Biology subjects

Braun, R.

Publications and source records attributed to Braun, R..

17 recordsLinked to original sources

bioRxiv2026

L1CAMxCD3 bispecific antibodies exert potent anti-tumor effects in preclinical pancreatic cancer models with representation of the complex tumor microenvironment

Pancreatic ductal adenocarcinoma (PDAC) is characterized by an immunosuppressive tumor microenvironment (TME) with pancreatic myofibroblasts (PMF) and macrophages being two prominent cell populations essentially impairing tumor responses to (immuno)therapies. L1 cell adhesion molecule (L1CAM) is upregulated in PDAC cells in primary and metastatic tissues and associated with tumor progression and therapy resistance. Using L1CAM as tumor-associated antigen, two bispecific antibodies (bsAB) targeting L1CAM and CD3 were developed in the IgG-(L)-ScFv format and their anti-tumorigenic activity was investigated in different preclinical PDAC models. In 2D models, both L1-bsAB exerted L1CAM-specific anti-PDAC cell activity when co-cultured with activated CD8+ T cells. Strong anti-PDAC cell effects along with elevated release of T cell effector molecules were also observed upon co-culture with peripheral blood mononuclear cells (PMBC) from healthy donors and PDAC patients. Of note, both L1-bsAB were also effective in 3D PDAC cell spheroids and neither impaired by PMF nor macrophages. Finally, application of L1-bsAB on organotypic tissue slice cultures from PDAC tissues comprising the entire complex TME also induced PDAC cell apoptosis and release of T cell effector molecules. Overall, our results highlight relevant anti-PDAC cell activity of L1-bsAB in immunosuppressive contexts supporting their potential as immunotherapeutic strategy for PDAC.

cancer biology↗

bioRxiv2025

Copula modeling of gene coexpression in single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) has become an indispensable tool for studying biological systems at the cellular level. It has therefore has become increasingly important to develop accurate statistical models of scRNA-seq data. While many models have been proposed to characterize transcript expression of individual genes, comparatively little attention has been paid to modeling gene coexpression. Copula modeling offers a flexible approach to modeling gene coexpression by linking models of individual genes together using copula functions. Despite the growing popularity of copula models, their utility for modeling scRNA-seq data has not been thoroughly explored. Here we evaluated six copula models on their ability to model gene coexpression in scRNA-seq data. Using a diverse collection of reference datasets, we evaluated each copula models accuracy and efficiency in reproducing gene coexpression patterns. Our results show that Gaussian copulas provide the best balance between accuracy and speed, with more flexible but expensive copula models providing only a marginal improvement in accuracy while requiring a much longer time to fit. Vine copulas show promise in being able to achieve high accuracy, but current implementations are unable to scale to the large size of typical scRNA-seq datasets.

bioinformatics↗

bioRxiv2025

Identification of Distinct Topological Structures From High-Dimensional Data

Single-cell RNA sequencing allows the direct measurement of the expression of tens of thousands of genes, providing an unprecedented view of the transcriptomic state of a cell. Within each cell, different biological processes such as differentiation or cell cycle take place simultaneously, each contributing a different characterization of cell state. To identify gene sets that govern these processes for the purpose of disentangling convolved biological processes, we present "Identification of Distinct topological structures" (ID). ID works by constructing an alternative low-dimensional parametrization of the high-dimensional system, applying a finite perturbation to this alternative parametrization, and looking for genes that respond similarly. With this approach, we demonstrate that ID is capable of identifying structures within the data that will otherwise be missed. We further demonstrate the utility of ID in scRNA-seq datasets collected under various conditions, delineating cellular differentiation, characterizing cellular response to external perturbation, and dissecting the effect of genetic knock-outs.

bioinformatics↗

bioRxiv2025

Impact of Single Freeze-Thaw Cycles on Serum Protein Stability: Implications for Clinical Biomarker Validation Using Mass Spectrometry

BackgroundValidation of biomarkers for clinical diagnostics and therapy necessitates the availability of a substantial number of high-quality samples, along with a complete set of clinical data. Establishing standards for sample collection, storage, and quality control is essential to reduce the variability of sample quality. MethodsThis study evaluated the impact of a single freeze-and-thaw (FT) cycle on the quality of liquid nitrogen-stored clinical serum aliquots. Data-independent acquisition mass spectrometry (DIA-MS) was performed to measure serum protein abundance in a test cohort comprising 25 patients and 99 samples, and a validation cohort comprising 109 patients/samples. Abundance differences of paired fresh and FT samples were assessed by employing biostatistics and bioinformatics approaches, including clustering analyses, linear mixed models, machine learning, and functional annotation. ResultsFollowing the library-free data analysis and implementation of strict data preprocessing procedures, data on the relative abundance of 213 (test cohort) and 248 (validation cohort) human serum proteins was available. Overall, the proteomic data demonstrated high quality and reproducibility. Significant changes in abundance between fresh and FT samples was observed for 30 proteins in the test cohort (q-value [≤]0.05). Of these, 11 proteins (36.6%) were successfully validated in the validation cohort, including CP and IGHV6-1 which were identified as potential biomarker candidates for discriminating between patients with malignant or benign pancreatic disease. A correlation analysis of the measured protein intensity and storage duration revealed no significant association between the two variables. ConclusionsDistinct protein abundance patterns can be discerned between fresh serum samples and samples stored in liquid nitrogen. The findings of this study suggest that FT cycles are an important pre-analytical factor and should be addressed during the translation of protein-based biomarkers into clinical use.

molecular biology↗

bioRxiv2024

Variational inference of single cell time series

Time course single-cell RNA sequencing (scRNA-seq) enables researchers to probe expression dynamics at the resolution of individual cells. However, analyzing this rich data poses several challenges, including deconvolving the contributions of time and cell type, discriminating true dynamics from batch effects, and inferring per-cell dynamics despite cells being destroyed at each time point. We present SNOW (SiNgle cell flOW map), a deep learning algorithm that deconvolves single-cell time series into time-dependent and time-independent components. Using both synthetic and real scRNA-seq data, we show that SNOW constructs biologically meaningful latent spaces, removes batch effects, and generates realistic single-cell time series.

bioinformatics↗

bioRxiv2023

Detecting Rhythmic Gene Expression in Single Cell Transcriptomics

An autonomous, environmentally-synchronizable circadian rhythm is a ubiquitous feature of life on Earth. In multicellular organisms, this rhythm is generated by a transcription-translation feedback loop present in nearly every cell that drives daily expression of thousands of genes in a tissue-dependent manner. Identifying the genes that are under circadian control can elucidate the mechanisms by which physiological processes are coordinated in multicellular organisms. Today, transcriptomic profiling at the single-cell level provides an unprecedented opportunity to understand the function of cell-level clocks. However, while many cycling detection algorithms have been developed to identify genes under circadian control in bulk transcriptomic data, it is not known how best to adapt these algorithms to single-cell RNAseq data. Here, we benchmark commonly used circadian detection methods on their reliability and efficiency when applied to single cell RNAseq data. Our results provide guidance on adapting existing cycling detection methods to the single-cell domain, and elucidate opportunities for more robust and efficient rhythm detection in single-cell data. We also propose a subsampling procedure combined with harmonic regression as an efficient strategy to detect circadian genes in the single-cell setting.

bioinformatics↗

bioRxiv2023

Temperature-driven coordination of circadian transcriptome regulation

The circadian rhythm is an evolutionarily-conserved molecular oscillator that enables species to anticipate rhythmic changes in their environment. At a molecular level, the core clock genes induce a circadian oscillation in thousands of genes in a tissue-specific manner, orchestrating myriad biological processes. While studies have investigated how the core clock circuit responds to environmental perturbations such as temperature, the downstream effects of such perturbations on circadian regulation remain poorly understood. By analyzing bulk-RNA sequencing of Drosophila fat bodies harvested from flies subjected to different environmental conditions, we demonstrate a highly condition-specific circadian transcriptome. Further employing a reference-based gene regulatory network (Reactome), we find evidence of increased gene-gene coordination at low tem-peratures and synchronization of rhythmic genes that are network neighbors. Our results point to the mechanisms by which the circadian clock mediates the flys response to seasonal changes in temperature. Significance StatementThe circadian rhythm enables organisms to anticipate and adapt to changes in their environment. While behavioral changes have been observed in Drosophila melanogaster subjected to low temperatures, little is known regarding how these changes are enacted at a molecular level. By conducting bulk RNA sequencing from fruit flies, we observe that genome-wide circadian oscillation patterns are temperature dependent. Intriguingly, we find that morning and evening peaks of transcriptomic activity shift closer together, consistent with anticipation of a shorter photoperiod in cooler winter weather. We further find that the low-temperature dynamics are highly coordinated with respect to a reference-based gene regulatory network. Our findings provide insights into the mechanisms by which flies adapt to environmental temperature changes.

bioinformatics↗

bioRxiv2023

Expansion of profibrotic monocyte-derived alveolar macrophages in patients with persistent respiratory symptoms and radiographic abnormalities after COVID-19

As many as 10-30% of the over 760 million survivors of COVID-19 develop persistent symptoms, of which respiratory symptoms are among the most common. To understand the cellular and molecular basis for respiratory PASC, we combined a machine learning based analysis of lung computed tomography (CT) with flow cytometry, single-cell RNA-sequencing analysis of bronchoalveolar lavage fluid and nasal curettage samples, and alveolar cytokine profiling in a cohort of thirty-five patients with respiratory symptoms and radiographic abnormalities more than 90 days after infection with COVID-19. CT images from patients with PASC revealed abnormalities involving 73% of the lung, which improved on subsequent imaging. Interstitial abnormalities suggestive of fibrosis on CT were associated with the increased numbers of neutrophils and presence of profibrotic monocyte-derived alveolar macrophages in BAL fluid, reflecting unresolved epithelial injury. Persistent infection with SARS-CoV-2 was identified in six patients and secondary bacterial or viral infections in two others. These findings suggest that despite its heterogenous clinical presentations, respiratory PASC with radiographic abnormalities results from a common pathobiology characterized by the ongoing recruitment of neutrophils and profibrotic monocyte-derived alveolar macrophages driving lung fibrosis with implications for diagnosis and therapy.

immunology↗

bioRxiv2023

Reconstruction of 3-dimensional tissue organization at the single-cell resolution

Recent advances in spatial transcriptomics (ST) have allowed for the mapping of tissue heterogeneity, but this technique lacks the resolution to investigate gene expression patterns, cell-cell communications and tissue organization at the single-cell resolution. ST data contains a mixed transcriptome from multiple heterogeneous cells, and current methods predict two-dimensional (2D) coordinates for individual cells within a predetermined space, making it difficult to reconstruct and study three-dimensional (3D) tissue organization. Here we present a new computational method called scHolography that uses deep learning to map single-cell transcriptome data to 3D space. Unlike existing methods, which generate a projection between transcriptome data and 2D spatial coordinates, scHolography uses neural networks to create a high-dimensional transcriptome-to-space map that preserves the distance information between cells, allowing for the construction of a cell-cell proximity matrix beyond the 2D ST scaffold. Furthermore, the neighboring cell profile of a given cell type can be extracted to study spatial cell heterogeneity. We apply scHolography to human skin, human skin cancer and mouse brain datasets, providing new insights into gene expression patterns, cell-cell interactions and spatial microenvironment. Together, scHolography offers a computational solution for digitizing transcriptome and spatial information into high-dimensional data for neural network-based mapping and the reconstruction of 3D tissue organization at the single-cell resolution.

bioinformatics↗

bioRxiv2023

Dietary Restriction Impacts Peripheral Circadian Clock Output Important for Longevity in Drosophila

Circadian clocks may mediate lifespan extension by caloric or dietary restriction (DR). We find that the core clock transcription factor Clock is crucial for a robust longevity and fecundity response to DR in Drosophila. To identify clock-controlled mediators, we performed RNA-sequencing from abdominal fat bodies across the 24 h day after just 5 days under control or DR diets. In contrast to more chronic DR regimens, we did not detect significant changes in the rhythmic expression of core clock genes. Yet we discovered that DR induced de novo rhythmicity or increased expression of rhythmic clock output genes. Network analysis revealed that DR increased network connectivity in one module comprised of genes encoding proteasome subunits. Adult, fat body specific RNAi knockdown demonstrated that proteasome subunits contribute to DR-mediated lifespan extension. Thus, clock control of output links DR-mediated changes in rhythmic transcription to lifespan extension.

genetics↗

bioRxiv2022

Quantitative Analysis of Transcriptome Dynamics Provides Novel Insights into Developmental State Transitions

During embryogenesis, the developmental potential of initially pluripotent cells becomes progressively restricted as they transit to lineage restricted states. The pluripotent cells of Xenopus blastula-stage embryos are an ideal system in which to study cell state transitions during developmental decision-making, as gene expression dynamics can be followed at high temporal resolution. Here we use transcriptomics to interrogate the process by which pluripotent cells transit to four different lineage-restricted states: neural progenitors, epidermis, endoderm and ventral mesoderm, providing quantitative insights into the dynamics of Waddingtons landscape. Our findings shed light on why the neural progenitor state is the default lineage state for pluripotent cells, and uncover novel components of lineage-specific gene regulation. These data reveal an unexpected overlap in the transcriptional responses to BMP4/7 and activin signaling, and provide mechanistic insight into how the timing of signaling inputs such as BMP are temporally controlled to ensure correct lineage decisions. Together these analyses provide quantitative insights into the logic and dynamics of developmental decision making in early embryos.

developmental biology↗

bioRxiv2022

period Translation as a Core Mechanism Controlling Temperature Compensation in an Animal Circadian Clock

The most enigmatic of the canonical properties of circadian clocks is temperature compensation where circadian period length is stable across a wide temperature range despite the temperature dependence of most biochemical reactions. While the core mechanisms of circadian clocks have been well described, the molecular mechanisms of temperature compensation are poorly understood especially in animals. A major gap is the lack of temperature compensation mutants that do not themselves unambiguously affect the temperature dependence of the encoded protein. Here we show that null alleles of two genes encoding components of a complex important for translation of the core clock component period in circadian pacemaker neurons robustly alter the temperature dependence of circadian behavioral period length. These changes are accompanied by parallel temperature dependent changes in oscillations of the PER protein and are consistent with the model that these translation factors mediate the temperature-dependence of PER translation. Consistent with findings from modeling studies, we find that translation of the key negative feedback factor PER plays an instrumental role in temperature compensation.

genetics↗

bioRxiv2021

Laser facilitated epicutaneous peptide immunization using dry patch technology

The skin has been intensely investigated as a target tissue for immunization because it is populated by multiple types of antigen presenting cells. Directly addressing dendritic cells or Langerhans cells in vivo represents an attractive strategy for inducing T cell responses in cancer immunotherapy. We and others have studied fractional laser ablation as a novel method combining efficient delivery of macromolecules to the skin with an inherent adjuvant effect of laser illumination. In this proof of concept study, we demonstrate the feasibility of peptide delivery to the skin using the P.L.E.A.S.E. professional Erb:YAG fractional infrared laser together with EPIMMUN patches. In an ovalbumin mouse model we demonstrate that a dry patch formulation of SIINFEKL peptide in combination with CpG-ODN1826, but not imiquimod or polyI:C, induces potent cytotoxic T cell responses, which can be further boosted by co-delivery of the pan-helper T cell epitope PADRE.

immunology↗

bioRxiv2021

Laser-facilitated epicutaneous immunization with SARS-CoV-2 spike protein induces ACE2 blocking antibodies in mice

The skin represents an attractive target tissue for vaccination against respiratory viruses such as SARS-CoV-2. Laser-facilitated epicutaneous immunization (EPI) has been established as a novel technology to overcome the skin barrier, which combines efficient delivery via micropores with an inherent adjuvant effect due to the release of danger-associated molecular patterns. Here we delivered the S1 subunit of the Spike protein of SARS-CoV-2 to the skin of BALB/c mice via laser-generated micropores with or without CpG-ODN1826 or the B subunit of heat-labile enterotoxin of E.coli (LT-B). EPI induced serum IgG titers of 1:3200 that could be boosted 5 to 10-fold by co-administration of LT-B and CpG, respectively. Sera were able to inhibit binding of the spike protein to its receptor ACE2. Our data indicate that delivery of recombinant spike protein via the skin may represent an alternative route for vaccines against Covid-19.

immunology↗

bioRxiv2020

TimeCycle: Topology Inspired MEthod for the Detection of Cycling Transcripts in Circadian Time-Series Data

MotivationThe circadian rhythm drives the oscillatory expression of thousands of genes across all tissues. The recent revolution in high-throughput transcriptomics, coupled with the significant implications of the circadian clock for human health, has sparked an interest in circadian profiling studies to discover genes under circadian control. ResultWe present TimeCycle: a topology-based rhythm detection method designed to identify cycling transcripts. For a given time-series, the method reconstructs the state space using time-delay embedding, a data transformation technique from dynamical systems theory. In the embedded space, Takens theorem proves that the dynamics of a rhythmic signal will exhibit circular patterns. The degree of circularity of the embedding is calculated as a persistence score using persistent homology, an algebraic method for discerning the topological features of data. By comparing the persistence scores to a bootstrapped null distribution, cycling genes are identified. Results in both synthetic and biological data highlight Time-Cycles ability to identify cycling genes across a range of sampling schemes, number of replicates, and missing data. Comparison to competing methods highlights their relative strengths, providing guidance as to the optimal choice of cycling detection method. Availability and ImplementationA fully documented open-source R package implementing Time-Cycle is available at: https://nesscoder.github.io/TimeCycle/.

bioinformatics↗

bioRxiv2020

TimeTrial: An Interactive Application for Optimizing the Design and Analysis of Transcriptomic Times-Series Data in CircadianBiology Research

The circadian rhythm drives the oscillatory expression of thousands of genes across all tissues, coordinating physiological processes. The effect of this rhythm on health has generated increasing interest in discovering genes under circadian control by searching for periodic patterns in transcriptomic time-series experiments. While algorithms for detecting cycling transcripts have advanced, there remains little guidance quantifying the effect of experimental design and analysis choices on cycling detection accuracy. We present TimeTrial, a user-friendly benchmarking framework using both real and synthetic data to investigate cycle detection algorithms performance and improve circadian experimental design. Results show that the optimal choice of analysis method depends on the sampling scheme, noise level, and shape of the waveform of interest; and provides guidance on the impact of sampling frequency and duration on cycling detection accuracy. The TimeTrial software is freely available for download and may also be accessed through a web interface. By supplying a tool to vary and optimize experimental design considerations, TimeTrial will enhance circadian transcriptomics studies.

bioinformatics↗

bioRxiv2019

Cancer-type specific aneuploidies hard-wire chromosome-wide gene expression patterns of their tissue of origin

Most carcinomas have characteristic chromosomal aneuploidies specific to the tissue of tumor origin. The reason for this specificity is unknown. As aneuploidies directly affect gene expression, we hypothesized that cancer-type specific aneuploidies, which emerge at early stages of tumor evolution, confer adaptive advantages to the physiological requirements of the tissue of origin. To test this hypothesis, we compared chromosomal aneuploidies reported in the TCGA database to chromosome arm-wide gene expression levels of normal tissues from the GTEx database. We find that cancer-type specific chromosomal aneuploidies mirror differential gene expression levels specific to the respective normal tissues which cannot be explained by copy number alterations of resident cancer driver genes. We propose that cancer-type specific aneuploidies "hard-wire" chromosome arm-wide gene expression levels present in normal tissues, favoring clonal expansion and tumorigenesis. One sentence summaryThe clonal evolution of cancer is initiated by tissue-specific transcriptional requirements

cancer biology↗