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BackgroundThe composition of the tumor immune microenvironment (TIME) associated with good prognosis generally also predicts the success of immunotherapy, and both entail the presence of pre-existing tumor-specific T cells. Here, the blueprint of the TIME associated with such an ongoing tumor-specific T-cell response was dissected in a unique prospective oropharyngeal squamous cell carcinoma (OPSCC) cohort, in which tumor-specific tumor-infiltrating T cells were detected (immune responsiveness (IR+)) or not (lack of immune responsiveness (IR−)).MethodsA comprehensive multimodal, high-dimensional strategy was applied to dissect the TIME of treatment-naive IR+ and IR− OPSCC tissue, including bulk RNA sequencing (NanoString), imaging mass cytometry (Hyperion) for phenotyping and spatial interaction analyses of immune cells, and combined single-cell gene expression profiling and T-cell receptor (TCR) sequencing (single-cell RNA sequencing (scRNAseq)) to characterize the transcriptional states of clonally expanded tumor-infiltrating T cells.ResultsIR+ patients had an excellent survival during >10 years follow-up. The tumors of IR+ patients expressed higher levels of genes strongly related to interferon gamma signaling, T-cell activation, TCR signaling, and mononuclear cell differentiation, as well as genes involved in several immune signaling pathways, than IR− patients. The top differently overexpressed genes included CXCL12 and LTB, involved in ectopic lymphoid structure development. Moreover, scRNAseq not only revealed that CD4+ T cells were the main producers of LTB but also identified a subset of clonally expanded CD8+ T cells, dominantly present in IR+ tumors, which secreted the T cell and dendritic cell (DC) attracting chemokine CCL4. Indeed, immune cell infiltration in IR+ tumors is stronger, highly coordinated, and has a distinct spatial phenotypical signature characterized by intratumoral microaggregates of CD8+CD103+ and CD4+ T cells with DCs. In contrast, the IR− TIME comprised spatial interactions between lymphocytes and various immunosuppressive myeloid cell populations. The impact of these chemokines on local immunity and clinical outcome was confirmed in an independent The Cancer Genome Atlas OPSCC cohort.ConclusionThe production of lymphoid cell attracting and organizing chemokines by tumor-specific T cells in IR+ tumors constitutes a positive feedback loop to sustain the formation of the DC–T-cell microaggregates and identifies patients with excellent survival after standard therapy.

BackgroundOropharyngeal squamous cell carcinoma (OPSCC) is the most prevalent type of head and neck cancer. The survival of patients with OPSCC is tightly linked to the intratumoral presence of tumor-specific CD4+ and CD8+ T cells. Yet, immunotherapy is currently far from effective in OPSCC partly due to our limited understanding of its immune microenvironment.MethodsHere a multi-modal, high-dimensional approach was used to dissect the immune landscape in a unique cohort of pre-therapy OPSCC patient samples (n=20) in which intratumoral tumor-specific T cells were either detected (immune response positive, IR+) or not (IR-). This included imaging mass cytometry (Hyperion) for high-dimensional phenotyping, spatial localization and interaction analyses of the cells in the tumor mircoenvironment with our newly developed imaging processing pipeline employing machine learning, Nanostring PanCancer IO360 panel analysis of immune signaling pathways, and combined single-cell gene expression profiling and T cell receptor sequencing (scRNAseq) to characterize the transcriptional states of clonally expanded tumor-infiltrating T cells.ResultsImmune cell infiltration in IR+ tumors is stronger and highly coordinated, with a distinct spatial phenotypic signature characterized by microaggregates of tumor-resident (CD103+) CD8+ and CD4+ T cells and dendritic cells within the tumor cell beds, which retained after permutation based correction for differences in cell frequencies. Furthermore, the increased expression of CXCL12 and LTB produced by CD4+ T cells, both involved in the spatial organization of immune cell infiltration, and the clonal expansion of CD8+ T cells producing the DC-attracting chemokines CCL4 or XCL1 in IR+ OPSCC, indicate that tumor-reactive T cells act as a positive feedback loop in the formation of these aggregates. The impact of these chemokines on local immunity and clinical outcome was confirmed in an independent TCGA OPSCC cohort. In contrast, the IR- OPSCC signature comprised spatial interactions between lymphocytes and different subpopulations of immunosuppressive myeloid cells.ConclusionsOur study reveals that the chemokine-driven spatial immune signature of OPSCC has strong potential as a prognostic and predictive biomarker. While the immune signature of IR+ OPSCC suggests potential benefit from neoadjuvant immunotherapeutic approaches to limit the side effects of current radio(chemo)therapy, that of IR- OPSCC calls for strategies focused on stimulating T cells and counteracting immune suppressive mechanisms.

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., An examination of motor cortex in humans, marmosets and mice reveals a generally conserved cellular makeup that is likely to extend to many mammalian species, but also differences in gene expression, DNA methylation and chromatin state that lead to species-dependent specializations.

Spatially-resolved omics-data enable researchers to precisely distinguish cell types in tissue and explore their spatial interactions, enabling deep understanding of tissue functionality. To understand what causes or deteriorates a disease and identify related biomarkers, clinical researchers regularly perform large-scale cohort studies, requiring the comparison of such data at cellular level. In such studies, with little a-priori knowledge of what to expect in the data, explorative data analysis is a necessity. Here, we present an interactive visual analysis workflow for the comparison of cohorts of spatially-resolved omics-data. Our workflow allows the comparative analysis of two cohorts based on multiple levels-of-detail, from simple abundance of contained cell types over complex co-localization patterns to individual comparison of complete tissue images. As a result, the workflow enables the identification of cohort-differentiating features, as well as outlier samples at any stage of the workflow. During the development of the workflow, we continuously consulted with domain experts. To show the effectiveness of the workflow we conducted multiple case studies with domain experts from different application areas and with different data modalities., Comment: 11 pages, 10 figures, 2 tables. Revised based on IEEE VIS 2020 reviewers comments. ACM 2012 CCS - Human-centered computing, Visualization, Visualization application domains, Visual analytics. Binary of the presented tool is available is our repository: https://doi.org/10.5281/zenodo.3885814

Brain iron accumulation has been found to accelerate disease progression in amyloid-β(Aβ) positive Alzheimer patients, though the mechanism is still unknown. Microglia have been identified as key players in the disease pathogenesis, and are highly reactive cells responding to aberrations such as increased iron levels. Therefore, using histological methods, multispectral immunofluorescence and an automated in-house developed microglia segmentation and analysis pipeline, we studied the occurrence of iron-accumulating microglia and the effect on its activation state in human Alzheimer brains. We identified a subset of microglia with increased expression of the iron storage protein ferritin light chain (FTL), together with increased Iba1 expression, decreased TMEM119 and P2RY12 expression. This activated microglia subset represented iron-accumulating microglia and appeared morphologically dystrophic. Multispectral immunofluorescence allowed for spatial analysis of FTL+Iba1+-microglia, which were found to be the predominant Aβ-plaque infiltrating microglia. Finally, an increase of FTL+Iba1+-microglia was seen in patients with high Aβ load and Tau load. These findings suggest iron to be taken up by microglia and to influence the functional phenotype of these cells, especially in conjunction with Aβ.

Diffusion Tensor Imaging (DTI) is a non-invasive magnetic resonance imaging technique that, combined with fiber tracking algorithms, allows the characterization and visualization of white matter structures in the brain. The resulting fiber tracts are used, for example, in tumor surgery to evaluate the potential brain functional damage due to tumor resection. The DTI processing pipeline from image acquisition to the final visualization is rather complex generating undesirable uncertainties in the final results. Most DTI visualization techniques do not provide any information regarding the presence of uncertainty. When planning surgery, a fixed safety margin around the fiber tracts is often used; however, it cannot capture local variability and distribution of the uncertainty, thereby limiting the informed decision-making process. Stochastic techniques are a possibility to estimate uncertainty for the DTI pipeline. However, it has high computational and memory requirements that make it infeasible in a clinical setting. The delay in the visualization of the results adds hindrance to the workflow. We propose a progressive approach that relies on a combination of wild-bootstrapping and fiber tracking to be used within the progressive visual analytics paradigm. We present a local bootstrapping strategy, which reduces the computational and memory costs, and provides fiber-tracking results in a progressive manner. We have also implemented a progressive aggregation technique that computes the distances in the fiber ensemble during progressive bootstrap computations. We present experiments with different scenarios to highlight the benefits of using our progressive visual analytic pipeline in a clinical workflow along with a use case and analysis obtained by discussions with our collaborators.

All authors: Andrea Cossarizza Hyun‐Dong Chang Andreas Radbruch Andreas Acs Dieter Adam Sabine Adam‐Klages William W. Agace Nima Aghaeepour Mübeccel Akdis Matthieu Allez Larissa Nogueira Almeida Giorgia Alvisi Graham Anderson Immanuel Andrä Francesco Annunziato Achille Anselmo Petra Bacher Cosima T. Baldari Sudipto Bari Vincenzo Barnaba Joana Barros‐Martins Luca Battistini Wolfgang Bauer Sabine Baumgart Nicole Baumgarth Dirk Baumjohann Bianka Baying Mary Bebawy Burkhard Becher Wolfgang Beisker Vladimir Benes Rudi Beyaert Alfonso Blanco Dominic A. Boardman Christian Bogdan Jessica G. Borger Giovanna Borsellino Philip E. Boulais Jolene A. Bradford Dirk Brenner Ryan R. Brinkman Anna E. S. Brooks Dirk H. Busch Martin Büscher Timothy P. Bushnell Federica Calzetti Garth Cameron Ilenia Cammarata Xuetao Cao Susanna L. Cardell Stefano Casola Marco A. Cassatella Andrea Cavani Antonio Celada Lucienne Chatenoud Pratip K. Chattopadhyay Sue Chow Eleni Christakou Luka Čičin‐Šain Mario Clerici Federico S. Colombo Laura Cook Anne Cooke Andrea M. Cooper Alexandra J. Corbett Antonio Cosma Lorenzo Cosmi Pierre G. Coulie Ana Cumano Ljiljana Cvetkovic Van Duc Dang Chantip Dang‐Heine Martin S. Davey Derek Davies Sara De Biasi Genny Del Zotto Gelo Victoriano Dela Cruz Michael Delacher Silvia Della Bella Paolo Dellabona Günnur Deniz Mark Dessing James P. Di Santo Andreas Diefenbach Francesco Dieli Andreas Dolf Thomas Dörner Regine J. Dress Diana Dudziak Michael Dustin Charles‐Antoine Dutertre Friederike Ebner Sidonia B. G. Eckle Matthias Edinger Pascale Eede Götz R.A. Ehrhardt Marcus Eich Pablo Engel Britta Engelhardt Anna Erdei Charlotte Esser Bart Everts Maximilien Evrard Christine S. Falk Todd A. Fehniger Mar Felipo‐Benavent Helen Ferry Markus Feuerer Andrew Filby Kata Filkor Simon Fillatreau Marie Follo Irmgard Förster John Foster Gemma A. Foulds Britta Frehse Paul S. Frenette Stefan Frischbutter Wolfgang Fritzsche David W. Galbraith Anastasia Gangaev Natalio Garbi Brice Gaudilliere Ricardo T. Gazzinelli Jens Geginat Wilhelm Gerner Nicholas A. Gherardin Kamran Ghoreschi Lara Gibellini Florent Ginhoux Keisuke Goda Dale I. Godfrey Christoph Goettlinger Jose M. González‐Navajas Carl S. Goodyear Andrea Gori Jane L. Grogan Daryl Grummitt Andreas Grützkau Claudia Haftmann Jonas Hahn Hamida Hammad Günter Hämmerling Leo Hansmann Goran Hansson Christopher M. Harpur Susanne Hartmann Andrea Hauser Anja E. Hauser David L. Haviland David Hedley Daniela C. Hernández Guadalupe Herrera Martin Herrmann Christoph Hess Thomas Höfer Petra Hoffmann Kristin Hogquist Tristan Holland Thomas Höllt Rikard Holmdahl Pleun Hombrink Jessica P. Houston Bimba F. Hoyer Bo Huang Fang‐Ping Huang Johanna E. Huber Jochen Huehn Michael Hundemer Christopher A. Hunter William Y. K. Hwang Anna Iannone Florian Ingelfinger Sabine M Ivison Hans‐Martin Jäck Peter K. Jani Beatriz Jávega Stipan Jonjic Toralf Kaiser Tomas Kalina Thomas Kamradt Stefan H. E. Kaufmann Baerbel Keller Steven L. C. Ketelaars Ahad Khalilnezhad Srijit Khan Jan Kisielow Paul Klenerman Jasmin Knopf Hui‐Fern Koay Katja Kobow Jay K. Kolls Wan Ting Kong Manfred Kopf Thomas Korn Katharina Kriegsmann Hendy Kristyanto Thomas Kroneis Andreas Krueger Jenny Kühne Christian Kukat Désirée Kunkel Heike Kunze‐Schumacher Tomohiro Kurosaki Christian Kurts Pia Kvistborg Immanuel Kwok Jonathan Landry Olivier Lantz Paola Lanuti Francesca LaRosa Agnès Lehuen Salomé LeibundGut‐Landmann Michael D. Leipold Leslie Y.T. Leung Megan K. Levings Andreia C. Lino Francesco Liotta Virginia Litwin Yanling Liu Hans‐Gustaf Ljunggren Michael Lohoff Giovanna Lombardi Lilly Lopez Miguel López‐Botet Amy E. Lovett‐Racke Erik Lubberts Herve Luche Burkhard Ludewig Enrico Lugli Sebastian Lunemann Holden T. Maecker Laura Maggi Orla Maguire Florian Mair Kerstin H. Mair Alberto Mantovani Rudolf A. Manz Aaron J. Marshall Alicia Martínez‐Romero Glòria Martrus Ivana Marventano Wlodzimierz Maslinski Giuseppe Matarese Anna Vittoria Mattioli Christian Maueröder Alessio Mazzoni James McCluskey Mairi McGrath Helen M. McGuire Iain B. McInnes Henrik E. Mei Fritz Melchers Susanne Melzer Dirk Mielenz Stephen D. Miller Kingston H.G. Mills Hans Minderman Jenny Mjösberg Jonni Moore Barry Moran Lorenzo Moretta Tim R. Mosmann Susann Müller Gabriele Multhoff Luis Enrique Muñoz Christian Münz Toshinori Nakayama Milena Nasi Katrin Neumann Lai Guan Ng Antonia Niedobitek Sussan Nourshargh Gabriel Núñez José‐Enrique O'Connor Aaron Ochel Anna Oja Diana Ordonez Alberto Orfao Eva Orlowski‐Oliver Wenjun Ouyang Annette Oxenius Raghavendra Palankar Isabel Panse Kovit Pattanapanyasat Malte Paulsen Dinko Pavlinic Livius Penter Pärt Peterson Christian Peth Jordi Petriz Federica Piancone Winfried F. Pickl Silvia Piconese Marcello Pinti A. Graham Pockley Malgorzata Justyna Podolska Zhiyong Poon Katharina Pracht Immo Prinz Carlo E. M. Pucillo Sally A. Quataert Linda Quatrini Kylie M. Quinn Helena Radbruch Tim R. D. J. Radstake Susann Rahmig Hans‐Peter Rahn Bartek Rajwa Gevitha Ravichandran Yotam Raz Jonathan A. Rebhahn Diether Recktenwald Dorothea Reimer Caetano Reis e Sousa Ester B.M. Remmerswaal Lisa Richter Laura G. Rico Andy Riddell Aja M. Rieger J. Paul Robinson Chiara Romagnani Anna Rubartelli Jürgen Ruland Armin Saalmüller Yvan Saeys Takashi Saito Shimon Sakaguchi Francisco Sala‐de‐Oyanguren Yvonne Samstag Sharon Sanderson Inga Sandrock Angela Santoni Ramon Bellmàs Sanz Marina Saresella Catherine Sautes‐Fridman Birgit Sawitzki Linda Schadt Alexander Scheffold Hans U. Scherer Matthias Schiemann Frank A. Schildberg Esther Schimisky Andreas Schlitzer Josephine Schlosser Stephan Schmid Steffen Schmitt Kilian Schober Daniel Schraivogel Wolfgang Schuh Thomas Schüler Reiner Schulte Axel Ronald Schulz Sebastian R. Schulz Cristiano Scottá Daniel Scott‐Algara David P. Sester T. Vincent Shankey Bruno Silva‐Santos Anna Katharina Simon Katarzyna M. Sitnik Silvano Sozzani Daniel E. Speiser Josef Spidlen Anders Stahlberg Alan M. Stall Natalie Stanley Regina Stark Christina Stehle Tobit Steinmetz Hannes Stockinger Yousuke Takahama Kiyoshi Takeda Leonard Tan Attila Tárnok Gisa Tiegs Gergely Toldi Julia Tornack Elisabetta Traggiai Mohamed Trebak Timothy I.M. Tree Joe Trotter John Trowsdale Maria Tsoumakidou Henning Ulrich Sophia Urbanczyk Willem van de Veen Maries van den Broek Edwin van der Pol Sofie Van Gassen Gert Van Isterdael René A.W. van Lier Marc Veldhoen Salvador Vento‐Asturias Paulo Vieira David Voehringer Hans‐Dieter Volk Anouk von Borstel Konrad von Volkmann Ari Waisman Rachael V. Walker Paul K. Wallace Sa A. Wang Xin M. Wang Michael D. Ward Kirsten A Ward‐Hartstonge Klaus Warnatz Gary Warnes Sarah Warth Claudia Waskow James V. Watson Carsten Watzl Leonie Wegener Thomas Weisenburger Annika Wiedemann Jürgen Wienands Anneke Wilharm Robert John Wilkinson Gerald Willimsky James B. Wing Rieke Winkelmann Thomas H. Winkler Oliver F. Wirz Alicia Wong Peter Wurst Jennie H. M. Yang Juhao Yang Maria Yazdanbakhsh Liping Yu Alice Yue Hanlin Zhang Yi Zhao Susanne Maria Ziegler Christina Zielinski Jakob Zimmermann Arturo Zychlinsky., These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer‐reviewed by leading experts in the field, making this an essential research companion., This work was supported by the Netherlands Organisation for Scientific Research – Domain Applied and Engineering Sciences (NWO-TTW), research program VENI 15924. This work was funded by the Deutsche Forschungsgemeinschaft. European Union Innovative Medicines Initiative - Joint Undertaking - RTCure Grant Agreement 777357 and innovation program (Grant Agreement 695551).

The primary motor cortex (M1) is essential for voluntary fine motor control and is functionally conserved across mammals. Using high-throughput transcriptomic and epigenomic profiling of over 450,000 single nuclei in human, marmoset monkey, and mouse, we demonstrate a broadly conserved cellular makeup of this region, whose similarity mirrors evolutionary distance and is consistent between the transcriptome and epigenome. The core conserved molecular identity of neuronal and non-neuronal types allowed the generation of a cross-species consensus cell type classification and inference of conserved cell type properties across species. Despite overall conservation, many species specializations were apparent, including differences in cell type proportions, gene expression, DNA methylation, and chromatin state. Few cell type marker genes were conserved across species, providing a short list of candidate genes and regulatory mechanisms responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allowed the Patch-seq identification of layer 5 (L5) corticospinal Betz cells in non-human primate and human and characterization of 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.

In recent years the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm has become one of the most used and insightful techniques for exploratory data analysis of high-dimensional data. It reveals clusters of high-dimensional data points at different scales while only requiring minimal tuning of its parameters. However, the computational complexity of the algorithm limits its application to relatively small datasets. To address this problem, several evolutions of t-SNE have been developed in recent years, mainly focusing on the scalability of the similarity computations between data points. However, these contributions are insufficient to achieve interactive rates when visualizing the evolution of the t-SNE embedding for large datasets. In this work, we present a novel approach to the minimization of the t-SNE objective function that heavily relies on graphics hardware and has linear computational complexity. Our technique decreases the computational cost of running t-SNE on datasets by orders of magnitude and retains or improves on the accuracy of past approximated techniques. We propose to approximate the repulsive forces between data points by splatting kernel textures for each data point. This approximation allows us to reformulate the t-SNE minimization problem as a series of tensor operations that can be efficiently executed on the graphics card. An efficient implementation of our technique is integrated and available for use in the widely used Google TensorFlow.js, and an open-source C++ library.

Helminth infections induce strong type 2 and regulatory responses, but the degree of heterogeneity of such cells is not well characterized. Using mass cytometry, we profiled these cells in Europeans and Indonesians not exposed to helminths and in Indonesians residing in rural areas infected with soil-transmitted helminths. To assign immune alteration to helminth infection, the profiling was performed before and 1 year after deworming. Very distinct signatures were found in Europeans and Indonesians, showing expanded frequencies of T helper 2 cells, particularly CD161+ cells and ILC2s in helminth-infected Indonesians, which was confirmed functionally through analysis of cytokine-producing cells. Besides ILC2s and CD4+ T cells, CD8+ T cells and γδ T cells in Indonesians produced type 2 cytokines. Regulatory T cells were also expanded in Indonesians, but only those expressing CTLA-4, and some coexpressed CD38, HLA-DR, ICOS, or CD161. CD11c+ B cells were found to be the main IL-10 producers among B cells in Indonesians, a subset that was almost absent in Europeans. A number of the distinct immune profiles were driven by helminths as the profiles reverted after clearance of helminth infections. Moreover, Indonesians with no helminth infections residing in an urban area showed immune profiles that resembled Europeans rather than rural Indonesians, which excludes a major role for ethnicity. Detailed insight into the human type 2 and regulatory networks could provide opportunities to target these cells for more precise interventions.

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 single-cell 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. Availability and implementation Implementation is available on GitHub (https://github.com/tabdelaal/CyTOFmerge). Supplementary information Supplementary data are available at Bioinformatics online.

Elucidating the cellular architecture of the human cerebral cortex is central to understanding our cognitive abilities and susceptibility to disease. Here we used single-nucleus RNA-sequencing analysis to perform a comprehensive study of cell types in the middle temporal gyrus of human cortex. We identified a highly diverse set of excitatory and inhibitory neuron types that are mostly sparse, with excitatory types being less layer-restricted than expected. Comparison to similar mouse cortex single-cell RNA-sequencing datasets revealed a surprisingly well-conserved cellular architecture that enables matching of homologous types and predictions of properties of human cell types. Despite this general conservation, we also found extensive differences between homologous human and mouse cell types, including marked alterations in proportions, laminar distributions, gene expression and morphology. These species-specific features emphasize the importance of directly studying human brain.

Hierarchical embeddings, such as HSNE, address critical visual and computational scalability issues of traditional techniques for dimensionality reduction. The improved scalability comes at the cost of the need for increased user interaction for exploration. In this paper, we provide a solution for the interactive visual Focus+Context exploration of such embeddings. We explain how to integrate embedding parts from different levels of detail, corresponding to focus and context groups, in a joint visualization. We devise an according interaction model that relates typical semantic operations on a Focus+Context visualization with the according changes in the level-of-detail-hierarchy of the embedding, including also a mode for comparative Focus+Context exploration and extend HSNE to incorporate the presented interaction model. In order to demonstrate the effectiveness of our approach, we present a use case based on the visual exploration of multi-dimensional images.

The fetus is thought to be protected from exposure to foreign antigens, yet CD45RO+ T cells reside in the fetal intestine. Here we combined functional assays with mass cytometry, single-cell RNA-sequencing and high-throughput T cell antigen receptor (TCR) sequencing to characterize the CD4+ T cell compartment in the human fetal intestine. We identified 22 CD4+ T cell clusters, including naive-like, regulatory-like and memory-like subpopulations, which were confirmed and further characterized at the transcriptional level. Memory-like CD4+ T cells had high expression of Ki-67, indicative of cell division, and CD5, a surrogate marker of TCR avidity, and produced the cytokines IFN-γ and IL-2. Pathway analysis revealed a differentiation trajectory associated with cellular activation and proinflammatory effector functions, and TCR repertoire analysis indicated clonal expansions, distinct repertoire characteristics and interconnections between subpopulations of memory-like CD4+ T cells. Imaging-mass cytometry indicated that memory-like CD4+ T cells colocalized with antigen-presenting cells. Collectively, these results provide evidence for the generation of memory-like CD4+ T cells in the human fetal intestine that is consistent with exposure to foreign antigens.

A Diffusion Tensor Imaging (DTI) group study consists of a collection of volumetric diffusion tensor datasets (i.e., an ensemble) acquired from a group of subjects. The multivariate nature of the diffusion tensor imposes challenges on the analysis and the visualization. These challenges are commonly tackled by reducing the diffusion tensors to scalar-valued quantities that can be analyzed with common statistical tools. However, reducing tensors to scalars poses the risk of losing intrinsic information about the tensor. Visualization of tensor ensemble data without loss of information is still a largely unsolved problem. In this work, we propose an overview + detail visualization to facilitate the tensor ensemble exploration. We define an ensemble representative tensor and variations in terms of the three intrinsic tensor properties (i.e., scale, shape, and orientation) separately. The ensemble summary information is visually encoded into the newly designed aggregate tensor glyph which, in a spatial layout, functions as the overview. The aggregate tensor glyph guides the analyst to interesting areas that would need further detailed inspection. The detail views reveal the original information that is lost during aggregation. It helps the analyst to further understand the sources of variation and formulate hypotheses. To illustrate the applicability of our prototype, we compare with most relevant previous work through a user study and we present a case study on the analysis of a brain diffusion tensor dataset ensemble from healthy volunteers.

Purpose: The tumor immune microenvironment determines clinical outcome. Whether the original tissue in which a primary tumor develops influences this microenvironment is not well understood. Experimental Design: We applied high-dimensional single-cell mass cytometry [Cytometry by Time-Of-Flight (CyTOF)] analysis and functional studies to analyze immune cell populations in human papillomavirus (HPV)–induced primary tumors of the cervix (cervical carcinoma) and oropharynx (oropharyngeal squamous cell carcinoma, OPSCC). Results: Despite the same etiology of these tumors, the composition and functionality of their lymphocytic infiltrate substantially differed. Cervical carcinoma displayed a 3-fold lower CD4:CD8 ratio and contained more activated CD8+CD103+CD161+ effector T cells and less CD4+CD161+ effector memory T cells than OPSCC. CD161+ effector cells produced the highest cytokine levels among tumor-specific T cells. Differences in CD4+ T-cell infiltration between cervical carcinoma and OPSCC were reflected in the detection rate of intratumoral HPV-specific CD4+ T cells and in their impact on OPSCC and cervical carcinoma survival. The peripheral blood mononuclear cell composition of these patients, however, was similar. Conclusions: The tissue of origin significantly affects the overall shape of the immune infiltrate in primary tumors.

ObjectiveA comprehensive understanding of anticancer immune responses is paramount for the optimal application and development of cancer immunotherapies. We unravelled local and systemic immune profiles in patients with colorectal cancer (CRC) by high-dimensional analysis to provide an unbiased characterisation of the immune contexture of CRC.DesignThirty-six immune cell markers were simultaneously assessed at the single-cell level by mass cytometry in 35 CRC tissues, 26 tumour-associated lymph nodes, 17 colorectal healthy mucosa and 19 peripheral blood samples from 31 patients with CRC. Additionally, functional, transcriptional and spatial analyses of tumour-infiltrating lymphocytes were performed by flow cytometry, single-cell RNA-sequencing and multispectral immunofluorescence.ResultsWe discovered that a previously unappreciated innate lymphocyte population (Lin–CD7+CD127–CD56+CD45RO+) was enriched in CRC tissues and displayed cytotoxic activity. This subset demonstrated a tissue-resident (CD103+CD69+) phenotype and was most abundant in immunogenic mismatch repair (MMR)-deficient CRCs. Their presence in tumours was correlated with the infiltration of tumour-resident cytotoxic, helper and γδ T cells with highly similar activated (HLA-DR+CD38+PD-1+) phenotypes. Remarkably, activated γδ T cells were almost exclusively found in MMR-deficient cancers. Non-activated counterparts of tumour-resident cytotoxic and γδ T cells were present in CRC and healthy mucosa tissues, but not in lymph nodes, with the exception of tumour-positive lymph nodes.ConclusionThis work provides a blueprint for the understanding of the heterogeneous and intricate immune landscape of CRC, including the identification of previously unappreciated immune cell subsets. The concomitant presence of tumour-resident innate and adaptive immune cell populations suggests a multitargeted exploitation of their antitumour properties in a therapeutic setting.

MotivationMass cytometry (CyTOF) is a valuable technology for high-dimensional analysis at the single cell level. Identification of different cell populations is an important task during the data analysis. Many clustering tools can perform this task, however, they are time consuming, often involve a manual step, and lack reproducibility when new data is included in the analysis. Learning cell types from an annotated set of cells solves these problems. However, currently available mass cytometry classifiers are either complex, dependent on prior knowledge of the cell type markers during the learning process, or can only identify canonical cell types.ResultsWe propose to use a Linear Discriminant Analysis (LDA) classifier to automatically identify cell populations in CyTOF data. LDA shows comparable results with two state-of-the-art algorithms on four benchmark datasets and also outperforms a non-linear classifier such as the k-nearest neighbour classifier. To illustrate its scalability to large datasets with deeply annotated cell subtypes, we apply LDA to a dataset of ~3.5 million cells representing 57 cell types. LDA has high performance on abundant cell types as well as the majority of rare cell types, and provides accurate estimates of cell type frequencies. Further incorporating a rejection option, based on the estimated posterior probabilities, allows LDA to identify cell types that were not encountered during training. Altogether, reproducible prediction of cell type compositions using LDA opens up possibilities to analyse large cohort studies based on mass cytometry data.AvailabilityImplementation is available on GitHub (https://github.com/tabdelaal/CyTOF-Linear-Classifier).Contacta.mahfouz@lumc.nl

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.

Auto-reactive CD8 T-cells play an important role in the destruction of pancreatic β-cells resulting in type 1 diabetes (T1D). However, the phenotype of these auto-reactive cytolytic CD8 T-cells has not yet been extensively described. We used high-dimensional mass cytometry to phenotype autoantigen- (pre-proinsulin), neoantigen- (insulin-DRIP) and virus- (cytomegalovirus) reactive CD8 T-cells in peripheral blood mononuclear cells (PBMCs) of T1D patients. A panel of 33 monoclonal antibodies was designed to further characterise these cells at the single-cell level. HLA-A2 class I tetramers were used for the detection of antigen-specific CD8 T-cells. Using a novel Hierarchical Stochastic Neighbor Embedding (HSNE) tool (implemented in Cytosplore), we identified 42 clusters within the CD8 T-cell compartment of three T1D patients and revealed profound heterogeneity between individuals, as each patient displayed a distinct cluster distribution. Single-cell analysis of pre-proinsulin, insulin-DRIP and cytomegalovirus-specific CD8 T-cells showed that the detected specificities were heterogeneous between and within patients. These findings emphasize the challenge to define the obscure nature of auto-reactive CD8 T-cells.

Mass cytometry allows high-resolution dissection of the cellular composition of the immune system. However, the high-dimensionality, large size, and non-linear structure of the data poses considerable challenges for data analysis. In particular, dimensionality reduction-based techniques like t-SNE offer single-cell resolution but are limited in the number of cells that can be analysed. Here we introduce Hierarchical Stochastic Neighbor Embedding (HSNE) for the analysis of mass cytometry datasets. HSNE constructs a hierarchy of non-linear similarities that can be interactively explored with a stepwise increase in detail up to the single-cell level. We applied HSNE to a study on gastrointestinal disorders and three other available mass cytometry datasets. We found that HSNE efficiently replicates previous observations. Moreover, HSNE identifies rare cell populations that were previously missed. Thus, HSNE removes the scalability limit of conventional t-SNE analysis, a feature that makes it highly suitable for the analysis of massive high-dimensional datasets. Mass cytometry allows high-resolution dissection of the cellular composition of the immune system. However, the high-dimensionality, large size, and non-linear structure of the data poses considerable challenges for data analysis. In particular, dimensionality reduction-based techniques like t-SNE offer single-cell resolution but are limited in the number of cells that can be analysed. Here we introduce Hierarchical Stochastic Neighbor Embedding (HSNE) for the analysis of mass cytometry datasets. HSNE constructs a hierarchy of non-linear similarities that can be interactively explored with a stepwise increase in detail up to the single-cell level. We applied HSNE to a study on gastrointestinal disorders and three other available mass cytometry datasets. We found that HSNE efficiently replicates previous observations and identifies rare cell populations that were previously missed due to downsampling. Thus, HSNE removes the scalability limit of conventional t-SNE analysis, a feature that makes it highly suitable for the analysis of massive high-dimensional datasets.

Progressive Visual Analytics aims at improving the interactivity in existing analytics techniques by means of visualization as well as interaction with intermediate results. One key method for data analysis is dimensionality reduction, for example, to produce 2D embeddings that can be visualized and analyzed efficiently. t-Distributed Stochastic Neighbor Embedding (tSNE) is a well-suited technique for the visualization of several high-dimensional data. tSNE can create meaningful intermediate results but suffers from a slow initialization that constrains its application in Progressive Visual Analytics. We introduce a controllable tSNE approximation (A-tSNE), which trades off speed and accuracy, to enable interactive data exploration. We offer real-time visualization techniques, including a density-based solution and a Magic Lens to inspect the degree of approximation. With this feedback, the user can decide on local refinements and steer the approximation level during the analysis. We demonstrate our technique with several datasets, in a real-world research scenario and for the real-time analysis of high-dimensional streams to illustrate its effectiveness for interactive data analysis.

To understand how the immune system works, one needs to have a clear picture of its cellular compositon and the cells' corresponding properties and functionality. Mass cytometry is a novel technique to determine the properties of single-cells with unprecedented detail. This amount of detail allows for much finer differentiation but also comes at the cost of more complex analysis. In this work, we present Cytosplore, implementing an interactive workflow to analyze mass cytometry data in an integrated system, providing multiple linked views, showing different levels of detail and enabling the rapid definition of known and unknown cell types. Cytosplore handles millions of cells, each represented as a high-dimensional data point, facilitates hypothesis generation and confirmation, and provides a significant speed up of the current workflow. We show the effectiveness of Cytosplore in a case study evaluation.

In recent years, dimensionality-reduction techniques have been developed and are widely used for hypothesis generation in Exploratory Data Analysis. However, these techniques are confronted with overcoming the trade-off between computation time and the quality of the provided dimensionality reduction. In this work, we address this limitation, by introducing Hierarchical Stochastic Neighbor Embedding (Hierarchical-SNE). Using a hierarchical representation of the data, we incorporate the well-known mantra of Overview-First, Details-On-Demand in non-linear dimensionality reduction. First, the analysis shows an embedding, that reveals only the dominant structures in the data (Overview). Then, by selecting structures that are visible in the overview, the user can filter the data and drill down in the hierarchy. While the user descends into the hierarchy, detailed visualizations of the high-dimensional structures will lead to new insights. In this paper, we explain how Hierarchical-SNE scales to the analysis of big datasets. In addition, we show its application potential in the visualization of Deep-Learning architectures and the analysis of hyperspectral images.

Inflammatory intestinal diseases are characterized by abnormal immune responses and affect distinct locations of the gastrointestinal tract. Although the role of several immune subsets in driving intestinal pathology has been studied, a system-wide approach that simultaneously interrogates all major lineages on a single-cell basis is lacking. We used high-dimensional mass cytometry to generate a system-wide view of the human mucosal immune system in health and disease. We distinguished 142 immune subsets and through computational applications found distinct immune subsets in peripheral blood mononuclear cells and intestinal biopsies that distinguished patients from controls. In addition, mucosal lymphoid malignancies were readily detected as well as precursors from which these likely derived. These findings indicate that an integrated high-dimensional analysis of the entire immune system can identify immune subsets associated with the pathogenesis of complex intestinal disorders. This might have implications for diagnostic procedures, immune-monitoring, and treatment of intestinal diseases and mucosal malignancies.