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Multiplexed imaging enables the simultaneous spatial profiling of dozens of biological molecules in tissues at single-cell resolution. Extracting biologically relevant information, such as the spatial distribution of cell phenotypes from multiplexed tissue imaging data, involves a number of computational tasks, including image segmentation, feature extraction and spatially resolved single-cell analysis. Here, we present an end-to-end workflow for multiplexed tissue image processing and analysis that integrates previously developed computational tools to enable these tasks in a user-friendly and customizable fashion. For data quality assessment, we highlight the utility of napari-imc for interactively inspecting raw imaging data and the cytomapper R/Bioconductor package for image visualization in R. Raw data preprocessing, image segmentation and feature extraction are performed using the steinbock toolkit. We showcase two alternative approaches for segmenting cells on the basis of supervised pixel classification and pretrained deep learning models. The extracted single-cell data are then read, processed and analyzed in R. The protocol describes the use of community-established data containers, facilitating the application of R/Bioconductor packages for dimensionality reduction, single-cell visualization and phenotyping. We provide instructions for performing spatially resolved single-cell analysis, including community analysis, cellular neighborhood detection and cell–cell interaction testing using the imcRtools R/Bioconductor package. The workflow has been previously applied to imaging mass cytometry data, but can be easily adapted to other highly multiplexed imaging technologies. This protocol can be implemented by researchers with basic bioinformatics training, and the analysis of the provided dataset can be completed within 5–6 h. An extended version is available at https://bodenmillergroup.github.io/IMCDataAnalysis/ . Key points The protocol describes the analysis of data generated by highly multiplexed tissue imaging approaches, such as imaging mass cytometry. The presented workflow includes steps for imaging data visualization, data preprocessing, image segmentation, single-cell feature extraction, reading data into R, spillover correction, quality control, cell phenotyping and spatially resolved single-cell analysis. The software packages used include napari, steinbock, DeepCell/Mesmer, Ilastik, CellProfiler, cytomapper and imcRtools. An integrated workflow for multiplexed tissue image processing and analysis, including interactive inspection of raw data, cell segmentation, feature extraction, single-cell analysis and spatial analysis.

The histology topography cytometry analysis toolbox (histoCAT) enables quantitative analysis and exploration of highly multiplexed imaging data for better understanding of individual cells in the context of tissue architecture. Single-cell, spatially resolved omics analysis of tissues is poised to transform biomedical research and clinical practice. We have developed an open-source, computational histology topography cytometry analysis toolbox (histoCAT) to enable interactive, quantitative, and comprehensive exploration of individual cell phenotypes, cell–cell interactions, microenvironments, and morphological structures within intact tissues. We highlight the unique abilities of histoCAT through analysis of highly multiplexed mass cytometry images of human breast cancer tissues.

Phagocytosis is a process in which target cells or particles are engulfed and taken up by other cells, typically professional phagocytes; this process is crucial in many physiological processes and disease states. The detection of targets for phagocytosis is directed by a complex repertoire of cell surface receptors. Pattern recognition receptors directly detect targets for binding and uptake, while opsonic and complement receptors detect objects coated by soluble factors. However, the importance of single and combinatorial surface marker expression across different phenotypes of professional phagocytes is not known. Here we developed a novel mass cytometry-based phagocytosis assay that enables the simultaneous detection of phagocytic events in combination with up to 40 other protein markers. We applied this assay to distinct monocyte derived macrophage (MDM) populations and found that prototypic M2-like MDMs phagocytose more E. coli than M1-like MDMs. Surface markers such as CD14, CD206, and CD163 rendered macrophages phagocytosis competent, but only CD209 directly correlated with the amount of particle uptake. Similarly, M2-like MDMs also phagocytosed more cancer cells than M1-like MDMs but, unlike M1-like MDMs, were insensitive to anti-CD47 opsonization. Our approach facilitates the simultaneous study of single-cell phenotypes, phagocytic activity, signaling and transcriptional events in complex cell mixtures.

Pedestrian detection based on deep learning methods have reached great success in the past few years with several possible real-world applications including autonomous driving, robotic navigation, and video surveillance. In this work, a new neural network two-stage pedestrian detector with a new custom classification head, adding the triplet loss function to the standard bounding box regression and classification losses, is presented. This aims to improve the domain generalization capabilities of existing pedestrian detectors, by explicitly maximizing inter-class distance and minimizing intra-class distance. Triplet loss is applied to the features generated by the region proposal network, aimed at clustering together pedestrian samples in the features space. We used Faster R-CNN and Cascade R-CNN with the HRNet backbone pre-trained on ImageNet, changing the standard classification head for Faster R-CNN, and changing one of the three heads for Cascade R-CNN. The best results were obtained using a progressive training pipeline, starting from a dataset that is further away from the target domain, and progressively fine-tuning on datasets closer to the target domain. We obtained state-of-the-art results, MR−2 of 9.9, 11.0, and 36.2 for the reasonable, small, and heavy subsets on the CityPersons benchmark with outstanding performance on the heavy subset, the most difficult one.

Ludwigsburg Palace, built in 1704-1733 by Duke Eberhard Ludwig of Württemberg, is one of the great baroque residences of Germany. While a lot of palaces were destroyed during World War II, Ludwigsburg was hardly damaged; therefore, its original surfaces are still to be seen. In the eighteenth century, craftsmen used the walls in the shell to leave graffiti, they made jokes about other people, but they also used the walls instead of paper for drawings or calculations. These graffiti are a historical source which illustrates the building of the palace. When the palace was finished, most of these graffiti remained hidden behind paint and tapestries; but people still left their traces: inhabitants, staff, guards, visitors, tourists, travellers, lovers have left their mark on the palace walls, doors or windows. Names, figures, sayings, drawings and cartoons can be found ranging from incised monograms to hooks, over a period from 1704 until today. In addition to this historical source, under the wood floor panels, legacies of the inhabitants like letters, bills, clothing, shoes, ceramics and utensils were found. Thus, the walls and floors of the building become a living history book, a huge stone calendar, which has lasted up to the present and is still ongoing.

As Brexit removes the Nordic countries’ most powerful ally from the EU, what does this imply for their approach to European affairs? The literature on small states within the EU suggests that they can counterbalance limited bargaining capacities by entering two types of alliances: strategic partnerships with bigger member states and institutionalised cooperation on a regional basis. Against this backdrop we ask whether, by significantly raising the costs of non-cooperation for Nordic governments, the Brexit referendum has triggered a revival of Nordic political cooperation. We scrutinise this conjecture by analysing Nordic strategies of coalition-building on EU financial and budgetary policy, specifically looking at attempts to reform Europe’s Economic and Monetary Union and proposals to strengthen the EU’s fiscal powers. We find that Nordic governments have successfully collaborated on these issues in the context of new alliances such as the ‘New Hanseatic League’ or the ‘Frugal Four.’ Yet, their coalition-building strategies rely on relatively loose and issue-specific alliances rather than an institutionalisation of Nordic political cooperation, implying that this revival of Nordic political cooperation hardly involves the institutions of ‘official’ Nordic cooperation. We argue that this reflects lasting differences among the Nordics’ approach to the EU as well as electorates’ scepticism about supranational institution-building, implying that ‘reluctant Europeans’ are often also ‘reluctant Scandinavians.’

To obtain rates of mRNA synthesis and decay in yeast, we established dynamic transcriptome analysis (DTA). DTA combines non‐perturbing metabolic RNA labeling with dynamic kinetic modeling. DTA reveals that most mRNA synthesis rates are around several transcripts per cell and cell cycle, and most mRNA half‐lives range around a median of 11 min. DTA can monitor the cellular response to osmotic stress with higher sensitivity and temporal resolution than standard transcriptomics. In contrast to monotonically increasing total mRNA levels, DTA reveals three phases of the stress response. During the initial shock phase, mRNA synthesis and decay rates decrease globally, resulting in mRNA storage. During the subsequent induction phase, both rates increase for a subset of genes, resulting in production and rapid removal of stress‐responsive mRNAs. During the recovery phase, decay rates are largely restored, whereas synthesis rates remain altered, apparently enabling growth at high salt concentration. Stress‐induced changes in mRNA synthesis rates are predicted from gene occupancy with RNA polymerase II. DTA‐derived mRNA synthesis rates identified 16 stress‐specific pairs/triples of cooperative transcription factors, of which seven were known. Thus, DTA realistically monitors the dynamics in mRNA metabolism that underlie gene regulatory systems. Synopsis Nascent transcriptome analysis reveals dynamics of mRNA synthesis and decay in yeast. The first step in the expression of the genome is the synthesis of messenger‐RNA (mRNA). In all cells, the regulation of mRNA levels in response to changing environmental conditions is a fundamental process. Classical methods to study such changes in mRNA levels, however, fail to unravel whether such changes are due to changes in mRNA synthesis (transcription) or changes in mRNA decay, which both contribute to setting mRNA levels. Therefore, the regulation of mRNA stability and turnover is poorly understood, and new methods for a quantitative analysis of mRNA synthesis and decay are urgenlty sought. In this study, we describe a novel method termed dynamic transcriptome analysis (DTA), which can be used to determine synthesis and decay rates of mRNAs on a genome‐wide level in yeast and other eukaryotic cells. We applied DTA to the model organism Saccharomyces cerevisiae and analyzed the dynamics of the transcriptome under standard growth conditions as well as under osmotic stress conditions. DTA relies on a combination of biochemistry, high‐throughput data acquisition, and computational biology. It uses metabolic labeling of newly synthesised RNA with the nucleoside analogon 4‐thiouridine (4sU), purification of labeled, newly synthesized RNA, and subsequent microarray hybridization. An improved mathematical model enables synthesis and decay rates of esentially all mRNAs in the cell to be determined with accuracy. In this study, we found that under normal growth conditions the synthesis rates for most mRNAs are low and that the decay rates are not correlated with synthesis. Addition of salt to the culture, however, induced three phases of changes in mRNA synthesis and decay. During the initial shock phase, there is a global repression of synthesis and a reduction of decay of most mRNAs. The subsequent induction phase involves strongly increased synthesis of stress mRNAs, which are also destabilized. Finally, the recovery phase restores decay rates, but leaves synthesis rates altered, apparently to allow for cellular growth under the new conditions. DTA shows a higher sensitivity and better temporal resolution than classical methods such as transcriptomics. Also, DTA is non‐perturbing and allows for an unbiased monitoring of genomic regulatory systems in living cells. Previously used methods are invasive and likely alter cellular physiology and thereby mRNA dynamics. DTA has a high potential to become a standard technique in molecular biology that may replace standard transcriptomics to study gene regulatory systems. In the future, DTA may be used to study dynamic changes in cellular mRNA metabolism induced by chemical inhibitors or defined mutations or changes in the environment. Rates of mRNA synthesis and decay can be measured on a genome‐wide scale in yeast by dynamic transcriptome analysis (DTA), which combines non‐perturbing metabolic RNA labeling with dynamic kinetic modeling. DTA reveals that most mRNA synthesis rates are around several transcripts per cell and cell cycle, and most mRNA half‐lives range around a median of 11 min. DTA realistically monitors the cellular response to osmotic stress with higher sensitivity and temporal resolution than transcriptomics, and can be used to follow changes in RNA metabolism in gene regulatory systems.

Next Generation EU, specifically its Recovery and Resilience Facility (RRF), has been a groundbreaking new experiment for the EU. From the speed of the reaction at the EU level with an agreement between leaders a few weeks after the COVID-19 crisis erupted, the size of the instrument (being the largest EU fund ever created), to the RRF's design features (including the performance nature of the instrument, its leverage on reforms, and its method of financing), it is a fundamentally novel EU instrument. Aimed at both recovery and resilience, it first led to a firm common response to a simultaneous economic downturn across the EU, ensuring rapid macroeconomic stabilization and preservation of public investment levels, in contrast with previous crises. It has also planted the seeds of a significant increase in the resilience of the EU economy by fostering the implementation of major structural reforms in line with the common priorities of the EU. Lessons about absorption capacity, incentives, flexibility, and governance will all advance future program design in the EU and beyond.

At present, it is not clear how memory B lymphocytes are maintained over time, and whether only as circulating cells or also residing in particular tissues. Here we describe distinct populations of isotype-switched memory B lymphocytes (Bsm) of murine spleen and bone marrow, identified according to individual transcriptional signature and B cell receptor repertoire. A population of marginal zone-like cells is located exclusively in the spleen, while a population of quiescent Bsm is found only in the bone marrow. Three further resident populations, present in spleen and bone marrow, represent transitional and follicular B cells and B1 cells, respectively. A population representing 10-20% of spleen and bone marrow memory B cells is the only one qualifying as circulating. In the bone marrow, all cells individually dock onto VCAM1 + stromal cells and, reminiscent of resident memory T and plasma cells, are void of activation, proliferation and mobility. Memory B cells are important for protecting the host from pathogen rechallenge, but their properties and locations remain ill-defined. Here the authors show, using single-cell transcriptomics and repertoire analyses, that mouse spleen and bone marrow host distinct populations of isotype-switched memory B cells to potentially optimize for rapid recall responses.

Acute and chronic dermatological injuries need rapid tissue repair due to the susceptibility to infections. To effectively promote cutaneous wound recovery, it is essential to develop safe, low-cost, and affordable regenerative tools. Therefore, we aimed to identify the biological mechanisms involved in the wound healing properties of the glycosaminoglycan dermatan sulfate (DS), obtained from ascidian Styela plicata, a marine invertebrate, which in preliminary work from our group showed no toxicity and promoted a remarkable fibroblast proliferation and migration. In this study, 2,4-DS (50 µg/mL)-treated and control groups had the relative gene expression of 84 genes participating in the healing pathway evaluated. The results showed that 57% of the genes were overexpressed during treatment, 16% were underexpressed, and 9.52% were not detected. In silico analysis of metabolic interactions exhibited overexpression of genes related to: extracellular matrix organization, hemostasis, secretion of inflammatory mediators, and regulation of insulin-like growth factor transport and uptake. Furthermore, in C57BL/6 mice subjected to experimental wounds treated with 0.25% 2,4-DS, the histological parameters demonstrated a great capacity for vascular recovery. Additionally, this study confirmed that DS is a potent inducer of wound-healing cellular pathways and a promoter of neovascularization, being a natural ally in the tissue regeneration strategy.