Biology subjects

Freytag, S.

Publications and source records attributed to Freytag, S..

15 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

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

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↗

bioRxiv2022

A modular dCas9-based recruitment platform for combinatorial epigenome editing

CRISPR-dCas9 based targeted epigenome editing tools allow precise manipulation and functional investigation of various genome modifications. However, these tools often display substantial context dependency, with highly variable efficacy between target genes and cell types, potentially due to underlying variation in the chromatin modifications present. While simultaneous recruitment of multiple distinct effector chromatin regulators has improved efficacy, these systems typically lack control over which effectors bind and their spatial organisation. To overcome this we have created a new modular combinatorial epigenome editing platform, called SSSavi. This system acts as an interchangeable and reconfigurable docking platform fused to dCas9 to enable simultaneous recruitment of up to four different effectors, allowing precise control and reconfiguration of the effector composition and spatial ordering of their binding. We demonstrate the activity and specificity of the SSSavi system and compare it to existing multi-effector targeting systems, establishing its efficacy. Furthermore, by altering the spatial ordering of effector recruitment, across multiple target genes and cell lines, we demonstrate the importance of effector recruitment order for effective transcriptional regulation. Together, this system offers the capacity to explore effector co-recruitment to specific loci to potentially enhance the manipulation of chromatin contexts previously resistant to targeted epigenomic editing.

synthetic biology↗

bioRxiv2020

Guiding and interpreting brain network classification with transcriptional data

The investigation of brain networks has yielded many insights that have helped to characterise many neurological and psychiatric disorders. In particular, network classification of functional magnetic resonance imaging (fMRI) data is an important tool for the identification of prognostic and diagnostic biomarkers of brain connectivity disorders such as schizophrenia and depression. However, existing generic network classification methods provide no direct information on the underlying molecular mechanisms of the selected functional connectivity features when applied to fMRI data. To address this, we propose a novel fMRI network classification method that incor-porates brain transcriptional data using a user-specified gene set collection (GSC) to construct feature groups for use in classification of brain connectivity data. The use of GSCs are an opportunity to incorporate knowledge of potential molecular mechanisms which may be associated with a disease. The inclusion of transcriptional data yields improved prediction accuracy on publicly available schizophrenia fMRI data for several of the GSCs we consider. We also introduce a post-hoc interpretation framework to provide transcriptional-data-guided biological interpretations for discriminative functional connectivity features identified by existing fMRI network classification methods.

bioinformatics↗

bioRxiv2019

Cell type and cortex-specific RNA editing in single human neurons informs neuropsychiatric disorders

BACKGROUNDConversion of adenosine to inosine in RNA by ADAR enzymes occurs at thousands of sites in the human transcriptome, and is essential for healthy brain development. This RNA editing process is dysregulated in many neuropsychiatric diseases, but is little understood at the level of individual neurons. METHODSWe quantified RNA editing sites in full-length capture nuclear transcriptomes of 3055 neurons from six cortical regions of a neurotypical post-mortem female donor. Putative editing sites were intersected with sites in bulk human tissue transcriptomes including healthy and neuropsychiatric brain tissue, and sites identified in single nuclei from unrelated brain donors. Differential editing between cell types and cortical regions, and individual sites and genes therein, was quantified using linear models. Associations between gene expression and editing were also tested. RESULTSWe identified 41,930 RNA editing sites with robust read coverage in at least ten neuronal nuclei. Most sites were located within Alu repeats in introns or 3 UTRs, and approximately 80% were catalogued in published RNA editing databases. We identified 9285 putative novel RNA editing sites, 29% of which were also detectable neuronal transcriptomes from unrelated donors. Inhibitory neurons showed higher overall transcriptome editing than excitatory neurons. Among the strongest correlates of global editing rates were snoRNAs from the SNORD115 and SNORD116 cluster (15q11), known to modulate serotonin receptor processing and to colocalize with ADAR2. We identified 29 genes preferentially edited in excitatory neurons and 44 genes edited more heavily in inhibitory neurons including RBFOX1, its target genes and small nucleolar RNA-associated genes in the autism-associated Prader-Willi locus 15q11. These results provide cell-type and spatial context for 1730 and 910 sites that are also edited in the brains of schizophrenic and autistic patients respectively, and a reference for future studies of RNA editing in single brain cells from these cohorts. CONCLUSIONSRNA editing, including thousands of previously unreported sites, is robustly detectable in single neuronal nuclei, where gene editing differences are stronger between cell subtypes than between cortical regions. Insufficient editing of ASD-related genes in inhibitory neurons may manifest in the specific perturbation of these cells in autism.

neuroscience↗

bioRxiv2018

scRNA-seq mixology: towards better benchmarking of single cell RNA-seq protocols and analysis methods

Single cell RNA sequencing (scRNA-seq) technology has undergone rapid development in recent years, bringing with it new challenges in data processing and analysis. This has led to an explosion of tailored analysis methods for scRNA-seq to address various biological questions. However, the current lack of gold-standard benchmarking datasets makes it difficult for researchers to evaluate the performance of the many methods. Here, we designed and carried out a realistic benchmark experiment that included mixtures of single cells or pseudo-cells created by sampling admixtures of cells or RNA from 3 distinct cancer cell lines. Altogether we generated 10 datasets using a combination of droplet and plate-based scRNA-seq protocols, with varying data quality, population heterogeneity and noise levels. Using these benchmark datasets, we compared different protocols, evaluated the spike-in standard and multiple data analysis methods for tasks ranging from normalization and imputation, to clustering, trajectory analysis and data integration. Evaluation of methods across multiple datasets revealed some that performed well in general and others that suited specific situations. Our dataset and analysis provide a comprehensive comparison framework for benchmarking most popular scRNA-seq analysis tasks.

bioinformatics↗

bioRxiv2018

dtangle: accurate and fast cell-type deconvolution

MotivationUnderstanding cell type composition is important to understanding many biological processes. Furthermore, in gene expression studies cell type composition can confound differential expression analysis (DEA). To aid understanding cell type composition, methods of estimating (deconvolving) cell type proportions from gene expression data have been developed.\n\nResultsWe propose dtangle, a new cell-type deconvolution method. dtangle works on a range of DNA microarray and bulk RNA-seq platforms. It estimates cell-type proportions using publicly available, often cross-platform, reference data. To comprehensively evaluate dtangle, we assemble ten benchmark data sets. Here, dtangle is competitive with published deconvolution methods, is robust to selection of tuning parameters and is quicker than other methods. As a case study, we investigate the human immune response to Lyme disease. dtangles estimates reveal a temporal trend consistent with previous findings and are important covariates for DEA across disease status.\n\nAvailabilitydtangle is on CRAN (cran.r-project.org/package=dtangle) or github (dtangle.github.io).\n\nContactgjhunt@umich.edu

bioinformatics↗

bioRxiv2017

Cluster Headache: Comparing Clustering Tools for 10X Single Cell Sequencing Data

The commercially available 10X Genomics protocol to generate droplet-based single cell RNA-seq (scRNA-seq) data is enjoying growing popularity among researchers. Fundamental to the analysis of such scRNA-seq data is the ability to cluster similar or same cells into non-overlapping groups. Many competing methods have been proposed for this task, but there is currently little guidance with regards to which method offers most accuracy. Answering this question is complicated by the fact that 10X Genomics data lack cell labels that would allow a direct performance evaluation. Thus in this review, we focused on comparing clustering solutions of a dozen methods for three datasets on human peripheral mononuclear cells generated with the 10X Genomics technology. While clustering solutions appeared robust, we found that solutions produced by different methods have little in common with each other. They also failed to replicate cell type assignment generated with supervised labeling approaches. Furthermore, we demonstrate that all clustering methods tested clustered cells to a large degree according to the amount of genes coding for ribosomal protein genes in each cell.

bioinformatics↗