across diverse sample types such as human peripheral blood mononuclear cells (PBMCs), mouse embryonic brain and patient-derived xenografts (PDXs). We found that all multiplexing reagents worked well in cell types robust to ex vivo manipulation but suffered from signal-to-noise issues in more delicate sample types. We compared multiple demultiplexing algorithms which differed in performance depending on data quality. We find that minor improvements to laboratory workflows such as titration and rapid processing are critical to optimal performance. We also compared the performance of fixed scRNA-Seq kits and highlight the advantages of the Parse Biosciences kit for fragile samples. Highly multiplexed scRNA-Seq experiments require more sequencing resources, therefore we evaluated CRISPR-based destruction of non-informative genes to enhance sequencing value. Our comprehensive analysis provides insights into the selection of appropriate sample multiplexing reagents and protocols for scRNASeq experiments, facilitating more accurate and cost-effective studies.

genomics↗

bioRxiv2023

PRMT1 Inhibition Selectively Targets BNC1-Dependent Proliferation, but not Migration in Squamous Cell Carcinoma

Squamous Cell Carcinoma (SCC) develops in stratified epithelial tissues and demonstrates frequent alterations in transcriptional regulators. We sought to discover SCC-specific transcriptional programs and identified the transcription factor Basonuclin 1 (BNC1) as highly expressed in SCC compared to other tumor types. RNA-seq and ChIP-seq analysis identified pro-proliferative genes activated by BNC1 in SCC cells and keratinocytes. Inhibition of BNC1 in SCC cells suppressed proliferation and increased migration via FRA1. In contrast, BNC1 reduction in keratinocytes caused differentiation, which was abrogated by IRF6 knockdown, leading to increased migration. Protein interactome analysis identified PRMT1 as a co-activator of BNC1-dependent proliferative genes. Inhibition of PRMT1 resulted in a dose-dependent reduction in SCC cell proliferation without increasing migration. Importantly, therapeutic inhibition of PRMT1 in SCC xenografts significantly reduced tumor size, resembling functional effects of BNC1 knockdown. Together, we identify BNC1-PRMT1 as an SCC-lineage specific transcriptional axis that promotes cancer growth, which can be therapeutically targeted to inhibit SCC tumorigenesis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/533164v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@97dd68org.highwire.dtl.DTLVardef@48ebaaorg.highwire.dtl.DTLVardef@167eb2corg.highwire.dtl.DTLVardef@180b325_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2023

Spatial architecture of high-grade glioma reveals tumor heterogeneity within distinct domains

High-grade gliomas are aggressive primary brain cancers with poor response to standard regimens, driven by immense heterogeneity. In isocitrate dehydrogenase (IDH) wild-type high-grade glioma (glioblastoma, GBM), increased intra-tumoral heterogeneity is associated with more aggressive disease. Recently, spatial technologies have emerged to dissect this complex heterogeneity within the tumor ecosystem by preserving cellular organization in situ. Here, we construct a high-resolution molecular landscape of GBM and IDH-mutant high-grade glioma patient samples to investigate the cellular subtypes and spatial communities that compose high-grade glioma using digital spatial profiling and spatial molecular imaging. This uncovered striking diversity of the tumor and immune microenvironment, that is embodied by the heterogeneity of the inferred copy-number alterations in the tumor. Reconstructing the tumor architecture revealed two distinct niches, one composed of tumor cell states that most closely resemble normal glial cells, associated with microglia; and the other niche populated by monocytes and mesenchymal tumor cells. We further reveal that communication between tumor and immune cells is underpinned by tumor-specific ligands, such as TGF{beta} signaling in astrocyte-like tumor cells. This primary study reveals high levels of intra-tumoral heterogeneity in high-grade gliomas, associated with a diverse immune landscape within spatially localized regions.

cancer biology↗