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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.

DNA methylation (DNAm) is a key epigenetic mark that shows profound alterations in cancer. Read-level methylomes enable more in-depth analyses, due to their broad genomic coverage and preservation of rare cell-type signals, compared to summarized data such as 450K/EPIC microarrays. Here, we propose MethylBERT, a Transformer-based model for read-level methylation pattern classification. MethylBERT identifies tumour-derived sequence reads based on their methylation patterns and local genomic sequence, and estimates tumour cell fractions within bulk samples. In our evaluation, MethylBERT outperforms existing deconvolution methods and demonstrates high accuracy regardless of methylation pattern complexity, read length and read coverage. Moreover, we show its applicability to cell-type deconvolution as well as non-invasive early cancer diagnostics using liquid biopsy samples. MethylBERT represents a significant advancement in read-level methylome analysis and enables accurate tumour purity estimation. The broad applicability of MethylBERT will enhance studies on both tumour and non-cancerous bulk methylomes. Mapping DNA methylomes in single cells is challenging, and thus studies using bulk samples remain common. Here, authors develop a transformer-based method for methylation pattern analysis to enhance bulk methylome deconvolution and cancer detection.

This analysis describes the results of three Cell Tracking Challenge editions for examining the performance of cell segmentation and tracking algorithms and provides practical feedback for users and developers. We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge.

Plasmodium sporozoites are the crescent-shaped forms of malaria parasites injected from the salivary glands of mosquitoes into the skins of their vertebrate hosts. To proceed towards the liver of the host, sporozoites individually migrate at very high speeds and with relatively few adhesive interactions. By contrast, in the mosquito sporozoites often exist as collectives. Here we study their motion in collectives extracted from salivary glands, a situation in which dozens of sporozoites form rotating vortices. Complementing our experiments with quantitative image analysis and agent-based computer simulations, we find that, owing to their mechanical flexibility, single sporozoites are sorted according to their curvatures and speeds, and that these effects increase with vortex size. We also find that the vortices undergo oscillatory breathing because the thrust from the motility force of the single sporozoites can be stored in their elastic energy. Our findings suggest that the malaria parasite has evolved flexibility as an essential means to adapt to its mechanical environment and to ensure efficient transmission. In general, our work demonstrates how single-particle shape and mechanics can determine the dynamics of large, active collectives. The collective motion of malaria parasites is analyzed as a model system for active elastic matter and suggests that mechanical flexibility is favourable for parasite transmission.

The first community competition designed to objectively compare the performance of particle tracking algorithms provides valuable practical information for both users and developers. Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.

Although essential for many cellular processes, the sequence of structural and molecular events during clathrin-mediated endocytosis remains elusive. While it was long believed that clathrin-coated pits grow with a constant curvature, it was recently suggested that clathrin first assembles to form flat structures that then bend while maintaining a constant surface area. Here, we combine correlative electron and light microscopy and mathematical growth laws to study the ultrastructural rearrangements of the clathrin coat during endocytosis in BSC-1 mammalian cells. We confirm that clathrin coats initially grow flat and demonstrate that curvature begins when around 70% of the final clathrin content is acquired. We find that this transition is marked by a change in the clathrin to clathrin-adaptor protein AP2 ratio and that membrane tension suppresses this transition. Our results support the notion that BSC-1 mammalian cells dynamically regulate the flat-to-curved transition in clathrin-mediated endocytosis by both biochemical and mechanical factors. The sequence of structural and molecular events during clathrin-mediated endocytosis is unclear. Here the authors combine correlative microscopy and simple mathematical growth laws to demonstrate that the flat patch starts to curve when around 70% of the final clathrin content is reached.

Some bacteria, such as Bacillus subtilis , withstand starvation by forming dormant spores that revive when nutrients become available. Although sporulation and spore revival jointly determine survival in fluctuating environments, the relationship between them has been unclear. Here we show that these two processes are linked by a phenotypic “memory” that arises from a carry-over of molecules from the vegetative cell into the spore. By imaging life histories of individual B. subtilis cells using fluorescent reporters, we demonstrate that sporulation timing controls nutrient-induced spore revival. Alanine dehydrogenase contributes to spore memory and controls alanine-induced outgrowth, thereby coupling a spore’s revival capacity to the gene expression and growth history of its progenitors. A theoretical analysis, and experiments with signaling mutants exhibiting altered sporulation timing, support the hypothesis that such an intrinsically generated memory leads to a tradeoff between spore quantity and spore quality, which could drive the emergence of complex microbial traits. Bacillus subtilis withstands starvation by forming dormant spores that revive when nutrients become available. Here, Mutlu et al. show that sporulation timing controls spore revival through a phenotypic ‘memory’ that arises from the carry-over of a metabolic enzyme from the vegetative cell into the spore.

To understand the spatial organization as well as long- and short-range connections of the human brain at microscopic resolution, 3D reconstruction of histological sections is important. We approach this challenge by reconstructing series of unstained histological sections of multi-scale (1.3μm and 64μm) and multi-modal 3D polarized light imaging (3D-PLI) data. Since spatial coherence is lost during the sectioning procedure, image registration is the major step in 3D reconstruction. We propose a non-rigid registration method which comprises of a novel multi-modal similarity metric and an improved regularization scheme to cope with deformations inevitably introduced during the sectioning procedure, as well as a rigid registration approach using a robust similarity metric for improved initial alignment. We also introduce a multi-scale feature-based localization and registration approach for mapping of 1.3μm sections to 64μm sections and a scale-adaptive method that can handle challenging sections with large semi-global deformations due to tissue splits. We have applied our registration method to 126 consecutive sections of the temporal lobe of the human brain with 64μm and 1.3μm resolution. Each step of the registration method was quantitatively evaluated using 10 different sections and manually determined ground truth, and a quantitative comparison with previous methods was performed. Visual assessment of the reconstructed volumes and comparison with reference volumes confirmed the high quality of the registration result. •Robust and accurate non-rigid registration based on a physical deformation model.•3D reconstruction of high-resolution and ultra-high resolution 3D-PLI data.•Novel similarity metric using the canonical form of correlation transform and different regularizers.•Scale-adaptive non-rigid registration to cope with large semi-global deformations.•Feature-based localization and registration of ultra-high resolution 3D-PLI data.

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. Here, using an integrative experimental and computational approach, Imle et al. show how cell motility and density affect HIV cell-associated transmission in a three-dimensional tissue-like culture system of CD4 + T cells and collagen, and how different collagen matrices restrict infection by cell-free virions.

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.