Biology subjects

Schroeter, M.

Publications and source records attributed to Schroeter, M..

10 recordsLinked to original sources

bioRxiv2026

Label-Free Multimodal Volumetric Imaging of Colon Cancer Tissue via Registration of Propagation-Based Phase-Contrast CT, Light-Sheet, and Three-Photon Microscopy

Multimodal 3D imaging has emerged as a powerful approach for investigating complex tissue architecture in pathological specimens. Techniques such as propagation-based phase-contrast computed tomography (PCT), light-sheet microscopy (LSM), and three-photon microscopy (3PM) provide complementary information on unlabeled tissue morphology based on distinct intrinsic contrast mechanisms. However, integrating these heterogeneous datasets into a unified spatial framework remains challenging due to differences in imaging geometry, spatial resolution, and modality-specific distortions. In this study, we present a registration pipeline for spatially aligning volumetric datasets acquired with PCT, LSM, and 3PM from formalin-fixed paraffin-embedded (FFPE) human colon cancer specimens. Biopsies from theses specimens were optically cleared and imaged sequentially using the three high-resolution modalities. To compensate for large positional differences between acquisitions, a three-stage cascade registration strategy was developed, consisting of coarse global alignment on down-sampled data, followed by rigid refinement at intermediate resolution. Mutual information was used as the similarity metric to ensure robust multimodal registration. The resulting framework enables the generation of spatially aligned multi-channel 3D datasets that combine structural information from X-ray phase-contrast imaging with complementary optical contrast signals. Beyond registration, we demonstrate that the fused six-dimensional feature space can be further exploited for unsupervised tissue characterization using a Gaussian Mixture Model (GMM), enabling data-driven identification of spatially coherent tissue regions without manual annotation. Qualitative evaluation confirms consistent alignment of major anatomical structures across modalities, while the unsupervised clustering reveals biologically meaningful patterns despite modality-specific noise and resolution differences. While further optimization and validation across larger datasets will enhance its computational efficiency and breadth of application, the approach already demonstrates strong potential for comprehensive tissue analysis and enables scalable, label-free 3D characterization of colon cancer tissue architecture.

pathology↗

bioRxiv2026

Functional Cell-Type Identification in Neuronal Networks Using High-Density Microelectrode Arrays

The reliable identification of neuronal cell types - in particular, the distinction of excitatory (E) and inhibitory (I) neurons on the basis of extracellular recordings without post-hoc immunostaining or genetic labeling - remains a key challenge in neural-circuit analysis. High-density microelectrode arrays (HD-MEAs) have emerged as a powerful tool to address this issue, enabling simultaneous single-cell and network-level electrophysiology. Here, we present two complementary strategies for establishing cell-type ground truth based on HD-MEA recordings: (i) chemogenetic interneuron activation to label putative inhibitory neurons according to their functional response, and (ii) controlled mixing of excitatory and inhibitory hiPSC-derived populations at defined ratios. A classifier combining action potential waveform morphology and autocorrelogram-based discharge dynamics achieves robust cell-type discrimination in in vitro recordings of rat primary cortical cultures and hiPSC-derived networks, as well as in in vivo recordings of rat and mouse - i.e., across several species, recording modalities, and preparation types. Applied to unlabeled data, the classifier reveals cell-type-specific network dynamics during bursts, including an inhibition activity signature preceding burst onsets. Leveraging the HD-MEA spatiotemporal resolution, label-free electrophysiological footprint reconstruction enables a morphological characterization of putative E and I neurons without post-hoc staining. The classification pipeline represents a scalable framework for functional cell-type phenotyping with broad relevance for precision neural-circuit analysis and disease modeling.

neuroscience↗

bioRxiv2024

Deciphering molecular determinants of GPCR-G protein receptor interactions by complementary integrative structural biology methods

Many physiological processes are dependent on G protein-coupled receptors (GPCRs), the biggest family of human membrane proteins and a significant class of therapeutic targets. Once activated by external stimuli, GPCRs use G proteins and arrestins as transducers to generate second messengers and trigger downstream signaling, leading to diverse signaling profiles. The G protein-coupled bile acid receptor 1 (GPBAR1, also known as Takeda G protein-coupled receptor 5, TGR5) is a class A bile acid membrane receptor that regulates energy homeostasis and glucose and lipid metabolism. GPBAR1/G protein interactions are implicated in the prevention of diabetes and the reduction of inflammatory responses, making GPBAR1 a potential therapeutic target for metabolic disorders. Here, we present an integrated structural biology approach combining hydrogen/deuterium exchange mass spectrometry (HDX-MS) and cryo-electron microscopy (cryo-EM) to identify the molecular determinants of GPBAR1 conformational dynamics upon G protein binding. Thanks to extensive optimization of both HDX-MS and cryo-EM workflows, we achieved over 99% sequence coverage along with a 2.5-[A] resolution structure, both of which are state-of-the-art and solely obtained for complete GPCR complexes. Altogether, our results provide information on the under-investigated GPBAR1 binding mode to its cognate G protein, pinpointing the synergic and powerful combination of higher (cryo-EM) and lower (HDX-MS) resolution structural biology techniques to dissect GPCR/G protein binding characteristics. Short statementThis work highlights the utility of integrating cryo-EM and HDX-MS for studying large multiprotein complexes such as GPCR/G protein complexes. Cryo-EM offers high-resolution structural details, while HDX-MS reveals the dynamic conformational changes during assembly, providing a comprehensive structural view of difficult-to-study membrane protein systems.

biophysics↗

bioRxiv2024

Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains

Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, for the inference of neuronal connectivity from large-scale extracellular recordings. Despite the popularity of this approach and a number of elaborate methods to reconstruct networks, the degree to which synaptic connections can be reconstructed from spike-train recordings alone remains controversial. Here, we provide a framework to probe and compare connectivity inference algorithms, using a combination of synthetic and empirical ground-truth data sets, obtained from simulations and parallel single-cell patch-clamp and high-density microelectrode array (HD-MEA) recordings in vitro. We find that reconstruction performance critically depends on the regularity of the recorded spontaneous activity, i.e., their dynamical regime, the type of connectivity, and the amount of available spike train data. We find gross differences between different algorithms, and many algorithms have difficulties in detecting inhibitory connections. We therefore introduce an ensemble artificial neural network (eANN) to improve connectivity inference. We train the eANN on the validated outputs of six established inference algorithms, and show how it improves network reconstruction accuracy and robustness. Overall, the eANN was robust across different dynamical regimes, with shorter recording time, and ameliorated the identification of synaptic connections, in particular inhibitory ones. Results indicated that the eANN also improved the topological characterization of neuronal networks. The presented methodology contributes to advancing the performance of inference algorithms and facilitates our understanding of how neuronal activity relates to synaptic connectivity. Author summaryThis study introduces an ensemble artificial neural network (eANN) to infer neuronal connectivity from multi-unit spike time recordings. We compare the eANN to previous algorithms and validate it using simulations and HD-MEA/patch-clamp datasets. The latter is obtained from three single-cell patch-clamp recordings and high-density microelectrode array (HD-MEA) measurements, in parallel. Our results demonstrate that the eANN outperforms all other algorithms across different dynamical regimes and provides a more accurate description of the underlying topological organization of the studied networks. We also provide a SHAP analysis of the trained eANN to understand which input features of the eANN contribute most to this superior performance. The eANN is a promising approach to improve connectivity inference from spike-train data.

neuroscience↗

bioRxiv2023

(CA)n oligonucleotides correct RNA mis-splicing in TDP-43 pathology

Withdrawal StatementThe authors have withdrawn their manuscript owing to not all authors having approved the manuscript. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

molecular biology↗

bioRxiv2023

Human neuron subtype programming through combinatorial patterning with scSeq readouts

Human neurons programmed through transcription factor (TF) overexpression model neuronal differentiation and neurological diseases. However, programming specific neuron types remains challenging. Here, we modulate developmental signaling pathways combined with TF overexpression to explore the spectrum of neuron subtypes generated from pluripotent stem cells. We screened 480 morphogen signaling modulations coupled with NGN2 or ASCL1/DLX2 induction using a multiplexed single-cell transcriptomic readout. Analysis of 700,000 cells identified diverse excitatory and inhibitory neurons patterned along the anterior-posterior and dorsal-ventral axes of neural tube development. We inferred signaling and TF interaction networks guiding differentiation of forebrain, midbrain, hindbrain, spinal cord, peripheral sympathetic and sensory neurons. Our approach provides a strategy for cell subtype programming and to investigate how cooperative signaling drives neuronal fate.

bioengineering↗

bioRxiv2022

Downregulating α-synuclein in iPSC-derived dopaminergic neurons mimics electrophysiological phenotype of the A53T mutation

Parkinsons disease (PD) is a common debilitating neurodegenerative disorder, characterized by a progressive loss of dopaminergic (DA) neurons. Mutations, gene dosage increase, and single nucleotide polymorphisms in the -synuclein-encoding gene SNCA either cause or increase the risk for PD. However, neither the function of -synuclein in health and disease, nor its role throughout development is fully understood. Here, we introduce DeePhys, a new tool that allows for data-driven functional phenotyping of neuronal cell lines by combining electrophysiological features inferred from high-density microelectrode array (HD-MEA) recordings with a robust machine learning workflow. We apply DeePhys to human induced pluripotent stem cell (iPSC)-derived DA neuron-astrocyte co-cultures harboring the prominent SNCA mutation A53T and an isogenic control line. Moreover, we demonstrate how DeePhys can facilitate the assessment of cellular and network-level electrophysiological features to build functional phenotypes and to evaluate potential treatment interventions. We find that electrophysiological features across all scales proved to be highly specific for the A53T phenotype, enabled to predict the genotype and age of individual cultures with high accuracy, and revealed a mutant-like phenotype after downregulation of -synuclein.

neuroscience↗

bioRxiv2021

pH-Triggered Assembly of Endomembrane Multicompartments in Synthetic Cells

Bottom-up synthetic biology thrives to reconstruct basic cellular processes into a minimalist cellular replica to foster their investigation in greater details with a reduced number of variables. Among these cellular features, the endomembrane system is an important aspect of cells which is at the origin of many of their functions. Still, the reconstruction of these inner compartments within a lipid-based vesicle remains challenging and poorly controlled. Herein, we report the use of pH as external trigger to self-assemble compartmentalized giant unilamellar vesicles (GUVs) by either bulk, or droplet-based microfluidics. By co-encapsulating pH sensitive small unilamellar vesicles (SUVs), negatively charged SUVs and/or proteins, we show that acidification of the droplets efficiently produces GUVs while sequestrating the co-encapsulated material with flexibility and robustness. The method enables the simultaneous reconstruction of more than a single cellular phenotype from the bottom-up, corresponding to an important advancement in the current status quo of bottom-up synthetic biology.

synthetic biology↗

bioRxiv2020

Electrophysiological Phenotype Characterization of Human iPSC-Derived Neuronal Cell Lines by Means of High-Density Microelectrode Arrays

Recent advances in the field of cellular reprogramming have opened a route to study the fundamental mechanisms underlying common neurological disorders. High-density microelectrode-arrays (HD-MEAs) provide unprecedented means to study neuronal physiology at different scales, ranging from network through single-neuron to subcellular features. In this work, we used HD-MEAs in vitro to characterize and compare human induced-pluripotent-stem-cell (iPSC)-derived dopaminergic and motor neurons, including isogenic neuronal lines modeling Parkinsons disease and amyotrophic lateral sclerosis. We established reproducible electrophysiological network, single-cell and subcellular metrics, which were used for phenotype characterization and drug testing. Metrics such as burst shapes and axonal velocity enabled the distinction of healthy and diseased neurons. The HD-MEA metrics could also be used to detect the effects of dosing the drug retigabine to human motor neurons. Finally, we showed that the ability to detect drug effects and the observed culture-to-culture variability critically depend on the number of available recording electrodes.

neuroscience↗

bioRxiv2018

Neuroanatomical correlates of food addiction and obesity in the general population

The food addiction model suggests neurobiological similarities between substance-related and addictive disorders and obesity. While structural brain differences have been consistently reported in these conditions, little is known about the neuroanatomical correlates of food addiction. We therefore assessed whether food addiction, assessed with the Yale Food Addiction Scale (YFAS), related to obesity, personality and brain structure in a large population-based sample (n=625; 20-59 years old, 45% women). A higher YFAS symptom score correlated with obesity and disinhibited eating. In a whole-brain analysis, YFAS symptom score was not associated with cortical thickness nor subcortical gray matter volumes. Higher body mass index (BMI) correlated with reduced thickness of (pre)frontal, temporal and occipital cortex. Bayes factor analysis suggested that BMI and - to a smaller extent - YFAS symptom score contributed independently to right lateral orbitofrontal cortex thickness. Our study shows that food addiction is not associated with neuroanatomical differences in a large population-based sample, and does not account for the major part of obesity-associated gray matter alterations. Yet, food addiction might explain additional variance in orbitofrontal cortex, a hub area of the reward network. Longitudinal studies implementing both anatomical and functional MRI could further disentangle the neural mechanisms of addictive eating behaviors.

neuroscience↗