Biology subjects

Schroeder, A.

Publications and source records attributed to Schroeder, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Glutamine and GLS1 are key metabolic checkpoints that Limit T cell Infiltration and Function in HNSCC

Head and neck squamous cell carcinoma (HNSCC) is characterized by poor survival and limited benefit from immune checkpoint blockade (ICB), underscoring the need to define targetable mechanisms of immune escape. In a prospective, patient-centered study, we investigated how tumor metabolic reprogramming impairs T cell function and drives ICB resistance. Targeted metabolomics identified two dominant and distinct tumor states: the Warburg phenotype and glutamine deprivation and revealed increased tumor-specific glutamine to glutamate turnover. Integrating spatial metabolic imaging with immunohistochemistry linked these states to immune-cold and immune-excluded tumor architectures. Metabolic states selectively reduced the expression of T cell chemokine receptors highly abundant and prognostic in HNSCC. Accordingly, T cell migration into tumor spheroids was reduced at lower glutamine concentrations. Interfering with glutamine metabolism by pharmacologic inhibition or genetic deletion of glutaminase-1 markedly increased tumor susceptibility to immune cell-mediated cytotoxicity in vitro and ex vivo and sensitized patient-derived tumor fragments (PDTFs) to ICB. This effect extended beyond glutamine restoration and involved disruption of glutathione-dependent ROS defense, impaired tumor proliferation, and increased MHC expression in tumor cells. Consistently, high expression of genes involved in glutamine and glutathione metabolism associated with reduced ICB response in patients. In contrast to HNSCC, other tumor entities including breast cancer show low glutaminase-1 expression. Accordingly, breast cancer PDTFs were not responsive to glutaminase-1 inhibition. Together, these data identify glutaminase-1 as a metabolic checkpoint driving immune escape in HNSCC, and support guided patient stratification for metabolism-targeted immunotherapy, similar to current precision approaches for mutation targeted drugs.

cancer biology↗

bioRxiv2026

The role of sleep in the retention and long-term maintenance of newly formed ability self-beliefs

Sleep supports the consolidation of newly acquired information, however its role in the formation of self-beliefs has not yet been examined. Here, we investigated whether sleep facilitates the retention, stabilization, revision and long-term maintenance of newly formed ability beliefs. Using a Sleep vs. Wake between-subjects design (N=54), participants completed the Learning Of Own Performance (LOOP) task, which allowed them to form new positive and negative ability self-beliefs based on mocked feedback at a first session, and to revise them when encountering contradictory feedback at a second session. Belief formation was followed by either a sleep or wake retention interval. Belief states were tested at four time points: immediately after belief formation, after the 12-h retention interval, following the revision phase and after three weeks. Participants successfully formed and revised self-beliefs using the LOOP task. Compared with wakefulness, sleep following belief formation did not affect belief retention across the 12-h interval, nor did it stabilize beliefs against their revision. However, after three weeks, participants who slept after belief formation showed a shift towards more positivity for the initially formed low ability self-beliefs. Contrary to our hypotheses, sleep did not enhance short-term retention or stabilization of newly formed self-beliefs. However, the more positively recalled ability self-belief associated with the initially low-performance condition suggest that sleep may facilitate a positive long-term shift.

neuroscience↗

bioRxiv2026

Orthogonal Acoustic Control of Gene Expression Using a Synthetic T7RNAP Rapa-Inducible Dimerization System

Precise spatiotemporal regulation of engineered microbes remains a critical bottleneck in synthetic biology. While ultrasound is extensively utilized for imaging and drug delivery, its translation into bacterial chassis is hindered by the lack of stringent biochemical triggers. Here, we present a rationally designed, ultrasound-responsive hybrid molecular switch based on a strict acoustic-biochemical "AND-gate". We engineered a highly sensitive split-T7 RNA polymerase system, which the dimerization and subsequent gene transcription can only be triggered in the presence of both ultrasound and a PEG-modified rapamycin. By systematically optimizing the acoustic parameters, we deployed this spatiotemporal switch to dynamically regulate a microbial consortium. With lysisE suicide protein as the output module, we achieved precise and programmable tuning of bacterial population in a co-culture system. This acoustic gating strategy may provide a robust and versatile toolkit for complex microbiome engineering and dynamic biomanufacturing. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=158 SRC="FIGDIR/small/733485v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@6d229corg.highwire.dtl.DTLVardef@fb5d1eorg.highwire.dtl.DTLVardef@9e905dorg.highwire.dtl.DTLVardef@17e3a1_HPS_FORMAT_FIGEXP M_FIG Graphic Abstract C_FIG

Synthetic Biology↗

bioRxiv2026

Toward Computationally Complete Spatial Omics

Multimodal spatial omics has transformed biology by mapping molecular complexity within intact tissues, yet current technologies remain limited in the number of modalities measured simultaneously and often produce lower-quality data than single-modality assays. We present COSIE, a computational framework that generates high-resolution, multilayered molecular landscapes across tissue sections, individuals, and platforms. COSIE integrates histology, epigenome, transcriptome, proteome, and metabolome into a unified representation. Applied to 12 datasets spanning 10 spatial technologies, eight modalities, and nine tissue types, ranging from thousands of spots to millions of cells, COSIE outperforms existing methods. It resolves tissue structures, enhances noisy measurements, predicts unmeasured modalities, and captures dynamic processes. In human tumors, COSIE identifies invasive subregions linked to clinical outcomes and predicts spatial gene expression in TCGA samples using only histology images. By transforming fragmented data into comprehensive spatial maps, COSIE advances computationally complete spatial omics and the creation of digital tissue twins for biomedicine.

genomics↗

bioRxiv2026

Multiscale confidence quantification for virtual spatial transcriptomics with UTOPIA

Virtual spatial transcriptomics (ST) methods predict gene expression or cell types from histology images, extending molecular readouts beyond the limited regions or samples directly measured by ST platforms. However, the statistical reliability of these predictions remains unclear. Here, we present UTOPIA, a model-agnostic framework for multiscale confidence quantification in virtual ST. UTOPIA assigns statistically calibrated confidence scores to predictions across spatial resolutions and biological granularities, ranging from single genes to metagenes and from specific cell types to broader cell classes. UTOPIA controls false discovery rates for detecting genes, metagenes, or cell types while accounting for local tissue context. We show that prediction confidence depends critically on both spatial resolution and biological granularity, with reliable inference often emerging only at coarser, biologically meaningful scales. Across multiple ST platforms and in both in-sample and out-of-sample settings, UTOPIA enhances interpretability, prevents false biological conclusions, and enables more trustworthy downstream analyses of virtual ST.

genomics↗

bioRxiv2026

NLRP3 activators disrupt the endocytic AP2 complex and plasma membrane signaling

Organellar perturbations are linked to NLRP3 inflammasome activation, however, it remains unclear whether unrelated agonists converge on a common upstream pathway. Here, we traced intracellular organelle and protein movements by differential ultracentrifugation combined with mass spectrometry-based proteomics. We show that NLRP3 activators uniformly disrupt the endocytic Adaptor Protein 2 (AP2) complex, whereas other subcellular rearrangements are stimulus-specific. We discovered Dynasore as a K-efflux-independent NLRP3 activator that engages this signaling node irrespective of endocytosis inhibition. Pharmacological and genetic perturbation of AP2 renders cells unresponsive to extracellular cues, blunting GPCR signaling, cAMP production, and chemotaxis, thereby enforcing a frozen signaling state that propagates NLRP3 inflammasome activation. Collectively, our study reveals a common surveillance checkpoint linking impaired plasma membrane signaling to the execution of inflammation and cell death. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/707400v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@178eb1dorg.highwire.dtl.DTLVardef@193cb02org.highwire.dtl.DTLVardef@1f50080org.highwire.dtl.DTLVardef@1f3e3e4_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2026

Pixel2Gene enables histology-guided reconstruction and prediction of spatial gene expression

Advances in spatial transcriptomics (ST) have fundamentally transformed our understanding of tissue biology by enabling gene expression profiling within intact spatial contexts and uncovering tissue organization and microenvironmental interactions. However, current high-resolution ST platforms remain constrained by high costs, limited tissue coverage, and technical artifacts, often yielding noisy, sparse, and incomplete data that compromise analytical accuracy, biological interpretation, and clinical utility. To address these challenges, we introduce Pixel2Gene, a deep learning framework that integrates co-registered histology images with ST data to enable histology-guided reconstruction and prediction of spatial gene expression. Pixel2Gene enhances existing expression measurements by denoising low-confidence data and reconstructing coherent expression patterns, while also predicting gene expression in unmeasured tissue regions and new samples lacking direct transcriptomic profiling. We systematically evaluated Pixel2Gene across multiple high-resolution ST platforms, including Visium HD, Xenium, and CosMx, spanning diverse tissue types and disease contexts using downsampling simulations and cross-platform comparisons in clinical samples. Across all settings, Pixel2Gene consistently improved data consistency, mitigated dropout effects, restored biologically meaningful spatial structure, and enabled accurate downstream analyses. By leveraging the scalability and ubiquity of routine histology, Pixel2Gene facilitates comprehensive, cost-effective ST profiling at whole-tissue scale, supporting large cohort studies, translational research, and next-generation biomarker discovery.

genomics↗

bioRxiv2026

HistoSweep enables cellular-resolution tissue quality control for gigapixel images in digital pathology and spatial omics

High-resolution histology images are indispensable for pathology and increasingly serve as the structural backbone for spatial omics. Yet whole-slide images (WSIs) frequently contain artifacts, acellular voids, and background regions that, when included in computational workflows, introduce noise, degrade model accuracy, and compromise biological interpretation. Existing tools provide only coarse foreground-background separation, leaving a gap in fine-grained quality control (QC). Here we present HistoSweep, a scalable framework that generates morphology-aware tissue masks at cellular resolution. By integrating density filtering, texture descriptors, and adaptive thresholding, HistoSweep systematically removes non-informative tissue regions while preserving biologically meaningful microstructures. It processes billion-pixel WSIs in minutes on standard CPUs, requiring no GPU acceleration, and is deployable across research and clinical settings. Across 25 WSIs spanning distinct tissues, disease states, and spatial omics platforms, HistoSweep consistently outperformed existing methods. It enhanced visualization and segmentation, improved virtual cell type predictions, and safeguarded spatial transcriptomics integrity by detecting transcript leakage and transcript-histology misalignment. By enabling fine-grained, scalable QC, HistoSweep provides a foundational preprocessing step for reliable and reproducible digital pathology and spatial omics analyses.

bioinformatics↗

bioRxiv2025

Self-esteem modulates beneficial causal attributions in the formation of novel self-beliefs

Healthy individuals typically attribute successes to internal causes, such as their abilities, and failures to external factors, like bad luck. In contrast, individuals with depression and low self-esteem are more likely to attribute failures to internal causes and successes to external causes. At the same time, depression and low self-esteem are associated with negatively biased self-related learning and self-beliefs. Although causal attributions have been shown to influence belief formation and updating, the dynamic interaction between real-time attributions and self-related learning remains poorly understood. In this study, we used a validated self-related learning task to investigate how internal versus external attributions of performance feedback affect the formation of self-beliefs and how these processes relate to depressive symptoms and self-esteem. Drawing on a computational model that incorporates prediction error valence and causal attributions, we found that participants updated their self-beliefs less when feedback was attributed to external causes. Furthermore, individuals with higher levels of depression and lower self-esteem showed a stronger negativity bias in learning. Lower self-esteem was also linked to a reduced self-serving bias in attributions. These findings provide insight into the cognitive mechanisms that may contribute to the development and maintenance of negative self-beliefs commonly observed in depression.

neuroscience↗

bioRxiv2025

Synthetic Mimetics of Exosomal Lipid Profile Enhance Gemcitabine Delivery in Pancreatic Cancer

Pancreatic cancer ranks fourth among cancer-related deaths. Despite decades of research, the cure rate of disease remains disappointingly low. This dismal prognosis is due to late detection and to resistance of tumors to all known systemic therapies. Here, we build upon our previous findings that highlighted the selective uptake of tumor-associated macrophage-derived exosomes by cancer cells. We hypothesize that the prominent lipid contents on the exosome surface play a crucial role in facilitating selective cellular interactions, offering a novel avenue for developing efficient drug delivery platforms for pancreatic cancer. Lipids affinity toward cancer cells was measured, following mass spectral lipidomic analysis for screening the lipidic composition of macrophages and their derived exosomes. Subsequently, optimized and characterized synthetic exosomes underwent detailed intracellular trafficking studies in human pancreatic cancer cells, demonstrating enhanced cellular uptake kinetics. Notably, gemcitabine-loaded synthetic exosomes exhibited superior efficacy in inducing programmed cell death compared to both the free drug and conventional liposome formulations. The biodistribution examination of these synthetic exosomes underscored their potential for tumor specificity. In vivo experiments further demonstrated that the treatment with gemcitabine-incorporated synthetic exosomes inhibited tumor growth by nearly 50% compared to the administration of the free drug, indicating a significantly enhanced treatment efficacy. Our findings indicate that leveraging the lipid membrane composition of exosomes could lead to breakthroughs in drug delivery efficiency. This innovative strategy offers a promising direction in the ongoing battle against pancreatic cancer, highlighting the potential of exosomal lipid profiles for cancer therapy.

cell biology↗

bioRxiv2025

Intracellular delivery of full-length antibodies via organ targeted lipid nanoparticles

Antibodies are proteins prized for their ability to bind to extracellular antigens with exceptionally high affinities and specificities. These features have motivated researchers to utilize antibody-antigen binding to inhibit intracellular disease targets in the proteome, yet delivery of antibodies into the cytosol of cells has long been a considerable challenge. Here, we outline the development of a novel lipid nanoparticle (LNP) platform for delivering antibodies into cells to selectively inhibit disease-relevant cytosolic targets. This approach efficiently delivers various therapeutic antibodies into multiple cancer cell lines, inhibiting key transcription factors in inflammatory and cancer signaling pathways. We further demonstrate systemic delivery of therapeutic antibodies in disease models, including alpha-synuclein-specific antibodies for Parkinsons disease and RelA-specific IgGs for acute lung injury using targeted LNP formulations. This work establishes a promising method for using LNPs for the delivery of antibody and antibody-derived therapeutics intracellularly to treat numerous proteome targets.

bioengineering↗

bioRxiv2025

Designing smart spatial omics experiments with S2Omics

Spatial omics technologies have transformed biomedical research by enabling high-resolution molecular profiling while preserving the native tissue architecture. These advances provide unprecedented insights into tissue structure and function. However, the high cost and time-intensive nature of spatial omics experiments necessitate careful experimental design, particularly in selecting regions of interest (ROIs) from large tissue sections. Currently, ROI selection is performed manually, which introduces subjectivity, inconsistency, and a lack of reproducibility. Previous studies have shown strong correlations between spatial molecular patterns and histological features, suggesting that readily available and cost-effective histology images can be leveraged to guide spatial omics experiments. Here, we present S2Omics, an end-to-end workflow that automatically selects ROIs from histology images with the goal of maximizing molecular information content in the ROIs. Through comprehensive evaluations across multiple spatial omics platforms and tissue types, we demonstrate that S2Omics enables systematic and reproducible ROI selection and enhances the robustness and impact of downstream biological discovery.

genomics↗

bioRxiv2025

Structural Basis for a Scaffolding Role of the COM Domain in Nonribosomal Peptide Synthetases

Nonribosomal peptide synthetases (NRPSs) are multi-domain enzymes that catalyze the biosynthesis of therapeutically relevant natural products. Efficient peptide synthesis relies on intricate domain interactions, whose underlying principles remain poorly understood. The communication-mediating (COM) domains facilitate interactions between separate NRPS subunits like other docking domains, however, exhibit distinctive features that are unusual within this family: COM domains co-occur with epimerization (E) domains, are partially embedded within the adjacent condensation (C) domain and can also be found as an internal cis-COM domain with unknown function. We present the first crystal structure of a cis-COM domain within an E-COM-C domain arrangement from modules 4 and 5 of bacitracin synthetase 3 (BacC). The structure reveals a compactly folded COM domain sandwiched between E and C domains, suggesting a role in orienting these domains for efficient peptidyl carrier protein (PCP) shuttling. Through mutational analyses, dipeptide formation assays, and proximity-dependent photo-crosslinking experiments, we investigated both cis- and trans-COM domains and provide evidence supporting a principal role of COM domains as scaffolds of NRPS architecture. Their function as docking domains may be a secondary consequence of their division into separate donor and acceptor parts.

biochemistry↗

bioRxiv2025

Scaling up spatial transcriptomics for large-sized tissues: uncovering cellular-level tissue architecture beyond conventional platforms with iSCALE

Recent advances in spatial transcriptomics (ST) technologies have transformed our ability to profile gene expression while retaining the crucial spatial context within tissues. However, existing ST platforms suffer from high costs, long turnaround times, low resolution, limited gene coverage, and small tissue capture areas, which hinder their broad applications. Here we present iSCALE, a method that predicts super-resolution gene expression and automatically annotates cellular-level tissue architecture for large-sized tissues that exceed the capture areas of standard ST platforms. The accuracy of iSCALE were validated by comprehensive evaluations, involving benchmarking experiments, immunohistochemistry staining, and manual annotation by pathologists. When applied to multiple sclerosis human brain samples, iSCALE uncovered lesion associated cellular characteristics that were undetectable by conventional ST experiments. Our results demonstrate iSCALEs utility in analyzing large-sized tissues with automatic and unbiased tissue annotation, inferring cell type composition, and pinpointing regions of interest for features not discernible through human visual assessment.

genomics↗

bioRxiv2023

Layer 1 NDNF Interneurons are Specialized Top-Down Master Regulators of Cortical Circuits

Diverse types of inhibitory interneurons (INs) impart computational power and flexibility to neocortical circuits. Whereas markers for different IN types in cortical layers (L) 2-6 have been instrumental for generating a wealth of functional insights, only the recent identification of the first selective marker (NDNF) has opened comparable opportunities for INs in L1. However, at present we know very little about the connectivity of NDNF L1INs with other IN types, their input-output conversion, and the existence of potential NDNF L1IN subtypes. Here, we report pervasive inhibition of L2/3 INs (including PV- and VIPINs) by NDNF L1INs. Intersectional genetics revealed similar physiology and connectivity in the NDNF L1IN subpopulation co-expressing NPY. Finally, NDNF L1INs prominently and selectively engage in persistent firing, a physiological hallmark disconnecting their output from the current input. Collectively, our work therefore identifies NDNF L1INs as specialized master regulators of superficial neocortex according to their pervasive top-down afferents.

neuroscience↗

bioRxiv2023

Validating a low-cost, open-source, locally manufactured workstation and computational pipeline for automated histopathology evaluation using deep learning

Deployment and access to state-of-the-art diagnostic technologies remains a fundamental challenge in providing equitable global cancer care to low-resource settings. The expansion of digital pathology in recent years and its interface with computational biomarkers provides an opportunity to democratize access to personalized medicine. Here we describe a low-cost platform for digital side capture and computational analysis composed of open-source components. The platform provides low-cost ($200) digital image capture from glass slides and is capable of real-time computational image analysis using an open-source deep learning (DL) algorithm and Raspberry Pi ($35) computer. We validate the performance of deep learning models performance using images captured from the open-source workstation and show similar model performance when compared against significantly more expensive standard institutional hardware.

pathology↗

bioRxiv2023

SpaDecon: cell-type deconvolution in spatial transcriptomics with semi-supervised learning

Spatially resolved transcriptomics (SRT) has advanced our understanding of the spatial patterns of gene expression, but the lack of single-cell resolution in spatial barcoding-based SRT hinders the inference of specific locations of individual cells. To determine the spatial distribution of cell types in SRT, we present SpaDecon, a semi-supervised learning approach that incorporates gene expression, spatial location, and histology information for cell-type deconvolution. SpaDecon was evaluated through analyses of four real SRT datasets using knowledge of the expected distributions of cell types. Quantitative evaluations were performed for four pseudo-SRT datasets constructed according to benchmark proportions. Using mean squared error and Jensen-Shannon divergence with the benchmark proportions as evaluation criteria, we show that SpaDecon performance surpasses that of published cell-type deconvolution methods. Given the accuracy and computational speed of SpaDecon, we anticipate it will be valuable for SRT data analysis and will facilitate the integration of genomics and digital pathology.

genomics↗

bioRxiv2022

Ultrastructural effects of sleep and wake on the parallel fiber synapses of the cerebellum

Multiple evidence in rodents shows that the strength of excitatory synapses in the cerebral cortex and hippocampus is greater after wake than after sleep. The widespread synaptic weakening afforded by sleep is believed to keep the cost of synaptic activity under control, promote memory consolidation, and prevent synaptic saturation, thus preserving the brains ability to learn day after day. The cerebellum is highly plastic and the Purkinje cells, the sole output neurons of the cerebellar cortex, are endowed with a staggering number of excitatory parallel fiber synapses. However, whether these synapses are affected by sleep and wake is unknown. Here we used serial block face scanning electron microscopy to obtain the full 3D reconstruction of more than 7,000 spines and their parallel fiber synapses in the mouse posterior vermis. We find that most Purkinje cell spines carry a synapse, but some do not. The latter, which we call "naked" spines, are [~]5% of all spines after wake but grow to [~]10% of all spines after sleep. Further analysis shows that the changes in the number of naked synapses with wake and sleep can be accounted for by a change in the number of "branched" synapses, which are housed in two or more spines sharing the same neck. Thus, during sleep branched spines may lose one or more synapses or convert to single spines, while the opposite changes occur after wake. Because branched synapses almost always contact different parallel fibers, these results also suggest that during wake, coincidences of firing over parallel fibers may translate into the formation of synapses converging on the same branched spine, which may be especially effective at driving the soma of Purkinje cells. Sleep, on the other hand, may promote the pruning of branched synapses that were formed due to spurious coincidences.

neuroscience↗