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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.

ABSTRACTSatellite imagery constitutes an affordable solution to map coastal bathymetry at very high resolution (VHR). Bathymetry retrieval is commonly based on regression analysis linking depth response with remotely-sensed spectral predictors. Most studies have focused on a single regressor using conventional three visible bands over clear waters. However the interdependence of added visible bands in respect to linear and non-linear regressors is poorly studied and strongly lacks for turbid waters. Here we investigate the single and joint contribution of both spectral bands (visible 1.24 m WorldView-3, WV-3) and regressor types on the performance of retrieval. A case study over Saint-Malo (Brittany, France) megatidal turbid waters enables to quantify the accuracy gain related to Coastal and yellow bands as well as ordinary least squares (OLS), generalized linear model (GLM) and artificial neural network (ANN). Correlation analysis reveals that both Coastal and yellow bands do not significantly increase conventional performance for the three regressors. ANN surpassed GLM and OLS for both conventional and boosted WV-3 visible spectral datasets, reaching 0.94 correlation coefficient and 0.52 m accuracy. Comparisons’ significance indicates that selecting a robust regression method (including parameterization) is more efficient than adding spectral bands for mapping VHR bathymetry of coastal turbid waters.

The Support Vector Machines (SVM) are increasingly far-reaching in remotely sensed data classification. As supervised classifiers, the SVM output depends on the input pixels, pointing out that training is potentially an important stage for optimizing classification accuracy. The SVM consist in projecting pixels into a high-dimensional feature space and then fitting in a hyperplane, maximizing the distance between the closest vectors and the hyperplane itself. This study aims to locate the pixels acting as support vectors and identify the ecological features they contain in a tropical vegetation context. The analyses focused on a Quickbird-2 image where two vegetation types occur. The physical boundary between classes was delineated in the field, and we used an iterative method to mark and localize the pixels acting as support vectors on the image. Our results highlight that vegetation sampling should focus on ecotones (the transition area between two different and adjacent vegetation classes) in order to minimize the field survey effort and maximize the mapping accuracy.