Biology subjects

Funke, J.

Publications and source records attributed to Funke, J..

At least 19 recordsLinked to original sources

bioRxiv2026

A multi-resolution imaging and analysis pipeline for comparative circuit reconstruction in insects

Connectomics has become essential for the study of brain function, yet for most research groups it remains prohibitively costly in imaging time, data storage, and analysis. Here, we present an imaging, processing, and analysis pipeline for multi-resolution image acquisition and circuit reconstruction. Applied to the central complex of six insect species, we were able to obtain global projectomes at cellular resolution (40-50 nm) with embedded local connectomes describing key computational compartments at synaptic resolution (8-12 nm). We provide standardized protocols for volume EM sample preparation, image acquisition and image alignment, combined with existing methods for {micro}CT block trimming, automatic segmentation, synapse detection, collaborative skeleton tracing with CATMAID, and segmentation proofreading via CAVE. We validated our workflow by reconstructing head direction cells across all six insect species, which revealed deep conservation at the level of cell types, cell numbers and projection patterns, while also revealing circuit level specializations. Overall, our pipeline democratizes comparative connectomics by making this method accessible for small research groups with modest resources.

neuroscience↗

bioRxiv2026

Machine learning-guided spatial omics for tissue-scale discovery of cell-type-specific architectures

Multiplexed protein imaging enables spatially resolved analysis of molecular organization in tissues, but existing spatial proteomics platforms remain constrained in scalability, throughput, and integration with RNA measurements and interpretable computational analysis. Here, we present an integrated spatial omics framework that combines highly multiplexed protein and RNA imaging with explainable machine learning to map cell-type-specific molecular and structural architectures at tissue scale. Using this platform, we simultaneously profiled up to 46 proteins and 79 RNA species across [~]370,000 cells in intact mouse brain tissue at diffraction-limited subcellular resolution ([~]260 nm). We developed a scalable, open-source computational pipeline for large-scale image processing and analysis, and show that nuclear protein and chromatin features alone are sufficient to accurately classify brain cell types and their spatial organization. Incorporation of explainable deep learning further enabled identification of human-interpretable, cell-type-specific subnuclear structural features directly from imaging data, with independent quantitative validation. Together, this integrated experimental and computational framework enables tissue-scale spatial proteomics-based cell-type classification and structural feature discovery, providing a broadly applicable platform for mechanistic studies, high-content screening, and translational applications.

systems biology↗

bioRxiv2025

Combinatorial protein barcodes enable self-correcting neuron tracing with nanoscale molecular context

Mapping nanoscale neuronal morphology with molecular annotations is critical for understanding healthy and dysfunctional brain circuits. Current methods are constrained by image segmentation errors and by sample defects (e.g., signal gaps, section loss). Genetic strategies promise to overcome these challenges by using easily distinguishable cell identity labels. However, multicolor approaches are spectrally limited in diversity, whereas nucleic acid barcoding lacks a cellfilling morphology signal for segmentation. Here, we introduce PRISM (Protein-barcode Reconstruction via Iterative Staining with Molecular annotations), a platform that integrates combinatorial delivery of antigenically distinct, cell-filling proteins with tissue expansion, multi-cycle imaging, barcode-augmented reconstruction, and molecular annotation. Protein barcodes increase label diversity by >750-fold over multicolor labeling and enable morphology reconstruction with intrinsic error correction. We acquired a [~]10 million {micro}m3 volume of mouse hippocampal area CA2/3, multiplexed across 23 barcode antigen and synaptic marker channels. By combining barcodes with shape information, we achieve an 8x increase in automatic tracing accuracy of genetically labelled neurons. We demonstrate PRISM supports automatic proofreading across micron-scale spatial gaps and reconnects neurites across discontinuities spanning hundreds of microns. Using PRISMs molecular annotation capability, we map the distribution of synapses onto traced neural morphology, characterizing challenging synaptic structures such as thorny excrescences (TEs), and discovering a size correlation among spatially proximal TEs on the same dendrite. PRISM thus supports selfcorrecting neuron reconstruction with molecular context.

neuroscience↗

bioRxiv2024

Spatial single-cell Organellomics reveals nutrient dependent hepatocyteheterogeneity and predicts pathophysiological status in vivo

Cell state diversity drives tissue adaptability, repair, and disease resilience, but fully capturing this cellular complexity remains a challenge. Most current approaches rely on transcriptional profiling and often overlook functional insights embedded in organelle structure, key indicators of cellular metabolism and stress. Here, we introduce spatial Organellomics (sOrganellomics), an imaging workflow integrating automated segmentation with machine learning to classify and spatially map cell states based on multi-organelle features. Across metabolically specialized organs, these features distinguish organ-specific cellular identities. In the liver, sOrganellomics reveals a previously unrecognized hepatocyte organization: different hepatocyte states form intermixed communities rather than classical homogeneous graded zones, a pattern disrupted by nutritional stress. Intravital imaging further links fasting-associated architectural remodeling to altered mitochondrial membrane potential and bioenergetic heterogeneity in vivo. This approach maps metabolic transitions and early disease progression, establishing organelle architecture as a scalable readout of functional cell state diversity in tissues.

cell biology↗

bioRxiv2024

Quantitative Attributions with Counterfactuals

We address the problem of explaining the decision process of deep neural network classifiers on images, which is of particular importance in biomedical datasets where class-relevant differences are not always obvious to a human observer. Our proposed solution, termed quantitative attribution with counterfactuals (QuAC), generates visual explanations that highlight class-relevant differences by attributing the classifier decision to changes of visual features in small parts of an image. To that end, we train a separate network to generate counterfactual images (i.e., to translate images between different classes). We then find the most important differences using novel discriminative attribution methods. Crucially, QuAC allows scoring of the attribution and thus provides a measure to quantify and compare the fidelity of a visual explanation. We demonstrate the suitability and limitations of QuAC on two datasets: (1) a synthetic dataset with known class differences, representing different levels of protein aggregation in cells and (2) an electron microscopy dataset of D. melanogaster synapses with different neurotransmitters, where QuAC reveals so far unknown visual differences. We further discuss how QuAC can be used to interrogate mispredictions to shed light on unexpected inter-class similarities and intra-class differences.

bioinformatics↗

bioRxiv2024

A Bayesian Solution to Count the Number of Molecules within a Diffraction Limited Spot

We address the problem of inferring the number of independently blinking fluorescent light emitters, when only their combined intensity contributions can be observed at each timepoint. This problem occurs regularly in light microscopy of objects that are smaller than the diffraction limit, where one wishes to count the number of fluorescently labelled subunits. Our proposed solution directly models the photo-physics of the system, as well as the blinking kinetics of the fluorescent emitters as a fully differentiable hidden Markov model. Given a trace of intensity over time, our model jointly estimates the parameters of the intensity distribution per emitter, their blinking rates, as well as a posterior distribution of the total number of fluorescent emitters. We show that our model is consistently more accurate and increases the range of countable subunits by a factor of two compared to current state-of-the-art methods, which count based on autocorrelation and blinking frequency. Furthermore, we demonstrate that our model can be used to investigate the effect of blinking kinetics on counting ability, and therefore can inform experimental conditions that will maximize counting accuracy.

biophysics↗

bioRxiv2023

Model Based Inference of Synaptic Plasticity Rules

Inferring the synaptic plasticity rules that govern learning in the brain is a key challenge in neuroscience. We present a novel computational method to infer these rules from experimental data, applicable to both neural and behavioral data. Our approach approximates plasticity rules using a parameterized function, employing either truncated Taylor series for theoretical interpretability or multilayer perceptrons. These plasticity parameters are optimized via gradient descent over entire trajectories to align closely with observed neural activity or behavioral learning dynamics. This method can uncover complex rules that induce long nonlinear time dependencies, particularly involving factors like postsynaptic activity and current synaptic weights. We validate our approach through simulations, successfully recovering established rules such as Ojas, as well as more intricate plasticity rules with reward-modulated terms. We assess the robustness of our technique to noise and apply it to behavioral data from Drosophila in a probabilistic reward-learning experiment. Notably, our findings reveal an active forgetting component in reward learning in flies, improving predictive accuracy over previous models. This modeling framework offers a promising new avenue for elucidating the computational principles of synaptic plasticity and learning in the brain.

neuroscience↗

bioRxiv2023

Neural-circuit basis of song preference learning in fruit flies

As observed in human language learning and song learning in birds, the fruit fly Drosophila melanogaster changes its auditory behaviors according to prior sound experiences. Female flies that have heard male courtship songs of the same species are less responsive to courtship songs of different species. This phenomenon, known as song preference learning in flies, requires GABAergic input to pC1 neurons in the central brain, with these neurons playing a key role in mating behavior by integrating multimodal sensory and internal information. The neural circuit basis of this GABAergic input, however, has not yet been identified. Here, we find that pCd-2 neurons, totaling four cells per hemibrain and expressing the sex-determination gene doublesex, provide the GABAergic input to pC1 neurons for song preference learning. First, RNAi-mediated knockdown of GABA production in pCd-2 neurons abolished song preference learning. Second, pCd-2 neurons directly, and in many cases mutually, connect with pC1 neurons, suggesting the existence of reciprocal circuits between pC1 and pCd-2 neurons. Finally, GABAergic and dopaminergic inputs to pCd-2 neurons are necessary for song preference learning. Together, this study suggests that reciprocal circuits between pC1 and pCd-2 neurons serve as a sensory and internal state-integrated hub, allowing flexible control over female copulation. Consequently, this provides a neural circuit model that underlies experience-dependent auditory plasticity. SignificanceTo find a suitable mate, an organism must adapt its behavior based on past experiences. In the case of Drosophila, female assessments of male song signals, which contain information about the status and species of the sender, are experience dependent. Here, we show that reciprocal circuits in the central brain modulate the females song response depending on her previous auditory experiences. These circuits exhibit feedback and lateral inhibition motifs, and are regulated by dopaminergic and GABAergic inputs. While the effects of prior auditory experiences on sound responsiveness have been extensively studied in other species, our research advances the use of Drosophila as a model for dissecting the circuitry underlying experience-dependent auditory plasticity at single-cell resolution.

neuroscience↗

bioRxiv2023

Network Statistics of the Whole-Brain Connectome of Drosophila

Brains comprise complex networks of neurons and connections. Network analysis applied to the wiring diagrams of brains can offer insights into how brains support computations and regulate information flow. The completion of the first whole-brain connectome of an adult Drosophila, the largest connectome to date, containing 130,000 neurons and millions of connections, offers an unprecedented opportunity to analyze its network properties and topological features. To gain insights into local connectivity, we computed the prevalence of two- and three-node network motifs, examined their strengths and neurotransmitter compositions, and compared these topological metrics with wiring diagrams of other animals. We discovered that the network of the fly brain displays rich club organization, with a large population (30% percent of the connectome) of highly connected neurons. We identified subsets of rich club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex and will serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

neuroscience↗

bioRxiv2023

Neuronal wiring diagram of an adult brain

Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5x107 chemical synapses between [~]130,000 neurons reconstructed from a female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.

neuroscience↗

bioRxiv2023

A leaky integrate-and-fire computational model based on the connectome of the entire adult Drosophila brain reveals insights into sensorimotor processing

The forthcoming assembly of the adult Drosophila melanogaster central brain connectome, containing over 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain. Here, we create a leaky integrate-and-fire computational model of the entire Drosophila brain, based on neural connectivity and neurotransmitter identity, to study circuit properties of feeding and grooming behaviors. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation. Computational activation of neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing, a testable hypothesis that we validate by optogenetic activation and behavioral studies. Moreover, computational activation of different classes of gustatory neurons makes accurate predictions of how multiple taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Our computational model predicts that the sugar and water pathways form a partially shared appetitive feeding initiation pathway, which our calcium imaging and behavioral experiments confirm. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit that do not overlap with gustatory circuits, and accurately describes the circuit response upon activation of different mechanosensory subtypes. Our results demonstrate that modeling brain circuits purely from connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can accurately describe complete sensorimotor transformations.

neuroscience↗

bioRxiv2022

Hierarchical architecture of dopaminergic circuits enables second-order conditioning in Drosophila

Dopaminergic neurons with distinct projection patterns and physiological properties compose memory subsystems in a brain. However, it is poorly understood whether or how they interact during complex learning. Here, we identify a feedforward circuit formed between dopamine subsystems and show that it is essential for second-order conditioning, an ethologically important form of higher-order associative learning. The Drosophila mushroom body comprises a series of dopaminergic compartments, each of which exhibits distinct memory dynamics. We find that a slow and stable memory compartment can serve as an effective "teacher" by instructing other faster and transient memory compartments via a single key interneuron, which we identify by connectome analysis and neurotransmitter prediction. This excitatory interneuron acquires enhanced response to reward-predicting odor after first-order conditioning and, upon activation, evokes dopamine release in the "student" compartments. These hierarchical connections between dopamine subsystems explain distinct properties of first- and second-order memory long known by behavioral psychologists.

neuroscience↗

bioRxiv2021

Structured connectivity in the cerebellum enables noise-resilient pattern separation

The cerebellum is thought to detect and correct errors between intended and executed commands1-3 and is critical for social behaviors, cognition and emotion4-6. Computations for motor control must be performed quickly to correct errors in real time and should be sensitive to small differences between patterns for fine error correction while being resilient to noise7. Influential theories of cerebellar information processing have largely assumed random network connectivity, which increases the encoding capacity of the networks first layer8-13. However, maximizing encoding capacity reduces resiliency to noise7. To understand how neuronal circuits address this fundamental tradeoff, we mapped the feedforward connectivity in the mouse cerebellar cortex using automated large-scale transmission electron microscopy (EM) and convolutional neural network-based image segmentation. We found that both the input and output layers of the circuit exhibit redundant and selective connectivity motifs, which contrast with prevailing models. Numerical simulations suggest these redundant, non-random connectivity motifs increase discriminability of similar input patterns at a minimal cost to the networks overall encoding capacity. This work reveals how neuronal network structure can balance encoding capacity and redundancy, unveiling principles of biological network architecture with implications for artificial neural network design.

neuroscience↗

bioRxiv2021

Automated Reconstruction of Whole-Embryo Cell Lineages by Learning from Sparse Annotations

We present a method for automated nucleus identification and tracking in time-lapse microscopy recordings of entire developing embryos. Our method combines deep learning and global optimization to enable complete lineage reconstruction from sparse point annotations, and uses parallelization to process multi-terabyte light-sheet recordings, which we demonstrate on three common model organisms: mouse, zebrafish, Drosophila. On the most difficult dataset (mouse), our method correctly reconstructs 75.8% of cell lineages spanning 1 hour, compared to 31.8% for the previous state of the art, thus enabling biologists to determine where and when cell fate decisions are made in developing embryos, tissues, and organs.

bioinformatics↗

bioRxiv2021

Ultrastructural readout of in vivo synaptic activity for functional connectomics.

Large-volume ultrastructural mapping approaches yield detailed circuit wiring diagrams but lack an integrated synaptic activity readout which is essential for functional interpretation of the connectome. Here we resolve this limitation by combining functional synaptic labelling in vivo with focused ion-beam scanning electron microscopy (FIBSEM) and machine learning-based segmentation. Our approach generates high-resolution near-isotropic three-dimensional readouts of activated vesicle pools across large populations of individual synapses in a volume of tissue, opening the way for detailed functional connectomics studies. We apply this method to measure presynaptic activity in an ultrastructural context in synapses activated by sensory input in primary visual cortex in awake head-fixed mice, showing that the numbers of recycling and non-recycling vesicles approximate to a lognormal distribution across a large number of synapses. We also demonstrate that neighbouring boutons of the same axon, which share the same spiking activity, can differ greatly in their presynaptic release probability.

neuroscience↗

bioRxiv2021

Local Shape Descriptors for Neuron Segmentation

We present a simple, yet effective, auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of Local Shape Descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors are designed to capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a large study comparing several existing methods across various specimen, imaging techniques, and resolutions, we find that auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (Flood-Filling Networks, FFN), while being two orders of magnitudes more efficient--a critical requirement for the processing of future petabyte-sized datasets. Implementations of the new auxiliary learning task, network architectures, training, prediction, and evaluation code, as well as the datasets used in this study are publicly available as a benchmark for future method contributions.

neuroscience↗

bioRxiv2020

Automatic whole cell organelle segmentation in volumetric electron microscopy

Cells contain hundreds of different organelle and macromolecular assemblies intricately organized relative to each other to meet any cellular demands. Obtaining a complete understanding of their organization is challenging and requires nanometer-level, threedimensional reconstruction of whole cells. Even then, the immense size of datasets and large number of structures to be characterized requires generalizable, automatic methods. To meet this challenge, we developed an analysis pipeline for comprehensively reconstructing and analyzing the cellular organelles in entire cells imaged by focused ion beam scanning electron microscopy (FIB-SEM) at a near-isotropic size of 4 or 8 nm per voxel. The pipeline involved deep learning architectures trained on diverse samples for automatic reconstruction of 35 different cellular organelle classes - ranging from endoplasmic reticulum to microtubules to ribosomes - from multiple cell types. Automatic reconstructions were used to directly quantify various previously inaccessible metrics about these structures, including their spatial interactions. We show that automatic organelle reconstructions can also be used to automatically register light and electron microscopy images for correlative studies. We created an open data and open source web repository, OpenOrganelle, to share the data, computer code, and trained models, enabling scientists everywhere to query and further reconstruct the datasets.

cell biology↗

bioRxiv2020

Neurotransmitter Classification from Electron Microscopy Images at Synaptic Sites in Drosophila

High-resolution electron microscopy of nervous systems enables the reconstruction of connectomes. A key piece of missing information from connectomes is the synaptic sign. We show that for D. melanogaster, artificial neural networks can predict the transmitter type released at synapses from electron micrographs and thus add putative signs to connections. Our network discriminates between six transmitters (acetylcholine, glutamate, GABA, serotonin, dopamine, octopamine) with an average accuracy of 87%/94% for synapses/entire neurons. We developed an explainability method to reveal which features our network is using and found significant ultrastructural differences between the classical transmitters. We predict transmitters in two connectomes and characterize morphological and connection properties of tens of thousands of neurons classed by predicted transmitter expression. We find that hemilineages in D. melanogaster largely express only one fastacting transmitter among their neurons. Furthermore, we show that neurons with different transmitters may differ in features like polarization and projection targets.

neuroscience↗