Spatial multi-omic map of human myocardial infarction.

Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.

Human pluripotent stem cell-derived kidney organoids for personalized congenital and idiopathic nephrotic syndrome modeling.

Nephrotic syndrome (NS) is characterized by severe proteinuria as a consequence of kidney glomerular injury due to podocyte damage. In vitro models mimicking in vivo podocyte characteristics are a prerequisite to resolve NS pathogenesis. The detailed characterization of organoid podocytes resulting from a hybrid culture protocol showed a podocyte population that resembles adult podocytes and was superior compared with 2D counterparts, based on single-cell RNA sequencing, super-resolution imaging and electron microscopy. In this study, these next-generation podocytes in kidney organoids enabled personalized idiopathic nephrotic syndrome modeling, as shown by activated slit diaphragm signaling and podocyte injury following protamine sulfate, puromycin aminonucleoside treatment and exposure to NS plasma containing pathogenic permeability factors. Organoids cultured from cells of a patient with heterozygous NPHS2 mutations showed poor NPHS2 expression and aberrant NPHS1 localization, which was reversible after genetic correction. Repaired organoids displayed increased VEGFA pathway activity and transcription factor activity known to be essential for podocyte physiology, as shown by RNA sequencing. This study shows that organoids are the preferred model of choice to study idiopathic and congenital podocytopathies.

Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT).

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.

CrossTalkeR: analysis and visualization of ligand-receptorne tworks.

MOTIVATION: Ligand-receptor (LR) network analysis allows the characterization of cellular crosstalk based on single cell RNA-seq data. However, current methods typically provide a list of inferred LR interactions and do not allow the researcher to focus on specific cell types, ligands or receptors. In addition, most of these methods cannot quantify changes in crosstalk between two biological phenotypes. RESULTS: CrossTalkeR is a framework for network analysis and visualization of LR interactions. CrossTalkeR identifies relevant ligands, receptors and cell types contributing to changes in cell communication when contrasting two biological phenotypes, i.e. disease versus homeostasis. A case study on scRNA-seq of human myeloproliferative neoplasms reinforces the strengths of CrossTalkeR for characterization of changes in cellular crosstalk in disease. AVAILABILITY AND IMPLEMENTATION: CrosstalkeR is an R package available at: Github: https://github.com/CostaLab/CrossTalkeR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Correction: A stomata classification and detection system in microscope images of maize cultivars.

[This corrects the article DOI: 10.1371/journal.pone.0258679.].

Non-canonical Hedgehog signaling mediates profibrotic hematopoiesis-stroma crosstalk in myeloproliferative neoplasms.

The role of hematopoietic Hedgehog signaling in myeloproliferative neoplasms (MPNs) remains incompletely understood despite data suggesting that Hedgehog (Hh) pathway inhibitors have therapeutic activity in patients. We aim to systematically interrogate the role of canonical vs. non-canonical Hh signaling in MPNs. We show that Gli1 protein levels in patient peripheral blood mononuclear cells (PBMCs) mark fibrotic progression and that, in murine MPN models, absence of hematopoietic Gli1, but not Gli2 or Smo, significantly reduces MPN phenotype and fibrosis, indicating that GLI1 in the MPN clone can be activated in a non-canonical fashion. Additionally, we establish that hematopoietic Gli1 has a significant effect on stromal cells, mediated through a druggable MIF-CD74 axis. These data highlight the complex interplay between alterations in the MPN clone and activation of stromal cells and indicate that Gli1 represents a promising therapeutic target in MPNs, particularly that Hh signaling is dispensable for normal hematopoiesis.

scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference.

Summary: The increasing availability of single-cell multi-omics data allows to quantitatively characterize gene regulation. We here describe scMEGA (Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference) that enables an end-to-end analysis of multi-omics data for gene regulatory network inference including modalities integration, trajectory analysis, enhancer-to-promoter association, network analysis and visualization. This enables to study the complex gene regulation mechanisms for dynamic biological processes, such as cellular differentiation and disease-driven cellular remodeling. We provide a case study on gene regulatory networks controlling myofibroblast activation in human myocardial infarction. Availability and implementation: scMEGA is implemented in R, released under the MIT license and available from https://github.com/CostaLab/scMEGA. Tutorials are available from https://costalab.github.io/scMEGA. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data.

The growing availability of single-cell data, especially transcriptomics, has sparked an increased interest in the inference of cell-cell communication. Many computational tools were developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events. Yet the impact of the choice of resource and method on the resulting predictions is largely unknown. To shed light on this, we systematically compare 16 cell-cell communication inference resources and 7 methods, plus the consensus between the methods' predictions. Among the resources, we find few unique interactions, a varying degree of overlap, and an uneven coverage of specific pathways and tissue-enriched proteins. We then examine all possible combinations of methods and resources and show that both strongly influence the predicted intercellular interactions. Finally, we assess the agreement of cell-cell communication methods with spatial colocalisation, cytokine activities, and receptor protein abundance and find that predictions are generally coherent with those data modalities. To facilitate the use of the methods and resources described in this work, we provide LIANA, a LIgand-receptor ANalysis frAmework as an open-source interface to all the resources and methods.

Single-cell analysis of cultured bone marrow stromal cells reveals high similarity to fibroblasts in situ.

Within the heterogenous pool of bone marrow stromal cells, mesenchymal stromal cells (MSCs) are of particular interest because of their hematopoiesis-supporting capacities, contribution to disease progression, therapy resistance, and leukemic initiation. Cultured bone marrow-derived stromal cells (cBMSCs) are used for in vitro modeling of hematopoiesis-stroma interactions, validation of disease mechanisms, and screening for therapeutic targets. Here, we place cBMSCs (mouse and human) in a bone marrow tissue context by systematically comparing the transcriptome of plastic-adherent cells on a single-cell level with in vivo counterparts. Cultured BMSCs encompass a rather homogenous cell population, independent of the isolation method used and, although still possessing hematopoiesis-supporting capacity, are distinct from freshly isolated MSCs and more akin to in vivo fibroblast populations. Informed by combined cell trajectories and pathway analyses, we illustrate that TGFb inhibition in vitro can preserve a more "MSC"-like phenotype.

Dissecting CD8+ T cell pathology of severe SARS-CoV-2 infection by single-cell immunoprofiling.

Introduction: SARS-CoV-2 infection results in varying disease severity, ranging from asymptomatic infection to severe illness. A detailed understanding of the immune response to SARS-CoV-2 is critical to unravel the causative factors underlying differences in disease severity and to develop optimal vaccines against new SARS-CoV-2 variants. Methods: We combined single-cell RNA and T cell receptor sequencing with CITE-seq antibodies to characterize the CD8+ T cell response to SARS-CoV-2 infection at high resolution and compared responses between mild and severe COVID-19. Results: We observed increased CD8+ T cell exhaustion in severe SARS-CoV-2 infection and identified a population of NK-like, terminally differentiated CD8+ effector T cells characterized by expression of FCGR3A (encoding CD16). Further characterization of NK-like CD8+ T cells revealed heterogeneity among CD16+ NK-like CD8+ T cells and profound differences in cytotoxicity, exhaustion, and NK-like differentiation between mild and severe disease conditions. Discussion: We propose a model in which differences in the surrounding inflammatory milieu lead to crucial differences in NK-like differentiation of CD8+ effector T cells, ultimately resulting in the appearance of NK-like CD8+ T cell populations of different functionality and pathogenicity. Our in-depth characterization of the CD8+ T cell-mediated response to SARS-CoV-2 infection provides a basis for further investigation of the importance of NK-like CD8+ T cells in COVID-19 severity.

SARS-CoV-2 infects the human kidney and drives fibrosis in kidney organoids.

Kidney failure is frequently observed during and after COVID-19, but it remains elusive whether this is a direct effect of the virus. Here, we report that SARS-CoV-2 directly infects kidney cells and is associated with increased tubule-interstitial kidney fibrosis in patient autopsy samples. To study direct effects of the virus on the kidney independent of systemic effects of COVID-19, we infected human-induced pluripotent stem-cell-derived kidney organoids with SARS-CoV-2. Single-cell RNA sequencing indicated injury and dedifferentiation of infected cells with activation of profibrotic signaling pathways. Importantly, SARS-CoV-2 infection also led to increased collagen 1 protein expression in organoids. A SARS-CoV-2 protease inhibitor was able to ameliorate the infection of kidney cells by SARS-CoV-2. Our results suggest that SARS-CoV-2 can directly infect kidney cells and induce cell injury with subsequent fibrosis. These data could explain both acute kidney injury in COVID-19 patients and the development of chronic kidney disease in long COVID.

A stomata classification and detection system in microscope images of maize cultivars.

Plant stomata are essential structures (pores) that control the exchange of gases between plant leaves and the atmosphere, and also they influence plant adaptation to climate through photosynthesis and transpiration stream. Many works in literature aim for a better understanding of these structures and their role in the evolution process and the behavior of plants. Although stomata studies in dicots species have advanced considerably in the past years, even there is not much knowledge about the stomata of cereal grasses. Due to the high morphological variation of stomata traits intra- and inter-species, detecting and classifying stomata automatically becomes challenging. For this reason, in this work, we propose a new system for automatic stomata classification and detection in microscope images for maize cultivars based on transfer learning strategy of different deep convolution neural netwoks (DCNN). Our performed experiments show that our system achieves an approximated accuracy of 97.1% in identifying stomata regions using classifiers based on deep learning features, which figures out as a nearly perfect classification system. As the stomata are responsible for several plant functionalities, this work represents an important advance for maize research, providing an accurate system in replacing the current manual task of categorizing these pores on microscope images. Furthermore, this system can also be a reference for studies using images from different cereal grasses.

Heterogeneous bone-marrow stromal progenitors drive myelofibrosis via a druggable alarmin axis.

Functional contributions of individual cellular components of the bone-marrow microenvironment to myelofibrosis (MF) in patients with myeloproliferative neoplasms (MPNs) are incompletely understood. We aimed to generate a comprehensive map of the stroma in MPNs/MFs on a single-cell level in murine models and patient samples. Our analysis revealed two distinct mesenchymal stromal cell (MSC) subsets as pro-fibrotic cells. MSCs were functionally reprogrammed in a stage-dependent manner with loss of their progenitor status and initiation of differentiation in the pre-fibrotic and acquisition of a pro-fibrotic and inflammatory phenotype in the fibrotic stage. The expression of the alarmin complex S100A8/S100A9 in MSC marked disease progression toward the fibrotic phase in murine models and in patient stroma and plasma. Tasquinimod, a small-molecule inhibiting S100A8/S100A9 signaling, significantly ameliorated the MPN phenotype and fibrosis in JAK2V617F-mutated murine models, highlighting that S100A8/S100A9 is an attractive therapeutic target in MPNs.