A reversible state of hypometabolism in a human cellular model of sporadic Parkinson's disease

Sporadic Parkinson's Disease (sPD) is a progressive neurodegenerative disorder caused by multiple genetic and environmental factors. Mitochondrial dysfunction is one contributing factor, but its role at different stages of disease progression is not fully understood. Here, we showed that neural precursor cells and dopaminergic neurons derived from induced pluripotent stem cells (hiPSCs) from sPD patients exhibited a hypometabolism. Further analysis based on transcriptomics, proteomics, and metabolomics identified the citric acid cycle, specifically the alpha-ketoglutarate dehydrogenase complex (OGDHC), as bottleneck in sPD metabolism. A follow-up study of the patients approximately 10 years after initial biopsy demonstrated a correlation between OGDHC activity in our cellular model and the disease progression. In addition, the alterations in cellular metabolism observed in our cellular model were restored by interfering with the enhanced SHH signal transduction in sPD. Thus, inhibiting overactive SHH signaling may have potential as neuroprotective therapy during early stages of sPD. Mitochondrial dysfunction is a contributing factor in Parkinson's disease. Here the authors carry out a multilayered omics analysis of Parkinson's disease patient-derived neuronal cells, which reveals a reversible hypometabolism mediated by alpha-ketoglutarate dehydrogenase deficiency, which is correlated with disease progression in the donating patients

The discovAIR project:a roadmap towards the Human Lung Cell Atlas

The Human Cell Atlas (HCA) consortium aims to establish an atlas of all organs in the healthy human body at single-cell resolution to increase our understanding of basic biological processes that govern development, physiology and anatomy, and to accelerate diagnosis and treatment of disease. The lung biological network of the HCA aims to generate the Human Lung Cell Atlas as a reference for the cellular repertoire, molecular cell states and phenotypes, and the cell-cell interactions that characterise normal lung homeostasis in healthy lung tissue. Such a reference atlas of the healthy human lung will facilitate mapping the changes in the cellular landscape in disease. The discovAIR project is one of six pilot actions for the HCA funded by the European Commission in the context of the H2020 framework program. DiscovAIR aims to establish the first draft of an integrated Human Lung Cell Atlas, combining single-cell transcriptional and epigenetic profiling with spatially resolving techniques on matched tissue samples, as well as including a number of chronic and infectious diseases of the lung. The integrated Lung Cell Atlas will be available as a resource for the wider respiratory community, including basic and translational scientists, clinical medicine, and the private sector, as well as for patients with lung disease and the interested lay public. We anticipate that the Lung Cell Atlas will be the founding stone for a more detailed understanding of the pathogenesis of lung diseases, guiding the design of novel diagnostics and preventive or curative interventions

Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics

Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2, TMPRSS2 and CTSL. Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial-macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas

Current best practices in single‐cell RNA‐seq analysis: a tutorial

Abstract Single‐cell RNA‐seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single‐cell analysis methods. As more analysis tools are becoming available, it is becoming increasingly difficult to navigate this landscape and produce an up‐to‐date workflow to analyse one's data. Here, we detail the steps of a typical single‐cell RNA‐seq analysis, including pre‐processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell‐ and gene‐level downstream analysis. We formulate current best‐practice recommendations for these steps based on independent comparison studies. We have integrated these best‐practice recommendations into a workflow, which we apply to a public dataset to further illustrate how these steps work in practice. Our documented case study can be found at https://www.github.com/theislab/single-cell-tutorial. This review will serve as a workflow tutorial for new entrants into the field, and help established users update their analysis pipelines

Feature selection methods affect the performance of scRNA-seq data integration and querying [Registered Reports Stage 1 manuscript]

The continued accessibility of technologies for single-cell transcriptomics has led to the increased production of single-cell datasets and the development of computational methods for analysing them. Several efforts have now been made to bring together the available data to construct reference atlases that attempt to catalogue the cell types present in various tissues and organs. While these atlases have the potential to be valuable resources, their usefulness depends on the quality of integration of multiple datasets from different labs and experimental conditions to create the atlas, as well as the ability to map new query samples to the completed reference. Previous benchmarking studies have compared integration methods and found that there were significant differences in performance and that methods generally perform better when applied to a subset of highly-variable genes rather than the full feature set. While the field has converged on this approach it is not clear how these genes should be selected to generate the best integrated reference or how feature selection affects the mapping of query datasets. Here, we propose a study that benchmarks feature selection methods for single-cell cell integration and reference usage. We investigate deep learning integration methods as these methods have been found to be among the top performers and have been used to construct existing large-scale atlases as well as an integration approach based on a corrected PCA space. Our benchmark will evaluate the most commonly-used highly-variable gene selection methods in popular packages such as Seurat and scanpy as well as alternative approaches. Variants of methods that differ in the number of genes selected or whether they are selected across all the datasets to be integrated rather than in a batch-aware fashion will also be tested. To extend beyond the integration step to how a reference is used we include evaluation metrics for assessing the quality of query mapping, label transfer and the detection of previously unseen populations. 1 The results of this study will be of broad interest and importance to the single-cell analysis community as data integration has been named a key challenge in single-cell data science and most studies now include several batches or conditions. We also expect comprehensive, well-annotated references to play a bigger role in future analyses. These questions are also vitally important for large-scale initiatives such as the Human Cell Atlas as encountered during the construction of the Human Lung Cell Atlas, both for informing how to construct tissue-scale atlases as well as how they can be used as resources.</p

Mapping single-cell data to reference atlases by transfer learning

Large single-cell atlases are now routinely generated to serve as references for analysis of smaller-scale studies. Yet learning from reference data is complicated by batch effects between datasets, limited availability of computational resources and sharing restrictions on raw data. Here we introduce a deep learning strategy for mapping query datasets on top of a reference called single-cell architectural surgery (scArches). scArches uses transfer learning and parameter optimization to enable efficient, decentralized, iterative reference building and contextualization of new datasets with existing references without sharing raw data. Using examples from mouse brain, pancreas, immune and whole-organism atlases, we show that scArches preserves biological state information while removing batch effects, despite using four orders of magnitude fewer parameters than de novo integration. scArches generalizes to multimodal reference mapping, allowing imputation of missing modalities. Finally, scArches retains coronavirus disease 2019 (COVID-19) disease variation when mapping to a healthy reference, enabling the discovery of disease-specific cell states. scArches will facilitate collaborative projects by enabling iterative construction, updating, sharing and efficient use of reference atlases

A Roadmap for a Consensus Human Skin Cell Atlas and Single-Cell Data Standardization

Single-cell technologies have become essential to driving discovery in both basic and translational investigative dermatology. Despite the multitude of available datasets, a central reference atlas of normal human skin, which can serve as a reference resource for skin cell types, cell states, and their molecular signatures, is still lacking. For any such atlas to receive broad acceptance, participation by many investigators during atlas construction is an essential prerequisite. As part of the Human Cell Atlas project, we have assembled a Skin Biological Network to build a consensus Human Skin Cell Atlas and outline a roadmap toward that goal. We define the drivers of skin diversity to be considered when selecting sequencing datasets for the atlas and list practical hurdles during skin sampling that can result in data gaps and impede comprehensive representation and technical considerations for tissue processing and computational analysis, the accounting for which should minimize biases in cell type enrichments and exclusions and decrease batch effects. By outlining our goals for Atlas 1.0, we discuss how it will uncover new aspects of skin biology