Flow-mediated olfactory communication in honeybee swarms.

Honeybee swarms are a landmark example of collective behavior. To become a coherent swarm, bees locate their queen by tracking her pheromones. But how can distant individuals exploit these chemical signals, which decay rapidly in space and time? Here, we combine a behavioral assay with the machine vision detection of organism location and scenting (pheromone propagation via wing fanning) behavior to track the search and aggregation dynamics of the honeybee Apis mellifera L. We find that bees collectively create a scenting-mediated communication network by arranging in a specific spatial distribution where there is a characteristic distance between individuals and directional signaling away from the queen. To better understand such a flow-mediated directional communication strategy, we developed an agent-based model where bee agents obeying simple, local behavioral rules exist in a flow environment in which the chemical signals diffuse and decay. Our model serves as a guide to exploring how physical parameters affect the collective scenting behavior and shows that increased directional bias in scenting leads to a more efficient aggregation process that avoids local equilibrium configurations of isotropic (nondirectional and axisymmetric) communication, such as small bee clusters that persist throughout the simulation. Our results highlight an example of extended classical stigmergy: Rather than depositing static information in the environment, individual bees locally sense and globally manipulate the physical fields of chemical concentration and airflow.

Deep learning-based segmentation and quantification of podocyte foot process morphology suggests differential patterns of foot process effacement across kidney pathologies.

Morphological alterations at the kidney filtration barrier increase intrinsic capillary wall permeability resulting in albuminuria. However, automated, quantitative assessment of these morphological changes has not been possible with electron or light microscopy. Here we present a deep learning-based approach for segmentation and quantitative analysis of foot processes in images acquired with confocal and super-resolution fluorescence microscopy. Our method, Automatic Morphological Analysis of Podocytes (AMAP), accurately segments podocyte foot processes and quantifies their morphology. AMAP applied to a set of kidney diseases in patient biopsies and a mouse model of focal segmental glomerulosclerosis allowed for accurate and comprehensive quantification of various morphometric features. With the use of AMAP, detailed morphology of podocyte foot process effacement was found to differ between categories of kidney pathologies, showed detailed variability between diverse patients with the same clinical diagnosis, and correlated with levels of proteinuria. AMAP could potentially complement other readouts such as various omics, standard histologic/electron microscopy and blood/urine assays for future personalized diagnosis and treatment of kidney disease. Thus, our novel finding could have implications to afford an understanding of early phases of kidney disease progression and may provide supplemental information in precision diagnostics.

Predicting the HER2 status in oesophageal cancer from tissue microarrays using convolutional neural networks.

BACKGROUND: Fast and accurate diagnostics are key for personalised medicine. Particularly in cancer, precise diagnosis is a prerequisite for targeted therapies, which can prolong lives. In this work, we focus on the automatic identification of gastroesophageal adenocarcinoma (GEA) patients that qualify for a personalised therapy targeting epidermal growth factor receptor 2 (HER2). We present a deep-learning method for scoring microscopy images of GEA for the presence of HER2 overexpression. METHODS: Our method is based on convolutional neural networks (CNNs) trained on a rich dataset of 1602 patient samples and tested on an independent set of 307 patient samples. We additionally verified the CNN's generalisation capabilities with an independent dataset with 653 samples from a separate clinical centre. We incorporated an attention mechanism in the network architecture to identify the tissue regions, which are important for the prediction outcome. Our solution allows for direct automated detection of HER2 in immunohistochemistry-stained tissue slides without the need for manual assessment and additional costly in situ hybridisation (ISH) tests. RESULTS: We show accuracy of 0.94, precision of 0.97, and recall of 0.95. Importantly, our approach offers accurate predictions in cases that pathologists cannot resolve and that require additional ISH testing. We confirmed our findings in an independent dataset collected in a different clinical centre. The attention-based CNN exploits morphological information in microscopy images and is superior to a predictive model based on the staining intensity only. CONCLUSIONS: We demonstrate that our approach not only automates an important diagnostic process for GEA patients but also paves the way for the discovery of new morphological features that were previously unknown for GEA pathology.

In vivo characterization of a podocyte-expressed short podocin isoform.

The most common genetic causes of steroid-resistant nephrotic syndrome (SRNS) are mutations in the NPHS2 gene, which encodes the cholesterol-binding, lipid-raft associated protein podocin. Mass spectrometry and cDNA sequencing revealed the existence of a second shorter isoform in the human kidney in addition to the well-studied canonical full-length protein. Distinct subcellular localization of the shorter isoform that lacks part of the conserved PHB domain suggested a physiological role. Here, we analyzed whether this protein can substitute for the canonical full-length protein. The short isoform of podocin is not found in other organisms except humans. We therefore analysed a mouse line expressing the equivalent podocin isoform (podocinΔexon5) by CRISPR/Cas-mediated genome editing. We characterized the phenotype of these mice expressing podocinΔexon5 and used targeted mass spectrometry and qPCR to compare protein and mRNA levels of podocinwildtype and podocinΔexon5. After immunolabeling slit diaphragm components, STED microscopy was applied to visualize alterations of the podocytes' foot process morphology.Mice homozygous for podocinΔexon5 were born heavily albuminuric and did not survive past the first 24 h after birth. Targeted mass spectrometry revealed massively decreased protein levels of podocinΔexon5, whereas mRNA abundance was not different from the canonical form of podocin. STED microscopy revealed the complete absence of podocin at the podocytes' slit diaphragm and severe morphological alterations of podocyte foot processes. Mice heterozygous for podocinΔexon5 were phenotypically and morphologically unaffected despite decreased podocin and nephrin protein levels.The murine equivalent to the human short isoform of podocin cannot stabilize the lipid-protein complex at the podocyte slit diaphragm. Reduction of podocin levels at the site of the slit diaphragm complex has a detrimental effect on podocyte function and morphology. It is associated with decreased protein abundance of nephrin, the central component of the filtration-slit forming slit diaphragm protein complex.

A C. elegans model for neurodegeneration in Cockayne syndrome.

Cockayne syndrome (CS) is a congenital syndrome characterized by growth and mental retardation, and premature ageing. The complexity of CS and mammalian models warrants simpler metazoan models that display CS-like phenotypes that could be studied in the context of a live organism. Here, we provide a characterization of neuronal and mitochondrial aberrations caused by a mutation in the csb-1 gene in Caenorhabditis elegans. We report a progressive neurodegeneration in adult animals that is enhanced upon UV-induced DNA damage. The csb-1 mutants show dysfunctional hyperfused mitochondria that degrade upon DNA damage, resulting in diminished respiratory activity. Our data support the role of endogenous DNA damage as a driving factor of CS-related neuropathology and underline the role of mitochondrial dysfunction in the disease.

Lipidome Evolution in Mammalian Tissues.

Lipids are essential structural and functional components of cells. Little is known, however, about the evolution of lipid composition in different tissues. Here, we report a large-scale analysis of the lipidome evolution in six tissues of 32 species representing primates, rodents, and bats. While changes in genes' sequence and expression accumulate proportionally to the phylogenetic distances, <2% of the lipidome evolves this way. Yet, lipids constituting this 2% cluster in specific functions shared among all tissues. Among species, human show the largest amount of species-specific lipidome differences. Many of the uniquely human lipidome features localize in the brain cortex and cluster in specific pathways implicated in cognitive disorders.

Changes in Lipidome Composition during Brain Development in Humans, Chimpanzees, and Macaque Monkeys.

Lipids are essential components of the brain. Here, we conducted a comprehensive mass spectrometry-based analysis of lipidome composition in the prefrontal cortex of 40 humans, 40 chimpanzees, and 40 rhesus monkeys over postnatal development and adulthood. Of the 11,772 quantified lipid peaks, 7,589 change significantly along the lifespan. More than 60% of these changes occur prior to adulthood, with less than a quarter associated with myelination progression. Evolutionarily, 36% of the age-dependent lipids exhibit concentration profiles distinct to one of the three species; 488 (18%) of them were unique to humans. In both humans and chimpanzees, the greatest extent of species-specific differences occurs in early development. Human-specific lipidome differences, however, persist over most of the lifespan and reach their peak from 20 to 35 years of age, when compared with chimpanzee-specific ones.

Exceptional evolutionary divergence of human muscle and brain metabolomes parallels human cognitive and physical uniqueness.

Metabolite concentrations reflect the physiological states of tissues and cells. However, the role of metabolic changes in species evolution is currently unknown. Here, we present a study of metabolome evolution conducted in three brain regions and two non-neural tissues from humans, chimpanzees, macaque monkeys, and mice based on over 10,000 hydrophilic compounds. While chimpanzee, macaque, and mouse metabolomes diverge following the genetic distances among species, we detect remarkable acceleration of metabolome evolution in human prefrontal cortex and skeletal muscle affecting neural and energy metabolism pathways. These metabolic changes could not be attributed to environmental conditions and were confirmed against the expression of their corresponding enzymes. We further conducted muscle strength tests in humans, chimpanzees, and macaques. The results suggest that, while humans are characterized by superior cognition, their muscular performance might be markedly inferior to that of chimpanzees and macaque monkeys.

Analysis of physicochemical and structural properties determining HIV-1 coreceptor usage.

The relationship of HIV tropism with disease progression and the recent development of CCR5-blocking drugs underscore the importance of monitoring virus coreceptor usage. As an alternative to costly phenotypic assays, computational methods aim at predicting virus tropism based on the sequence and structure of the V3 loop of the virus gp120 protein. Here we present a numerical descriptor of the V3 loop encoding its physicochemical and structural properties. The descriptor allows for structure-based prediction of HIV tropism and identification of properties of the V3 loop that are crucial for coreceptor usage. Use of the proposed descriptor for prediction results in a statistically significant improvement over the prediction based solely on V3 sequence with 3 percentage points improvement in AUC and 7 percentage points in sensitivity at the specificity of the 11/25 rule (95%). We additionally assessed the predictive power of the new method on clinically derived 'bulk' sequence data and obtained a statistically significant improvement in AUC of 3 percentage points over sequence-based prediction. Furthermore, we demonstrated the capacity of our method to predict therapy outcome by applying it to 53 samples from patients undergoing Maraviroc therapy. The analysis of structural features of the loop informative of tropism indicates the importance of two loop regions and their physicochemical properties. The regions are located on opposite strands of the loop stem and the respective features are predominantly charge-, hydrophobicity- and structure-related. These regions are in close proximity in the bound conformation of the loop potentially forming a site determinant for the coreceptor binding. The method is available via server under http://structure.bioinf.mpi-inf.mpg.de/.

HoLy-Net: Segmentation of histological images of diffuse large B-cell lymphoma.

Over the last years, there has been large progress in automated segmentation and classification methods in histological whole slide images (WSIs) stained with hematoxylin and eosin (H&E). Current state-of-the-art (SOTA) techniques are based on diverse datasets of H&E-stained WSIs of different types of predominantly solid cancer. However, there is a scarcity of methods and datasets enabling segmentation of tumors of the lymphatic system (lymphomas). Here, we propose a solution for segmentation of diffuse large B-cell lymphoma (DLBCL), the most common non-Hodgkin's lymphoma. Our method applies to both H&E-stained slides and to a broad range of markers stained with immunohistochemistry (IHC). While IHC staining is an important tool in cancer diagnosis and treatment decisions, there are few automated segmentation and classification methods for IHC-stained WSIs. To address the challenges of nuclei segmentation in H&E- and IHC-stained DLBCL images, we propose HoLy-Net - a HoVer-Net-based deep learning model for lymphoma segmentation. We train two different models, one for segmenting H&E- and one for IHC-stained images and compare the test results with the SOTA methods as well as with the original version of HoVer-Net. Subsequently, we segment patient WSIs and perform single cell-level analysis of different cell types to identify patient-specific tumor characteristics such as high level of immune infiltration. Our method outperforms general-purpose segmentation methods for H&E staining in lymphoma WSIs (with an F1 score of 0.899) and is also a unique automated method for IHC slide segmentation (with an F1 score of 0.913). With our solution, we provide a new dataset we denote LyNSeC (lymphoma nuclear segmentation and classification) containing 73,931 annotated cell nuclei from H&E and 87,316 from IHC slides. Our method and dataset open up new avenues for quantitative, large-scale studies of morphology and microenvironment of lymphomas overlooked by the current automated segmentation methods.

First experiences with machine learning predictions of accelerated declining eGFR slope of living kidney donors 3 years after donation.

BACKGROUND: Living kidney donors are screened pre-donation to estimate the risk of end-stage kidney disease (ESKD). We evaluate Machine Learning (ML) to predict the progression of kidney function deterioration over time using the estimated GFR (eGFR) slope as the target variable. METHODS: We included 238 living kidney donors who underwent donor nephrectomy. We divided the dataset based on the eGFR slope in the third follow-up year, resulting in 185 donors with an average eGFR slope and 53 donors with an accelerated declining eGFR-slope. We trained three Machine Learning-models (Random Forest [RF], Extreme Gradient Boosting [XG], Support Vector Machine [SVM]) and Logistic Regression (LR) for predictions. Predefined data subsets served for training to explore whether parameters of an ESKD risk score alone suffice or additional clinical and time-zero biopsy parameters enhance predictions. Machine learning-driven feature selection identified the best predictive parameters. RESULTS: None of the four models classified the eGFR slope with an AUC greater than 0.6 or an F1 score surpassing 0.41 despite training on different data subsets. Following machine learning-driven feature selection and subsequent retraining on these selected features, random forest and extreme gradient boosting outperformed other models, achieving an AUC of 0.66 and an F1 score of 0.44. After feature selection, two predictive donor attributes consistently appeared in all models: smoking-related features and glomerulitis of the Banff Lesion Score. CONCLUSIONS: Training machine learning-models with distinct predefined data subsets yielded unsatisfactory results. However, the efficacy of random forest and extreme gradient boosting improved when trained exclusively with machine learning-driven selected features, suggesting that the quality, rather than the quantity, of features is crucial for machine learning-model performance. This study offers insights into the application of emerging machine learning-techniques for the screening of living kidney donors.

WormSwin: Instance segmentation of C. elegans using vision transformer.

The possibility to extract motion of a single organism from video recordings at a large-scale provides means for the quantitative study of its behavior, both individual and collective. This task is particularly difficult for organisms that interact with one another, overlap, and occlude parts of their bodies in the recording. Here we propose WormSwin-an approach to extract single animal postures of Caenorhabditis elegans (C. elegans) from recordings of many organisms in a single microscope well. Based on transformer neural network architecture our method segments individual worms across a range of videos and images generated in different labs. Our solutions offers accuracy of 0.990 average precision ([Formula: see text]) and comparable results on the benchmark image dataset BBBC010. Finally, it allows to segment challenging overlapping postures of mating worms with an accuracy sufficient to track the organisms with a simple tracking heuristic. An accurate and efficient method for C. elegans segmentation opens up new opportunities for studying of its behaviors previously inaccessible due to the difficulty in the worm extraction from the video frames.

LYN kinase programs stromal fibroblasts to facilitate leukemic survival via regulation of c-JUN and THBS1.

Microenvironmental bystander cells are essential for the progression of chronic lymphocytic leukemia (CLL). We have discovered previously that LYN kinase promotes the formation of a microenvironmental niche for CLL. Here we provide mechanistic evidence that LYN regulates the polarization of stromal fibroblasts to support leukemic progression. LYN is overexpressed in fibroblasts of lymph nodes of CLL patients. LYN-deficient stromal cells reduce CLL growth in vivo. LYN-deficient fibroblasts show markedly reduced leukemia feeding capacity in vitro. Multi-omics profiling reveals that LYN regulates the polarization of fibroblasts towards an inflammatory cancer-associated phenotype through modulation of cytokine secretion and extracellular matrix composition. Mechanistically, LYN deletion reduces inflammatory signaling including reduction of c-JUN expression, which in turn augments the expression of Thrombospondin-1, which binds to CD47 thereby impairing CLL viability. Together, our findings suggest that LYN is essential for rewiring fibroblasts towards a leukemia-supportive phenotype.

An expanded model of HIV cell entry phenotype based on multi-parameter single-cell data.

BACKGROUND: Entry of human immunodeficiency virus type 1 (HIV-1) into the host cell involves interactions between the viral envelope glycoproteins (Env) and the cellular receptor CD4 as well as a coreceptor molecule (most importantly CCR5 or CXCR4). Viral preference for a specific coreceptor (tropism) is in particular determined by the third variable loop (V3) of the Env glycoprotein gp120. The approval and use of a coreceptor antagonist for antiretroviral therapy make detailed understanding of tropism and its accurate prediction from patient derived virus isolates essential. The aim of the present study is the development of an extended description of the HIV entry phenotype reflecting its co-dependence on several key determinants as the basis for a more accurate prediction of HIV-1 entry phenotype from genotypic data. RESULTS: Here, we established a new protocol of quantitation and computational analysis of the dependence of HIV entry efficiency on receptor and coreceptor cell surface levels as well as viral V3 loop sequence and the presence of two prototypic coreceptor antagonists in varying concentrations. Based on data collected at the single-cell level, we constructed regression models of the HIV-1 entry phenotype integrating the measured determinants. We developed a multivariate phenotype descriptor, termed phenotype vector, which facilitates a more detailed characterization of HIV entry phenotypes than currently used binary tropism classifications. For some of the tested virus variants, the multivariant phenotype vector revealed substantial divergences from existing tropism predictions. We also developed methods for computational prediction of the entry phenotypes based on the V3 sequence and performed an extrapolating calculation of the effectiveness of this computational procedure. CONCLUSIONS: Our study of the HIV cell entry phenotype and the novel multivariate representation developed here contributes to a more detailed understanding of this phenotype and offers potential for future application in the effective administration of entry inhibitors in antiretroviral therapies.

Interleukin-4 receptor signaling modulates neuronal network activity.

Evidence is emerging that immune responses not only play a part in the central nervous system (CNS) in diseases but may also be relevant for healthy conditions. We discovered a major role for the interleukin-4 (IL-4)/IL-4 receptor alpha (IL-4Rα) signaling pathway in synaptic processes, as indicated by transcriptome analysis in IL-4Rα-deficient mice and human neurons with/without IL-4 treatment. Moreover, IL-4Rα is expressed presynaptically, and locally available IL-4 regulates synaptic transmission. We found reduced synaptic vesicle pools, altered postsynaptic currents, and a higher excitatory drive in cortical networks of IL-4Rα-deficient neurons. Acute effects of IL-4 treatment on postsynaptic currents in wild-type neurons were mediated via PKCγ signaling release and led to increased inhibitory activity supporting the findings in IL-4Rα-deficient neurons. In fact, the deficiency of IL-4Rα resulted in increased network activity in vivo, accompanied by altered exploration and anxiety-related learning behavior; general learning and memory was unchanged. In conclusion, neuronal IL-4Rα and its presynaptic prevalence appear relevant for maintaining homeostasis of CNS synaptic function.

Markerless tracking of an entire honey bee colony.

From cells in tissue, to bird flocks, to human crowds, living systems display a stunning variety of collective behaviors. Yet quantifying such phenomena first requires tracking a significant fraction of the group members in natural conditions, a substantial and ongoing challenge. We present a comprehensive, computational method for tracking an entire colony of the honey bee Apis mellifera using high-resolution video on a natural honeycomb background. We adapt a convolutional neural network (CNN) segmentation architecture to automatically identify bee and brood cell positions, body orientations and within-cell states. We achieve high accuracy (~10% body width error in position, ~10° error in orientation, and true positive rate > 90%) and demonstrate months-long monitoring of sociometric colony fluctuations. These fluctuations include ~24 h cycles in the counted detections, negative correlation between bee and brood, and nightly enhancement of bees inside comb cells. We combine detected positions with visual features of organism-centered images to track individuals over time and through challenging occluding events, recovering ~79% of bee trajectories from five observation hives over 5 min timespans. The trajectories reveal important individual behaviors, including waggle dances and crawling inside comb cells. Our results provide opportunities for the quantitative study of collective bee behavior and for advancing tracking techniques of crowded systems.

Positive selection of HIV host factors and the evolution of lentivirus genes.

BACKGROUND: Positive selection of host proteins that interact with pathogens can indicate factors relevant for infection and potentially be a measure of pathogen driven evolution. RESULTS: Our analysis of 1439 primate genes and 175 lentivirus genomes points to specific host factors of high genetic variability that could account for differences in susceptibility to disease and indicate specific mechanisms of host defense and pathogen adaptation. We find that the largest amount of genetic change occurs in genes coding for cellular membrane proteins of the host as well as in the viral envelope genes suggesting cell entry and immune evasion as the primary evolutionary interface between host and pathogen. We additionally detect the innate immune response as a gene functional group harboring large differences among primates that could potentially account for the different levels of immune activation in the HIV/SIV primate infection. We find a significant correlation between the evolutionary rates of interacting host and viral proteins pointing to processes of the host-pathogen biology that are relatively conserved among species and to those undergoing accelerated genetic evolution. CONCLUSIONS: These results indicate specific host factors and their functional groups experiencing pathogen driven evolutionary selection pressures. Individual host factors pointed to by our analysis might merit further study as potential targets of antiretroviral therapies.

A single amino acid substitution of the human immunodeficiency virus type 1 capsid protein affects viral sensitivity to TRIM5 alpha.

BACKGROUND: Human immunodeficiency virus type 1 (HIV-1) productively infects only humans and chimpanzees but not Old World monkeys, such as rhesus and cynomolgus (CM) monkeys. To establish a monkey model of HIV-1/AIDS, several HIV-1 derivatives have been constructed. We previously reported that efficient replication of HIV-1 in CM cells was achieved after we replaced the loop between alpha-helices 6 and 7 (L6/7) of the capsid protein (CA) with that of SIVmac239 in addition to the loop between alpha-helices 4 and 5 (L4/5) and vif. This virus (NL-4/5S6/7SvifS) was supposed to escape from host restriction factors cyclophilin A, CM TRIM5 alpha, and APOBEC3G. However, the replicative capability of NL-4/5S6/7SvifS in human cells was severely impaired. RESULTS: By long-term cultivation of human CEMss cells infected with NL-4/5S6/7SvifS, we succeeded in rescuing the impaired replicative capability of the virus in human cells. Sequence analysis of the CA region of the adapted virus revealed a G-to-E substitution at the 116 th position of the CA (G116E). Introduction of this substitution into the molecular DNA clone of NL-4/5S6/7SvifS indeed improved the virus' replicative capability in human cells. Although the G116E substitution occurred during long-term cultivation of human cells infected with NL-4/5S6/7SvifS, the viruses with G116E unexpectedly became resistant to CM, but not human TRIM5 alpha-mediated restriction. The 3-D model showed that position 116 is located in the 6th helix near L4/5 and L6/7 and is apparently exposed to the protein surface. The amino acid substitution at the 116 th position caused a change in the structure of the protein surface because of the replacement of G (which has no side chain) with E (which has a long negatively charged side chain). CONCLUSIONS: We succeeded in rescuing the impaired replicative capability of NL-4/5S6/7SvifS and report a mutation that improved the replicative capability of the virus. Unexpectedly, HIV-1 with this mutation became resistant to CM TRIM5 alpha-mediated restriction.

OrganoidTracker: Efficient cell tracking using machine learning and manual error correction.

Time-lapse microscopy is routinely used to follow cells within organoids, allowing direct study of division and differentiation patterns. There is an increasing interest in cell tracking in organoids, which makes it possible to study their growth and homeostasis at the single-cell level. As tracking these cells by hand is prohibitively time consuming, automation using a computer program is required. Unfortunately, organoids have a high cell density and fast cell movement, which makes automated cell tracking difficult. In this work, a semi-automated cell tracker has been developed. To detect the nuclei, we use a machine learning approach based on a convolutional neural network. To form cell trajectories, we link detections at different time points together using a min-cost flow solver. The tracker raises warnings for situations with likely errors. Rapid changes in nucleus volume and position are reported for manual review, as well as cases where nuclei divide, appear and disappear. When the warning system is adjusted such that virtually error-free lineage trees can be obtained, still less than 2% of all detected nuclei positions are marked for manual analysis. This provides an enormous speed boost over manual cell tracking, while still providing tracking data of the same quality as manual tracking.

Publisher Correction: Lipidome determinants of maximal lifespan in mammals.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.