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 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

Программное обеспечение для оценки динамики состояния растительного покрова с использованием данных спутникового мониторинга Земли

The article presents the results of software development for predictive maps modeling of the earth’s surface processes based on time-varying satellite data using the probabilistic and spatial characteristics of various types of the earth’s surface in the image. The analysis of existing methods for the assessment and modeling of the state of landscapes of various territories using satellite Earth monitoring data is presented. The review of existing systems of the earth’s surface dynamics analysis and their main advantages and disadvantages is given. The cellular automata method was used to implement the forecast. This method allows complex systems modeling using a simple set of rules and is the most convenient and accurate method for working with a space images. Algorithms and basic modeling parameters for using this method are described. The developed software makes it possible to forecast the state of the surface of the territories under consideration based on a series of time-varying data, and also to specify various modeling parameters in order to improve the accuracy of forecast maps. The results of the developed software testing with MODIS and Landsat data are presented, the accuracy of the forecast and the influence of simulation parameters on the result were estimatedВ статье изложены результаты разработки программного обеспечения для построения прогнозных карт развития процессов на земной поверхности на основе разновременных данных спутникового мониторинга с использованием вероятностных и пространственных характеристик различных типов земной поверхности на изображении. Представлен анализ существующих методов для оценки и моделирования состояния ландшафтов различных территорий с использованием данных спутникового мониторинга Земли. Приведен обзор существующих систем анализа динамики земной поверхности, их основных достоинств и недостатков. Для разработки использован метод клеточных автоматов, который позволяет моделировать сложные системы с помощью простого набора правил и является наиболее удобным и точным методом для работы с аэрокосмоснимками. Описаны алгоритмы и основные параметры моделирования, необходимые для использования данного метода. Разработанное программное обеспечение позволяет производить прогноз состояния поверхности рассматриваемых территорий на основе серии разновременных данных, а также задавать различные параметры моделирования с целью повышения точности прогнозных карт. Приведены результаты тестирования разработанного программного обеспечения на данных MODIS и Landsat, произведена оценка точности прогноза, влияния параметров моделирования на полученный результа

Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers

Abstract Large language models such as ChatGPT can produce increasingly realistic text, with unknown information on the accuracy and integrity of using these models in scientific writing. We gathered fifth research abstracts from five high-impact factor medical journals and asked ChatGPT to generate research abstracts based on their titles and journals. Most generated abstracts were detected using an AI output detector, ‘GPT-2 Output Detector’, with % ‘fake’ scores (higher meaning more likely to be generated) of median [interquartile range] of 99.98% ‘fake’ [12.73%, 99.98%] compared with median 0.02% [IQR 0.02%, 0.09%] for the original abstracts. The AUROC of the AI output detector was 0.94. Generated abstracts scored lower than original abstracts when run through a plagiarism detector website and iThenticate (higher scores meaning more matching text found). When given a mixture of original and general abstracts, blinded human reviewers correctly identified 68% of generated abstracts as being generated by ChatGPT, but incorrectly identified 14% of original abstracts as being generated. Reviewers indicated that it was surprisingly difficult to differentiate between the two, though abstracts they suspected were generated were vaguer and more formulaic. ChatGPT writes believable scientific abstracts, though with completely generated data. Depending on publisher-specific guidelines, AI output detectors may serve as an editorial tool to help maintain scientific standards. The boundaries of ethical and acceptable use of large language models to help scientific writing are still being discussed, and different journals and conferences are adopting varying policies

UCSC Cell Browser: visualize your single-cell data

SummaryAs the use of single-cell technologies has grown, so has the need for tools to explore these large, complicated datasets. The UCSC Cell Browser is a tool that allows scientists to visualize gene expression and metadata annotation distribution throughout a single-cell dataset or multiple datasets.Availability and implementationWe provide the UCSC Cell Browser as a free website where scientists can explore a growing collection of single-cell datasets and a freely available python package for scientists to create stable, self-contained visualizations for their own single-cell datasets. Learn more at https://cells.ucsc.edu.Supplementary informationSupplementary data are available at Bioinformatics online

Introduced, Mixed, and Peripheral: Conservation of Mitochondrial-DNA Lineages in the Wild Boar (<i>Sus scrofa</i> L.) Population in the Urals

Translocations and introductions are important events that allow organisms to overcome natural barriers. The genetic background of colonization success and genetic consequences of the establishment of populations in new environments are of great interest for predicting species’ colonization success. The wild boar has been introduced into many parts of the world. We analyzed sequences of the mitochondrial-DNA control region in the wild boars introduced into the Ural region and compared them with sequences from founder populations (from Europe, the Caucasus, Central Asia, and the Far East). We found that the introduced population has high genetic diversity. Haplotypes from all the major phylogenetic clades were detected in the analyzed group of the animals from the Urals. In this group, no haplotypes identical to Far Eastern sequences were detectable despite a large number of founders from that region. The contribution of lineages originating from Eastern Europe was greater than expected from the proportions (%) of European and Asian animals in the founder populations. This is the first study on the genetic diversity and structure of a wild boar population of mixed origin at the northern periphery of this species’ geographical range

Aging imparts cell-autonomous dysfunction to regulatory T cells during recovery from influenza pneumonia

Regulatory T (Treg) cells orchestrate resolution and repair of acute lung inflammation and injury after viral pneumonia. Compared with younger patients, older individuals experience impaired recovery and worse clinical outcomes after severe viral infections, including influenza and SARS coronavirus 2 (SARS-CoV-2). Whether age is a key determinant of Treg cell prorepair function after lung injury remains unknown. Here, we showed that aging results in a cell-autonomous impairment of reparative Treg cell function after experimental influenza pneumonia. Transcriptional and DNA methylation profiling of sorted Treg cells provided insight into the mechanisms underlying their age-related dysfunction, with Treg cells from aged mice demonstrating both loss of reparative programs and gain of maladaptive programs. Strategies to restore youthful Treg cell functional programs could be leveraged as therapies to improve outcomes among older individuals with severe viral pneumonia