Biology subjects

Hierlemann, A.

Publications and source records attributed to Hierlemann, A..

At least 19 recordsLinked to original sources

bioRxiv2026

A perturbation atlas of mouse neural organoids resolves macroglial reactivity and nominates drivers of degeneration

Understanding neuro-glial roles in homeostasis and disease requires scalable central nervous system (CNS) models, particularly as macroglia like astrocytes and Muller glia remain challenging to study in slow-developing human systems. Here, we generate and map the cellular diversity of mouse neural organoids spanning multiple brain regions and the retina. Benchmarking transcriptomic maturation against in vivo references captures postnatal neuro-macroglial states within a month. We show that macroglia are highly responsive to inflammatory cues and adopt specific transcriptomic states also found in vivo. Through a single-cell perturbation screen targeting 13 Muller-glia-specific pathways, we map how certain inflammatory cytokines drive degeneration-like stress programs in photoreceptors and distinct reactive states in Muller glia, including an Oncostatin M-driven chaperone-associated state. Mechanistically, we identify gp130-STAT3 signaling as a targetable driver of macroglial reactivity and photoreceptor distress. Together, these single-cell atlases provide a foundation for dissecting cellular crosstalk and neuro-glial pathology in the mature CNS.

neuroscience↗

bioRxiv2026

Rapid estimation of receptive fields across stages of the early visual system

Estimating receptive fields in large neural populations is often limited by experimental time: stimuli with theoretically favorable statistics for receptive-field estimation may, in practice, evoke too few informative spikes in neurons with high specificity for structured features, requiring impracticably long recordings. Therefore, we asked whether a structured stimulus could broadly engage neural populations across stages of the early visual system while permitting a tractable and interpretable reverse-correlation analysis. To address this question, we developed "reverse correlation against stimulus elements (RCASE)", an analysis framework for stimuli that can be represented frame by frame through the presence or absence of specific stimulus elements. We then designed such a stimulus consisting of random moving objects (RMO) and compared RMO/RCASE with conventional spike-triggered averaging using a dense binary white noise (WN/STA) stimulus across different stages of the early visual system of the mouse, namely the retina, the nucleus of the optic tract, the superior colliculus and the primary visual cortex, and in the primate retina. To distinguish the contributions of spatial and temporal scales, motion, stimulus structure, and analysis choice, we additionally compared against WN/STA with different square sizes and frame rates, spike-triggered covariance analysis, as well as random static objects and spatially correlated cloud stimuli. RMO evoked stronger and more reliable responses than WN in the mouse retina and across the investigated mouse visual areas. RMO/RCASE yielded discernible receptive fields for a substantially larger proportion of neurons and in a fraction of the recording time. Although the stimulus objects featured localized moving contrast edges, this advantage was not restricted to direction-selective cell populations. In the primate retina, in contrast, WN/STA approached the performance of RMO/RCASE. RMO/RCASE also supported simultaneous estimation of stimulus direction, speed, and color tuning. Our approach enables rapid and interpretable functional characterization of large populations of visual neurons, enabling to simultaneously recover receptive fields and feature tuning, whenever a low-dimensional feature space can be specified beforehand.

neuroscience↗

bioRxiv2026

Microfluidic System for On-Demand Imaging of Organotypic Slices

Organotypic brain slice cultures (OSCs) provide a physiologically relevant model for recapitulating complex tissues in vitro. However, the longitudinal monitoring of dynamic processes in OSCs through high-resolution imaging is limited in standard culturing methods, as they rely on slice placement on porous membranes at the air-liquid interface to preserve tissue viability. Here, a new experimental paradigm based on a modular slice culture insert is introduced, which is compatible with static and perfused culturing, and facilitates longitudinal observation of cellular and tissue dynamics at high spatiotemporal resolution. Besides compatibility with conventional culturing in well plates, the insert is integrated into an automated microfluidic instrument to provide user-friendly operation while enabling precise temporal fluid control and compound delivery. The system is applied to monitor calcium responses and cellular morphology of virally labeled cerebellar astrocytes and Purkinje cells exposed to different concentrations of glutamate. Cellular responses were evaluated at baseline, during glutamate exposure, and 48 h and 96 h after stimulation to identify acute and long-term excitotoxic effects. Our results indicate that the developed system enables high-resolution monitoring of cellular morphology and dynamic events of the same intact tissue over multiple days, which provides a robust foundation for mechanistic investigations of neural signaling.

bioengineering↗

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↗

bioRxiv2026

Monitoring intracellular antibiotic concentrations in real-time using allosteric biosensors

Antibiotic treatment can fail due to insufficient drug availability at the site of infection or limited accumulation within bacterial pathogens. However, it is poorly understood how antibiotics penetrate infected tissues and complex bacterial aggregates, limiting insights into the mechanisms of treatment failure. Here, we present genetically-encoded allosteric biosensors for two antibiotic classes, trimethoprim and tetracycline, which enable real-time monitoring of antibiotic concentrations inside bacterial cells. The biosensors consist of circularly permuted EGFP linked to the sensory domains DHFR or TetR. To extend this approach to low oxygen environments, we engineered an oxygen-independent trimethoprim biosensor by fusing DHFR to a circularly permuted version of the fluorogenic protein FAST. Using these biosensors, we monitored the antibiotic exposure dynamics of intracellular Salmonella enterica during macrophage infection at the single-cell level, and antibiotic penetration into anaerobic regions of Vibrio cholerae biofilms, as well as antibiotic availability in microoxic conditions in a human bladder tissue model infected with uropathogenic Escherichia coli. These fluorescent biosensors have the potential to be broadly applied for determining antibiotic distributions at infection sites with high spatial and temporal resolution.

microbiology↗

bioRxiv2025

Perfusable 3D human urothelial model for real-time analysis of bacterial infection dynamics and therapeutic interventions

Urinary tract infections (UTIs) remain a major health burden, yet mechanistic studies are limited by the lack of experimental models that enable high spatiotemporal resolution tracking of infection dynamics, while recapitulating the stratified architecture of the bladder epithelium, urine tolerance and fluid dynamics. Here, we present a modular microphysiological platform integrating a fully stratified, urine-tolerant human urothelium cultured on standard transwell inserts within a custom-designed perfusion device compatible with live imaging. Urine flow enables real-time, high-resolution imaging of uropathogenic Escherichia coli (UPEC) infections under physiologically relevant conditions, including clearance of planktonic bacteria and nutrients replenishment, while retaining tissue-associated populations. This system revealed UPEC attachment via the type 1 fimbrial adhesin FimH and its inhibition by D-mannose treatment. Moreover, the platform captured L-form formation upon treatment with the frontline antibiotic fosfomycin and regrowth of walled bacteria following drug withdrawal. The platform further uncovered strain-specific lysis through bacteriophages in contrast to the activity of broad-spectrum antibiotics. In summary, this system constitutes a scalable platform with high predictive power for studying UTI pathogenesis and preclinical therapeutic testing.

bioengineering↗

bioRxiv2025

Adaptation of Enterococcus faecalis to intestinal mucus revealed by a human colonic organoid model

The human gastrointestinal tract hosts a diverse population of microorganisms that have a significant impact on host health. Among this population, Enterococcus faecalis (Ef) represents a common member of intestinal microbiota colonizing humans early in life, but which can also opportunistically infect its host. Despite its importance in human health, investigations of its physiological adaptation to the mucosal environment remain limited. Building on recent advances in tissue engineering, we here leverage human colonic organoids (colonoids) to investigate the Efs mechanisms of mucosal surface colonization across space and time. Using high-resolution microscopy, we visualized Ef growth within the natively formed colonic mucus layer in colonoids. Leveraging a custom perfusion chamber, we tracked Ef growth within the mucus of live colonoids over time under flow, which revealed specific colonization strategies, including biofilm-like microcolony formation. To identify Ef fitness determinants in this niche, we implemented transposon insertion sequencing (Tn-seq) in the natively formed mucus of live colonoids. This approach revealed a large fitness rearrangement compared to typical liquid culture, mainly involving metabolic activity and regulatory response during mucosal colonization, as well as factors that may contribute to colony formation at the mucosal surface. Altogether, our results show important physiological and biophysical adaptation of Ef to the mucosal surface that are not captured by in vitro conditions and that cannot be revealed in vivo at high resolution.

microbiology↗

bioRxiv2025

Real-Time Tracking of Tumor Invasion Dynamics in Organotypic Brain Slices at Subcellular Resolution

Replicating the cytoarchitecture and cell heterogeneity of the brain in vitro remains challenging. Although ex vivo organotypic slices preserve native tissue complexity, current culturing methods limit long-term, high-resolution imaging and restrict temporally controlled perturbations. Here, we present PHIROS, a microfluidic platform for high-resolution imaging of organotypic slices that supports static culturing of excised tissue at the air-liquid interface and provides continuous optical access for extended imaging at subcellular resolution. Controlled perfusion with oxygenated medium preserves tissue viability over several days, enabling, e.g., the monitoring of spontaneous and pharmacologically modulated astrocytic calcium activity. Using PHIROS we characterized medulloblastoma (MB) cell behavior in a physiological tumor microenvironment and observed dynamic F-actin-driven interactions with tissue-resident astrocytes, as well as leading edge localization of the immune-checkpoint marker B7-H3 in invading tumor cells. Quantitative assessment of mitochondria transfer across heterotypic actin-rich connections evidences the potential of PHIROS as a versatile system for mechanistic studies in a tissue context, enabling controlled compound exposure and high-resolution imaging in physiologically relevant tissue settings.

bioengineering↗

bioRxiv2024

CardioMEA: Comprehensive Data Analysis Platform for Studying Cardiac Diseases and Drug Responses

In recent years, high-density microelectrode arrays (HD-MEAs) have emerged as a valuable tool in preclinical research for characterizing the electrophysiology of human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). HD-MEAs enable the capturing of both extracellular and intracellular signals on a large scale, while minimizing potential damage to the cell. However, a gap exists between technological advancements of HD-MEAs and the availability of effective data-analysis platforms. To address this need, we introduce CardioMEA, a comprehensive data-analysis platform designed specifically for HD-MEA data that have been obtained from iPSC-CMs. CardioMEA features scalable data processing pipelines and an interactive web-based dashboard for advanced visualization and analysis. In addition to its core functionalities, CardioMEA incorporates modules designed to discern crucial electrophysiological features between diseased and healthy iPSC-CMs. Notably, CardioMEA has the unique capability to analyze both extracellular and intracellular signals, thereby facilitating customized analyses for specific research tasks. We demonstrate the practical application of CardioMEA by analyzing electrophysiological signals from iPSC-CM cultures exposed to seven antiarrhythmic drugs. CardioMEA holds great potential as an intuitive, user-friendly platform for studying cardiac diseases and assessing drug effects.

bioengineering↗

bioRxiv2024

Action potential propagation speed compensates for traveling distance in the human retina

Neural information processing requires accurately timed action potentials arriving from presynaptic neurons at the postsynaptic neuron. However, axons of ganglion cells in the human retina feature low axonal conduction speeds and vastly different lengths, which poses a challenge to the brain for constructing a temporally coherent image over the visual field. Combining results from microelectrode array recordings, human behavioral measurements, transmission electron microscopy, and mathematical modelling of the retinal nerve fiber layer, we demonstrate that axonal propagation speeds compensate for variations in axonal length across the human retina including the fovea. The human brain synchronizes the arrival times of action potentials at the optic disc by increasing the diameters of longer axons, which increases their propagation speeds.

neuroscience↗

bioRxiv2024

RT-Sort: an action potential propagation-based algorithm for real time spike detection and sorting with millisecond latencies

With the use of high density multi electrode recording devices, electrophysiological signals resulting from action potentials of individual neurons can now be reliably detected on multiple adjacent recording electrodes both in vivo and in vitro. Spike sorting assigns these signals to putative neural sources. However, until now, spike sorting can only be performed after completion of the recording, preventing true real time usage of spike sorting algorithms. Utilizing the unique propagation patterns of action potentials along axons detected as high fidelity sequential activations on adjacent electrodes, together with a convolutional neural network based spike detection algorithm, we introduce RT-Sort (Real Time Sorting), a spike sorting algorithm that enables the sorted detection of action potentials within 7.5ms{+/-}1.5ms (mean{+/-}STD) after the waveform trough while the recording remains ongoing. RT-Sorts true real-time spike sorting capabilities enable closed loop experiments with latencies comparable to synaptic delay times. We show RT-Sorts performance on both Multi-Electrode Arrays as well as Neuropixels probes to exemplify RT-Sorts functionality on different types of recording hardware and electrode configurations.

neuroscience↗

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

Protosequences in human cortical organoids model intrinsic states in the developing cortex

Neuronal firing sequences are thought to be the basic building blocks of neural coding and information broadcasting within the brain. However, when sequences emerge during neurodevelopment remains unknown. We demonstrate that structured firing sequences are present in spontaneous activity of human and murine brain organoids and ex vivo neonatal brain slices from the murine somatosensory cortex. We observed a balance between temporally rigid and flexible firing patterns that are emergent phenomena in human and murine brain organoids and early postnatal murine somatosensory cortex, but not in primary dissociated cortical cultures. Our findings suggest that temporal sequences do not arise in an experience-dependent manner, but are rather constrained by an innate preconfigured architecture established during neurogenesis. These findings highlight the potential for brain organoids to further explore how exogenous inputs can be used to refine neuronal circuits and enable new studies into the genetic mechanisms that govern assembly of functional circuitry during early human brain development.

neuroscience↗

bioRxiv2023

Transwell-based microphysiological platform for high-resolution imaging of airway tissues

Transwell-based airway models have become increasingly important to study the effects of respiratory diseases and drug treatment at the air-liquid interface of the lung epithelial barrier. However, the underlying mechanisms at tissue and cell level often remain unclear, as transwell inserts feature limited live-cell imaging compatibility. Here, we report on a novel microphysiological platform for the cultivation of transwell-based lung tissues providing the possibility to alternate between air-liquid and liquid-liquid interfaces. While the air-liquid interface recapitulates physiological conditions for the lung model, the liquid-liquid interface enables live-imaging of the tissue at high spatiotemporal resolution. The plastics-based microfluidic platform enables insertion and recuperation of the transwell inserts, which allows for tissue cultivation and analysis under standardized well plate conditions. We used the device to monitor infections of Pseudomonas aeruginosa in human stem-cell-derived bronchial epithelial tissue. We continuously imaged the progression of a P. aeruginosa infection in real time at high resolution, which provided insights into bacterial spreading and invasion on the apical tissue surface, as well as insights into tissue breaching and destruction over time. The airway tissue culture system is a powerful tool to visualize and elucidate key processes of developing respiratory diseases and to facilitate drug testing and development.

bioengineering↗

bioRxiv2023

Scalable covariance-based connectivity inference for synchronous neuronal networks

We present a novel method for inferring connectivity from large-scale neuronal networks with synchronous activity. Our approach leverages Dynamic Differential Covariance to address the associated computational challenges. First, we analyze spike trains generated from Leaky Integrate-and-Fire network simulations and evaluate the performance of several off-the-shelf multivariate connectivity inference methods. Next, we introduce a new approach, Fractional Dynamic Differential Covariance (FDDC), and demonstrate that it consistently outperforms the other methods. Finally, we apply FDDC to experimental data to assess the topological organization of inferred graphs of in vitro neural network recordings obtained using high-density microelectrode arrays (HD-MEAs). Our results indicate that FDDC-derived graphs exhibit a significant negative correlation between small-worldness and measures of network synchrony. In contrast, graphs inferred through the well-established pairwise correlation method do not show such a correlation. This finding implies that the graphs obtained through FDDC provide stronger evidence in support of the theoretical notion that networks with clustered connections tend to exhibit higher levels of synchronizability. We hypothesize that our findings have implications for the development of scalable connectivity inference methods for large-scale neural network data.

neuroscience↗

bioRxiv2023

Direct-print three-dimensional electrodes for large-scale, high-density, and customizable neural interfaces

Silicon-based planar microelectronics is a powerful tool for scalably recording and modulating neural activity at high spatiotemporal resolution, but it remains challenging to target neural structures in three dimensions (3D). We present a method for directly fabricating 3D arrays of tissue-penetrating microelectrodes onto silicon microelectronics. Leveraging a high-resolution 3D printing technology based on 2-photon polymerization and scalable microfabrication processes, we fabricated arrays of 6,600 microelectrodes 10-130 {micro}m tall and at 35-m pitch onto a planar silicon-based microelectrode array. The process enables customizable electrode shape, height and positioning for precise targeting of neuron populations distributed in 3D. As a proof of concept, we addressed the challenge of specifically targeting retinal ganglion cell (RGC) somas when interfacing with the retina. The array was customized for insertion into the retina and recording from somas while avoiding the axon layer. We verified locations of the microelectrodes with confocal microscopy and recorded high-resolution spontaneous RGC activity at cellular resolution. This revealed strong somatic and dendritic components with little axon contribution, unlike recordings with planar microelectrode arrays. The technology could be a versatile solution for interfacing silicon microelectronics with neural structures and modulating neural activity at large scale with single-cell resolution.

neuroscience↗

bioRxiv2023

Harnessing PROTAC technology to combat stress hormone receptor activation

Counteracting the overactivation of glucocorticoid receptors (GR) is an important therapeutic goal in stress-related psychiatry and beyond. The only clinically approved GR antagonist lacks selectivity and induces unwanted side effects. To complement existing tools of small-molecule-based inhibitors, we present a highly potent, novel catalytically-driven GR degrader, KH-103, based on proteolysis-targeting chimera technology. This selective degrader enables immediate and reversible GR depletion that is independent of genetic manipulation and circumvents transcriptional adaptations to inhibition. KH-103 achieves passive inhibition, preventing agonistic induction of gene expression, and significantly averts the GRs genomic effects compared to two currently available inhibitors. Application in primary-neuron cultures revealed the dependency of a glucocorticoid-induced increase in spontaneous calcium activity on GR. Finally, we present a proof of concept for application in-vivo. KH-103 opens opportunities for a more lucid interpretation of GR functions with translational potential.

molecular biology↗

bioRxiv2023

A detailed input-output characterization of single neurons reveals the synaptic basis of spontaneous spiking in recurrent networks

Self-sustained recurrent activity in cortical networks is thought to be important for multiple crucial processes, including circuit development and homeostasis. Yet, the precise relationship between synaptic input patterns and the spiking output of individual neurons remains largely unresolved. Here, we developed, validated and applied a novel in vitro experimental platform and analytical procedures that provide - for individual neurons - simultaneous excitatory and inhibitory synaptic activity estimates during recurrent network activity. Our approach combines whole-network high-density microelectrode array (HD-MEA) recordings from rat neuronal cultures with patch clamping and enables a comprehensive mapping and characterization of active incoming connections to single postsynaptic neurons. We found that, during network states with excitation(E)-inhibition(I) balance, postsynaptic spiking often coincided with the maxima of fast fluctuations in the input E/I ratio. These spike-associated E/I ratio escalations were largely due to a rapid bidirectional change in synaptic inhibition that was modulated by the network-activity level. Our approach also uncovered the underlying circuit architecture and we show that individual neurons received a few key inhibitory connections - often from special hub neurons - that were instrumental in controlling postsynaptic spiking. Balanced network theory predicts dynamical regimes governed by small and rapid input fluctuation and featuring a fast neuronal responsiveness. Our findings - obtained in self-organized neuronal cultures - suggest that the emergence of these favorable regimes and associated network architectures is an inherent property of cortical networks in general.

neuroscience↗