Biology subjects

Rupprecht, P.

Publications and source records attributed to Rupprecht, P..

16 recordsLinked to original sources

bioRxiv2026

Scan field rotation with a K-mirror for fast two-photon imaging along curved planes

In contrast to the curved geometry of biological structures, the imaging plane of conventional microscopes is flat. In principle, axial scanning can adjust the focus to follow curved structures. In resonant scanning microscopes, which are broadly used for video-rate functional imaging, however, such focus adjustment is typically possible at rates faster than the slow (~30 Hz) scan axis but not as fast as the fast (~8000 Hz) resonant axis. The fixed direction of the fast resonant axis therefore constrains how the imaging plane can be tilted or curved using axial scanning, and limits how well it can be aligned with biological structures of interest. Here, we present an optical modification that overcomes this constraint. Specifically, we combine scan field rotation using a K-mirror with fast axial scanning using an electrically tunable lens. We demonstrate the versatility of our approach for calcium imaging by aligning the imaging plane with the curved pyramidal cell layer in mouse hippocampus, increasing the number of simultaneously imaged neurons. In addition, we image dendritic arbors of hippocampal and cortical pyramidal neurons that would otherwise be only partially accessible for fast imaging, and we apply our method to the curved layers of cerebellar Purkinje cells. Furthermore, we demonstrate how scan field rotation can be used to optimally orient a dendritic branch for high-speed subcellular glutamate imaging. Our approach is based on a single module and can therefore be retrofitted to standard two-photon systems. We openly share design specifications, 3D-printed parts, and custom ScanImage-compatible code to facilitate rapid adoption.

neuroscience↗

bioRxiv2026

PURELIGHT: a quantitative photon-counting framework unifying intensity and lifetime imaging at video rate across detector technologies

Quantitative fluorescence microscopy requires photon-efficiency, speed and accurate intensity and lifetime measurements. Time-correlated single-photon counting (TCSPC) simultaneously captures intensity and lifetime, but photon pile-up distorts both signals at high count rates, preventing fast acquisitions. Existing corrections discard photons, distort intensity, or require specialized detectors. Here we introduce PURELIGHT, an integrated hardware and software framework that simultaneously recovers undistorted intensities and lifetimes at count rates far beyond conventional pile-up limits. PURELIGHT works with hybrid photodetectors, silicon photomultipliers and photomultiplier tubes while retaining over three times more photons than alternative approaches. Using two-photon imaging, we showcase PURELIGHT's superior accuracy and spatial contrast, demonstrating video-rate subcellular lifetime imaging in awake mice, a unique lifetime-calibrated ratiometric modality and crosstalk-free temporal multiplexing. By removing the limits that have confined TCSPC to low-signal applications, PURELIGHT promotes the adoption of quantitative, photon-efficient microscopy across the life sciences.

neuroscience↗

bioRxiv2026

Locus coeruleus noradrenaline elicits response profiles distinct from natural arousal in hippocampal neurons and astrocytes

The locus coeruleus (LC)-noradrenaline (NA) system is a central component of the brains response to arousal and stress. Yet how LC activity contributes to the cellular response profiles observed during natural arousal remains unclear. Here, we directly compared natural arousal with selective LC activation in mouse CA1, using physiologically titrated optogenetics, fiber photometry of NA and calcium signals, chronic two-photon imaging, and behavioral monitoring. While natural arousal robustly activated astrocytes, pyramidal cells, and interneurons, direct LC stimulation revealed a striking divergence in cellular response polarity and sensitivity. At levels of LC activation that correspond to arousal and moderate stress, we observed strong and reliable calcium responses in astrocytes, whereas pyramidal neurons and interneurons remained largely unaffected. Only at high-intensity LC stimulation did neurons exhibit a response, characterized by broad population-level inhibition of both pyramidal cells and interneurons, alongside the transient activation of an interneuron subpopulation that occupied distinct laminar positions in CA1. Thus, LC-driven NA release produces cell-specific effects in hippocampal CA1 that are distinct from and, at the population level for neurons even opposite to, cellular dynamics during natural arousal. Together, our results reveal a divergence in how astrocytes and neurons respond to LC-driven NA release and suggest that noradrenergic effects in the hippocampus at moderate levels of arousal and stress are predominantly mediated by astrocytes.

neuroscience↗

bioRxiv2025

aDISCO: A Broadly Applicable Method for 3D Microscopy of Archival Paraffin-Embedded Human Tissues

Human surgery and autopsy specimens are routinely stored as formalin-fixed paraffin-embedded (FFPE) tissue blocks. Diagnoses are based on microscopic examination of two-dimensional sections. Good clinical practice requires that samples be retained for decades, thus giving rise to vast archives of healthy and diseased tissues. While tissue clearing and whole-mount microscopy enable 3D analysis, FFPE human tissue blocks are often unsuitable for clearing and immunolabeling due to their large size, extensive cross-linking, and wax embedding. Here, we introduce archival DISCO (aDISCO), a versatile and robust clearing method designed to overcome these challenges. aDISCO achieves complete clearing and immunolabeling of large samples stored for 15 years or more. We applied aDISCO to human brain, spinal cord, peripheral nerve, skin, muscle, heart, kidney, liver, spleen, colon, and lung, using a broad range of antibodies. To investigate the clinical usefulness of quantitative 3D histology by aDISCO, we performed a case study of focal cortical dysplasia, a neurodevelopmental disease causing epilepsy. Using deep-learning-based analysis, we found disrupted cortical layering and both focal and global neuronal density variations in FCD, features that are likely to be overlooked by conventional histology. In summary, aDISCO delivers large datasets suitable for deep-learning-based processing, enabling the detection of subtle and sparse pathologies in large archival human tissue specimens.

bioengineering↗

bioRxiv2025

Spike inference from calcium imaging data acquired with GCaMP8 indicators

For neuroscience to be reproducible, its key methods must be quantitative and interpretable. Calcium imaging is such a key method, but it records neuronal activity only indirectly and is therefore difficult to interpret. These difficulties arise from the kinetics, nonlinearity, and sensitivity of calcium indicators, but also from the methods used for signal analysis. Here, we evaluate how methods for spike inference can be optimized to interpret data with the recently developed calcium indicator GCaMP8. We find that the indicator linearity of GCaMP8 enables more accurate recovery of high-frequency spiking events and the detection of single action potentials. Ground truth recordings from mouse neocortex show that the most linear calcium variants, GCaMP8s and GCaMP8m - but not GCaMP6, GCaMP7f, or GCaMP8f - robustly detect isolated spikes in pyramidal neurons under realistic noise conditions. In addition, we fine-tune and benchmark existing algorithms for spike inference (CASCADE, OASIS, MLSpike) with GCaMP8 data, investigate spike inference for interneurons, and demonstrate how the fast rise time of GCaMP8 enables low-latency real-time detection of neuronal activity. Together, our study provides tools and guidelines to optimally process calcium signals with GCaMP8 and highlights the key role of linearity in improving the interpretability of calcium imaging.

neuroscience↗

bioRxiv2024

Representational learning by optimization of neural manifolds in an olfactory memory network

Cognitive brain functions rely on experience-dependent internal representations of relevant information. Such representations are organized by attractor dynamics or other mechanisms that constrain population activity onto "neural manifolds". Quantitative analyses of representational manifolds are complicated by their potentially complex geometry, particularly in the absence of attractor states. Here we trained juvenile and adult zebrafish in an odor discrimination task and measured neuronal population activity to analyze representations of behaviorally relevant odors in telencephalic area pDp, the homolog of piriform cortex. No obvious signatures of attractor dynamics were detected. However, olfactory discrimination training selectively enhanced the separation of neural manifolds representing task-relevant odors from other representations, consistent with predictions of autoassociative network models endowed with precise synaptic balance. Analytical approaches using the framework of manifold capacity revealed multiple geometrical modifications of representational manifolds that supported the classification of task-relevant sensory information. Manifold capacity predicted odor discrimination across individuals better than other descriptors of population activity, indicating a close link between manifold geometry and behavior. Hence, pDp and possibly related recurrent networks store information in the geometry of representational manifolds, resulting in joint sensory and semantic maps that may support distributed learning processes.

neuroscience↗

bioRxiv2024

Spike inference from mouse spinal cord calcium imaging data

Calcium imaging is a key method to record the spiking activity of identified and genetically targeted neurons. However, the observed calcium signals are only an indirect readout of the underlying electrophysiological events (single spikes or bursts of spikes) and require dedicated algorithms to recover the spike rate. These algorithms for spike inference can be optimized using ground truth data from combined electrical and optical recordings, but it is not clear how such optimized algorithms perform on cell types and brain regions for which ground truth does not exist. Here, we use a state-of-the-art algorithm based on supervised deep learning (CASCADE) and a non-supervised algorithm based on non-negative deconvolution (OASIS) to test spike rate inference in spinal cord neurons. To enable these tests, we recorded specific ground truth from glutamatergic and GABAergic somatosensory neurons in the superficial dorsal horn of spinal cord in mice of both sexes. We find that CASCADE and OASIS algorithms that were designed for cortical excitatory neurons generalize well to both spinal cord cell types. However, CASCADE models re-trained on our ground truth further improved the performance, resulting in a more accurate inference of spiking activity from spinal cord neurons. We openly provide re-trained models that can be applied to spinal cord data of variable noise levels and frame rates. Together, our ground-truth recordings and analyses provide a solid foundation for the interpretation of calcium imaging data from spinal cord dorsal horn and showcase how spike rate inference can generalize between different regions of the nervous system. Significance StatementCalcium imaging is a powerful method for measuring the activity of genetically identified neurons. However, accurate interpretation of calcium transients depends on having a detailed understanding of how neuronal activity correlates with fluorescence. Such calibration recordings have been performed for cerebral cortex but not yet for most other CNS regions and neuron types. Here, we obtained ground truth data in spinal cord by conducting simultaneous calcium and electrophysiology recordings in excitatory and inhibitory neurons. We systematically investigated the transferability of cortical algorithms to spinal neuron subpopulations and generated inference algorithms optimized to excitatory and inhibitory neurons. Our study provides a foundation for the rigorous interpretation of calcium imaging data from spinal cord. Conflict of interest statementThe authors declare no competing financial conflicts of interest.

neuroscience↗

bioRxiv2024

Computational functions of precisely balanced neuronal assemblies in an olfactory memory network

Structured connectivity in the brain organizes information by constraining neuronal dynamics. Theoretical models predict that memories are represented by balanced assemblies of excitatory and inhibitory neurons, but the existence and functions of such EI assemblies are difficult to explore. We addressed these issues in telencephalic area Dp of adult zebrafish, the homolog of piriform cortex, using computational modeling, population activity measurements, and optogenetic perturbations. Modeling revealed that precise balance of EI assemblies is important to prevent not only excessive firing rates ("runaway activity") but also the stochastic occurrence of high pattern correlations ("runaway correlations"). Consistent with model-derived predictions, runaway correlations emerged in Dp when synaptic balance was perturbed by optogenetic manipulations of fast-spiking feedback interneurons. Moreover, runaway correlations were driven by sparse subsets of strongly active neurons, rather than by a general broadening of tuning curves. These results reveal novel computational functions of EI assemblies in an autoassociative olfactory memory network and support the hypothesis that EI assemblies organize information on continuous representational manifolds rather than discrete attractor landscapes.

neuroscience↗

bioRxiv2023

Aberrant hippocampal Ca2+ micro-waves following synapsin-dependent adenoviral expression of Ca2+ indicators

Genetically encoded calcium indicators (GECIs) such as GCaMP are invaluable tools in neuroscience to monitor neuronal activity using optical imaging. The viral transduction of GECIs is commonly used to target expression to specific brain regions, can be conveniently used with any mouse strain of interest without the need for prior crossing with a GECI mouse line and avoids potential hazards due to the chronic expression of GECIs during development. A key requirement for monitoring neuronal activity with an indicator is that the indicator itself minimally affects activity. Here, using common adeno-associated viral (AAV) transduction procedures, we describe spatially confined aberrant Ca2+ micro-waves slowly travelling through the hippocampus following expression of GCaMP6, GCaMP7 or R-CaMP1.07 driven by the synapsin promoter with AAV-dependent gene transfer, in a titre-dependent fashion. Ca2+ micro-waves developed in hippocampal CA1 and CA3, but not dentate gyrus (DG) nor neocortex, were typically first observed at 4 weeks after viral transduction, and persisted up to at least 8 weeks. The phenomenon was robust, observed across laboratories with various experimenters and setups. Our results indicate that aberrant hippocampal Ca2+ micro-waves depend on the promoter and viral titre of the GECI, density of expression as well as the targeted brain region. We used an alternative viral transduction method of GCaMP which avoids this artifact. The results show that commonly used Ca2+-indicator AAV transduction procedures can produce artefactual Ca2+ responses. Our aim is to raise awareness in the field of these artefactual transduction-induced Ca2+ micro-waves and we provide a potential solution. Impact statement: Common AAV transduction procedures induce artefactual spatially confined Ca2+ waves in the hippocampus.

neuroscience↗

bioRxiv2023

Deep learning-based synaptic event detection

Quantitative information about synaptic transmission is key to our understanding of neural function. Spontaneously occurring synaptic events carry fundamental information about synaptic function and plasticity. However, their stochastic nature and low signal-to-noise ratio present major challenges for the reliable and consistent analysis. Here, we introduce miniML, a supervised deep learning- based method for accurate classification and automated detection of spontaneous synaptic events. Comparative analysis using simulated ground-truth data shows that miniML outperforms existing event analysis methods in terms of both precision and recall. miniML enables precise detection and quantification of synaptic events in electrophysiological recordings. We demonstrate that the deep learning approach generalizes easily to diverse synaptic preparations, different electrophysiological and optical recording techniques, and across animal species. miniML provides not only a comprehensive and robust framework for automated, reliable, and standardized analysis of synaptic events, but also opens new avenues for high-throughput investigations of neural function and dysfunction.

neuroscience↗

bioRxiv2023

Distinct hypothalamus-habenula circuits govern risk preference

Appropriate risk evaluation is essential for survival in complex, uncertain environments. Confronted with choosing between certain (safe) and uncertain (risky) options, animals show strong preference for either option consistently across extended time periods. How such risk preference is encoded in the brain remains elusive. A candidate region is the lateral habenula (LHb), which is prominently involved in value-guided behavior. Here, using a balanced two-alternative choice task and longitudinal two-photon calcium imaging, we identify LHb neurons with risk-preference-selective activity reflecting individual risk preference prior to action selection. By employing whole-brain anatomical tracing, multi-fiber photometry, and projection- and cell-type-specific optogenetics, we find that glutamatergic LHb projections from the medial (MH) but not lateral (LH) hypothalamus provide behavior-relevant synaptic input before action selection. Optogenetic stimulation of MH{lozenge}LHb axons evoked excitatory and inhibitory postsynaptic responses, whereas LH{lozenge}LHb projections were excitatory. We thus reveal functionally distinct hypothalamus-habenula circuits for risk preference in habitual economic decision-making.

neuroscience↗

bioRxiv2022

Centripetal integration of past events by hippocampal astrocytes

An essential feature of neurons is their ability to centrally integrate information from their dendrites. The activity of astrocytes, in contrast, has been described as mostly uncoordinated across cellular compartments without clear central integration. Here, we describe conditional centripetal integration as a principle of how astrocytes integrate calcium signals from their distal processes to induce somatic activation. We found in mouse hippocampus that global astrocytic activity, as recorded with population calcium imaging, is well explained as a leaky integration of past neuronal and behavioral events on a timescale of seconds. Salient past events, indicated by pupil dilations, facilitated propagation of calcium signals from distal processes to the soma on this slow timescale. Centripetal propagation was reproduced by optogenetic activation of the locus coeruleus, a key regulator of arousal, and reduced by pharmacological inhibition of 1-adrenergic receptors. Together, our results establish astrocytes as computational units of the brain that slowly and conditionally integrate calcium signals to activate their somata upon behaviorally relevant events.

neuroscience↗

bioRxiv2021

In vivo calcium imaging of CA3 pyramidal neuron populations in adult mouse hippocampus

Neuronal population activity in the hippocampal CA3 subfield is implicated in cognitive brain functions such as memory processing and spatial navigation. However, because of its deep location in the brain, the CA3 area has been difficult to target with modern calcium imaging approaches. Here, we achieved chronic two-photon calcium imaging of CA3 pyramidal neurons with the red fluorescent calcium indicator R-CaMP1.07 in anesthetized and awake mice. We characterize CA3 neuronal activity at both the single-cell and population level and assess its stability across multiple imaging days. During both anesthesia and wakefulness, nearly all CA3 pyramidal neurons displayed calcium transients. Most of the calcium transients were consistent with a high incidence of bursts of action potentials, based on calibration measurements using simultaneous juxtacellular recordings and calcium imaging. In awake mice, we found state-dependent differences with striking large and prolonged calcium transients during locomotion. We estimate that trains of >30 action potentials over 3 s underlie these salient events. Their abundance in particular subsets of neurons was relatively stable across days. At the population level, we found that coactivity within the CA3 network was above chance level and that co-active neuron pairs maintained their correlated activity over days. Our results corroborate the notion of state-dependent spatiotemporal activity patterns in the recurrent network of CA3 and demonstrate that at least some features of population activity, namely coactivity of cell pairs and likelihood to engage in prolonged high activity, are maintained over days.

neuroscience↗

bioRxiv2020

A deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging data

Calcium imaging is a key method to record patterns of neuronal activity across populations of identified neurons. Inference of temporal patterns of action potentials ( spikes) from calcium signals is, however, challenging and often limited by the scarcity of ground truth data containing simultaneous measurements of action potentials and calcium signals. To overcome this problem, we compiled a large and diverse ground truth database from publicly available and newly performed recordings. This database covers various types of calcium indicators, cell types, and signal-to-noise ratios and comprises a total of >35 hours from 298 neurons. We then developed a novel algorithm for spike inference (CASCADE) that is based on supervised deep networks, takes advantage of the ground truth database, infers absolute spike rates, and outperforms existing model-based algorithms. To optimize performance for unseen imaging data, CASCADE retrains itself by resampling ground truth data to match the respective sampling rate and noise level. As a consequence, no parameters need to be adjusted by the user. To facilitate routine application of CASCADE we developed systematic performance assessments for unseen data, we openly release all resources, and we provide a user-friendly cloud-based implementation.

neuroscience↗

bioRxiv2019

Predictive neural processing in adult zebrafish depends on shank3b

Intelligent behavior requires a comparison between the predicted and the actual consequences of behavioral actions. According to the theory of predictive processing, this comparison relies on a neuronal error signal that reflects the mismatch between an internal prediction and sensory input. Inappropriate error signals may generate pathological experiences in neuropsychiatric conditions. To examine the processing of sensorimotor prediction errors across different telencephalic brain areas we optically measured neuronal activity in head-fixed, adult zebrafish in a virtual reality. Brief perturbations of visuomotor feedback triggered distinct changes in swimming behavior and different neuronal responses. Neuronal activity reflecting sensorimotor mismatch, rather than sensory input or motor output alone, was prominent throughout multiple forebrain areas. This activity preceded and predicted the transition in motor behavior. Error signals were altered in specific forebrain regions by a mutation in the autism-related gene shank3b. Predictive processing is therefore a widespread phenomenon that may contribute to disease phenotypes.

neuroscience↗

bioRxiv2017

Community-based benchmarking improves spike inference from two-photon calcium imaging data

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations1, 2. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators3. Different algorithms for estimating spike trains from noisy calcium measurements have been proposed in the past4-8, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

neuroscience↗