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. </p

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

A cooperative mechanism drives budding yeast kinetochore assembly downstream of CENP-A

Kinetochores are megadalton-sized protein complexes that mediate chromosome–microtubule interactions in eukaryotes. How kinetochore assembly is triggered specifically on centromeric chromatin is poorly understood. Here we use biochemical reconstitution experiments alongside genetic and structural analysis to delineate the contributions of centromere-associated proteins to kinetochore assembly in yeast. We show that the conserved kinetochore subunits Ame1$^{CENP-U}$ and Okp1$^{CENP-Q}$ form a DNA-binding complex that associates with the microtubule-binding KMN network via a short Mtw1 recruitment motif in the N terminus of Ame1. Point mutations in the Ame1 motif disrupt kinetochore function by preventing KMN assembly on chromatin. Ame1–Okp1 directly associates with the centromere protein C (CENP-C) homologue Mif2 to form a cooperative binding platform for outer kinetochore assembly. Our results indicate that the key assembly steps, CENP-A recognition and outer kinetochore recruitment, are executed through different yeast constitutive centromere-associated network subunits. This two-step mechanism may protect against inappropriate kinetochore assembly similar to rate-limiting nucleation steps used by cytoskeletal polymers

A mass spectrometry-based hybrid method for structural modeling of protein complexes

We describe a method that integrates data derived from different mass spectrometry (MS)-based techniques with a modeling strategy for structural characterization of protein assemblies. We encoded structural data derived from native MS, bottom-up proteomics, ion mobility–MS and chemical cross-linking MS into modeling restraints to compute the most likely structure of a protein assembly. We used the method to generate near-native models for three known structures and characterized an assembly intermediate of the proteasomal base

Probing Native Protein Structures by Chemical Cross-linking, Mass Spectrometry, and Bioinformatics*

Chemical cross-linking of reactive groups in native proteins and protein complexes in combination with the identification of cross-linked sites by mass spectrometry has been in use for more than a decade. Recent advances in instrumentation, cross-linking protocols, and analysis software have led to a renewed interest in this technique, which promises to provide important information about native protein structure and the topology of protein complexes. In this article, we discuss the critical steps of chemical cross-linking and its implications for (structural) biology: reagent design and cross-linking protocols, separation and mass spectrometric analysis of cross-linked samples, dedicated software for data analysis, and the use of cross-linking data for computational modeling. Finally, the impact of protein cross-linking on various biological disciplines is highlighted

A mass spectrometry-based hybrid method for structural modeling of protein complexes.

We describe a method that integrates data derived from different mass spectrometry (MS)-based techniques with a modeling strategy for structural characterization of protein assemblies. We encoded structural data derived from native MS, bottom-up proteomics, ion mobility-MS and chemical cross-linking MS into modeling restraints to compute the most likely structure of a protein assembly. We used the method to generate near-native models for three known structures and characterized an assembly intermediate of the proteasomal base