Lisa Sikkema

The rise of single-cell atlases has opened up the use of reference atlases as a comprehensive healthy control for the study of disease. A key step for using atlases is the mapping of “query” disease or perturbed datasets onto a healthy reference atlas, which allows for comparison of the query to healthy controls and thereby the identification of perturbation-specific transcriptional cell states. However, the mapping success, i.e. the extent to which these mappings correctly reflect biological similarities and differences between query and reference, varies with e.g. experimental confounders in the data (dissociation protocol, single-cell assay) or the mapping method used, and cannot be predicted in advance. Moreover, an unsuccessful mapping can result in falsely reporting technical artifacts as disease or perturbation-specific cellular changes and in overlooking true altered states. Here, we present MapQC, a method that quantifies query-to-reference mapping success by leveraging the information present in the reference embedding. Specifically, MapQC tests the presence of remaining batch effect in the embedding after mapping by comparing inter-sample distances in the healthy reference itself to distance of query control samples to the reference. Similarly, it tests whether disease or perturbation-specific variation has been retained during mapping by testing whether query perturbed samples are more distant to the reference than reference samples are from each other. We apply MapQC to lung and endometrial query datasets, including data of idiopathic pulmonary fibrosis and Asherman Syndrome, mapped to large-scale healthy references. We show that MapQC correctly distinguishes successful mappings from unsuccessful ones where data visualization techniques such as UMAP can be deceiving, and that mapQC outperforms integration metrics often re-purposed for mapping qualitycontrol. Moreover, only mappings that MapQC identified as successful result in the correct identification of cell state changes specific to disease. Taken together, MapQC provides a critical quality-control metric for a more reliable, robust, and accurate use of reference atlases to study disease.

The rapid adoption of single-cell technologies has created an opportunity to build single-cell ‘atlases’ integrating diverse datasets across many laboratories. Such atlases can serve as a reference for analyzing and interpreting current and future data. However, it has become apparent that atlasing approaches differ, and the impact of these differences are often unclear. Here we review the current atlasing literature and present considerations for building and using atlases. Importantly, we find that no one-size-fits-all protocol for atlas building exists, but rather we discuss context-specific considerations and workflows, including atlas conceptualization, data collection, curation and integration, atlas evaluation and atlas sharing. We further highlight the benefits of integrated atlases for analyses of new datasets and deriving biological insights beyond what is possible from individual datasets. Our overview of current practices and associated recommendations will improve the quality of atlases to come, facilitating the shift to a unified, reference-based understanding of single-cell biology.

With progressive digitalization of healthcare systems worldwide, large-scale collection of electronic health records (EHRs) has become commonplace. However, an extensible framework for comprehensive exploratory analysis that accounts for data heterogeneity is missing. Here we introduce ehrapy, a modular open-source Python framework designed for exploratory analysis of heterogeneous epidemiology and EHR data. ehrapy incorporates a series of analytical steps, from data extraction and quality control to the generation of low-dimensional representations. Complemented by rich statistical modules, ehrapy facilitates associating patients with disease states, differential comparison between patient clusters, survival analysis, trajectory inference, causal inference and more. Leveraging ontologies, ehrapy further enables data sharing and training EHR deep learning models, paving the way for foundational models in biomedical research. We demonstrate ehrapy’s features in six distinct examples. We applied ehrapy to stratify patients affected by unspecified pneumonia into finer-grained phenotypes. Furthermore, we reveal biomarkers for significant differences in survival among these groups. Additionally, we quantify medication-class effects of pneumonia medications on length of stay. We further leveraged ehrapy to analyze cardiovascular risks across different data modalities. We reconstructed disease state trajectories in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) based on imaging data. Finally, we conducted a case study to demonstrate how ehrapy can detect and mitigate biases in EHR data. ehrapy, thus, provides a framework that we envision will standardize analysis pipelines on EHR data and serve as a cornerstone for the community.

With progressive digitalization of healthcare systems worldwide, large-scale collection of electronic health records (EHRs) has become commonplace. However, an extensible framework for comprehensive exploratory analysis that accounts for data heterogeneity is missing. Here, we introduce ehrapy, a modular open-source Python framework designed for exploratory end-to-end analysis of heterogeneous epidemiology and electronic health record data. Ehrapy incorporates a series of analytical steps, from data extraction and quality control to the generation of low-dimensional representations. Complemented by rich statistical modules, ehrapy facilitates associating patients with disease states, differential comparison between patient clusters, survival analysis, trajectory inference, causal inference, and more. Leveraging ontologies, ehrapy further enables data sharing and training EHR deep learning models paving the way for foundational models in biomedical research. We demonstrated ehrapy’s features in five distinct examples: We first applied ehrapy to stratify patients affected by unspecified pneumonia into finer-grained phenotypes. Furthermore, we revealed biomarkers for significant differences in survival among these groups. Additionally, we quantify medication-class effects of pneumonia medications on length of stay. We further leveraged ehrapy to analyze cardiovascular risks across different data modalities. Finally, we reconstructed disease state trajectories in SARS-CoV-2 patients based on imaging data. Ehrapy thus provides a framework that we envision will standardize analysis pipelines on EHR data and serve as a cornerstone for the community.

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.

PDF file - 151K, Supplemental Table 1. mutations found in NYU cohort and previously discovered by Hodis et al; and Krauthammer et al; Supplemental Table 2. Breslow-Day test of homogeneity for association between PP6C, BRAF, or NRAS mutations in Primary and Metastatic patients respectively. Supplemental Table 3. Bbaseline characteristics and prognostic factors by PP6C mutation status in primary and metastatic patients. Supplemental Table 4: PP6C, BRAF and NRAS mutation allele:wild-type allele ratios. Supplemental Table 5. Correlation of PP6R binding and Aurora A Kinase phosphorylation(primary patients only).

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 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 programme. 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 Human 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 Human 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 genomics is now producing an ever-increasing amount of datasets that, when integrated, could provide large-scale reference atlases of tissue in health as well as disease. Such large-scale atlases not only increase the scale and generalizability of analyses but also enable combining the knowledge generated by individual studies. Specifically, individual studies often differ in terms of cell annotation terminology and depth, with different groups specializing in different cell type compartments, often using distinct terminology. Understanding how these distinct sets of annotations are related and complement each other would mark a major step towards a consensus-based cell type annotation that reflects the latest knowledge in the field. Whereas recent computational techniques, referred to as “reference mapping” methods, facilitate the usage and expansion of existing reference atlases by mapping new datasets (i.e. queries) onto an atlas; a systematic approach towards harmonizing dataset-specific cell type terminology and annotation depth is still lacking. Here, we present “treeArches”, a framework to automatically build and extend reference atlases while enriching them with an updatable hierarchy of cell type annotations across different datasets. We demonstrate various use cases for treeArches, from automatically resolving relations between reference and query cell types to identifying unseen cell types not present in the reference, such as disease-associated cell states. We envision treeArches enabling data-driven construction of consensus, atlas-level cell type hierarchies, as well as facilitating efficient usage of reference atlases.

Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. The growth in both size and number of single-cell datasets, combined with recent advances in computational techniques, for the first time makes it possible to generate such comprehensive large-scale atlases through integration of multiple datasets. Here, we present the integrated Human Lung Cell Atlas (HLCA) combining 46 datasets of the human respiratory system into a single atlas spanning over 2.2 million cells from 444 individuals across health and disease. The HLCA contains a consensus re-annotation of published and newly generated datasets, resolving under- or misannotation of 59% of cells in the original datasets. The HLCA enables recovery of rare cell types, provides consensus marker genes for each cell type, and uncovers gene modules associated with demographic covariates and anatomical location within the respiratory system. To facilitate the use of the HLCA as a reference for single-cell lung research and allow rapid analysis of new data, we provide an interactive web portal to project datasets onto the HLCA. Finally, we demonstrate the value of the HLCA reference for interpreting disease-associated changes. Thus, the HLCA outlines a roadmap for the development and use of organ-scale cell atlases within the Human Cell Atlas.