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

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

RIPK1 kinase-dependent inflammation and cell death contribute to the pathogenesis of COPD.

RATIONALE: Receptor-interacting protein kinase 1 (RIPK1) is a key mediator of regulated cell death (including apoptosis and necroptosis) and inflammation, both drivers of chronic obstructive pulmonary disease (COPD) pathogenesis. OBJECTIVE: We aimed to define the contribution of RIPK1 kinase-dependent cell death and inflammation in the pathogenesis of COPD. METHODS: We assessed RIPK1 expression in single-cell RNA-sequencing data from human and mouse lungs and validated RIPK1 levels in lung tissue of COPD patients via immunohistochemistry. Next, we assessed the consequences of genetic and pharmacological inhibition of RIPK1 kinase activity in experimental COPD, using Ripk1S25D /S25D kinase deficient mice and the RIPK1 kinase inhibitor GSK'547. MEASUREMENTS AND MAIN RESULTS: RIPK1 expression increased in alveolar type I (AT1), AT2, ciliated and neuroendocrine cells in human COPD. RIPK1 protein levels were significantly increased in airway epithelium of COPD patients, compared to never smokers and smokers without airflow limitation. In mice, exposure to cigarette smoke (CS) increased Ripk1 expression similarly in AT2 cells, and further in alveolar macrophages and T cells. Genetic and/or pharmacological inhibition of RIPK1 kinase activity significantly attenuated airway inflammation upon acute and subacute CS-exposure, as well as airway remodeling, emphysema and apoptotic and necroptotic cell death upon chronic CS-exposure. Similarly, pharmacological RIPK1 kinase inhibition significantly attenuated elastase-induced emphysema and lung function decline. Finally, RNA-sequencing on lung tissue of CS-exposed mice revealed downregulation of cell death and inflammatory pathways upon pharmacological RIPK1 kinase inhibition. CONCLUSIONS: RIPK1 kinase inhibition is protective in experimental models of COPD and may represent a novel promising therapeutic approach

Vertical distribution of heavy metals in soil profile in a seasonally waterlogging agriculture field in Eastern Ganges Basin

The accumulation of heavy metals in soil and water is a serious concern due to their persistence and toxicity. This study investigated the vertical distribution of heavy metals, possible sources and their relation with soil texture in a soil profile from seasonally waterlogged agriculture fields of Eastern Ganges basin. Fifteen samples were collected at ~0.90-m interval during drilling of 13.11 mbgl and analysed for physical parameters (moisture content and grain size parameters: sand, silt, clay ratio) and heavy metals (Fe, Mn, Cr, Cu, Pb, Zn, Co, Ni and Cd). The average metal content was in the decreasing order of Fe > Mn > Cr > Zn > Ni > Cu > Co > Pb > Cd. Vertical distribution of Fe, Mn, Zn and Ni shows more or less similar trends, and clay zone records high concentration of heavy metals. The enrichment of heavy metals in clay zone with alkaline pH strongly implies that the heavy metal distributions in the study site are effectively regulated by soil texture and reductive dissolution of Fe and Mn oxy-hydroxides. Correlation coefficient analysis indicates that most of the metals correlate with Fe, Mn and soil texture (clay and silt). Soil quality assessment was carried out using geoaccumulation index (I geo), enrichment factor (EF) and contamination factor (CF). The enrichment factor values were ranged between 0.66 (Mn) and 2.34 (Co) for the studied metals, and the contamination factor values varied between 0.79 (Mn) and 2.55 (Co). Results suggest that the elements such as Cu and Co are categorized as moderate to moderately severe contamination, which are further confirmed by I geo values (0.69 for Cu and 0.78 for Co). The concentration of Ni exceeded the effects-range median values, and the biological adverse effect of this metal is 87 %. The average concentration of heavy metals was compared with published data such as concentration of heavy metals in Ganga River sediments, Ganga Delta sediments and upper continental crust (UCC), which apparently revealed that heavy metals such as Fe, Mn, Cr, Pb, Zn and Cd are influenced by the dynamic nature of flood plain deposits. Agricultural practice and domestic sewage are also influenced on the heavy metal content in the study area