@misc{21137, author = {Naik, Suyash}, publisher = {Institute of Science and Technology Austria}, title = {{Data associated with Keratins coordinate tissue spreading }}, doi = {10.15479/AT-ISTA-21137}, year = {2026}, } @article{19003, abstract = {Super-resolution methods provide far better spatial resolution than the optical diffraction limit of about half the wavelength of light (∼200-300 nm). Nevertheless, they have yet to attain widespread use in plants, largely due to plants’ challenging optical properties. Expansion microscopy improves effective resolution by isotropically increasing the physical distances between sample structures while preserving relative spatial arrangements and clearing the sample. However, its application to plants has been hindered by the rigid, mechanically cohesive structure of plant tissues. Here, we report on whole-mount expansion microscopy of thale cress (Arabidopsis thaliana) root tissues (PlantEx), achieving a four-fold resolution increase over conventional microscopy. Our results highlight the microtubule cytoskeleton organization and interaction between molecularly defined cellular constituents. Combining PlantEx with stimulated emission depletion (STED) microscopy, we increase nanoscale resolution and visualize the complex organization of subcellular organelles from intact tissues by example of the densely packed COPI-coated vesicles associated with the Golgi apparatus and put these into a cellular structural context. Our results show that expansion microscopy can be applied to increase effective imaging resolution in Arabidopsis root specimens. }, author = {Gallei, Michelle C and Truckenbrodt, Sven M and Kreuzinger, Caroline and Inumella, Syamala and Vistunou, Vitali and Sommer, Christoph M and Tavakoli, Mojtaba and Agudelo Duenas, Nathalie and Vorlaufer, Jakob and Jahr, Wiebke and Randuch, Marek and Johnson, Alexander J and Benková, Eva and Friml, Jiří and Danzl, Johann G}, issn = {1532-298X}, journal = {The Plant Cell}, number = {4}, publisher = {Oxford University Press}, title = {{Super-resolution expansion microscopy in plant roots}}, doi = {10.1093/plcell/koaf006}, volume = {37}, year = {2025}, } @article{19453, abstract = {A key feature of biological and artificial neural networks is the progressive refinement of their neural representations with experience. In neuroscience, this fact has inspired several recent studies in sensory and motor systems. However, less is known about how higher associational cortical areas, such as the hippocampus, modify representations throughout the learning of complex tasks. Here, we focus on associative learning, a process that requires forming a connection between the representations of different variables for appropriate behavioral response. We trained rats in a space-context associative task and monitored hippocampal neural activity throughout the entire learning period, over several days. This allowed us to assess changes in the representations of context, movement direction, and position, as well as their relationship to behavior. We identified a hierarchical representational structure in the encoding of these three task variables that was preserved throughout learning. Nevertheless, we also observed changes at the lower levels of the hierarchy where context was encoded. These changes were local in neural activity space and restricted to physical positions where context identification was necessary for correct decision-making, supporting better context decoding and contextual code compression. Our results demonstrate that the hippocampal code not only accommodates hierarchical relationships between different variables but also enables efficient learning through minimal changes in neural activity space. Beyond the hippocampus, our work reveals a representation learning mechanism that might be implemented in other biological and artificial networks performing similar tasks.}, author = {Chiossi, Heloisa and Nardin, Michele and Tkačik, Gašper and Csicsvari, Jozsef L}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences}, number = {11}, publisher = {National Academy of Sciences}, title = {{Learning reshapes the hippocampal representation hierarchy}}, doi = {10.1073/pnas.2417025122}, volume = {122}, year = {2025}, } @article{19704, abstract = {The information-processing capability of the brain’s cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can be achieved by volumetric imaging at nanoscale resolution1,2 with dense cellular labelling. Light microscopy is uniquely positioned to visualize specific molecules, but dense, synapse-level circuit reconstruction by light microscopy has been out of reach, owing to limitations in resolution, contrast and volumetric imaging capability. Here we describe light-microscopy-based connectomics (LICONN). We integrated specifically engineered hydrogel embedding and expansion with comprehensive deep-learning-based segmentation and analysis of connectivity, thereby directly incorporating molecular information into synapse-level reconstructions of brain tissue. LICONN will allow synapse-level phenotyping of brain tissue in biological experiments in a readily adoptable manner.}, author = {Tavakoli, Mojtaba and Lyudchik, Julia and Januszewski, Michał and Vistunou, Vitali and Agudelo Duenas, Nathalie and Vorlaufer, Jakob and Sommer, Christoph M and Kreuzinger, Caroline and Oliveira, Bárbara and Cenameri, Alban and Novarino, Gaia and Jain, Viren and Danzl, Johann G}, issn = {1476-4687}, journal = {Nature}, pages = {398--410}, publisher = {Springer Nature}, title = {{Light-microscopy-based connectomic reconstruction of mammalian brain tissue}}, doi = {10.1038/s41586-025-08985-1}, volume = {642}, year = {2025}, } @article{19795, abstract = {Super-resolution microscopy often entails long acquisition times of minutes to hours. Since drifts during the acquisition adversely affect data quality, active sample stabilization is commonly used for some of these techniques to reach their full potential. Although drifts in the lateral plane can often be corrected after acquisition, this is not always possible or may come with drawbacks. Therefore, it is appealing to stabilize sample position in three dimensions (3D) during acquisition. Various schemes for active sample stabilization have been demonstrated previously, with some reaching sub-nanometer stability in 3D. Here, we present a scheme for active drift correction that delivers the nanometer-scale 3D stability demanded by state-of-the-art super-resolution techniques and is straightforward to implement compared to previous schemes capable of reaching this level of stabilization precision. Using a refined algorithm that can handle various types of reference structure, without sparse signal peaks being mandatory, we stabilized sample position to ∼1 nm in 3D using objective lenses both with high and low numerical aperture. Our implementation requires only the addition of a simple widefield imaging path and we provide an open-source control software with graphical user interface to facilitate easy adoption of the module. Finally, we demonstrate how this has the potential to enhance data collection for diffraction-limited and super-resolution imaging techniques using single-molecule localization microscopy and cryo-confocal imaging as showcases.}, author = {Vorlaufer, Jakob and Semenov, Nikolai and Kreuzinger, Caroline and Javoor, Manjunath and Zens, Bettina and Agudelo Duenas, Nathalie and Tavakoli, Mojtaba and Suplata, Marek and Jahr, Wiebke and Lyudchik, Julia and Wartak, Andreas and Schur, Florian Km and Danzl, Johann G}, issn = {2667-0747}, journal = {Biophysical Reports}, number = {2}, publisher = {Elsevier}, title = {{Image-based 3D active sample stabilization on the nanometer scale for optical microscopy}}, doi = {10.1016/j.bpr.2025.100211}, volume = {5}, year = {2025}, } @article{20330, abstract = {The evolution of sexual dimorphism (the difference in average trait values between females and males, SD), is often thought to be constrained by shared genetic architecture between the sexes. Indeed, it is commonly expected that SD should negatively correlate with the intersex correlation (the genetic correlation between effects of segregating variants in females and males, r fm), either because (1) traits with ancestrally low r fm are less constrained in their ability to respond to sex-specific selection and thus evolve to be more dimorphic, or because (2) sex-specific selection, driving sexual dimorphism evolution, also acts to reduce r fm. Despite the intuitive appeal and prominence of these ideas, their generality and the conditions in which they hold remain unclear. Here, we develop models incorporating sex-specific stabilizing selection, mutation and genetic drift to examine the relationship between r fm and SD. We show that the two commonly-discussed mechanisms with the potential to generate a negative correlation between SD and r fm could just as easily generate a positive association, since the standard line of reasoning hinges on a hidden assumption that sex-specific adaptation more frequently favors increased dimorphism than reduced dimorphism. Our results provide, to our knowledge, the first mechanistic framework for understanding the conditions under which a correlation between r fm and SD may arise and offer a compelling explanation for inconsistent empirical evidence. We also make the intriguing observation that—even when selection between the two sexes is identical—drift generates nonzero SD. We quantify this effect and discuss its significance.}, author = {Puixeu Sala, Gemma and Hayward, Laura}, issn = {1943-2631}, journal = {Genetics}, number = {3}, publisher = {Oxford University Press}, title = {{The relationship between sexual dimorphism and intersex correlation: Do models support intuition?}}, doi = {10.1093/genetics/iyaf175}, volume = {231}, year = {2025}, } @phdthesis{20371, abstract = {Quantum mechanics reveals a world that defies classical determinism, where uncertainty, superposition, and fluctuations are fundamental aspects. Engineering devices that harness these quantum features requires not only precision, but also a deep understanding of how they interact with their surrounding environment. Superconducting circuits, which exploit macroscopic quantum coherence in low-loss superconducting materials, provide a scalable platform for implementing such systems. Among the critical elements in these circuits, superinductors—high-impedance, dissipation-free inductive components—play a central role by suppressing charge fluctuations. They allow quantum states to be delocalized in phase space, protect qubits from environmental noise, and facilitate access to phenomena such as dual Josephson physics and ultra-strong coupling regimes. This thesis explores two complementary implementations of high-impedance circuits: geometric superinductors, demonstrating that high impedance can be achieved beyond kinetic inductance, and Josephson junction chains, used to investigate both microwave mode properties and DC transport across the superconductor-to-insulator transition. Part I addresses geometric superinductors. Contrary to the common belief that high-impedance superconducting circuits require kinetic inductance, we demonstrate that purely geometric designs can achieve characteristic impedance exceeding the resistance quantum. By exploiting mutual coupling between adjacent turns, coil-based inductors achieve enhanced self-inductance, creating a reliable platform for qubits and resonators. Modeling, simulation, fabrication, and characterization confirm that these elements behave as superinductor. With low loss, high linearity, and minimal stray capacitance, these elements are reproducible, free of uncontrolled tunneling events, and capable of strong magnetic coupling. This establishes geometric superinductors as robust, single-wave-function superconducting devices suitable for hardware protected qubits and hybrid systems. Part II presents classical numerical simulations of a Quantum Phase Slip circuit to study dual Shapiro steps. The circuit consists of an ideal Quantum Phase Slip element embedded in a resistive-inductive environment with a parasitic capacitance. Part III extends the investigation of high characteristic-impedance circuit elements to one-dimensional Josephson junction chains, which act as a quantum simulator for many-body physics and the superconductor–insulator transition. Different devices are realized on both sides of the DC phase transition, showing either a supercurrent branch or Coulomb blockade at zero bias. The effect of the crossover on microwave modes, however, remains insufficiently investigated. Studying these modes provides insight into the interplay between disorder and phase-slip events. Small differences in circuit component sizes determine which side of the transition a device falls on, making these results relevant not only for fundamental understanding but also for the design of quantum devices, emphasizing the crucial role of the electromagnetic environment in stabilizing and controlling fragile quantum states. Together, these results illustrate how carefully engineered high characteristic-impedance elements provide a link between macroscopic circuits and the inherently uncertain quantum world, enabling experiments that probe, control, and ultimately exploit quantum fluctuations for applications in quantum information, metrology, solid state physics and beyond. }, author = {Trioni, Andrea}, isbn = {978-3-99078-067-1}, issn = {2663-337X}, pages = {202}, publisher = {Institute of Science and Technology Austria}, title = {{High-impedance quantum circuits for mesoscopic physics : Geometric superinductors and insulating Josephson Chains}}, doi = {10.15479/AT-ISTA-20371}, year = {2025}, } @phdthesis{20485, author = {Misova, Michaela}, isbn = {978-3-99078-068-8}, issn = {2663-337X}, pages = {155}, publisher = {Institute of Science and Technology Austria}, title = {{Dissecting gap junction biology using the C. elegans nervous system}}, doi = {10.15479/AT-ISTA-20485}, year = {2025}, } @inproceedings{20668, abstract = {The Message Layer Security (MLS) protocol has recently been standardized by the IETF. MLS is a scalable secure group messaging protocol expected to run more efficiently compared to the Signal protocol at scale, while offering a similar level of strong security. Even though MLS has undergone extensive examination by researchers, the majority of the works have focused on confidentiality. In this work, we focus on the authenticity of the application messages exchanged in MLS. Currently, MLS authenticates every application message with an EdDSA signature and while manageable, the overhead is greatly amplified in the post-quantum setting as the NIST-recommended Dilithium signature results in a 40x increase in size. We view this as an invitation to explore new authentication modes that can be used instead. We start by taking a systematic view on how application messages are authenticated in MLS and categorize authenticity into four different security notions. We then propose several authentication modes, offering a range of different efficiency and security profiles. For instance, in one of our modes, COSMOS++, we replace signatures with one-time tokens and a MAC tag, offering roughly a 75x savings in the post-quantum communication overhead. While this comes at the cost of weakening security compared to the authentication mode used by MLS, the lower communication overhead seems to make it a worthwhile trade-off with security.}, author = {Hashimoto, Keitaro and Katsumata, Shuichi and Pascual Perez, Guillermo}, booktitle = {34th Usenix Security Symposium}, isbn = {9781939133526}, location = {Seattle, WA, USA}, pages = {6699--6716}, publisher = {Usenix Association}, title = {{Exploring how to authenticate application messages in MLS: More efficient, post-quantum, and anonymous blocklistable}}, year = {2025}, } @phdthesis{19745, abstract = {Cell migration is a crucial process in animal development and maintenance. It is incredibly heterogeneous, with different cell types utilizing fundamentally distinct migration strategies. The strategies also depend on the cellular microenvironment, where cells can switch between migration modes as they encounter new environmental cues. In this thesis, we investigated how dendritic cells adapt their migration strategy when encountering geometrically, mechanically and chemically distinct environments. When dendritic cells are embedded in a homogeneous fibrous network, they migrate in a fast and directional amoeboid manner. In this migration strategy, extracellular proteolysis and integrin-mediated adhesions are dispensable. Instead, the cells use topography of the environment to propel their cell body forward. To migrate efficiently in the maze of different pore sizes, they position the nucleus ahead of the microtubule organizing center (MTOC) and use it to gauge the pores to identify the path of least resistance. Our aim was to identify whether dendritic cells adapt their migration strategy when encountering asymmetrical transitions into much denser environments with limited choice of large pores. In such invasive transitions it is unclear if the cells can cross tight pores without the use of adhesions and extracellular proteolysis and whether they maintain the nucleus in the cell front. Using various cell migration assays such as fibrous 3D collagen gels, geometrically defined microchannels with constrictions and simplistic under agarose migration assay, we provide a comprehensive characterization of invasive migration of dendritic cells. We show that during invasion the cells stall and stretch, reflecting the difficulty to translocate the bulky cell body into the dense environment. In collagen gels, we show that dendritic cells can invade without proteolysis and adhesions. Instead, they utilize contractility, which can lead to largescale collagen compressions. During invasion, the nucleus stalls at tight constrictions, leading to a transient organelle reorientation. To resolve the stalling, upregulated rear contractility is required. This contractile force is simultaneously necessary for reverting the nucleus back to the cell front after invasion and maintaining this positioning during permissive migration. A functional role of the reorientation was uncovered in the first collaboration project. A prominent central actin pool was identified around the MTOC, especially pronounced in dense and compressive environments. The actin pool was shown to generate pushing forces to dilate the space for cell translocation. These forces are only necessary in non-permissive environments, where the nucleus reorients to the cell rear, allowing the actin pool to generate space. In permissive environments where space generation is dispensable, the MTOC is located behind the nucleus and the actin cloud has reduced intensity, allowing more actin to be incorporated into the lamellipodium, speeding up migration. In the second collaboration project, we investigated the effects of distinct chemical environments on dendritic cell migration. The strikingly persistent migration of these cells was explained by their ability to modulate and even self-generate chemokine gradients. This allows the cells to migrate faster and more persistent in uniform chemokine fields compared to imposed chemokine gradients. The chemokine receptor CCR7 was identified as a crucial player in this process, both sensing the signal and internalizing the chemokine to create a sink.}, author = {Canigova, Nikola}, isbn = {978-3-99078-058-9}, issn = {2663-337X}, pages = {133}, publisher = {Institute of Science and Technology Austria}, title = {{Adaptive strategies of dendritic cell migration in response to environmental cues}}, doi = {10.15479/AT-ISTA-19745}, year = {2025}, } @phdthesis{19271, abstract = {The medial habenula (MHb) is implicated in regulating emotional responses to aversive events. Studies in zebrafish have identified a remarkable morphological left-right asymmetry in the dorsal habenula (zebrafish equivalent of mammalian MHb)-to-interpeduncular nucleus (IPN) pathway and its left-side specific role in modulating fear responses. However, there is little evidence for structural or functional lateralization in the mammalian MHb-IPN pathway. Here, I investigated the synaptic properties of the left and right MHb afferents to the IPN in mice and addressed whether these synaptic connections selectively influence the expression of conditioned fear in mice. My findings reveal that each individual IPN neuron receives inputs from both left and right MHb. Electrophysiological recordings from the same postsynaptic IPN neurons demonstrate that the left MHb-originating synapses exhibit lower release probability and higher 𝛾-aminobutyric acid type B receptor (GABABR)-mediated potentiation compared to the right MHb-originating synapses. Interestingly, chemogenetic inhibition of cholinergic neurons in the left but not the right MHb significantly attenuated cue-dependent fear recall. Furthermore, conditional deletion of GABABR in the left MHb interfered with the recall of cued fear memory, whereas that in the right MHb neurons spared fear memory expression. Collectively, I demonstrate a functional asymmetry of the MHb in mice, revealing a predominant role for GABABR-mediated signaling in the left MHb-IPN pathway in the modulation of fear memories. These findings suggest that lateralized pathways could represent a fundamental principle in the neural regulation of emotion across species.}, author = {Önal, Hüseyin C}, issn = {2663-337X}, publisher = {Institute of Science and Technology Austria}, title = {{Asymmetrical modulation of fear expression via GABAB receptors in the mouse medial habenula}}, doi = {10.15479/AT-ISTA-19271}, year = {2025}, } @phdthesis{19906, abstract = {Flows of ordinary fluids such as water or air transition from laminar to turbulent motion as the velocity increases. This simple dependence of the flow state solely on inertia, does not apply to more complex substances such as polymericand biofluids which commonly have elastic as well as viscous properties. Here various different instabilities and turbulent states can arise at low and even vanishing inertia, while high inertia turbulence counterintuitively is suppressed and its drag strongly reduced. We here show in experiments of a viscoelastic model fluid that the phenomena observed at low and high inertia have a common origin and that the same dynamical state, elasto-inertial turbulence, persists across four orders of magnitude in Reynolds number, ranging from very low inertia, all the way to high inertia Maximum drag reduction (MDR) asymptote. We also explore the transitions from Newtonian turbulence to MDR, and specific cases of flow at high polymer concentrations, exploring the relationship between flow at these wide range of control parameters. }, author = {Suresh, Sarath S}, issn = {2663-337X}, pages = {82}, publisher = {Institute of Science and Technology Austria}, title = {{Turbulence in polymeric flows : A characterisation of elasto-inertial turbulence and the maximum drag reduction asymptote}}, doi = {10.15479/AT-ISTA-19906}, year = {2025}, } @article{14257, abstract = {Mapping the complex and dense arrangement of cells and their connectivity in brain tissue demands nanoscale spatial resolution imaging. Super-resolution optical microscopy excels at visualizing specific molecules and individual cells but fails to provide tissue context. Here we developed Comprehensive Analysis of Tissues across Scales (CATS), a technology to densely map brain tissue architecture from millimeter regional to nanometer synaptic scales in diverse chemically fixed brain preparations, including rodent and human. CATS uses fixation-compatible extracellular labeling and optical imaging, including stimulated emission depletion or expansion microscopy, to comprehensively delineate cellular structures. It enables three-dimensional reconstruction of single synapses and mapping of synaptic connectivity by identification and analysis of putative synaptic cleft regions. Applying CATS to the mouse hippocampal mossy fiber circuitry, we reconstructed and quantified the synaptic input and output structure of identified neurons. We furthermore demonstrate applicability to clinically derived human tissue samples, including formalin-fixed paraffin-embedded routine diagnostic specimens, for visualizing the cellular architecture of brain tissue in health and disease.}, author = {Michalska, Julia M and Lyudchik, Julia and Velicky, Philipp and Korinkova, Hana and Watson, Jake and Cenameri, Alban and Sommer, Christoph M and Amberg, Nicole and Venturino, Alessandro and Roessler, Karl and Czech, Thomas and Höftberger, Romana and Siegert, Sandra and Novarino, Gaia and Jonas, Peter M and Danzl, Johann G}, issn = {1546-1696}, journal = {Nature Biotechnology}, pages = {1051--1064}, publisher = {Springer Nature}, title = {{Imaging brain tissue architecture across millimeter to nanometer scales}}, doi = {10.1038/s41587-023-01911-8}, volume = {42}, year = {2024}, } @phdthesis{14711, abstract = {In nature, different species find their niche in a range of environments, each with its unique characteristics. While some thrive in uniform (homogeneous) landscapes where environmental conditions stay relatively consistent across space, others traverse the complexities of spatially heterogeneous terrains. Comprehending how species are distributed and how they interact within these landscapes holds the key to gaining insights into their evolutionary dynamics while also informing conservation and management strategies. For species inhabiting heterogeneous landscapes, when the rate of dispersal is low compared to spatial fluctuations in selection pressure, localized adaptations may emerge. Such adaptation in response to varying selection strengths plays an important role in the persistence of populations in our rapidly changing world. Hence, species in nature are continuously in a struggle to adapt to local environmental conditions, to ensure their continued survival. Natural populations can often adapt in time scales short enough for evolutionary changes to influence ecological dynamics and vice versa, thereby creating a feedback between evolution and demography. The analysis of this feedback and the relative contributions of gene flow, demography, drift, and natural selection to genetic variation and differentiation has remained a recurring theme in evolutionary biology. Nevertheless, the effective role of these forces in maintaining variation and shaping patterns of diversity is not fully understood. Even in homogeneous environments devoid of local adaptations, such understanding remains elusive. Understanding this feedback is crucial, for example in determining the conditions under which extinction risk can be mitigated in peripheral populations subject to deleterious mutation accumulation at the edges of species’ ranges as well as in highly fragmented populations. In this thesis we explore both uniform and spatially heterogeneous metapopulations, investigating and providing theoretical insights into the dynamics of local adaptation in the latter and examining the dynamics of load and extinction as well as the impact of joint ecological and evolutionary (eco-evolutionary) dynamics in the former. The thesis is divided into 5 chapters. Chapter 1 provides a general introduction into the subject matter, clarifying concepts and ideas used throughout the thesis. In chapter 2, we explore how fast a species distributed across a heterogeneous landscape adapts to changing conditions marked by alterations in carrying capacity, selection pressure, and migration rate. In chapter 3, we investigate how migration selection and drift influences adaptation and the maintenance of variation in a metapopulation with three habitats, an extension of previous models of adaptation in two habitats. We further develop analytical approximations for the critical threshold required for polymorphism to persist. The focus of chapter 4 of the thesis is on understanding the interplay between ecology and evolution as coupled processes. We investigate how eco-evolutionary feedback between migration, selection, drift, and demography influences eco-evolutionary outcomes in marginal populations subject to deleterious mutation accumulation. Using simulations as well as theoretical approximations of the coupled dynamics of population size and allele frequency, we analyze how gene flow from a large mainland source influences genetic load and population size on an island (i.e., in a marginal population) under genetically realistic assumptions. Analyses of this sort are important because small isolated populations, are repeatedly affected by complex interactions between ecological and evolutionary processes, which can lead to their death. Understanding these interactions can therefore provide an insight into the conditions under which extinction risk can be mitigated in peripheral populations thus, contributing to conservation and restoration efforts. Chapter 5 extends the analysis in chapter 4 to consider the dynamics of load (due to deleterious mutation accumulation) and extinction risk in a metapopulation. We explore the role of gene flow, selection, and dominance on load and extinction risk and further pinpoint critical thresholds required for metapopulation persistence. Overall this research contributes to our understanding of ecological and evolutionary mechanisms that shape species’ persistence in fragmented landscapes, a crucial foundation for successful conservation efforts and biodiversity management.}, author = {Olusanya, Oluwafunmilola O}, issn = {2663-337X}, pages = {183}, publisher = {Institute of Science and Technology Austria}, title = {{Local adaptation, genetic load and extinction in metapopulations}}, doi = {10.15479/at:ista:14711}, year = {2024}, } @phdthesis{15020, abstract = {This thesis consists of four distinct pieces of work within theoretical biology, with two themes in common: the concept of optimization in biological systems, and the use of information-theoretic tools to quantify biological stochasticity and statistical uncertainty. Chapter 2 develops a statistical framework for studying biological systems which we believe to be optimized for a particular utility function, such as retinal neurons conveying information about visual stimuli. We formalize such beliefs as maximum-entropy Bayesian priors, constrained by the expected utility. We explore how such priors aid inference of system parameters with limited data and enable optimality hypothesis testing: is the utility higher than by chance? Chapter 3 examines the ultimate biological optimization process: evolution by natural selection. As some individuals survive and reproduce more successfully than others, populations evolve towards fitter genotypes and phenotypes. We formalize this as accumulation of genetic information, and use population genetics theory to study how much such information can be accumulated per generation and maintained in the face of random mutation and genetic drift. We identify the population size and fitness variance as the key quantities that control information accumulation and maintenance. Chapter 4 reuses the concept of genetic information from Chapter 3, but from a different perspective: we ask how much genetic information organisms actually need, in particular in the context of gene regulation. For example, how much information is needed to bind transcription factors at correct locations within the genome? Population genetics provides us with a refined answer: with an increasing population size, populations achieve higher fitness by maintaining more genetic information. Moreover, regulatory parameters experience selection pressure to optimize the fitness-information trade-off, i.e. minimize the information needed for a given fitness. This provides an evolutionary derivation of the optimization priors introduced in Chapter 2. Chapter 5 proves an upper bound on mutual information between a signal and a communication channel output (such as neural activity). Mutual information is an important utility measure for biological systems, but its practical use can be difficult due to the large dimensionality of many biological channels. Sometimes, a lower bound on mutual information is computed by replacing the high-dimensional channel outputs with decodes (signal estimates). Our result provides a corresponding upper bound, provided that the decodes are the maximum posterior estimates of the signal.}, author = {Hledik, Michal}, issn = {2663-337X}, keywords = {Theoretical biology, Optimality, Evolution, Information}, pages = {158}, publisher = {Institute of Science and Technology Austria}, title = {{Genetic information and biological optimization}}, doi = {10.15479/at:ista:15020}, year = {2024}, } @article{15330, abstract = {Clathrin-mediated endocytosis (CME) is vital for the regulation of plant growth and development by controlling plasma membrane protein composition and cargo uptake. CME relies on the precise recruitment of regulators for vesicle maturation and release. Homologues of components of mammalian vesicle scission are strong candidates to be part of the scission machinery in plants, but the precise roles of these proteins in this process are not fully understood. Here, we characterised the roles of Plant Dynamin-Related Proteins 2 (DRP2s) and SH3-domain containing protein 2 (SH3P2), the plant homologue to Dynamins’ recruiters, like Endophilin and Amphiphysin, in the CME by combining high-resolution imaging of endocytic events in vivo and characterisation of the purified proteins in vitro. Although DRP2s and SH3P2 arrive similarly late during CME and physically interact, genetic analysis of the sh3p123 triple-mutant and complementation assays with non-SH3P2-interacting DRP2 variants suggests that SH3P2 does not directly recruit DRP2s to the site of endocytosis. These observations imply that despite the presence of many well-conserved endocytic components, plants have acquired a distinct mechanism for CME.}, author = {Gnyliukh, Nataliia and Johnson, Alexander J and Nagel, MK and Monzer, Aline and Babic, David and Hlavata, Annamaria and Alotaibi, SS and Isono, E and Loose, Martin and Friml, Jiří}, issn = {1477-9137}, journal = {Journal of Cell Science}, number = {8}, publisher = {The Company of Biologists}, title = {{Role of dynamin-related proteins 2 and SH3P2 in clathrin-mediated endocytosis in Arabidopsis thaliana}}, doi = {10.1242/jcs.261720}, volume = {137}, year = {2024}, } @phdthesis{18568, abstract = {Locomotion is ubiquitous in the animal kingdom because an animal's survival depends on its ability to navigate its environment to find food, avoid predators and locate potential mates. These behaviours require control mechanisms that can extract information from the environment, particularly visual cues. Selective evolutionary pressures have thus refined such visuomotor transformations in a species-specific manner to meet the specific ecological and ethological challenges of each organism. However, a common challenge across organisms as visual information processing becomes increasingly detailed is the mechanisms required to synthesise disparate pieces of information into a coherent percept or unified picture of the world. In this thesis, I investigate how disparate visual information is combined in the brain of Drosophila melanogaster to effectively guide locomotion. For this, I first designed and built a behavioural setup to record locomotion and present visual stimuli to freely-walking fruit flies in a closed-loop manner. This setup allowed the investigation of innate visually-guided behaviours, including the optomotor reflex and courtship. Second, taking advantage of my system I investigated the optomotor response, a reflexive visual stabilisation behaviour in which flies turn in the direction of global motion to minimise retinal slip. This behaviour is thought to be mediated by Lobula plate tangential cells (LPTCs); a complex network of optic-flow-sensitive neurons essential for self-motion estimation. Using a novel genetic mutant, I demonstrate that electrical coupling between two LPTC subtypes, contralateral HS and H2 neurons, regulates the balance between smooth optomotor turning and saccadic anti-optomotor responses. These findings underscore the critical role of binocular motion cue integration in guiding course control. Finally, I developed a novel behavioural paradigm in which a sexually aroused male fruit fly is presented with an optomotor distractor. This setup creates competition between two visual behaviours, courtship tracking and the optomotor response, enabling me to explore how the visual system resolves this conflict. In this setting, males engaged in courtship selectively suppress their optomotor response based on the female's location. Furthermore, when this experiment is replicated with an “artificial female”, optogenetically aroused males alternate between tracking and optomotor responses. The probability and dynamics of this switching are determined by the relative strengths of the two competing stimuli. In summary, the results presented in this thesis explore two mechanisms – integration and competition - through which visual information is combined in the brain of the fruit fly to drive locomotion.}, author = {Satapathy, Roshan K}, isbn = {978-3-99078-047-3}, issn = {2663-337X}, pages = {114}, publisher = {Institute of Science and Technology Austria}, title = {{Mechanisms of visual integration and competition in innate behaviours in Drosophila melanogaster}}, doi = {10.15479/at:ista:18568}, year = {2024}, } @phdthesis{18674, abstract = {Mapping the complex and dense arrangement of cells and their connectivity in brain tissue requires volumetric imaging at nanoscale spatial resolution. While light microscopy excels at visualizing specific molecules and individual cells, achieving dense, synapse-level circuit reconstruction has not been possible with any light microscopy technique. Thus, the goal of my work was to develop image and data analysis pipelines for brain tissue visualization and reconstruction with light microscopy. To achieve dense circuit reconstruction with single-synapse resolution, I developed both conventional and deep-learning-based synapse detection algorithms, as well as connectivity analysis pipelines that integrate synapse detection with volumetric segmentation of brain tissue.}, author = {Lyudchik, Julia}, isbn = { 978-3-99078-051-0}, issn = {2663-337X}, pages = {217}, publisher = {Institute of Science and Technology Austria}, title = {{Image analysis for brain tissue reconstruction with super-resolution light microscopy}}, doi = {10.15479/at:ista:18674}, year = {2024}, } @unpublished{18677, abstract = {The information-processing capability of the brain’s cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can be achieved by volumetric imaging at nanoscale resolution with dense cellular labeling. Light microscopy is uniquely positioned to visualize specific molecules but dense, synapse-level circuit reconstruction by light microscopy has been out of reach due to limitations in resolution, contrast, and volumetric imaging capability. Here we developed light-microscopy based connectomics (LICONN). We integrated specifically engineered hydrogel embedding and expansion with comprehensive deep-learning based segmentation and analysis of connectivity, thus directly incorporating molecular information in synapse-level brain tissue reconstructions. LICONN will allow synapse-level brain tissue phenotyping in biological experiments in a readily adoptable manner.}, author = {Tavakoli, Mojtaba and Lyudchik, Julia and Januszewski, Michał and Vistunou, Vitali and Agudelo Duenas, Nathalie and Vorlaufer, Jakob and Sommer, Christoph M and Kreuzinger, Caroline and Oliveira, Bárbara and Cenameri, Alban and Novarino, Gaia and Jain, Viren and Danzl, Johann G}, booktitle = {bioRxiv}, title = {{Light-microscopy based dense connectomic reconstruction of mammalian brain tissue}}, doi = {10.1101/2024.03.01.582884}, year = {2024}, } @unpublished{18689, abstract = {Multiplexed fluorescence microscopy imaging is widely used in biomedical applications. However, simultaneous imaging of multiple fluorophores can result in spectral leaks and overlapping, which greatly degrades image quality and subsequent analysis. Existing popular spectral unmixing methods are mainly based on computational intensive linear models and the performance is heavily dependent on the reference spectra, which may greatly preclude its further applications. In this paper, we propose a deep learning-based blindly spectral unmixing method, termed AutoUnmix, to imitate the physical spectral mixing process. A tranfer learning framework is further devised to allow our AutoUnmix adapting to a variety of imaging systems without retraining the network. Our proposed method has demonstrated real-time unmixing capabilities, surpassing existing methods by up to 100-fold in terms of unmixing speed. We further validate the reconstruction performance on both synthetic datasets and biological samples. The unmixing results of AutoUnmix achieve a highest SSIM of 0.99 in both three- and four-color imaging, with nearly up to 20% higher than other popular unmixing methods. Due to the desirable property of data independency and superior blind unmixing performance, we believe AutoUnmix is a powerful tool to study the interaction process of different organelles labeled by multiple fluorophores.}, author = {Gallei, Michelle C and Truckenbrodt, Sven M and Kreuzinger, Caroline and Inumella, Syamala and Vistunou, Vitali and Sommer, Christoph M and Tavakoli, Mojtaba and Agudelo Duenas, Nathalie and Vorlaufer, Jakob and Jahr, Wiebke and Randuch, Marek and Johnson, Alexander J and Benková, Eva and Friml, Jiří and Danzl, Johann G}, booktitle = {bioRxiv}, title = {{Super-resolution expansion microscopy in plant roots}}, doi = {10.1101/2024.02.21.581330}, year = {2024}, }