DNA choreography: correlating mobility and organization of DNA across different resolutions from loops to chromosomes.

The dynamics of DNA in the cell nucleus plays a role in cellular processes and fates but the interplay of DNA mobility with the hierarchical levels of DNA organization is still underexplored. Here, we made use of DNA replication to directly label genomic DNA in an unbiased genome-wide manner. This was followed by live-cell time-lapse microscopy of the labeled DNA combining imaging at different resolutions levels simultaneously and allowing one to trace DNA motion across organization levels within the same cells. Quantification of the labeled DNA segments at different microscopic resolution levels revealed sizes comparable to the ones reported for DNA loops using 3D super-resolution microscopy, topologically associated domains (TAD) using 3D widefield microscopy, and also entire chromosomes. By employing advanced chromatin tracking and image registration, we discovered that DNA exhibited higher mobility at the individual loop level compared to the TAD level and even less at the chromosome level. Additionally, our findings indicate that chromatin movement, regardless of the resolution, slowed down during the S phase of the cell cycle compared to the G1/G2 phases. Furthermore, we found that a fraction of DNA loops and TADs exhibited directed movement with the majority depicting constrained movement. Our data also indicated spatial mobility differences with DNA loops and TADs at the nuclear periphery and the nuclear interior exhibiting lower velocity and radius of gyration than the intermediate locations. On the basis of these insights, we propose that there is a link between DNA mobility and its organizational structure including spatial distribution, which impacts cellular processes.

Replisome loading reduces chromatin motion independent of DNA synthesis.

Chromatin has been shown to undergo diffusional motion, which is affected during gene transcription by RNA polymerase activity. However, the relationship between chromatin mobility and other genomic processes remains unclear. Hence, we set out to label the DNA directly in a sequence unbiased manner and followed labeled chromatin dynamics in interphase human cells expressing GFP-tagged proliferating cell nuclear antigen (PCNA), a cell cycle marker and core component of the DNA replication machinery. We detected decreased chromatin mobility during the S-phase compared to G1 and G2 phases in tumor as well as normal diploid cells using automated particle tracking. To gain insight into the dynamical organization of the genome during DNA replication, we determined labeled chromatin domain sizes and analyzed their motion in replicating cells. By correlating chromatin mobility proximal to the active sites of DNA synthesis, we showed that chromatin motion was locally constrained at the sites of DNA replication. Furthermore, inhibiting DNA synthesis led to increased loading of DNA polymerases. This was accompanied by accumulation of the single-stranded DNA binding protein on the chromatin and activation of DNA helicases further restricting local chromatin motion. We, therefore, propose that it is the loading of replisomes but not their catalytic activity that reduces the dynamics of replicating chromatin segments in the S-phase as well as their accessibility and probability of interactions with other genomic regions.

Endosomal egress and intercellular transmission of hepatic ApoE-containing lipoproteins and its exploitation by the hepatitis C virus.

Liver-generated plasma Apolipoprotein E (ApoE)-containing lipoproteins (LPs) (ApoE-LPs) play central roles in lipid transport and metabolism. Perturbations of ApoE can result in several metabolic disorders and ApoE genotypes have been associated with multiple diseases. ApoE is synthesized at the endoplasmic reticulum and transported to the Golgi apparatus for LP assembly; however, the ApoE-LPs transport pathway from there to the plasma membrane is largely unknown. Here, we established an integrative imaging approach based on a fully functional fluorescently tagged ApoE. We found that newly synthesized ApoE-LPs accumulate in CD63-positive endosomes of hepatocytes. In addition, we observed the co-egress of ApoE-LPs and CD63-positive intraluminal vesicles (ILVs), which are precursors of extracellular vesicles (EVs), along the late endosomal trafficking route in a microtubule-dependent manner. A fraction of ApoE-LPs associated with CD63-positive EVs appears to be co-transmitted from cell to cell. Given the important role of ApoE in viral infections, we employed as well-studied model the hepatitis C virus (HCV) and found that the viral replicase component nonstructural protein 5A (NS5A) is enriched in ApoE-containing ILVs. Interaction between NS5A and ApoE is required for the efficient release of ILVs containing HCV RNA. These vesicles are transported along the endosomal ApoE egress pathway. Taken together, our data argue for endosomal egress and transmission of hepatic ApoE-LPs, a pathway that is hijacked by HCV. Given the more general role of EV-mediated cell-to-cell communication, these insights provide new starting points for research into the pathophysiology of ApoE-related metabolic and infection-related disorders.

Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network.

Purpose: The interpretation of image data plays a critical role during acute brain stroke diagnosis, and promptly defining the requirement of a surgical intervention will drastically impact the patient's outcome. However, determining stroke lesions purely from images can be a daunting task. Many studies proposed automatic segmentation methods for brain stroke lesions from medical images in different modalities, though heretofore results do not satisfy the requirements to be clinically reliable. We investigate the segmentation of brain stroke lesions using a geometric deep learning model that takes advantage of the intrinsic interconnected diffusion features in a set of multi-modal inputs consisting of computer tomography (CT) perfusion parameters. Approach: We propose a geometric deep learning model for the segmentation of ischemic stroke brain lesions that employs spline convolutions and unpooling/pooling operators on graphs to excerpt graph-structured features in a fully convolutional network architecture. In addition, we seek to understand the underlying principles governing the different components of our model. Accordingly, we structure the experiments in two parts: an evaluation of different architecture hyperparameters and a comparison with state-of-the-art methods. Results: The ablation study shows that deeper layers obtain a higher Dice coefficient score (DCS) of up to 0.3654. Comparing different pooling and unpooling methods shows that the best performing unpooling method is the proportional approach, yet it often smooths the segmentation border. Unpooling achieves segmentation results more adapted to the lesion boundary corroborated with systematic lower values of Hausdorff distance. The model performs at the level of state-of-the-art models without optimized training methods, such as augmentation or patches, with a DCS of 0.4553±0.0031. Conclusions: We proposed and evaluated an end-to-end trainable fully convolutional graph network architecture using spline convolutional layers for the ischemic stroke lesion prediction. We propose a model that employs graph-based operations to predict acute stroke brain lesions from CT perfusion parameters. Our results prove the feasibility of using geometric deep learning to solve segmentation problems, and our model shows a better performance than other models evaluated. The proposed model achieves improved metric values for the DCS metric, ranging from 8.61% to 69.05%, compared with other models trained under the same conditions. Next, we compare different pooling and unpooling operations in relation to their segmentation results, and we show that the model can produce segmentation outputs that adapt to irregular segmentation boundaries when using simple heuristic unpooling operations.

Superadditivity and Convex Optimization for Globally Optimal Cell Segmentation Using Deformable Shape Models.

Cell nuclei segmentation is challenging due to shape variation and closely clustered or partially overlapping objects. Most previous methods are not globally optimal, limited to elliptical models, or are computationally expensive. In this work, we introduce a globally optimal approach based on deformable shape models and global energy minimization for cell nuclei segmentation and cluster splitting. We propose an implicit parameterization of deformable shape models and show that it leads to a convex energy. Convex energy minimization yields the global solution independently of the initialization, is fast, and robust. To jointly perform cell nuclei segmentation and cluster splitting, we developed a novel iterative global energy minimization method, which leverages the inherent property of superadditivity of the convex energy. This property exploits the lower bound of the energy of the union of the models and improves the computational efficiency. Our method provably determines a solution close to global optimality. In addition, we derive a closed-form solution of the proposed global minimization based on the superadditivity property for non-clustered cell nuclei. We evaluated our method using fluorescence microscopy images of five different cell types comprising various challenges, and performed a quantitative comparison with previous methods. Our method achieved state-of-the-art or improved performance.

Pre-Synaptic GABAA in NaV1.8+ Primary Afferents Is Required for the Development of Punctate but Not Dynamic Mechanical Allodynia following CFA Inflammation.

Hypersensitivity to mechanical stimuli is a cardinal symptom of neuropathic and inflammatory pain. A reduction in spinal inhibition is generally considered a causal factor in the development of mechanical hypersensitivity after injury. However, the extent to which presynaptic inhibition contributes to altered spinal inhibition is less well established. Here, we used conditional deletion of GABAA in NaV1.8-positive sensory neurons (Scn10aCre;Gabrb3fl/fl) to manipulate selectively presynaptic GABAergic inhibition. Behavioral testing showed that the development of inflammatory punctate allodynia was mitigated in mice lacking pre-synaptic GABAA. Dorsal horn cellular circuits were visualized in single slices using stimulus-tractable dual-labelling of c-fos mRNA for punctate and the cognate c-Fos protein for dynamic mechanical stimulation. This revealed a substantial reduction in the number of cells activated by punctate stimulation in mice lacking presynaptic GABAA and an approximate 50% overlap of the punctate with the dynamic circuit, the relative percentage of which did not change following inflammation. The reduction in dorsal horn cells activated by punctate stimuli was equally prevalent in parvalbumin- and calretinin-positive cells and across all laminae I-V, indicating a generalized reduction in spinal input. In peripheral DRG neurons, inflammation following complete Freund's adjuvant (CFA) led to an increase in axonal excitability responses to GABA, suggesting that presynaptic GABA effects in NaV1.8+ afferents switch from inhibition to excitation after CFA. In the days after inflammation, presynaptic GABAA in NaV1.8+ nociceptors constitutes an "open gate" pathway allowing mechanoreceptors responding to punctate mechanical stimulation access to nociceptive dorsal horn circuits.

APPsα Rescues Tau-Induced Synaptic Pathology.

Alzheimer's disease (AD) is histopathologically characterized by Aß plaques and the accumulation of hyperphosphorylated Tau species, the latter also constituting key hallmarks of primary tauopathies. Whereas Aß is produced by amyloidogenic APP processing, APP processing along the competing nonamyloidogenic pathway results in the secretion of neurotrophic and synaptotrophic APPsα. Recently, we demonstrated that APPsα has therapeutic effects in transgenic AD model mice and rescues Aß-dependent impairments. Here, we examined the potential of APPsα to mitigate Tau-induced synaptic deficits in P301S mice (both sexes), a widely used mouse model of tauopathy. Analysis of synaptic plasticity revealed an aberrantly increased LTP in P301S mice that could be normalized by acute application of nanomolar amounts of APPsα to hippocampal slices, indicating a homeostatic function of APPsα on a rapid time scale. Further, AAV-mediated in vivo expression of APPsα restored normal spine density of CA1 neurons even at stages of advanced Tau pathology not only in P301S mice, but also in independent THY-Tau22 mice. Strikingly, when searching for the mechanism underlying aberrantly increased LTP in P301S mice, we identified an early and progressive loss of major GABAergic interneuron subtypes in the hippocampus of P301S mice, which may lead to reduced GABAergic inhibition of principal cells. Interneuron loss was paralleled by deficits in nest building, an innate behavior highly sensitive to hippocampal impairments. Together, our findings indicate that APPsα has therapeutic potential for Tau-mediated synaptic dysfunction and suggest that loss of interneurons leads to disturbed neuronal circuits that compromise synaptic plasticity as well as behavior.SIGNIFICANCE STATEMENT Our findings indicate, for the first time, that APPsα has the potential to rescue Tau-induced spine loss and abnormal synaptic plasticity. Thus, APPsα might have therapeutic potential not only because of its synaptotrophic functions, but also its homeostatic capacity for neuronal network activity. Hence, APPsα is one of the few molecules which has proven therapeutic effects in mice, both for Aß- and Tau-dependent synaptic impairments and might therefore have therapeutic potential for patients suffering from AD or primary tauopathies. Furthermore, we found in P301S mice a pronounced reduction of inhibitory interneurons as the earliest pathologic event preceding the accumulation of hyperphosphorylated Tau species. This loss of interneurons most likely disturbs neuronal circuits that are important for synaptic plasticity and behavior.

Single-dose ethanol intoxication causes acute and lasting neuronal changes in the brain.

Alcohol intoxication at early ages is a risk factor for the development of addictive behavior. To uncover neuronal molecular correlates of acute ethanol intoxication, we used stable-isotope-labeled mice combined with quantitative mass spectrometry to screen more than 2,000 hippocampal proteins, of which 72 changed synaptic abundance up to twofold after ethanol exposure. Among those were mitochondrial proteins and proteins important for neuronal morphology, including MAP6 and ankyrin-G. Based on these candidate proteins, we found acute and lasting molecular, cellular, and behavioral changes following a single intoxication in alcohol-naïve mice. Immunofluorescence analysis revealed a shortening of axon initial segments. Longitudinal two-photon in vivo imaging showed increased synaptic dynamics and mitochondrial trafficking in axons. Knockdown of mitochondrial trafficking in dopaminergic neurons abolished conditioned alcohol preference in Drosophila flies. This study introduces mitochondrial trafficking as a process implicated in reward learning and highlights the potential of high-resolution proteomics to identify cellular mechanisms relevant for addictive behavior.

TRPM3-mediated dynamic mitochondrial activity in nerve growth factor-induced latent sensitization of chronic low back pain.

ABSTRACT: The transient receptor potential ion channel TRPM3 is highly prevalent on nociceptive dorsal root ganglion (DRG) neurons, but its functions in neuronal plasticity of chronic pain remain obscure. In an animal model of nonspecific low back pain (LBP), latent spinal sensitization known as nociceptive priming is induced by nerve growth factor (NGF) injection. Here, we address the TRPM3-associated molecular basis of NGF-induced latent spinal sensitization at presynaptic level by studying TRPM3-mediated calcium transients in DRG neurons. By investigating TRPM3-expressing HEK cells, we further show the dynamic mitochondrial activity downstream of TRPM3 activation. NGF enhances TRPM3 function, attenuates TRPM3 tachyphylaxis, and slows intracellular calcium clearance; TRPM3 activation triggers more mitochondrial calcium loading than depolarization does, causing a steady-state mitochondrial calcium elevation and a delayed recovery of cytosolic calcium; mitochondrial calcium buffering accounts for approximately 40% of calcium influx subsequent to TRPM3 activation. TRPM3 activation provokes an outbreak of pulsatile superoxide production (mitoflash) that comes in the form of a surge in frequency being tunable. We suggest that mitoflash pulsations downstream of TRPM3 activation might be an early signaling event initiating pain sensitization. Tuning of mitoflash activity would be a novel bottom-up therapeutic strategy for chronic pain conditions such as LBP and beyond.

PITX1 Is a Regulator of TERT Expression in Prostate Cancer with Prognostic Power.

The current risk stratification in prostate cancer (PCa) is frequently insufficient to adequately predict disease development and outcome. One hallmark of cancer is telomere maintenance. For telomere maintenance, PCa cells exclusively employ telomerase, making it essential for this cancer entity. However, TERT, the catalytic protein component of the reverse transcriptase telomerase, itself does not suit as a prognostic marker for prostate cancer as it is rather low expressed. We investigated if, instead of TERT, transcription factors regulating TERT may suit as prognostic markers. To identify transcription factors regulating TERT, we developed and applied a new gene regulatory modeling strategy to a comprehensive transcriptome dataset of 445 primary PCa. Six transcription factors were predicted as TERT regulators, and most prominently, the developmental morphogenic factor PITX1. PITX1 expression positively correlated with telomere staining intensity in PCa tumor samples. Functional assays and chromatin immune-precipitation showed that PITX1 activates TERT expression in PCa cells. Clinically, we observed that PITX1 is an excellent prognostic marker, as concluded from an analysis of more than 15,000 PCa samples. PITX1 expression in tumor samples associated with (i) increased Ki67 expression indicating increased tumor growth, (ii) a worse prognosis, and (iii) correlated with telomere length.

Temporal control of the integrated stress response by a stochastic molecular switch.

Stress granules (SGs) are formed in the cytosol as an acute response to environmental cues and activation of the integrated stress response (ISR), a central signaling pathway controlling protein synthesis. Using chronic virus infection as stress model, we previously uncovered a unique temporal control of the ISR resulting in recurrent phases of SG assembly and disassembly. Here, we elucidate the molecular network generating this fluctuating stress response by integrating quantitative experiments with mathematical modeling and find that the ISR operates as a stochastic switch. Key elements controlling this switch are the cooperative activation of the stress-sensing kinase PKR, the ultrasensitive response of SG formation to the phosphorylation of the translation initiation factor eIF2α, and negative feedback via GADD34, a stress-induced subunit of protein phosphatase 1. We identify GADD34 messenger RNA levels as the molecular memory of the ISR that plays a central role in cell adaptation to acute and chronic stress.

Deep probabilistic tracking of particles in fluorescence microscopy images.

Tracking of particles in temporal fluorescence microscopy image sequences is of fundamental importance to quantify dynamic processes of intracellular structures as well as virus structures. We introduce a probabilistic deep learning approach for fluorescent particle tracking, which is based on a recurrent neural network that mimics classical Bayesian filtering. Compared to previous deep learning methods for particle tracking, our approach takes into account uncertainty, both aleatoric and epistemic uncertainty. Thus, information about the reliability of the computed trajectories is determined. Manual tuning of tracking parameters is not necessary and prior knowledge about the noise statistics is not required. Short and long-term temporal dependencies of individual object dynamics are exploited for state prediction, and assigned detections are used to update the predicted states. For correspondence finding, we introduce a neural network which computes assignment probabilities jointly across multiple detections as well as determines the probabilities of missing detections. Training requires only simulated data and therefore tedious manual annotation of ground truth is not needed. We performed a quantitative performance evaluation based on synthetic and real 2D as well as 3D fluorescence microscopy images. We used image data of the Particle Tracking Challenge as well as real time-lapse fluorescence microscopy images displaying virus structures and chromatin structures. It turned out that our approach yields state-of-the-art results or improves the tracking results compared to previous methods.

Long-term cancer survival prediction using multimodal deep learning.

The age of precision medicine demands powerful computational techniques to handle high-dimensional patient data. We present MultiSurv, a multimodal deep learning method for long-term pan-cancer survival prediction. MultiSurv uses dedicated submodels to establish feature representations of clinical, imaging, and different high-dimensional omics data modalities. A data fusion layer aggregates the multimodal representations, and a prediction submodel generates conditional survival probabilities for follow-up time intervals spanning several decades. MultiSurv is the first non-linear and non-proportional survival prediction method that leverages multimodal data. In addition, MultiSurv can handle missing data, including single values and complete data modalities. MultiSurv was applied to data from 33 different cancer types and yields accurate pan-cancer patient survival curves. A quantitative comparison with previous methods showed that Multisurv achieves the best results according to different time-dependent metrics. We also generated visualizations of the learned multimodal representation of MultiSurv, which revealed insights on cancer characteristics and heterogeneity.

Deep Consensus Network: Aggregating predictions to improve object detection in microscopy images.

Detection of cells and particles in microscopy images is a common and challenging task. In recent years, detection approaches in computer vision achieved remarkable improvements by leveraging deep learning. Microscopy images pose challenges like small and clustered objects, low signal to noise, and complex shape and appearance, for which current approaches still struggle. We introduce Deep Consensus Network, a new deep neural network for object detection in microscopy images based on object centroids. Our network is trainable end-to-end and comprises a Feature Pyramid Network-based feature extractor, a Centroid Proposal Network, and a layer for ensembling detection hypotheses over all image scales and anchors. We suggest an anchor regularization scheme that favours prior anchors over regressed locations. We also propose a novel loss function based on Normalized Mutual Information to cope with strong class imbalance, which we derive within a Bayesian framework. In addition, we introduce an improved algorithm for Non-Maximum Suppression which significantly reduces the algorithmic complexity. Experiments on synthetic data are performed to provide insights into the properties of the proposed loss function and its robustness. We also applied our method to challenging data from the TUPAC16 mitosis detection challenge and the Particle Tracking Challenge, and achieved results competitive or better than state-of-the-art.

A Recurrent Neural Network for Particle Tracking in Microscopy Images Using Future Information, Track Hypotheses, and Multiple Detections.

Automatic tracking of particles in time-lapse fluorescence microscopy images is essential for quantifying the dynamic behavior of subcellular structures and virus structures. We introduce a novel particle tracking approach based on a deep recurrent neural network architecture that exploits past and future information in both forward and backward direction. Assignment probabilities are determined jointly across multiple detections, and the probability of missing detections is computed. In addition, existence probabilities are determined by the network to handle track initiation and termination. For correspondence finding, track hypotheses are propagated to future time points so that information at later time points can be used to resolve ambiguities. A handcrafted similarity measure and handcrafted motion features are not necessary. Manually labeled data is not required for network training. We evaluated the performance of our approach using image data of the Particle Tracking Challenge as well as real fluorescence microscopy image sequences of virus structures. It turned out that the proposed approach outperforms previous methods.

Accessible and reproducible mass spectrometry imaging data analysis in Galaxy.

BACKGROUND: Mass spectrometry imaging is increasingly used in biological and translational research because it has the ability to determine the spatial distribution of hundreds of analytes in a sample. Being at the interface of proteomics/metabolomics and imaging, the acquired datasets are large and complex and often analyzed with proprietary software or in-house scripts, which hinders reproducibility. Open source software solutions that enable reproducible data analysis often require programming skills and are therefore not accessible to many mass spectrometry imaging (MSI) researchers. FINDINGS: We have integrated 18 dedicated mass spectrometry imaging tools into the Galaxy framework to allow accessible, reproducible, and transparent data analysis. Our tools are based on Cardinal, MALDIquant, and scikit-image and enable all major MSI analysis steps such as quality control, visualization, preprocessing, statistical analysis, and image co-registration. Furthermore, we created hands-on training material for use cases in proteomics and metabolomics. To demonstrate the utility of our tools, we re-analyzed a publicly available N-linked glycan imaging dataset. By providing the entire analysis history online, we highlight how the Galaxy framework fosters transparent and reproducible research. CONCLUSION: The Galaxy framework has emerged as a powerful analysis platform for the analysis of MSI data with ease of use and access, together with high levels of reproducibility and transparency.

DNA accessibility of chromatosomes quantified by automated image analysis of AFM data.

DNA compaction and accessibility in eukaryotes are governed by nucleosomes and orchestrated through interactions between DNA and DNA-binding proteins. Using QuantAFM, a method for automated image analysis of atomic force microscopy (AFM) data, we performed a detailed statistical analysis of structural properties of mono-nucleosomes. QuantAFM allows fast analysis of AFM images, including image preprocessing, object segmentation, and quantification of different structural parameters to assess DNA accessibility of nucleosomes. A comparison of nucleosomes reconstituted with and without linker histone H1 quantified H1's already described ability of compacting the nucleosome. We further employed nucleosomes bearing two charge-modifying mutations at position R81 and R88 in histone H2A (H2A R81E/R88E) to characterize DNA accessibility under destabilizing conditions. Upon H2A mutation, even in presence of H1, the DNA opening angle at the entry/exit site was increased and the DNA wrapping length around the histone core was reduced. Interestingly, a distinct opening of the less bendable DNA side was observed upon H2A mutation, indicating an enhancement of the intrinsic asymmetry of the Widom-601 nucleosomes. This study validates AFM as a technique to investigate structural parameters of nucleosomes and highlights how the DNA sequence, together with nucleosome modifications, can influence the DNA accessibility.

Spatiotemporal Coupling of the Hepatitis C Virus Replication Cycle by Creating a Lipid Droplet- Proximal Membranous Replication Compartment.

The hepatitis C virus (HCV) is a major cause of chronic liver disease, affecting around 71 million people worldwide. Viral RNA replication occurs in a membranous compartment composed of double-membrane vesicles (DMVs), whereas virus particles are thought to form by budding into the endoplasmic reticulum (ER). It is unknown how these steps are orchestrated in space and time. Here, we established an imaging system to visualize HCV structural and replicase proteins in live cells and with high resolution. We determined the conditions for the recruitment of viral proteins to putative assembly sites and studied the dynamics of this event and the underlying ultrastructure. Most notable was the selective recruitment of ER membranes around lipid droplets where structural proteins and the viral replicase colocalize. Moreover, ER membranes wrapping lipid droplets were decorated with double membrane vesicles, providing a topological map of how HCV might coordinate the steps of viral replication and virion assembly.

Hyperparameter optimization for image analysis: application to prostate tissue images and live cell data of virus-infected cells.

PURPOSE: Automated analysis of microscopy image data typically requires complex pipelines that involve multiple methods for different image analysis tasks. To achieve best results of the analysis pipelines, method-dependent hyperparameters need to be optimized. However, complex pipelines often suffer from the fact that calculation of the gradient of the loss function is analytically or computationally infeasible. Therefore, first- or higher-order optimization methods cannot be applied. METHODS: We developed a new framework for zero-order black-box hyperparameter optimization called HyperHyper, which has a modular architecture that separates hyperparameter sampling and optimization. We also developed a visualization of the loss function based on infimum projection to obtain further insights into the optimization problem. RESULTS: We applied HyperHyper in three different experiments with different imaging modalities, and evaluated in total more than 400.000 hyperparameter combinations. HyperHyper was used for optimizing two pipelines for cell nuclei segmentation in prostate tissue microscopy images and two pipelines for detection of hepatitis C virus proteins in live cell microscopy data. We evaluated the impact of separating the sampling and optimization strategy using different optimizers and employed an infimum projection for visualizing the hyperparameter space. CONCLUSIONS: The separation of sampling and optimization strategy of the proposed HyperHyper optimization framework improves the result of the investigated image analysis pipelines. Visualization of the loss function based on infimum projection enables gaining further insights on the optimization process.

Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures.

Pathogens face varying microenvironments in vivo, but suitable experimental systems and analysis tools to dissect how three-dimensional (3D) tissue environments impact pathogen spread are lacking. Here we develop an Integrative method to Study Pathogen spread by Experiment and Computation within Tissue-like 3D cultures (INSPECT-3D), combining quantification of pathogen replication with imaging to study single-cell and cell population dynamics. We apply INSPECT-3D to analyze HIV-1 spread between primary human CD4 T-lymphocytes using collagen as tissue-like 3D-scaffold. Measurements of virus replication, infectivity, diffusion, cellular motility and interactions are combined by mathematical analyses into an integrated spatial infection model to estimate parameters governing HIV-1 spread. This reveals that environmental restrictions limit infection by cell-free virions but promote cell-associated HIV-1 transmission. Experimental validation identifies cell motility and density as essential determinants of efficacy and mode of HIV-1 spread in 3D. INSPECT-3D represents an adaptable method for quantitative time-resolved analyses of 3D pathogen spread.