Biology subjects

Banovich, N. E.

Publications and source records attributed to Banovich, N. E..

At least 19 recordsLinked to original sources

bioRxiv2026

Therapeutic Targeting of the TGFβ Pathway Augments CAR T cell Efficacy in Glioblastoma

Chimeric Antigen Receptor (CAR) T therapy represents a promising approach to improve outcomes for patients with high-grade gliomas (HGG). Recently, we reported on a phase I trial assessing the local delivery of CAR T cells for HGG. Despite notable clinical benefits to CAR T treatment, clinical responses were highly variable between patients. Here we report that TGF{beta} levels in the tumor cavity fluid (TCF) decreased following CAR T treatment and that lower TGF{beta} levels positively correlated with improved patient responses to CAR T therapy. Patients were stratified based on baseline TGF{beta} levels in the TCF before CAR T therapy. Single-cell RNA sequencing of patient tumor tissues identified significant upregulation of genes associated with angiogenesis and epithelial-to-mesenchymal transition, and IL6 and SLRP pathways in TGF{beta} high patients, while IL2, chemokine signaling and NK-mediated cytotoxicity pathways were significantly upregulated in TGF{beta} low patients. Preclinically, we demonstrated that systemic treatment with a TGF{beta}R1 inhibitor markedly improved CAR T efficacy in syngeneic murine glioma models, correlating with changes in the tumor microenvironment (TME) that benefited both endogenous and CAR T cell phenotype and trafficking. Further studies demonstrated that inhibition of the TGF{beta} pathway in CAR T cells, through either TGF{beta}R1 inhibitor or CRISPR/Cas9 knockout of TGF{beta}R2 (TGF{beta}R2-KO), significantly enhanced CAR T cell expansion and antitumor activity. Collectively, our clinical and preclinical findings strongly indicate that targeting the TGF{beta} pathway could significantly enhance CAR T therapy for patients with high-grade gliomas.

cancer biology↗

bioRxiv2026

Inflammatory Fibroblasts Promote Repair After Injury Through Epithelial Proliferation

Fibroblast heterogeneity after lung injury is a well observed phenomenon made highly relevant by the widespread application of single cell RNA-sequencing. The characterization of homeostatic and injury associated states has led to the identification of a population of fibroblasts that emerge during inflammation and express cytokines that may potentially amplify the inflammatory circuit. However, whether these cells actively contribute to inflammation or serve an alternative function within the broader injury-repair cascade remains unclear. By integrating several robust murine lung injury data sets we establish the persistence of the inflammatory fibroblast across multiple injury models and identify a role for these cells in lung repair after injury through effects on alveolar epithelial proliferation. We validate this observation in-vivo using a genetic model of spontaneous lung fibrosis and in-vitro with mixed alveolar organoid cultures of various homeostatic and injury associated fibroblasts wherein we identify a mesenchymal-epithelial BMP signaling axis as a key driver of the AT2 cell injury repair response. Finally, we present supporting evidence from human disease, reinforcing the relevance of this fibroblast subset in pathological settings. These findings extend critical observations made prior to the single-cell era and contribute to our evolving understanding of fibroblast heterogeneity as a key feature of lung repair.

molecular biology↗

bioRxiv2026

Single-cell lung eQTL dataset of Asian never-smokers highlights the roles of alveolar cells in lung cancer etiology

Single-cell expression quantitative trait loci (sc-eQTL) analyses are powerful in identifying context-specific susceptibility genes from genome-wide association studies (GWAS) loci. However, few studies have comprehensively investigated cells of lung cancer origin in non-European populations. Here, we built a lung sc-eQTL dataset from 129 Korean women never-smokers with epithelial cell enrichment. eQTL mapping identified 2,229 genes with an eQTL in 33 cell types, including East Asian-specific findings when compared to predominantly European datasets. Integration with single-cell chromatin accessibility data demonstrated an enrichment of cell-type specific eQTLs in cell-type matched candidate enhancers, while shared eQTLs were more frequently found near promoters. Colocalization and transcriptome-wide association study unveiled 36 susceptibility genes from 22 cell types in 22 lung cancer loci, including 10 loci not achieving genome-wide significance in prior GWAS. Around 47% of these genes were from cells of the alveoli, underscoring their importance, especially in lung adenocarcinoma (LUAD) susceptibility. Focusing on the trajectory of alveolar epithelial cell regeneration, we detected 785 cell-state-interacting QTLs, which overlapped with 28% (10) of the identified susceptibility genes. Finally, we experimentally validated East Asian-and alveolar type 2 cell-specific eQTL of TCF7L2 underlying East Asian LUAD locus, 10q25.2. Consistent with its role as a Wnt/{beta}-catenin effector, TCF7L2 displayed significant effect on lung adenocarcinoma cell growth. Our data highlighted context-specific susceptibility genes, especially from alveolar cells of lung, contributing to lung cancer etiology.

genetics↗

bioRxiv2026

Ancestral and environmental diversity shape the immune landscape in Indonesia

Island Southeast Asia (ISEA) remains consistently underrepresented in human genomic resources despite its exceptional ancestral and lifestyle diversity. The interplay between the regions complex population history and its environmental variation provide a window into how ancestry and environment jointly shape the human immune system. Here we report the generation of single-cell PBMC profiles from 199 Indonesians sampled across four communities in the islands of Bali and New Guinea. These groups capture diversity in regional genetic ancestries (East Asian-like and Papuan-like) and lifestyle contrasts (urban versus rural communities in Bali; highland versus lowland communities in New Guinea). We identify over 4,000 expression quantitative trait loci (eQTLs) across nine immune cell types, including eQTLs driven by introgression from both Neanderthals and Denisovans at genes such as IL7R, HLA-E, or STAT2. We also find evidence of local ancestry driving gene-by-environment interactions at pathogen receptors such as MARCO, although the majority of gene-by-environment interactions are not driven by differences in genetic structure between populations. Beyond direct genetic effects, we construct gene co-expression networks that consistently identify environmental signatures, as well as T-cell receptor repertoires that distinguish specific communities, with excess representation of interferon-stimulated genes in rural, but not urban samples. This work establishes a framework for population-aware functional genomics in understudied regions and highlights how ancestral and environmental diversity jointly shape human immunity in this globally important yet underrepresented region.

genomics↗

bioRxiv2026

Fishing with Two Lines: A Hybrid Approach to Spatial Transcriptomic Discovery

Spatial transcriptomics faces a trade-off between the number of genes assayed and depth of per-gene sensitivity. We developed a dual chemistry method that combines the high sensitivity of a 10X Genomics Xenium V1 custom panel (up to 480 genes) with the broad coverage of the Prime 5K panel (5001 genes) on a single tissue section. This involved co-hybridizing Prime and V1 probes and sequentially running the V1 and Prime decoding chemistries. Applied to a human lung tissue microarray, we observed high concordance between the V1 and Prime chemistries when run independently (on serial sections) and the dual chemistry runs. Overlapping genes (profiled on both V1 and Prime chemistries) showed similar expression patterns in the dual run demonstrating the fidelity of the assay. By combining information from both the V1 and Prime chemistries within the same cell, we retain more cells, gain valuable additional information, and enable both high sensitivity profiling and discovery.

molecular biology↗

bioRxiv2025

Denoising image-based spatial transcriptomics data with DenoIST

Image-based spatial transcriptomics (IST) technologies provide unprecedented resolution of gene expression in tissue sections, but suffer from contamination of cells gene expression profiles due to imperfect cell segmentation. We present DenoIST (Denoising Image-based Spatial Transcriptomics data), a new computational tool that accurately identifies and removes contaminating transcripts from IST datasets. DenoIST models the observed transcript counts using a Poisson mixture model that explicitly accounts for local neighbourhood contamination. Applied to multiple real IST datasets of varying cell densities, DenoIST restores gene expression specificity and clarifies local biological structures by identifying and filtering transcripts spilt over from neighbouring cells. The denoised data enable more consistent and interpretable cell type annotation by minimising conflicting gene expression profiles, reducing the prevalence of hybrid or ambiguous cell types, and enhancing the contrast between distinct functional compartments. Overall, we demonstrate that DenoIST can be integrated to existing IST analysis workflows to improve biological interpretability and robustness of IST data.

bioinformatics↗

bioRxiv2025

A spatial transcriptomic atlas of acute neonatal lung injury across development and disease severity

A molecular understanding of lung organogenesis requires delineation of the timing and regulation of the cellular transitions that ultimately form and support a surface capable of gas exchange. While the advent of single-cell transcriptomics has allowed for the discovery and identification of transcriptionally distinct cell populations present during lung development, the spatiotemporal dynamics of these transcriptional shifts remain undefined. With imaging-based spatial transcriptomics, we analyzed the gene expression patterns in 17 human infant lungs at varying stages of development and injury, creating a spatial transcriptomic atlas of [~]1.2 million cells. We applied computational clustering approaches to identify shared molecular patterns among this cohort, informing how tissue architecture and molecular spatial relationships are coordinated during development and disrupted in disease. Recognizing that all preterm birth represents an injury to the developing lung, we created a simplified classification scheme that relies upon the routinely collected objective measures of gestational age and life span. Within this framework, we have identified cell type patterns across gestational age and life span variables that would likely be overlooked when using the conventional "disease vs. control" binary comparison. Together, these data represent an open resource for the lung research community, supporting discovery-based inquiry and identification of targetable molecular mechanisms in both normal and arrested human lung development.

genomics↗

bioRxiv2025

SpatialRNA: a Python package for easy application of Graph Neural Network models on single-molecule spatial transcriptomicsdataset

Image-based spatial transcriptomics deliver gene expression measurements of RNA transcripts in tissue slices with single-molecule resolution and spatial context preserved. Modern Graph Neural Network (GNN) models are promising methods for capturing the complex molecular and cellular phenotypes in tissues at single-transcript and single-cell levels. A key application of GNNs is the detection of spatial domains or niches, that is, groups of molecules and/or cells that collaboratively work together to produce complex phenotypes. Due to the vast number of detected transcripts in image-based spatial transcriptomics, applying GNNs on RNA molecule graphs is not trivial. We present a Python package, SpatialRNA, for easy (sub)graph generation from tissue samples and provide comprehensive tutorials for convenient and efficient application of Graph Neural Network models under the PyG framework. Availability and implementation: The SpatialRNA package is freely accessible from GitLab https://gitlab.svi.edu.au/biocellgen-public/spatialrna and can be installed via pip. Python notebooks and scripts used in case studies are freely accessible at https://gitlab.svi.edu.au/biocellgen-public/case_study_ipf and https://gitlab.svi.edu.au/biocellgen-public/case_study_xenium_5k_panel.

bioinformatics↗

bioRxiv2024

Germline and somatic genetic effects on gene expression and outcome in patients with Multiple Myeloma

Multiple Myeloma (MM) is a hematological malignancy associated with poor prognosis and relapses. MM has a strong inherited component, and while the mutational and transcriptomic landscapes of MM have been characterized extensively, the molecular consequences of germline genetic variation in MM have not been previously explored. Here, we have leveraged matched genotype and expression data from 607 patients to map the germline and somatic regulatory variation in MM. We find that non-coding variation shapes the transcriptional landscape of MM, contributing to overall survival. These regulatory variants alter the binding of transcription factors with known importance in MM, such as IRF4, providing insights into the underlying mechanisms. These data contribute to the increasing understanding of the genetic, molecular, and cellular forces shaping MM risk and outcomes.

genomics↗

bioRxiv2024

Association between mosaic loss of chromosome Y and pulmonary fibrosis susceptibility and severity

Background Pulmonary fibrosis (PF) is a rare lung disease with diverse pathogenesis and multiple interconnected underlying biological mechanisms. Mosaic loss of chromosome Y (mLOY) is one of the most common forms of acquired chromosome abnormality in men, which has been reported to be associated with increased risk of various chronic progressive diseases including fibrotic diseases. However, the exact role of mLOY in the development of PF remains elusive and to be elucidated. MethodsWe adopted three complementary approaches to explore the role of mLOY in the pathogenesis of PF. We used copy number on chromosome Y to estimate mLOY comparing patients in PROFILE and gnomAD cohorts and between cases and control patients from the GE100KGP cohort. Correlation of mLOY with demographic and clinical variables was tested using patients from PROFILE cohort. Lung single-cell transcriptomic data were analysed to assess the cell types implicated in mLOY. We performed Mendelian randomisation to examine the causal relationship between mLOY, IPF, and telomere length. ResultsThe genetic analysis suggests that mLOY is found in PF from both case cohorts but when compared with an age matched population the effect is minimal (P = 0.0032). mLOY is related to age (P = 0.00021) and shorter telomere length (P = 0.0081) rather than PF severity or progression. Single-cell analysis indicates that mLOY appears to be found primarily in immune cells and appears to be related to presence and severity of fibrosis. Mendelian randomisation demonstrates that mLOY is not on the causal pathway for IPF, but partial evidence supports that telomere shortening is on the causal pathway for mLOY. ConclusionOur study confirms the existence of mLOY in PF patients and suggests that mLOY is not a major driver of IPF. The combined evidence suggests a triangulation model where telomere shortening leads to both IPF and mLOY.

genomics↗

bioRxiv2023

Image-based spatial transcriptomics identifies molecular niche dysregulation associated with distal lung remodeling in pulmonary fibrosis

The human lung is structurally complex, with a diversity of specialized epithelial, stromal and immune cells playing specific functional roles in anatomically distinct locations, and large-scale changes in the structure and cellular makeup of this distal lung is a hallmark of pulmonary fibrosis (PF) and other progressive chronic lung diseases. Single-cell transcriptomic studies have revealed numerous disease-emergent/enriched cell types/states in PF lungs, but the spatial contexts wherein these cells contribute to disease pathogenesis has remained uncertain. Using sub-cellular resolution image-based spatial transcriptomics, we analyzed the gene expression of more than 1 million cells from 19 unique lungs. Through complementary cell-based and innovative cell-agnostic analyses, we characterized the localization of PF-emergent cell-types, established the cellular and molecular basis of classical PF histopathologic disease features, and identified a diversity of distinct molecularly-defined spatial niches in control and PF lungs. Using machine-learning and trajectory analysis methods to segment and rank airspaces on a gradient from normal to most severely remodeled, we identified a sequence of compositional and molecular changes that associate with progressive distal lung pathology, beginning with alveolar epithelial dysregulation and culminating with changes in macrophage polarization. Together, these results provide a unique, spatially-resolved characterization of the cellular and molecular programs of PF and control lungs, provide new insights into the heterogeneous pathobiology of PF, and establish analytical approaches which should be broadly applicable to other imaging-based spatial transcriptomic studies.

genomics↗

bioRxiv2023

Cell type-specific and disease-associated eQTL in the human lung

Common genetic variants confer substantial risk for chronic lung diseases, including pulmonary fibrosis (PF). Defining the genetic control of gene expression in a cell-type-specific and context-dependent manner is critical for understanding the mechanisms through which genetic variation influences complex traits and disease pathobiology. To this end, we performed single-cell RNA-sequencing of lung tissue from 67 PF and 49 unaffected donors. Employing a pseudo-bulk approach, we mapped expression quantitative trait loci (eQTL) across 38 cell types, observing both shared and cell type-specific regulatory effects. Further, we identified disease-interaction eQTL and demonstrated that this class of associations is more likely to be cell-type specific and linked to cellular dysregulation in PF. Finally, we connected PF risk variants to their regulatory targets in disease-relevant cell types. These results indicate that cellular context determines the impact of genetic variation on gene expression, and implicates context-specific eQTL as key regulators of lung homeostasis and disease.

genomics↗

bioRxiv2021

Club cells are the primary source of pIgR in small airways

BackgroundLoss of secretory immunoglobulin A (SIgA) is common in COPD small airways and likely contributes to disease progression. We hypothesized loss of SIgA results from reduced expression of pIgR, a chaperone protein needed for SIgA transcytosis, in the COPD small airway epithelium. MethodspIgR-expressing cells were defined and quantified at single-cell resolution in human airways using RNA in-situ hybridization, immunostaining, and single-cell RNA sequencing. Complementary studies in mice utilized immunostaining, primary murine tracheal epithelial cell (MTEC) culture, and transgenic mice with secretory or ciliated cell-specific knockout of pIgR. SIgA degradation by human neutrophil elastase or secreted bacterial proteases from non-typeable Haemophilus influenzae (NTHi) was evaluated in vitro. ResultsWe found that secretory cells are the predominant cell type responsible for pIgR expression in human and murine airways. Loss of SIgA in small airways was not associated with a reduction in secretory cells but rather a reduction in pIgR protein expression despite intact PIGR mRNA expression. Neutrophil elastase and NTHi-secreted proteases are both capable of degrading SIgA in vitro and may also contribute to a deficient SIgA immunobarrier in COPD. InterpretationLoss of the SIgA immunobarrier in small airways of patients severe COPD is complex and likely results from both pIgR-dependent defects in IgA transcytosis and SIgA degradation. Key MessagesWhat is the key question? Localized SIgA deficiency in small airways is an established driver of COPD pathogenesis, but the mechanism of loss remains unclear. We hypothesized loss of SIgA is due to reduced numbers of pIgR-expressing cells in SIgA-deficient small airways. What is the bottom line? pIgR is primarily expressed by secretory cells in human and murine airways. Although numbers of secretory cells are similar between SIgA-deficient and SIgA-replete airways in COPD, there is reduced expression of pIgR protein, but not mRNA, in SIgA-deficient airways. Additionally, host and bacterial proteases degrade SIgA in vitro, suggesting loss of SIgA may relate to both impaired transcytosis and increased degradation. Why read on? This study highlights the complexity of SIgA immunobarrier maintenance and suggests that strategies aimed at restoring the SIgA immunobarrier will need to account for both impaired transcytosis and degradation by host and/or bacterial proteases.

cell biology↗

bioRxiv2021

Single-cell transcriptomic assessment of cellular phenotype stability in human precision-cut lung slices

Precision-cut lung slices (PCLS) are increasingly utilized for ex vivo disease modeling, but a high-resolution characterization of cellular phenotype stability in PCLS has not been reported. Comparing the single-cell transcriptomic profile of human PCLS after five days of culture to freshly isolated human lung tissue, we found striking changes in endothelial cell and alveolar epithelial cell programs, reflecting both injury and pathways activated in static culture, while immune cell frequencies and programs remained largely intact and similar to the native lung. These cellular dynamics should be considered when utilizing PCLS as a model of the human lung.

cell biology↗

bioRxiv2021

A Single Cell Atlas of Lung Development

Lung organogenesis requires precisely timed shifts in the spatial organization and function of parenchymal cells, especially during the later stages of lung development. To investigate the mechanisms governing lung parenchymal dynamics during development, we performed a single cell RNA sequencing (scRNA-seq) time-series yielding 92,238 epithelial, endothelial, and mesenchymal cells across 8 time points from embryonic day 12 (E12) to postnatal day 14 (P14) in mice. We combined new computational analyses with RNA in situ hybridization to explore transcriptional velocity, fate likelihood prediction, and spatiotemporal localization of cell populations during the transition between the saccular and alveolar stages. We interrogated this atlas to illustrate the complexity of type 1 pneumocyte function during the saccular and alveolar stages, and we demonstrate an integrated view of the cellular dynamics during lung development.

developmental biology↗

bioRxiv2020

Single-cell RNA-sequencing reveals dysregulation of molecular programs associated with SARS-CoV-2 severity and outcomes in patients with chronic lung disease

Patients with chronic lung disease (CLD) have an increased risk for severe coronavirus disease-19 (COVID-19) and poor outcomes. Here, we analyzed the transcriptomes of 605,904 single cells isolated from healthy and CLD lungs to identify molecular characteristics of lung cells that may account for worse COVID-19 outcomes in patients with chronic lung diseases. We observed a similar cellular distribution and relative expression of SARS-CoV-2 entry factors in control and CLD lungs. CLD epithelial cells expressed higher levels of genes linked directly to the efficiency of viral replication and innate immune response. Additionally, we identified basal differences in inflammatory gene expression programs that highlight how CLD alters the inflammatory microenvironment encountered upon viral exposure to the peripheral lung. Our study indicates that CLD is accompanied by changes in cell-type-specific gene expression programs that prime the lung epithelium for and influence the innate and adaptive immune responses to SARS-CoV-2 infection.

cell biology↗

bioRxiv2020

Genetic architecture of gene regulation in Indonesian populations identifies QTLs associated with local ancestry and archaic introgression

Lack of diversity in human genomics limits our understanding of the genetic underpinnings of complex traits, hinders precision medicine, and contributes to health disparities. To map genetic effects on gene regulation in the underrepresented Indonesian population, we have integrated genotype, gene expression, and CpG methylation data from 115 participants across three island populations that capture the major sources of genomic diversity on the region. In a comparison with a European dataset, we identify 166 uniquely Indonesia-specific eQTLs, highlighting the benefits of performing association studies on non-European populations. By combining local ancestry and archaic introgression inference eQTLs and methylQTLs, we identify regulatory loci driven by modern Papuan ancestry as well as introgressed Denisovan and Neanderthal variation. GWAS colocalization connects QTLs detected here to hematological traits. Our findings illustrate how local ancestry and archaic introgression drive variation in gene regulation across genetically distinct and in admixed populations.

genomics↗

bioRxiv2020

Dynamic effects of genetic variation on gene expression revealed following hypoxic stress in cardiomyocytes

One life-threatening outcome of cardiovascular disease is myocardial infarction, where cardiomyocytes are deprived of oxygen. To study inter-individual differences in response to hypoxia, we established an in vitro model of induced pluripotent stem cell-derived cardiomyocytes from 15 individuals. We measured gene expression levels, chromatin accessibility, and methylation levels in four culturing conditions that correspond to normoxia, hypoxia and short or long-term re-oxygenation. We characterized thousands of gene regulatory changes as the cells transition between conditions. Using available genotypes, we identified 1,573 genes with a cis expression quantitative locus (eQTL) in at least one condition, as well as 367 dynamic eQTLs, which are classified as eQTLs in at least one, but not in all conditions. A subset of genes with dynamic eQTLs is associated with complex traits and disease. Our data demonstrate how dynamic genetic effects on gene expression, which are likely relevant for disease, can be uncovered under stress.

genomics↗