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BACKGROUND: Kidney transplantation is the preferred treatment option for patients with end-stage renal disease. However, long-term graft survival remains a challenge. The enzyme indoleamine 2,3 dioxygenase (IDO) has been reported to have immunomodulatory effects with IDO transcripts being elevated in both antibody-mediated rejection and T cell-mediated rejection.METHODS: A metal-conjugated antibody panel for the staining of kidney biopsies was developed, allowing the visualization of 41 structural and immune markers on a single tissue slide to gain in-depth insight into the composition and localization of the immune cell compartment. Staining was applied to week 4 and 24 protocol biopsies of 49 patients as well as on 15 indication biopsies of the TRITON study and 4 additional transplantation biopsies with glomerulitis.RESULTS: A highly distinctive and specific glomerular IDO expression was observed in biopsies from 3 of 49 patients in imaging mass cytometry. Immunohistochemistry confirmed IDO expression in glomeruli of 10 of 10 cases with glomerulitis. IDO was found to be expressed by CD31 + glomerular endothelial cells, accompanied by the presence of granzyme-B +Tbet +CD7 +CD45RA + natural killer cells and CD68 + macrophages. Furthermore, a proportion of both the immune cells and endothelial cells expressed Ki-67, indicative of cell proliferation, which was not observed in control glomeruli. CONCLUSIONS: Our results show glomerular IDO expression in transplanted kidneys with glomerulitis, which is accompanied by increased numbers of natural killer cells and macrophages and likely reflects local immune activation.

Exploration and analysis of high-dimensional data are important tasks in many fields that produce large and complex data, like the financial sector, systems biology, or cultural heritage. Tailor-made visual analytics software is developed for each specific application, limiting their applicability in other fields. However, as diverse as these fields are, their characteristics and requirements for data analysis are conceptually similar. Many applications share abstract tasks and data types and are often constructed with similar building blocks. Developing such applications, even when based mostly on existing building blocks, requires significant engineering efforts. We developed ManiVault, a flexible and extensible open-source visual analytics framework for analyzing high-dimensional data. The primary objective of ManiVault is to facilitate rapid prototyping of visual analytics workflows for visualization software developers and practitioners alike. ManiVault is built using a plugin-based architecture that offers easy extensibility. While our architecture deliberately keeps plugins self-contained, to guarantee maximum flexibility and re-usability, we have designed and implemented a messaging API for tight integration and linking of modules to support common visual analytics design patterns. We provide several visualization and analytics plugins, and ManiVault's API makes the integration of new plugins easy for developers. ManiVault facilitates the distribution of visualization and analysis pipelines and results for practitioners through saving and reproducing complete application states. As such, ManiVault can be used as a communication tool among researchers to discuss workflows and results. A copy of this paper and all supplemental material is available at osf.io/9k6jw, and source code at github.com/ManiVaultStudio., Computer Graphics and Visualisation, Pattern Recognition and Bioinformatics

With the rapid advances in single-cell sequencing technologies, novel types of studies into the cell-type makeup of the brain have become possible. Biologists often analyze large and complex single-cell transcriptomic datasets to enhance knowledge of the intricate features of cellular and molecular tissue organization. A particular area of interest is the study of whether cell types and their gene regulation are conserved across species during evolution. However, in-depth comparisons across species of such high-dimensional, multi-modal single-cell data pose considerable visualization challenges. This paper introduces Cytosplore Simian Viewer, a visualization system that combines various views and linked interaction methods for comparative analysis of single-cell transcriptomic datasets across multiple species. Cytosplore Simian Viewer enables biologists to help gain insights into the cell type and gene expression differences and similarities among different species, particularly focusing on comparing human data to other species. The system validation in discovery research on real-world datasets demonstrates its utility in visualizing valuable results related to the evolutionary development of the middle temporal gyrus., Computer Graphics and Visualisation

AbstractThe primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch–seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.

The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch–seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations., Author Correction: Comparative cellular analysis of motor cortex in human, marmoset and mouse Download PDF Author Correction Open Access Published: 22 March 2022 Author Correction: Comparative cellular analysis of motor cortex in human, marmoset and mouse Trygve E. Bakken, Nikolas L. Jorstad, …Ed S. Lein Nature (2022)Cite this article 399 Accesses 1 Altmetric Metrics details The Original Article was published on 06 October 2021 Correction to: Nature https://doi-org.tudelft.idm.oclc.org/10.1038/s41586-021-03465-8 Published online 6 October 2021 In the version of this article initially published, the Acknowledgements section was incomplete and has now been amended to include the following: “NIH BRAIN Initiative awards U01 MH121282 to J.R.E and M.M.B, U19 MH114831 to J.R.E. and E.M.C., U19 MH114830 to H.Z., U01 MH114819 to G.F., 1U01MH114828 to K.Z. and J.C., RF1MH123220 to M.H. and R.H.S., and U19 MH114821. NIH awards R01DC019370 to R.H., R24MH114815 to R.H. and O.R.W., and R24 MH114788 to O.R.W. Nancy and Buster Alvord Endowment to C.D.K.” The changes have been made to the HTML and PDF versions of the article, Comp Graphics & Visualisation, Pattern Recognition and Bioinformatics

The human fetal immune system must protect the infant against the sudden exposure to a large variety of pathogens upon birth. While it is known that the fetal immune system develops in sequential waves, relatively little is known about the composition of the innate and adaptive immune system in the tissues. Here, we applied high-dimensional mass cytometry to profile the immune system in human fetal liver, spleen, and intestine. With Hierarchical Stochastic Neighbor Embedding (HSNE) we distinguished 177 distinct immune cell clusters, including both previously identified and novel cell clusters. PCA analysis indicated substantial differences between the compositions of the immune system in the different organs. Through dual t-SNE we identified tissue-specific cell clusters, which were found both in the innate and adaptive compartment. To determine the spatial location of tissue-specific subsets we developed a 31-antibody panel to reveal both the immune compartment and surrounding stromal elements through analysis of snap-frozen tissue samples with imaging mass cytometry. Imaging mass cytometry reconstructed the tissue architecture and allowed both the characterization and determination of the location of the various immune cell clusters within the tissue context. Moreover, it further underpinned the distinctness of the immune system in the tissues. Thus, our results provide evidence for early compartmentalization of the adaptive and innate immune compartment in fetal spleen, liver, and intestine. Together, our data provide a unique and comprehensive overview of the composition and organization of the human fetal immune system in several tissues., Pattern Recognition and Bioinformatics

Purpose: Intravascular optical coherence tomography (OCT) is widely used for analysis of the coronary artery disease. Its high spatial resolution allows for visualization of arterial tissue components in detail. There are different OCT systems on the market, each of which produces data characterized by its own intensity range and distribution. These differences should be taken into account for the development of image processing algorithms. In order to overcome this difference in the intensity range and distribution, we developed a framework for matching intensities based on the exact histogram matching technique. Methods: In our method, the key step for using the exact histogram matching is to determine the target histogram. For this, we proposed two schemes: a global scheme that uses a single histogram as the target histogram for all the pullbacks, and a local scheme that selects for each single image a target histogram from a predefined database. These two schemes are compared on a unique dataset containing pairs of pullbacks that were acquired shortly after each other with systems from two vendors, St. Jude and Terumo. Pullbacks were aligned according to anatomical landmarks, and a database of matched histogram pairs was created. A leave-one-out cross validation was used to compare performance of the two schemes. The matching accuracy was evaluated by comparing: (a) histograms using Euclidean (dx2) and Kolmogorov–Smirnov (dKS) distances, and (b) median intensity level within anatomical regions of interest. Results: Leave-one-out validation indicated that both matching schemes yield comparably high accuracies across the entire validation dataset. The local scheme outperforms the global scheme with marginally lower dissimilarities at both histogram level and intensity level. High visual similarity was observed when comparing the matched images to their aligned counterparts. Conclusion: Both local and global schemes are robust and produce a, Pattern Recognition and Bioinformatics