Biology subjects

Cha, J.

Publications and source records attributed to Cha, J..

At least 19 recordsLinked to original sources

bioRxiv2026

MAFA drives pancreatic islet maturation through coordination of cell growth, metabolic maturation, and resilience to stress.

Stem cell-derived islets (SC-islets) are a promising source for replacing the loss of insulin-secreting {beta} cells in insulin-dependent diabetes patients, but compared to their primary human counter parts, SC-islets are considered functionally immature and do not immediately regulate blood glucose after transplantation. To better understand the molecular underpinnings of {beta} cell maturation, we genetically engineered human pluripotent stem cells (hPSCs) to control the induction of the {beta} cell-maturing transcription factor MAFA or a MAFA S64F mutant variant associated with premature {beta} cell aging and senescence in SC-islets. We employ a combination of in vitro assays, multi-omics analyses, and murine in vivo transplantation studies to demonstrate that MAFA remodeling of the SC-islet transcriptional landscape promotes SC-islet biogenesis and metabolic maturation giving rise to greater SC-islet resilience and insulin secretory capacity. Parallel MAFA S64F studies highlight the importance for compensatory mechanisms for sustaining accelerated oxidative metabolism and enhanced insulin secretion without inducing intracellular stress responses and islet dysfunction. Overall, these results reveal MAFA-governed transcriptional changes that drive SC-islet functional maturation, which provides the foundation for maturing SC-islets in vitro, preserving {beta} cell integrity, and improving SC-islet cell replacement therapies.

cell biology↗

bioRxiv2026

Slower cardiac-coupled cortical dynamics link depressive symptoms to inflexible affective updating

Depression is marked by blunted affective responses to context, which interoceptive accounts trace to altered neural representations of bodily states. Yet this evidence mainly concerns response magnitude, not how quickly affect is updated when contexts change. Here we tested whether depressive symptom severity is related to delayed affective updating, and whether cortical dynamics tracking cardiac states account for this delay. To this end, we applied a movie-watching paradigm with independently defined contextual shifts, continuous affect ratings, electroencephalography, and electrocardiography in individuals spanning a continuum of depressive symptoms. Combining deep representation learning and a dynamical systems framework, we quantified how quickly (speed) and how sharply (angle) cardiac-coupled cortical representations reorganized at each shift. Greater symptom severity predicted longer latency to enter the context-congruent affective state across contextual shifts, regardless of valence. In a cross-sectional mediation analysis, slower speed, but not angle, accounted for this association. This mediation was specific to depressive symptoms, contextual shifts, and cardiac-coupled neural dynamics. These findings extend the embodied account of depression from blunted affective intensity toward its inflexible updating at moments of contextual shift, and offer a broadly applicable framework for quantifying brain-body dynamics across affective dysfunctions.

neuroscience↗

bioRxiv2026

Prophage activity shapes the thermal ecology and evolution of a marine bacterial host

Predicting the response of populations to thermal change is challenging due to temperature-dependent interspecific interactions, such as those between bacteria and their integrated viruses (prophages). Temperature is known to modulate prophage activity, raising the question of whether and how prophages alter host responses to temperature. In this work, we explored how prophage infection shapes host thermal ecology and evolution in a novel marine bacterial isolate carrying a spontaneously inducing prophage. We first characterized the effect of temperature on bacteria and phage population dynamics, finding that high temperatures led to bacterial population crashes and reduced yield, with abundant phage production across all temperatures. To then determine the causal impacts of this phage, we infected a conspecific susceptible strain and found that performance in the novel lysogen was reduced across all temperatures and indistinguishable from the naturally infected strain. Further, we found that evolutionary rescue mutants of the naturally infected strain with an expanded upper thermal limit gained high temperature adaptation through a reduction in prophage activity. Overall, we found that intraspecific variation in thermal ecology was explained by prophage infection, and that adaptation to nonpermissive temperature occurred through modifications to prophage dynamics. This work demonstrates a critical link between viral mobile genetic elements and bacterial thermal responses, and emphasizes the importance of ecological interactions in thermal ecology and evolution.

ecology↗

bioRxiv2026

Exact formula of the total quasi-steady state approximation in competitive target-mediated drug disposition

Competitive target-mediated drug disposition (competitive TMDD) arises when two drugs compete for the same target receptor. These dynamics can be characterized by the full competitive TMDD model; yet, its complexity motivated the use of a reduced model, which is invalid under high receptor concentrations. While this problem can be resolved by using the total quasi-steady state approximation (tQSSA), which remains valid for all receptor conditions, the exact formula of the tQSSA-based reduced model--competitive qTMDD--has remained unknown for 15 years, as it requires solving a cubic equation and the nontrivial task of identifying a biologically meaningful solution. Consequently, researchers have relied on numerical approximation methods, which impose substantial computational costs to maintain accuracy and thereby hinder their practical use in complex real-world applications. To address this problem, we derive--for the first time--a real-valued exact formula for competitive qTMDD by leveraging analytic properties of cubic equations and geometric characteristics of their roots in the complex plane. This exact formula improved computational speed by more than 11-fold compared to previous numerical approximation methods, thereby enabling Bayesian inference using competitive qTMDD, which had been impractical due to excessive computational time. When applied to real-world data from clinical trials, competitive qTMDD estimated pharmacological parameter estimates comparable to those from the full competitive TMDD model while requiring only 30-43% of the computation time. Importantly, this estimation using competitive qTMDD remained consistently accurate regardless of data sparsity, whereas the previous reduced model produced biased estimates under sparse sampling conditions. By ensuring precise biological interpretation of drug systems even in complex real-world scenarios, the exact formula of competitive qTMDD has the potential to significantly streamline the drug development and clinical testing process. Our exact formula also consists entirely of real-valued terms, allowing seamless integration into existing pharmacometrics software. Author SummaryCompetitive target-mediated drug disposition (competitive TMDD) occurs when two drugs compete for the same target receptor. Analyzing this interaction has faced a dilemma for 15 years: choosing between a full model that is accurate but computationally intensive, and a reduced model that is fast but often loses accuracy under real-world clinical conditions. Researchers tried to solve this dilemma by deriving an accurate reduced model; however, it was mathematically challenging. As a result, they had to rely on numerical approximations--which require substantial computational power to maintain the accuracy needed for clinical use, making them impractical in real-world scenarios. In this study, we derive a first-ever exact formula for the new reduced model that is accurate across all biological conditions. This formula computes more than 11 times faster than previous numerical methods, making advanced statistical analyses--such as Bayesian inference--feasible for the first time in this context. When applied to real-world clinical data for Anakinra and rhIL-7-hyFc, our method yielded parameter estimates as accurate as the full model but required significantly less computation time. This breakthrough provides a more accurate biological interpretation and better guidance for determining the right drug dose, potentially accelerating drug development and reducing associated costs.

pharmacology and toxicology↗

bioRxiv2025

Longitudinal Changes in Cortical Response Dynamics with Deep Brain Stimulation to the Subcallosal Cingulate for Treatment-Resistant Depression

Deep brain stimulation (DBS) of the subcallosal cingulate cortex (SCC) is a promising intervention for treatment-resistant depression, yet objective biomarkers that track recovery remain limited. This study examined longitudinal changes in stimulation-evoked potentials (SEPs) to characterize how SCC-driven cortical communication evolves during treatment. Ten patients across three SCC-DBS trials underwent high-density EEG recordings at 4 and 24 weeks. SEP features were extracted from source-localized SCC signals and related to clinical outcomes and fractional anisotropy (FA) of midcingulate cingulum. Cortical responses showed a consistent reduction in latency and an increase in magnitude over time, indicating faster and stronger electrocortical signaling with chronic stimulation. Higher baseline midcingulate FA predicted greater latency acceleration, linking SEP timing to white-matter integrity. These findings identify temporal SEP dynamics as candidate mechanistic biomarkers that reflect circuit engagement during SCC-DBS and offer a pathway toward physiology-guided optimization of neuromodulation for depression. HighlightsO_LIReduced latency of stimulation evoked potential after 6 months of DBS C_LIO_LILarger cortical evoked response to single pulse perturbation after 6 months of DBS C_LIO_LIBaseline myelin integrity in MCC predicts magnitude of change in EP latency C_LI

neuroscience↗

bioRxiv2025

A more comprehensive and reliable analysis of individual differences with generalized random forest for high-dimensional data: validation and guidelines

Analyzing individual differences in treatment or exposure effects is a central challenge in psychology and behavioral sciences. Conventional statistical models have focused on average treatment effects, overlooking individual variability, and struggling to identify key moderators. Generalized Random Forest (GRF) can predict individualized treatment effects, but current implementations suffer from two critical limitations: (1) prediction performances vary substantially across random initializations, and (2) identification of key moderator is limited in high-dimensional settings. Here, we introduce two methodological advances to address these issues. First, a seed ensemble strategy stabilizes predictions by aggregating models trained under different random initializations. Second, a backward elimination procedure systematically identifies key moderators from high-dimensional inputs. Simulation analyses across diverse scenarios demonstrate that our approach achieves reliable and valid predictions across random seeds, improved performance in moderator identification, and robust generalization to independent data. To facilitate adoption and interpretation, we provide step-by-step guidance using large-scale neuroimaging dataset (N = 8,778) with reusable code. These enhancements make GRF more reliable for modeling individual differences in treatment effects, supporting data-driven hypothesis generation, and identification of responsive subgroups.

neuroscience↗

bioRxiv2025

RET Receptor Tyrosine Kinase Promotes Breast Cancer Metastasis to the Brain and RET Inhibitors Pralsetinib and Selpercatinib Suppress Breast Cancer Brain Metastases

Patients with breast cancer brain metastases (BCBM) exhibit dismal prognosis, largely due to the insufficient biological understanding of BCBM and the scarcity of therapeutics that can penetrate the blood-brain barrier. This study was focused on Rearranged during transfection (RET) receptor tyrosine kinase that has been implicated in tumorigenesis and metastatic progression of several solid tumor types including, non-small cell lung cancer (NSCLC), thyroid carcinomas, and luminal breast cancer subtypes. FDA-approved selective RET inhibitors, pralsetinib and selpercatinib, have demonstrated potent intracranial activity in brain metastases from NSCLC and thyroid cancer; however, their efficacy in BCBM has not been investigated. Here, we report that RET activation is elevated in brain metastases of breast cancer patients compared to matched primary tumors (N=30), and in three brain-tropic breast cancer cell lines compared to the parental lines. High RET pathway activation is associated with shorter overall metastasis-free survival and brain metastasis-free survival in patients with HER2-enriched and triple-negative breast cancer (TNBC). Using isogenic TNBC cells lines RET overexpression, we demonstrated that RET strongly promotes their preferential metastasis to the brain in mice with intracardiac injections to tumors cells. Using intracranial tumor implantation of the isogenic lines, we further found that RET significantly enhances the formation and progression of brain tumors in vivo. Moreover, we report that selective RET inhibition using pralsetinib and selpercatinib significantly reduces cell viability, enhances apoptosis, and attenuates migration of brain-tropic breast cancer cells in vitro. Using two mouse studies that model multi-organ metastases and breast tumor formation in the brain, we observed that RET inhibition significantly prevented the circulating tumor cells from forming brain metastases and suppressed the growth of intracranially implanted tumor cells, but did not significantly inhibit the progression of well-established brain metastases. Together, our findings demonstrated that RET is highly activated in BCBM and functioning as a novel mediator of BCBM, and that RET plays a new role as a viable therapeutic target for BCBM.

cancer biology↗

bioRxiv2025

Genetic background influences the heterogeneous phenotypes driven by the MAFAS64F MODY variant in male mice

Pancreatic {beta}-cells require the coordinated expression of transcription factors such as MAFA to dynamically secrete insulin to maintain euglycemia. A naturally occurring mutation in MAFA (MAFAS64F) produces a long-lived variant protein which predisposes carriers to dichotomous conditions of either maturity (adult)-onset diabetes of the young (MODY) or hypoglycemia in a sex-dependent manner. Here we show that genetic background modulates disease penetrance in MafAS64F/+ male mice. Specifically, MafAS64F/+ males backcrossed to a C57/Bl6J ("C57") background prevented dysglycemia, while C57 MafAS64F/+ males bred one generation to an SJL/J background ("mixed") manifested overt diabetes, impaired insulin secretion, and accelerated {beta}-cell senescence. MafA protein levels, phosphorylation status, and target gene expression in C57 MafAS64F/+ male islets were more comparable to wildtype males. RNA sequencing of C57 MafAS64F/+ male islets revealed fewer differentially expressed genes than mixed background male islets, including transcriptional signatures of {beta}-cell senescence. In addition, retinoic acid signaling, another signature of cellular aging, was uniquely downregulated in C57 MafAS64F/+ male islets. Indeed, core retinoic acid signaling receptor RAR and known target genes were downregulated in C57 MafAS64F/+ male islets. CUT&RUN mapping revealed that MafA directly impacted retinoic acid receptor alpha Rara expression. In sum, these data show that genetic factors can impact unique pathways to modulate disease penetrance in mice modeling MAFA-MODY. Article HighlightsO_LIGenetic background in mice or ancestry in humans can profoundly influence diabetes susceptibility. C_LIO_LIWe asked whether diabetes penetrance in our robust mouse model of MAFA-MODY on a mixed genetic background was impacted when backcrossed to C57/Bl6J ("C57"). C_LIO_LIHere we show that C57 MafAS64F/+ males have wildtype glucose-sensing properties, compared to overt diabetes which could be reproducibly re-created on a mixed background. Further analysis disclosed more muted gene expression changes in C57 MafAS64F/+ male islets including unique downregulation of senescence and retinoic acid signaling. C_LIO_LIThese results illustrate the influence of genetic landscape on {beta}-cell function in response to a pathogenic MODY variant. C_LI

physiology↗

bioRxiv2025

Stochastically Emergent Tumors offer in vivo whole genome interrogation of cancer evolution from non-malignant precursors

Interrogating the stochastic events underlying tumor evolution from non-malignant precursors is crucial for understanding therapy resistance. Current methods are complicated by chromosomal instability, obscuring driver identification and yielding non-representative genetics. Inspired by patient tumors that evolve without chromosomal instability, we developed Stochastically Emergent Tumors (SETs) by inducing mismatch repair deficiency in non-malignant precursors, then engrafting in mice. Barcoded SETs exhibited increased tumoral and drug target heterogeneity over current models. SETs delineated the stochastic contributions, mutational landscapes, and selective pressures distinguishing tumorigenesis from non-malignant precursor in vitro growth. SETs are an unlimited source for diverse Stochastically Emergent cell Lines (SELs), bolstering under-represented cancers. Since SETs composition dynamically reflects therapy exposure, they are a whole-genome platform for precision oncology. We identified three novel genetic drivers (ZFHX3, CIC, KMT2D) of differential prostate cancer therapy responses. These alterations are enriched in patients of African and Chinese ancestry and correlate with significant differences in survival.

cancer biology↗

bioRxiv2024

Association of Bile Acids with Connectivity of Executive Control and Default Mode Networks in Patients with Major Depression

Objective: Bile acids may contribute to pathophysiologic markers of Alzheimers disease, including disruptions of the executive control network (ECN) and the default mode network (DMN). Cognitive dysfunction is common in major depressive disorder (MDD), but whether bile acids impact these networks in MDD patients is unknown. Methods: Resting state functional magnetic resonance imaging (fMRI) scans and blood measures of four bile acids from 74 treatment-naive adults with MDD were analyzed. Dorsolateral prefrontal cortex (DLPFC) seeds were used to examine connectivity of the ECN and posterior cingulate cortex (PCC) seeds were used for the DMN. Using a whole-brain analysis, the functional connectivity of these seeds was correlated with serum levels chenodeoxycholic acid (CDCA) and its bacterially-derived secondary bile acid, lithocholic acid (LCA). Results: CDCA levels were strongly and inversely correlated with connectivity between DLPFC regions of the ECN (R2= .401, p<.001). LCA levels were strongly and positively correlated with connectivity of the DLPFC and left inferior temporal cortex of the ECN (R2=.263, p<.001). The LCA/CDCA ratio was strongly and positively correlated with connectivity of the DLPFC with two components of the ECN: bilateral inferior temporal cortex and the left superior and inferior parietal lobules (all R2>.24, all p<.001). For the DMN, the LCA/CDCA ratio was strongly and negatively correlated with connectivity of the PCC with multiple bilateral insula regions (all R2>0.25, all p<.001). Conclusions: The relationship between LCA and CDCA levels and functional connectivity of the ECN and DMN suggests potential shared pathophysiologic processes between Alzheimers disease and MDD.

neuroscience↗

bioRxiv2024

Augmenting the human interactome for disease prediction through gene networks inferred from human cell atlas

Gene co-expression network inference from bulk tissue samples often misses cell-type-specific interactions, which can be detected through single-cell gene expression data. However, the noise and sparsity of single-cell data challenge the inference of these networks. We developed scNET, a framework for integrative cell-type-specific co-expression network inference from single-cell transcriptome data, demonstrating its utility in augmenting the human interactome for more accurate disease gene prediction. We address the limitations in de novo network inference from single-cell expression data through dropout imputation, metacells formation, and data transformation. Employing this data preprocessing pipeline, we inferred cell-type-specific co-expression links from single-cell atlas data, covering various cell types and tissues, and integrated over 850K of these inferred links into a preexisting human interactome, HumanNet, resulting in HumanNet-plus. This integration notably enhanced accuracy of network-based disease gene prediction. These findings suggest that with proper data preprocessing, network inference from single-cell gene expression data can be highly effective, potentially enriching the human interactome and advancing the field of network medicine.

bioinformatics↗

bioRxiv2024

Tumor microenvironment distinctions between esophageal cancer subtypes explain varied immunotherapy responses

Esophageal cancer comprises two main subtypes: esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). Previous studies have revealed distinct genomic characteristics between these subtypes, with ESCC sharing similarities with head and neck squamous cell carcinoma (HNSCC), and EAC aligning with gastric adenocarcinoma (GAC). Additionally, recent immunotherapy clinical trials have shown a higher response rate in ESCC and HNSCC compared to EAC and GAC. However, how the tumor microenvironment contributes to these varied immunotherapy responses remains unclear. In this study, through a comparative analysis of single-cell tumor transcriptomes from 35 patients with ESCC, EAC, HNSCC, or GAC, we identified two groups with tumor microenvironment distinctions: ESCC and HNSCC versus EAC and GAC, consistent with their genomic classifications. Malignant epithelial cells displayed distinct separations based on histological origin. In the tumor immune microenvironment, we observed an enrichment of CXCL13+CD8+ T cells and CXCL9+CXCL10+ tumor-associated macrophages (TAMs) in ESCC and HNSCC, which activate cellular immunity through interferon-{gamma}. In contrast, EAC and GAC exhibited a high presence of heat-shock protein-expressing CD8+ T cells and MARCO+ TAMs. These immune signatures help explain the varied immunotherapy responses among these cancer subtypes and successfully predict immunotherapy outcomes across diverse cancer types, underscoring their clinical significance.

cancer biology↗

bioRxiv2024

Awe is characterized as an ambivalent experience in the human behavior and cortex: integrated virtual reality-electroencephalogram study

Ambivalent feelings are a defining feature of awe, which has been understood as a possible source of its psychosocial benefits. However, due to the conventional unidimensional model of affective valence, behavior and neural representation of ambivalent feelings during awe remain elusive. To address this gap, we combined awe-inducing virtual reality clips, electroencephalogram, and a deep learning-based dimensionality reduction technique (N = 43). Behaviorally, awe ratings were precisely predicted by the duration and intensity of ambivalent feelings, not by single valence-related metrics. In the electrophysiological analysis, we identified latent neural space for each participant sharing valence representation structures across individuals and stimuli. In these spaces, ambivalent feelings during awe were distinctly represented from positive and negative ones, and the variability in their distinctiveness specifically predicted awe ratings. Additionally, frontal delta oscillations mainly engaged in differentiating valence representations. Our findings demonstrate that awe is fundamentally an ambivalent experience reflected in both behavior and electrophysiological activities. This work provides a new framework for understanding complex emotions and their neural underpinnings, with potential implications for affective neuroscience and relevant fields.

neuroscience↗

bioRxiv2024

HCNetlas: Human cell network atlas enabling cell type-resolved disease genetics

Cell type-specific actions of disease genes add a significant layer of complexity to the genetic architecture underlying diseases, obscuring our understanding of disease mechanisms. Single-cell omics have revealed the functional roles of genes at the cellular level, identifying cell types critical for disease progression. Often, a gene impact on disease through its altered network within specific cell types, rather than mere changes in expression levels. To explore the cell type-specific roles of disease genes, we developed HCNetlas (human cell network atlas), a resource cataloging cell type-specific gene networks (CGNs) for various healthy tissue cells. We also devised three network analysis methods to investigate cell type-specific functions of disease genes. These methods involve comparing HCNetlas CGNs with those derived from disease-affected tissue samples. These methods find that systemic lupus erythematosus genes predominantly function in myeloid cells, and Alzheimers disease genes mainly play roles in inhibitory and excitatory neurons. Moreover, they show many lung cancer genes exert their roles in immune cells. These findings suggest that HCNetlas has the potential to link disease-associated genes to cell types of action, facilitating development of cell type-resolved diagnostics and therapeutic strategies for complex human diseases.

bioinformatics↗

bioRxiv2024

Predicting task-related brain activity from resting-state brain dynamics with fMRI Transformer

Accurate prediction of the brains task reactivity from resting-state functional magnetic resonance imaging (fMRI) data remains a significant challenge in neuroscience. Traditional statistical approaches often fail to capture the complex, nonlinear spatiotemporal patterns of brain function. This study introduces SwiFUN (Swin fMRI UNet Transformer), a novel deep-learning framework designed to predict 3D task activation maps directly from resting-state fMRI scans. SwiFUN leverages advanced techniques such as shifted window-based self-attention, which helps to understand complex patterns by focusing on varying parts of the data sequentially, and a contrastive learning strategy to better capture individual differences among subjects. When applied to predicting emotion-related task activation in adults (UK Biobank, n=7,038) and children (ABCD, n=4,944), SwiFUN consistently achieved higher overall prediction accuracy than existing methods across all contrasts; it demonstrated an improvement of up to 27% for the FACES-PLACES contrast in ABCD data. The resulting task activation maps revealed individual differences across cortical and subcortical regions associated with sex, age, and depressive symptoms. This scalable, transformer-based approach potentially reduces the need for task-based fMRI in clinical settings, marking a promising direction for future neuroscience and clinical research that enhances our ability to understand and predict brain function.

neuroscience↗

bioRxiv2024

Polygenic architecture of brain structure and function, behaviors, and psychopathologies in children

The human brain undergoes structural and functional changes during childhood, a critical period in cognitive and behavioral development. Understanding the genetic architecture of the brain development in children can offer valuable insights into the development of the brain, cognition, and behaviors. Here, we integrated brain imaging-genetic-phenotype data from over 8,600 preadolescent children of diverse ethnic backgrounds using multivariate statistical techniques. We found a low-to-moderate level of SNP-based heritability in most IDPs, which is lower compared to the adult brain. Using sparse generalized canonical correlation analysis (SGCCA), we identified several covariation patterns among genome-wide polygenic scores (GPSs) of 29 traits, 7 different modalities of brain imaging-derived phenotypes (IDPs), and 266 cognitive and psychological phenotype data. In structural MRI, significant positive associations were observed between total grey matter volume, left ventral diencephalon volume, surface area of right accumbens and the GPSs of cognition-related traits. Conversely, negative associations were found with the GPSs of ADHD, depression and neuroticism. Additionally, we identified a significant positive association between educational attainment GPS and regional brain activation during the N-back task. The BMI GPS showed a positive association with fractional anisotropy (FA) of connectivity between the cerebellum cortex and amygdala in diffusion MRI, while the GPSs for educational attainment and cannabis use were negatively associated with the same IDPs. Our GPS-based prediction models revealed substantial genetic contributions to cognitive variability, while the genetic basis for many mental and behavioral phenotypes remained elusive. This study delivers a comprehensive map of the relationships between genetic profiles, neuroanatomical diversity, and the spectrum of cognitive and behavioral traits in preadolescence.

genetics↗

bioRxiv2024

EMinsight: a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition.

The widespread adoption of cryoEM technologies for structural biology has pushed the discipline to new frontiers. A significant worldwide effort has refined the Single Particle Analysis (SPA) workflow into a reasonably standardised procedure. Significant investment of development time have been made particularly in sample preparation, microscope data collection efficiency, pipeline analyses and data archiving. The widespread adoption of specific commercial microscopes, software for controlling them and best practises developed at national facilities has also begun to establish a degree of standardisation to data structures coming from the SPA workflow. There is opportunity to capitalise on this moment in the fields maturation, to capture metadata from SPA experiments and correlate this with experimental outcomes, which is presented here in a set of programmes called EMinsight. This tool aims to prototype the framework and types of analyses that could lead to new insights into optimal microscope configurations as well as for defining methods for metadata capture to assist with archiving of cryoEM SPA data. We also envisage this tool to be useful to microscope operators and facilities looking to rapidly generate reports on SPA data collection and screening sessions. SynopsisEMinsight is a Python-based tool for systematically mining metadata from single particle analysis cryoEM experiments. The capture and analysis of metadata facilitates assessment of instrument performance, provides concise reporting of experiment performance and sample quality by analysing preprocessing results, and gathers metadata for deposition. We envisage this approach to benefit the microscope operator, facility managers, database developers and users.

biophysics↗

bioRxiv2023

Glioma Cells Secrete Collagen VI to Facilitate Invasion

While glioblastoma (GBM) progression is associated with extensive extracellular matrix (ECM) secretion, the causal contributions of ECM secretion to invasion remain unclear. Here we investigate these contributions by combining engineered materials, proteomics, analysis of patient data, and a model of bevacizumab-resistant GBM. We find that GBM cells cultured in engineered 3D hyaluronic acid hydrogels secrete ECM prior to invasion, particularly in the absence of exogenous ECM ligands. Proteomic measurements reveal extensive secretion of collagen VI, and collagen VI-associated transcripts are correspondingly enriched in microvascular proliferation regions of human GBMs. We further show that bevacizumab-resistant GBM cells deposit more collagen VI than their responsive counterparts, which is associated with marked cell-ECM stiffening. COL6A3 deletion in GBM cells reduces invasion, {beta}-catenin signaling, and expression of mesenchymal markers, and these effects are amplified in hypoxia. Our studies strongly implicate GBM cell-derived collagen VI in microenvironmental remodeling to facilitate invasion.

bioengineering↗