Biology subjects

Bader, G.

Publications and source records attributed to Bader, G..

At least 19 recordsLinked to original sources

bioRxiv2026

Multiscale volume electron microscopy of the human liver maps vascular-cellular architecture, organelle dynamics and inter-organelle communication

The human liver depends on multiscale structural organization from vasculature to cells to organelles to perform its diverse metabolic functions. A unified three-dimensional view linking these hierarchical scales in intact human tissue would be useful for better understanding these levels and how they relate to each other. We present a high-resolution volume electron microscopy reconstruction of human periportal liver tissue (152 x 140 x 33 {micro}m3 at 8 nm pixel size) acquired by serial block-face scanning electron microscopy. Using a multiscale deep learning approach, we performed automated segmentation across the entire volume, enabling comprehensive annotation of vasculature, cells, and organelles. Quantitative analysis of bile duct architecture revealed coordinated scaling between lumen geometry and cholangiocyte number and size. Sinusoidal capillary branches exhibited distinct structural profiles with differential endothelial coverage. Analysis of 35,790 complete mitochondria identified substantial morphological heterogeneity, with elongated mitochondria displaying preferential endoplasmic reticulum (ER) contacts at narrowing sites, a pattern consistent with ER-mediated fission or fusion. This multiscale reconstruction establishes an ultrastructural reference for the healthy human liver and provides a quantitative framework for investigating hepatic physiology and disease.

cell biology↗

bioRxiv2025

Scalable expansion and hepatic zone maturation of hepatic progenitor cells from human pluripotent stem cells

Scalable generation of functionally mature hepatocytes from human pluripotent stem cells (hPSCs) is vital for regenerative medicine, disease modeling, and drug screening. We present a modular differentiation platform that enables serial expansion and cryopreservation of hepatic progenitors (hepatoblasts; HBs), supporting efficient downstream maturation. By modulating bile acid signaling with FGF19 and culturing under hypoxia, we preserved HB proliferation and bipotency. Subsequent thyroid hormone treatment induced alpha-fetoprotein-negative, albumin-positive hepatocytes, while WNT pathway modulation promoted zonal identity, mimicking in vivo hepatic metabolic heterogeneity. Zonation was confirmed by differential drug-metabolizing enzyme activity, mitochondrial function, and enhanced engraftment in a liver injury mouse model. To evaluate translational relevance, mature hepatocytes were incorporated into extrusion-based 3D bioprinted constructs, which maintained hepatic viability and function. This integrated strategy of combining progenitor cell banking, zonation control, and tissue engineering offers a scalable and clinically relevant approach for generating functional human liver tissue suitable for therapeutic development.

cell biology↗

bioRxiv2025

FLASH-MM: fast and scalable single-cell differential expression analysis using linear mixed-effects models

Single-cell RNA sequencing (scRNA-seq) enables detailed comparisons of gene expression across cells and conditions. Single-cell differential expression analysis faces challenges like sample correlation, individual variation, and scalability. We developed a fast and scalable linear mixed-effects model (LMM) estimation algorithm, FLASH-MM, to address these issues. We reformulate aspects of the linear mixed model estimation procedure to make it faster, by reducing computational complexity and memory use in the case of working with a gene by cell matrix. Simulation studies with scRNA-seq data show that FLASH-MM is accurate, computationally efficient, effectively controls false positive rates, and maintains high statistical power in differential expression analysis. Tests on tuberculosis immune and kidney single cell data demonstrate FLASH-MMs utility in accelerating single-cell differential expression analysis across diverse biological contexts.

bioinformatics↗

bioRxiv2025

RETINA: Reconstruction-based Pre-Trained Enhanced TransUNet for Electron Microscopy Segmentation on the CEM500K Dataset

Electron microscopy (EM) has revolutionized our understanding of cellular structures at the nanoscale. Accurate image segmentation is required for analyzing EM images. While manual segmentation is reliable, it is labor-intensive, incentivizing the development of automated segmentation methods. Although deep learning-based segmentation has shown promise, its accuracy to automatically segment cellular structures in EM data remains insufficient compared to expert manual results. Current approaches usually use either convolutional or transformer-based neural networks for image feature extraction. We developed the RETINA method, which combines pre-training on the large, unlabeled CEM500K EM image dataset with a hybrid neural-network model architecture that integrates both local (convolutional layer) and global (transformer layer) image processing to learn from manual image annotations. RETINA outperformed existing models on cellular structure segmentation on five public EM datasets. This improvement works toward automated cellular structure segmentation for the EM community. Data and code are available at Zenodo and GitHub.

bioinformatics↗

bioRxiv2024

Frustration in the Protein-Protein interface Plays a Central Role in the Cooperativity of PROTAC Ternary Complexes

Targeted protein degradation of a protein of interest (POI) by Proteolysis Targeting Chimeras (PROTACs) is an attractive approach for dealing with formerly undruggable protein targets. PROTACs are heterobifunctional molecules that connect a POI-binding and an E3-ligase (E3) binding motif with a linker. The simultaneous binding and formation of a ternary POI::PROTAC::E3 complex (TC) induces proximity between the POI and the E3, allowing for POI-ubiquitination and subsequent induction of proteasomal degradation. Despite the availability of many three-dimensional structures of TCs, unveiling the structure-function relationships for the design of PROTACs remains a challenge. This is because the TCs can be dynamic with a complex conformational landscape that individual crystal structures may not capture. In this work, we used SMARCA2 as the POI and VHL as the E3-ligase and solved the X-ray crystal structures of the respective ternary complexes with four different PROTACs. Molecular dynamics (MD) simulations were used to show that the SMARCA2-VHL interface is flexible with multiple energy minima. The protein-protein (POI-E3) interactions are largely formed by residues located in structurally disordered loops in both VHL and SMARCA2. The residue pairs in the SMARCA2-VHL interface are frustrated, i.e., adopt a suboptimal energetic state. The number of frustrated residue pairs averaged over the MD ensemble shows a positive correlation with the experimentally determined cooperativity of the PROTACs. This indicates that protein-protein interface frustration can play an important role in PROTAC function. The TC ensembles of VHL, SMARCA2 and 11 different PROTACs were modeled by comparative modeling followed by MD. The frustration was subsequently calculated from the MD trajectories and correlated with the cooperativity. We found that identification of the dynamic protein-protein contacts and frustrated residue pairs in the interface can provide a rational framework for the structure-based design of PROTACs.

biophysics↗

bioRxiv2024

Single-cell transcriptome analysis reveals CD34 as a novel marker of human sinoatrial node pacemaker cardiomyocytes

The sinoatrial node (SAN) regulates the heart rate throughout life. Failure of this primary pacemaker results in life-threatening, slow heart rhythm. Despite its important function, the cellular and molecular composition of the human SAN is not completely resolved. Particularly, no cell surface marker to identify and isolate SAN pacemaker cells has been reported to date. Here we used single-nuclei/cell RNA sequencing of fetal and human pluripotent stem cell (hPSC)- derived SAN cells and show that the SAN consists of three subtypes of pacemaker cells, including Core SAN, SAN, and Transitional Cells. Our study identified a host of novel Core SAN markers including MYH11, BMP4, and the cell surface antigen CD34. We demonstrate that sorting for CD34+ cells from cardiac hPSC differentiations enriches for SAN cells with a functional pacemaker phenotype. This novel SAN pacemaker cell surface marker is highly valuable for future hPSC- based disease modelling, drug discovery, cell replacement therapies, as well as the delivery of therapeutics to SAN cells in vivo using antibody-drug conjugates.

cell biology↗

bioRxiv2024

Glioblastoma stem cells show transcriptionally correlated spatial organization

Glioblastoma (GBM) is an aggressive brain cancer with a poor survival rate. Despite hundreds of clinical trials, there is no effective targeted therapy. Glioblastoma stem cells (GSCs) are an important GBM model system. In culture, these cells form spatial structures that share morphological aspects with their source tumors. We collected 17,000 phase contrast images of 15 patient-derived GSC lines growing to confluence. We find that GSCs grow in characteristic multicellular patterns depending on their transcriptional state. Interpretable computer vision algorithms identified specific image features that predict transcriptional state across multiple cell confluency levels. This relationship will be useful in developing GSC screens where image features can be used to identify how GSC biology changes in response to perturbations simply by imaging cultured cells on plates.

cancer biology↗

bioRxiv2024

Interpretable single-cell factor decomposition using sciRED

Single-cell RNA sequencing (scRNA-seq) maps gene expression heterogeneity within a tissue. However, identifying biological signals in this data is challenging due to confounding technical factors, sparsity, and high dimensionality. Data factorization methods address this by separating and identifying signals in the data, such as gene expression programs, but the resulting factors must be manually interpreted. We developed Single-Cell Interpretable REsidual Decomposition (sciRED) to improve the interpretation of scRNA-seq factor analysis. sciRED removes known confounding effects, uses rotations to improve factor interpretability, maps factors to known covariates, identifies unexplained factors that may capture hidden biological phenomena and determines the genes and biological processes represented by the resulting factors. We apply sciRED to multiple scRNA-seq datasets and identify sex-specific variation in a kidney map, discern strong and weak immune stimulation signals in a PBMC dataset, reduce ambient RNA contamination in a rat liver atlas to help identify strain variation, and reveal rare cell type signatures and anatomical zonation gene programs in a healthy human liver map. These demonstrate that sciRED is useful in characterizing diverse biological signals within scRNA-seq data.

bioinformatics↗

bioRxiv2024

MALAT1 expression indicates cell quality in single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cell types and tissues. However, empty droplets and poor quality cells are often captured in single cell genomics experiments and need to be removed to avoid cell type interpretation errors. Many automated and manual methods exist to identify poor quality cells or empty droplets, such as minimum RNA count thresholds and comparing the gene expression profile of an individual cell to the overall background RNA expression of the experiment. A versatile approach is to use unbalanced overall RNA splice ratios of cells to identify poor quality cells or empty droplets. However, this approach is computationally intensive, requiring a detailed search through all sequence reads in the experiment to quantify spliced and unspliced reads. We found that the expression level of MALAT1, a non-coding RNA retained in the nucleus and ubiquitously expressed across cell types, is strongly correlated with this splice ratio measure and thus can be used to similarly identify low quality cells in scRNA-seq data. Since it is easy to visualize the expression of a single gene in single-cell maps, MALAT1 expression is a simple cell quality measure that can be quickly used during the cell annotation process to improve the interpretation of cells in tissues of human, mouse and other species with a conserved MALAT1 function.

bioinformatics↗

bioRxiv2024

Deep Lineage: Single-Cell Lineage Tracing and Fate Inference Using Deep Learning

Recent advances in single-cell RNA-sequencing and lineage tracing techniques have provided valuable insights into the temporal changes in gene expression during development, tumour progression, and disease onset. However, there are few computational methods available to analyze this information to help understand multicellular dynamics. We introduce Deep Lineage, a novel deep-learning method for analyzing time-series single-cell RNA-sequencing with matched lineage-tracing data. Our method accurately predicts early cell fate biases and gene expression profiles at different time points within a clone, surpassing current state-of-the-art methods in fate prediction accuracy. Additionally, through in silico perturbations in cellular reprogramming and hematopoiesis development data, we show that Deep Lineage can accurately model dynamic multicellular responses while identifying key genes and pathways associated with cell fate determination.

bioinformatics↗

bioRxiv2024

PRMT5 is required for full-length HTT expression by repressing multiple proximal intronic polyadenylation sites

Expansion of the CAG trinucleotide repeat tract in exon 1 of the Huntingtin (HTT) gene above a threshold of [~]36 repeats causes Huntingtons disease (HD) through the expression of a polyglutamine-expanded form of the HTT protein. This mutation triggers wide-ranging cellular and biochemical pathologies leading to cognitive, motor, and psychiatric symptoms in HD patients. As accurate splicing is required to produce the full-length HTT protein of [~]348 kDa, targeting HTT splicing with small molecule drugs is a compelling approach to lower HTT protein levels to treat HD, and splice modulators are being tested in the clinic. Here, we identify PRMT5 as a novel regulator of HTT mRNA splicing and alternative polyadenylation. PRMT5 inhibition disrupts the splicing of HTT introns 9 and 10, leading to activation of multiple proximal intronic polyadenylation sites within these introns and promoting premature termination, cleavage and polyadenylation (PCPA) of the HTT mRNA, thus lowering total HTT protein levels. We also detected increasing levels of these truncated, intron-containing HTT transcripts across a series of neuronal differentiation samples which correlated with lower PRMT5 expression. Notably, PRMT5 inhibition in glioblastoma (GBM) stem cells potently induced neuronal differentiation. We posit that PRMT5-mediated regulation of intronic polyadenylation, premature termination and cleavage of the HTT mRNA modulates HTT expression and plays an important role during embryonic development and neuronal differentiation.

neuroscience↗

bioRxiv2023

Single-nucleus multiomic atlas of frontal cortex in amyotrophic lateral sclerosis with a deep learning-based decoding of alternative polyadenylation mechanisms

Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are two age-related and fatal neurodegenerative disorders that lie on a shared disease spectrum. While both disorders involve complex interactions between neuronal and glial cells, the specific cell-type alterations and their contributions to disease pathophysiology remain incompletely understood. Here, we applied single-nucleus RNA sequencing of the orbitofrontal cortex, a region affected in ALS-FTLD, to map cell-type specific transcriptional signatures in C9orf72-related ALS (with and without FTLD) and sporadic ALS cases. Our findings reveal disease- and cell-type-specific transcriptional changes, with neurons exhibiting the most pronounced alterations, primarily affecting mitochondrial function, protein homeostasis, and chromatin remodeling. A comparison with independent datasets from different cortical regions of C9orf72 and sporadic ALS cases showed concordance in several pathways, with neuronal STMN2 and NEFL showing consistent up-regulation between brain regions and disease subtypes. We also interrogated alternative polyadenylation (APA) as an additional layer of transcriptional regulation, demonstrating that APA events are not correlated with identified gene expression changes. To interpret these events, we developed APA-Net, a deep learning model that integrates transcript sequences with RNA-binding protein expression profiles, revealing cell type-specific patterns of APA regulation. Our atlas illuminates cell type-specific pathomechanisms of ALS/FTLD, providing a valuable resource for further investigation.

neuroscience↗

bioRxiv2023

Self-renewing Sox9+ osteochondral stem cells in the postnatal skeleton

Postnatal skeletal growth, homeostatic maintenance, and regeneration is driven by skeletal stem cells. In addition, it is well established that skeletal tissues lose their regenerative potential with age, comorbidities, and repeated trauma, possibly through stem cell exhaustion or loss of function. However, it is largely unknown where these cells reside in skeletal tissues, what molecular mechanisms regulate their self-renewal and fate decisions, and how to isolate, purify, and expand them ex vivo. Therefore, there is an urgent need for a deeper understanding of postnatal skeletal stem cells. Here, we used genetic lineage tracing, thymidine analogues retention, whole bone microscopy, imaging cytometry, in vitro assays, and single cell transcriptomics and provide the first experimental evidence for the existence of self-renewing osteochondral stem cells in the postnatal skeleton in both males and females. We also show direct comparisons between adult, fetal, mouse, and human skeletal stem cells at the transcriptome level.

developmental biology↗

bioRxiv2023

Cell networks in the mouse liver during partial hepatectomy

In solid tissues homeostasis and regeneration after injury involve a complex interplay between many different cell types. The mammalian liver harbors numerous epithelial and non-epithelial cells and little is known about the global signaling networks that govern their interactions. To better understand the hepatic cell network, we isolated and purified 10 different cell populations from normal and regenerative mouse livers. Their transcriptomes were analyzed by bulk RNA-seq and a computational platform was used to analyze the cell-cell and ligand-receptor interactions among the 10 populations. Over 50,000 potential cell-cell interactions were found in both the ground state and after partial hepatectomy. Importantly, about half of these differed between the two states, indicating massive changes in the cell network during regeneration. Our study provides the first comprehensive database of potential cell-cell interactions in mammalian liver cell homeostasis and regeneration. With the help of this prediction model, we identified and validated two previously unknown signaling interactions involved in accelerating and delaying liver regeneration. Overall, we provide a novel platform for investigating autocrine/paracrine pathways in tissue regeneration, which can be adapted to other complex multicellular systems. HighlightsA platform predicting cell-cell interactions in liver regeneration was established This platform identified the BMP4 pathway antagonist Fstl1 as a stimulator of hepatocyte proliferation This platform also discovered the role of Wnt pathway inhibitor Sfrp1 delaying liver regeneration

cell biology↗

bioRxiv2023

Adversarial training improves model interpretability in single-cell RNA-seq analysis

For predictive computational models to be considered reliable in crucial areas such as biology and medicine, it is essential for them to be accurate, robust, and interpretable. A sufficiently robust model should not have its output affected significantly by a slight change in the input. Also, these models should be able to explain how a decision is made. Efforts have been made to improve the robustness and interpretability of these models as independent challenges, however, the effect of robustness and interpretability on each other is poorly understood. Here, we show that predicting cell type based on single-cell RNA-seq data is more robust by adversarially training a deep learning model. Surprisingly, we find this also leads to improved model interpretability, as measured by identifying genes important for classification. We believe that adversarial training will be generally useful to improve deep learning robustness and interpretability, thereby facilitating biological discovery.

bioinformatics↗

bioRxiv2022

Fatecode: Cell fate regulator prediction using classification autoencoder perturbation

Cell reprogramming, which guides the conversion between cell states, is a promising technology for tissue repair and regeneration. Typically, a group of key genes, or master regulators, are manipulated to control cell fate, with the ultimate goal of accelerating recovery from diseases or injuries. Of importance is the ability to correctly identify the master regulators from single-cell transcriptomics datasets. To accomplish that goal, we propose Fatecode, a computational method that combines in silico perturbation experiments with cell trajectory modeling using deep learning to predict master regulators and key pathways controlling cell fate. Fatecode uses only scRNA-seq data from wild-type samples to learn and predict how cell type distribution changes following a perturbation. We assessed Fatecodes performance using simulations from a mechanistic gene regulatory network model and diverse gene expression profiles covering blood and brain development. Our results suggest that Fatecode can detect known master regulators of cell fate from single-cell transcriptomics datasets. That capability points to Fatecodes potential in accelerating the discovery of cell fate regulators that can be used to engineer and grow cells for therapeutic use in regenerative medicine applications.

systems biology↗

bioRxiv2022

Single cell profiling reveals strain-specific differences in myeloid inflammatory potential in the rat liver

Liver transplantation is currently the only treatment for end-stage liver disease and acute liver failure. Liver transplant rejection is among the most lethal complications of transplantation, and therapeutic development is limited by our lack of a comprehensive understanding of the cellular landscape of the liver. The laboratory rat (Rattus norvegicus), ideal in size as a model for surgical procedures, is a strong platform to study liver biology in the context of liver transplantation. Liver allograft rejection is known to be strain-specific in the rat model, although the transplantation is accepted without rejection in some strains, it leads to acute rejection in others. To shed light on the cellular landscape of the rat liver and build a foundation for strain comparison, we present a comprehensive single-cell transcriptomics map of the healthy rat liver of Lewis and Dark Agouti strains. Using a novel computational pipeline we developed to guide the detailed annotation of our rat liver atlas, we discovered that hepatic myeloid cells have strong Lewis and Dark Agouti strain-specific differences focused on inflammatory signaling pathways. We experimentally validated these strain-specific differences in myeloid inflammatory potential in vitro using intracellular cytokine staining. Our work provides the first examination of the multi-strain healthy rat liver by single cell transcriptomics and uncovers key insights into strain-specific differences in this valuable model animal. SummaryThe laboratory rat (Rattus norvegicus) is a standard model animal for orthotopic liver transplantation. Transplanting a liver from a Dark agouti (DA) to a Lewis (LEW) strain rat leads to transplant rejection and the reverse procedure leads to tolerance. Understanding this strain difference may help explain the cellular drivers of liver allograft rejection post-transplant. This study uses single-cell transcriptomics to better understand the complex cellular composition of the rat liver and unravels cellular and molecular sources of inter-strain hepatic variation. We generated single-cell transcriptomic maps of the livers of healthy DA and LEW rat strains and developed a novel, factor analysis-based bioinformatics pipeline to study data covariates, such as strain and batch. Using this approach, we discovered variations within hepatocyte and myeloid populations that explain how the states of these cells differ between strains in the healthy rat, which may explain why these strains respond differently to liver transplants.

genomics↗

bioRxiv2021

Loss of epigenetic regulation disrupts lineage integrity, reactivatesmultipotency and promotes breast cancer

Systematically investigating the scores of genes mutated in cancer and discerning real drivers from inconsequential bystanders is a prerequisite for Precision Medicine, but remains challenging. Here, we developed a somatic CRISPR/Cas9 mutagenesis screen to study 215 recurrent long-tail breast cancer genes, which revealed epigenetic regulation as major tumor suppressive mechanism. We report that core or accessory components of the COMPASS histone methylase complex including KMT2C, KDM6A, BAP1 and ASXL2 ("EpiDrivers") cooperate with PIK3CAH1047R to transform mouse and human breast epithelial cells. Mechanistically, we find that Cre-mediated activation of PIK3CAH1047R elicited an aberrant alveolar lactation program in luminal cells, which was exacerbated upon loss of EpiDrivers. Remarkably, EpiDriver loss in basal cells also triggered an alveolar-like lineage conversion and accelerated formation of luminal-like tumors, suggesting a basal origin for luminal tumors. As EpiDrivers are mutated in 39% of human breast cancers, lineage infidelity and lactation mimicry may significantly contribute to early steps of breast cancer progression. Statement of significanceInfrequently mutated genes comprise most of the mutational burden in breast tumors but are poorly understood. In-vivo CRISPR screening identified functional tumor suppressors that converged on epigenetic regulation. Loss of epigenetic regulators accelerated tumorigenesis and revealed lineage infidelity and aberrant expression of lactation genes as potential early events in tumorigenesis.

cancer biology↗