RNA Velocity allows the inference of cellular differentiation trajectories from single-cell RNA sequencing (scRNA-seq) data. It would be highly interesting to study these differentiation dynamics in the spatial context of tissues. Estimating spatial RNA velocities is, however, limited by the inability to spatially capture spliced and unspliced mRNA molecules in high-resolution spatial transcriptomics. We present SIRV, a method to spatially infer RNA velocities at the single-cell resolution by enriching spatial transcriptomics data with the expression of spliced and unspliced mRNA from reference scRNA-seq data. We used SIRV to infer spatial differentiation trajectories in the developing mouse brain, including the differentiation of midbrain-hindbrain boundary cells and marking the forebrain origin of the cortical hem and diencephalon cells. Our results show that SIRV reveals spatial differentiation patterns not identifiable with scRNA-seq data alone. Additionally, we applied SIRV to mouse organogenesis data and obtained robust spatial differentiation trajectories. Finally, we verified the spatial RNA velocities obtained by SIRV using 10x Visium data of the developing chicken heart and MERFISH data from human osteosarcoma cells. Altogether, SIRV allows the inference of spatial RNA velocities at the single-cell resolution to facilitate studying tissue development.

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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.@en

In this paper we propose a supervised method to predict registration misalignment using convolutional neural networks (CNNs). This task is casted to a classification problem with multiple classes of misalignment: 'correct' 0-3 mm, 'poor' 3-6 mm and 'wrong' over 6 mm. Rather than a direct prediction, we propose a hierarchical approach, where the prediction is gradually refined from coarse to fine. Our solution is based on a convolutional Long Short-Term Memory (LSTM), using hierarchical misalignment predictions on three resolutions of the image pair, leveraging the intrinsic strengths of an LSTM for this problem. The convolutional LSTM is trained on a set of artificially generated image pairs obtained from artificial displacement vector fields (DVFs). Results on chest CT scans show that incorporating multi-resolution information, and the hierarchical use via an LSTM for this, leads to overall better F1 scores, with fewer misclassifications in a well-tuned registration setup. The final system yields an accuracy of 87.1%, and an average F1 score of 66.4% aggregated in two independent chest CT scan studies.

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Tissue functionality is determined by the characteristics of tissue-resident cells and their interactions within their microenvironment. Imaging Mass Cytometry offers the opportunity to distinguish cell types with high precision and link them to their spatial location in intact tissues at sub-cellular resolution. This technology produces large amounts of spatially-resolved high-dimensional data, which constitutes a serious challenge for the data analysis. We present an interactive visual analysis workflow for the end-to-end analysis of Imaging Mass Cytometry data that was developed in close collaboration with domain expert partners. We implemented the presented workflow in an interactive visual analysis tool; ImaCytE. Our workflow is designed to allow the user to discriminate cell types according to their protein expression profiles and analyze their cellular microenvironments, aiding in the formulation or verification of hypotheses on tissue architecture and function. Finally, we show the effectiveness of our workflow and ImaCytE through a case study performed by a collaborating specialist.@en

MOTIVATION: Single cell data measures multiple cellular markers at the single-cell level for thousands to millions of cells. Identification of distinct cell populations is a key step for further biological understanding, usually performed by clustering this data. Dimensionality reduction based clustering tools are either not scalable to large datasets containing millions of cells, or not fully automated requiring an initial manual estimation of the number of clusters. Graph clustering tools provide automated and reliable clustering for single cell data, but suffer heavily from scalability to large datasets. RESULTS: We developed SCHNEL, a scalable, reliable and automated clustering tool for high-dimensional single-cell data. SCHNEL transforms large high-dimensional data to a hierarchy of datasets containing subsets of data points following the original data manifold. The novel approach of SCHNEL combines this hierarchical representation of the data with graph clustering, making graph clustering scalable to millions of cells. Using seven different cytometry datasets, SCHNEL outperformed three popular clustering tools for cytometry data, and was able to produce meaningful clustering results for datasets of 3.5 and 17.2 million cells within workable time frames. In addition, we show that SCHNEL is a general clustering tool by applying it to single-cell RNA sequencing data, as well as a popular machine learning benchmark dataset MNIST. AVAILABILITY AND IMPLEMENTATION: Implementation is available on GitHub (https://github.com/biovault/SCHNELpy). All datasets used in this study are publicly available. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Background: Aortic pulse wave velocity (PWV) is an indicator of aortic stiffness and is used as a predictor of adverse cardiovascular events. PWV can be non-invasively assessed using magnetic resonance imaging (MRI). PWV computation requires two components, the length of the aortic arch and the time taken for the systolic pressure wave to travel through the aortic arch. The aortic length is calculated using a multi-slice 3D scan and the transit time is computed using a 2D velocity encoded MRI (VE) scan. In this study we present and evaluate an automatic method to quantify the aortic pulse wave velocity using a large population-based cohort. Methods: For this study 212 subjects were retrospectively selected from a large multi-center heart-brain connection cohort. For each subject a multi-slice 3D scan of the aorta was acquired in an oblique-sagittal plane and a 2D VE scan acquired in a transverse plane cutting through the proximal ascending and descending aorta. PWV was calculated in three stages: (i) a multi-atlas-based segmentation method was developed to segment the aortic arch from the multi-slice 3D scan and subsequently estimate the length of the proximal aorta, (ii) an algorithm that delineates the proximal ascending and descending aorta from the time-resolved 2D VE scan and subsequently obtains the velocity-time flow curves was also developed, and (iii) automatic methods that can compute the transit time from the velocity-time flow curves were implemented and investigated. Finally the PWV was obtained by combining the aortic length and the transit time. Results: Quantitative evaluation with respect to the length of the aortic arch as well as the computed PWV were performend by comparing the results of the novel automatic method to those obtained manually. The mean absolute difference in aortic length obtained automatically as compared to those obtained manually was 3.3 ± 2.8 mm (p < 0.05), the manual inter-observer variability on a subset of 45 scans was 3.4 ± 3.4 mm (p = 0.49). Bland-Altman analysis between the automataic method and the manual methods showed a bias of 0.0 (-5.0,5.0) m/s for the foot-to-foot approach, -0.1 (-1.2, 1.1) and -0.2 (-2.6, 2.1) m/s for the half-max and the cross-correlation methods, respectively. Conclusion: We proposed and evaluated a fully automatic method to calculate the PWV on a large set of multi-center MRI scans. It was observed that the overall results obtained had very good agreement with manual analysis. Our proposed automatic method would be very beneficial for large population based studies, where manual analysis requires a lot of manpower.@en

Recent advances in single-cell acquisition technology have led to a shift towards single-cell analysis in many fields of biology. In immunology, detailed knowledge of the cellular composition is of interest, as it can be the cause of deregulated immune responses, which cause diseases. Similarly, vaccination is based on triggering proper immune responses; however, many vaccines are ineffective or only work properly in a subset of those who are vaccinated. Identifying differences in the cellular composition of the immune system in such cases can lead to more precise treatment. Cytosplore is an integrated, interactive visual analysis framework for the exploration of large single-cell datasets. We have developed Cytosplore in close collaboration with immunology researchers and several partners use the software in their daily workflow. Cytosplore enables efficient data analysis and has led to several discoveries alongside high-impact publications.@en

Motivation: High-dimensional mass cytometry (CyTOF) allows the simultaneous measurement of multiple cellular markers at single-cell level, providing a comprehensive view of cell compositions.
However, the power of CyTOF to explore the full heterogeneity of a biological sample at the singlecell level is currently limited by the number of markers measured simultaneously on a single panel.
Results: To extend the number of markers per cell, we propose an in silico method to integrate CyTOF datasets measured using multiple panels that share a set of markers. Additionally, we present an approach to select the most informative markers from an existing CyTOF dataset to be used as a shared marker set between panels. We demonstrate the feasibility of our methods by
evaluating the quality of clustering and neighborhood preservation of the integrated dataset, on two public CyTOF datasets. We illustrate that by computationally extending the number of markers
we can further untangle the heterogeneity of mass cytometry data, including rare cell-population detection.@en

In Magn Reson Med. 2018; 79:1127-1134, the acquisition matrix of the coronal T1-weighted sequence acquired prior to contrast injection was mistyped. The correct acquisition matrix is 388 × 288 instead of: 388 × 88. We apologize for any inconvenience this may have caused to the readers.@en

Deep neural networks are now rivaling human accuracy in several pattern recognition problems. Compared to traditional classifiers, where features are handcrafted, neural networks learn increasingly complex features directly from the data. Instead of handcrafting the features, it is now the network architecture that is manually engineered. The network architecture parameters such as the number of layers or the number of filters per layer and their interconnections are essential for good performance. Even though basic design guidelines exist, designing a neural network is an iterative trial-and-error process that takes days or even weeks to perform due to the large datasets used for training. In this paper, we present DeepEyes, a Progressive Visual Analytics system that supports the design of neural networks during training. We present novel visualizations, supporting the identification of layers that learned a stable set of patterns and, therefore, are of interest for a detailed analysis. The system facilitates the identification of problems, such as superfluous filters or layers, and information that is not being captured by the network. We demonstrate the effectiveness of our system through multiple use cases, showing how a trained network can be compressed, reshaped and adapted to different problems.

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Purpose: To investigate the feasibility of automatic quantification of bone marrow edema (BME) on MRI of the wrist in patients with early arthritis.
Methods: For 485 early arthritis patients (clinically confirmed arthritis of one or more joints, symptoms for less than 2 years), MR scans of the wrist were processed in three automatic stages. First, super-resolution reconstruction was applied to fuse coronal and axial scans into a single high-resolution 3D image. Next, the carpal bones were located and delineated using atlas-based segmentation. Finally, the extent of BME within each bone was quantified by identifying image intensity values characteristic of BME by fuzzy clustering and measuring the fraction of voxels with these characteristic intensities within each bone. Correlation with visual BME scores was assessed through Pearson correlation coefficient.
Results: Pearson correlation between quantitative and visual BME scores across 485 patients was r=0.83, P<0.001. Conclusions: Quantitative measurement of BME on MRI of the wrist has the potential to provide a feasible alternative to visual scoring. Complete automation requires automatic detection and compensation of acquisition artifacts.

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In recent years, machine learning approaches have been successfully applied to the field of neuroimaging for classification and regression tasks. However, many approaches do not give an intuitive relation between the raw features and the diagnosis. Therefore, they are difficult for clinicians to interpret. Moreover, most approaches treat the features extracted from the brain (for example, voxelwise gray matter concentration maps from brain MRI) as independent variables and ignore their spatial and anatomical relations. In this paper, we present a new Support Vector Machine (SVM)-based learning method for the classification of Alzheimer's disease (AD), which integrates spatial-anatomical information. In this way, spatial-neighbor features in the same anatomical region are encouraged to have similar weights in the SVM model. Secondly, we introduce a group lasso penalty to induce structure sparsity, which may help clinicians to assess the key regions involved in the disease. For solving this learning problem, we use an accelerated proximal gradient descent approach. We tested our method on the subset of ADNI data selected by Cuingnet et al. (2011) for Alzheimer's disease classification, as well as on an independent larger dataset from ADNI. Good classification performance is obtained for distinguishing cognitive normals (CN) vs. AD, as well as on distinguishing between various sub-types (e.g. CN vs. Mild Cognitive Impairment). The model trained on Cuignet's dataset for AD vs. CN classification was directly used without re-training to the independent larger dataset. Good performance was achieved, demonstrating the generalizability of the proposed methods. For all experiments, the classification results are comparable or better than the state-of-the-art, while the weight map more clearly indicates the key regions related to Alzheimer's disease.

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Single-cell analysis through mass cytometry has become an increasingly important tool for immunologists to study the immune system in health and disease. Mass cytometry creates a high-dimensional description vector for single cells by time-of-flight measurement. Recently, t-Distributed Stochastic Neighborhood Embedding (t-SNE) has emerged as one of the state-of-the-art techniques for the visualization and exploration of single-cell data. Ever increasing amounts of data lead to the adoption of Hierarchical Stochastic Neighborhood Embedding (HSNE), enabling the hierarchical representation of the data. Here, the hierarchy is explored selectively by the analyst, who can request more and more detail in areas of interest. Such hierarchies are usually explored by visualizing disconnected plots of selections in different levels of the hierarchy. This poses problems for navigation, by imposing a high cognitive load on the analyst. In this work, we present an interactive summary-visualization to tackle this problem. CyteGuide guides the analyst through the exploration of hierarchically represented single-cell data, and provides a complete overview of the current state of the analysis. We conducted a two-phase user study with domain experts that use HSNE for data exploration. We first studied their problems with their current workflow using HSNE and the requirements to ease this workflow in a field study. These requirements have been the basis for our visual design. In the second phase, we verified our proposed solution in a user evaluation.@en

Background: Evaluating inflammatory changes over time on MR images of the spine in patients with suspected axial Spondyloarthritis (axSpA) can be a labor-intensive task, requiring readers to manually search for and perceptually align a set of vertebrae between two scans. The purpose of this study was to assess the feasibility of computer-aided (CA) evaluation of such inflammatory changes in a framework where scans from two time points are fused into a single color-encoded image integrated into an interactive scoring tool. Methods: For 30 patients from the SPondyloArthritis Caught Early (SPACE) cohort (back pain ≥ 3 months, ≤ 2 years, onset < 45 years), baseline and follow-up MR scans acquired 9-12 months apart were fused into a single color-encoded image through locally-rigid image registration to evaluate inflammatory changes in 23 vertebral units (VUs). Scoring was performed by two expert readers on a (-2, 2) scale using an interactive scoring tool. For comparison of direction of change (increase/decrease) indicated by an existing reference, Berlin method scores ((-3, 3) scale) of the same MR scans from a different ongoing study were used. The distributions of VU-level differences between CA readers and between the CA and Berlin methods (sign of change scores) across patients were analyzed descriptively. Patient-level agreement between CA readers was assessed by intraclass correlation coefficient (ICC). Results: Five patients were excluded from evaluation due to failed vertebrae segmentation. Patient-level inter-reader agreement ICC was 0.56 (95% CI: 0.22 to 0.78). Mean VU-level inter-reader differences across 25 patients ranged (-0.04, 0.12) with SD range (0, 0.45). Across all VUs, inter-reader differences ranged (-1, 1) in 573/575 VUs (99.7%). Mean VU-level inter-method differences across patients ranged (-0.04, 0.08) with SD range (0, 0.61). Across all VUs, inter-method differences ranged (-1, 1) in 572/575 VUs (99.5%). Conclusions: Fusion of MR scans of the spine from two time points into a single color-encoded image allows for direct visualization and measurement of inflammatory changes over time in patients with suspected axSpA.@en

The immense complexity of the mammalian brain is largely reflected in the underlying molecular signatures of its billions of cells. Brain transcriptome atlases provide valuable insights into gene expression patterns across different brain areas throughout the course of development. Such atlases allow researchers to probe the molecular mechanisms which define neuronal identities, neuroanatomy, and patterns of connectivity. Despite the immense effort put into generating such atlases, to answer fundamental questions in neuroscience, an even greater effort is needed to develop methods to probe the resulting high-dimensional multivariate data. We provide a comprehensive overview of the various computational methods used to analyze brain transcriptome atlases.@en

Purpose To evaluate viscous energy loss and the association with three-dimensional (3D) vortex ring formation in left ventricular (LV) blood flow during diastolic filling. Theory and Methods Thirty healthy volunteers were compared with 32 patients with corrected atrioventricular septal defect as unnatural mitral valve morphology and inflow are common in these patients. 4DFlow MRI was acquired from which 3D vortex ring formation was identified in LV blood flow at peak early (E)-filling and late (A)-filling and characterized by its presence/absence, orientation, and position from the lateral wall. Viscous energy loss was computed over E-filling, A-filling, and complete diastole using the Navier-Stokes energy equations. Results Compared with healthy volunteers, viscous energy loss was significantly elevated in patients with disturbed vortex ring formation as characterized by a significantly inclined orientation and/or position closer to the lateral wall. Highest viscous energy loss was found in patients without a ring-shaped vortex during E-filling (on average more than double compared with patients with ring-shape vortex, P < 0.003). Altered A-filling vortex ring formation was associated with significant increase in total viscous energy loss over diastole even in the presence of normal E-filling vortex ring. Conclusion Altered vortex ring formation during LV filling is associated with increased viscous energy loss. Magn Reson Med 77:794–805, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.@en

Spatial and temporal brain transcriptomics has recently emerged as an invaluable data source for molecular neuroscience. The complexity of such data poses considerable challenges for analysis and visualization. We present BrainScope: A web portal for fast, interactive visual exploration of the Allen Atlases of the adult and developing human brain transcriptome. Through a novel methodology to explore high-dimensional data (dual t-SNE), BrainScope enables the linked, all-in-one visualization of genes and samples across the whole brain and genome, and across developmental stages. We show that densities in t-SNE scatter plots of the spatial samples coincide with anatomical regions, and that densities in t-SNE scatter plots of the genes represent gene co-expression modules that are significantly enriched for biological functions. We also show that the topography of the gene t-SNE maps reflect brain region-specific gene functions, enabling hypothesis and data driven research. We demonstrate the discovery potential of BrainScope through three examples: (i) analysis of cell type specific gene sets, (ii) analysis of a set of stable gene co-expression modules across the adult human donors and (iii) analysis of the evolution of co-expression of oligodendrocyte specific genes over developmental stages. Brain- Scope is publicly accessible at www.brainscope.nl.@en

With the wide access to studies of selected gene expressions in transgenic animals,mice have become the dominant species as cerebral diseasemodels. Many of these studies are performed on animals of not more than eight weeks, declared as adult animals. Based on the earlier reports that full brain maturation requires at least three months in rats, there is a clear need to discern the corresponding minimal animal age to provide an “adult brain” in mice in order to avoid modulation of disease progression/therapy studies by ongoing developmental changes. For this purpose, we have studied anatomical brain alterations of mice during their first six months of age. Using T2-weighted and diffusion-weightedMRI, structural and volume changes of the brain were identified and compared with histological analysis of myelination. Mouse brain volume was found to be almost stable already at three weeks, but cortex thickness kept decreasing continuously with maximal changes during the first
three months. Myelination is still increasing between three and six months, although most dramatic changes are over by three months. While our results emphasize that mice should be at least three months old when adult animals are needed for brain studies, preferred choice of one particular metric for future investigation goals will result in somewhat varying age windows of stabilization.@en

Steroid receptors are pleiotropic transcription factors that coordinate adaptation to different physiological states. An important target organ is the brain, but even though their effects are well studied in specific regions, brain-wide steroid receptor targets and mediators remain largely unknown due to the complexity of the brain. Here, we tested the idea that novel aspects of steroid action can be identified through spatial correlation of steroid receptors with genome-wide mRNA expression across different regions in the mouse brain. First, we observed significant coexpression of six nuclear receptors (NRs) [androgen receptor (Ar), estrogen receptor alpha (Esr1), estrogen receptor beta (Esr2), glucocorticoid receptor (Gr), mineralocorticoid receptor (Mr), and progesterone receptor (Pgr)] with sets of steroid target genes that were identified in single brain regions. These coexpression relationships were also present in distinct other brain regions, suggestive of as yet unidentified coordinate regulation of brain regions by, for example, glucocorticoids and estrogens. Second, coexpression of a set of 62 known NR coregulators and the six steroid receptors in 12 nonoverlapping mouse brain regions revealed selective downstream pathways, such as Pak6 as a mediator for the effects of Ar and Gr on dopaminergic transmission. Third, Magel2 and Irs4 were identified and validated as strongly responsive targets to the estrogen diethylstilbestrol in the mouse hypothalamus. The brain- and genome-wide correlations of mRNA expression levels of six steroid receptors that we provide constitute a rich resource for further predictions and understanding of brain modulation by steroid hormones.                            @en