Biology subjects

Whittle, J. R.

Publications and source records attributed to Whittle, J. R..

7 recordsLinked to original sources

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

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↗