1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Molecular programs of fibrotic change in aging human lung

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Lung fibrosis is increasingly detected with aging and has been associated with poor outcomes in acute lung injury or infection. However, the molecular programs driving this pro-fibrotic evolution are unclear. Here we profile distal lung samples from healthy human donors across the lifespan. Gene expression profiling by bulk RNAseq reveals both increasing cellular senescence and pro-fibrotic pathway activation with age. Quantitation of telomere length shows progressive shortening with age, which is associated with DNA damage foci and cellular senescence. Cell type deconvolution analysis of the RNAseq data indicates a progressive loss of lung epithelial cells and an increasing proportion of fibroblasts with age. Consistent with this pro-fibrotic profile, second harmonic imaging of aged lungs demonstrates increased density of interstitial collagen as well as decreased alveolar expansion and surfactant secretion. In this work, we reveal the transcriptional and structural features of fibrosis and associated functional impairment in normal lung aging.

          Abstract

          Age is associated with increasing vulnerability to both acute and chronic lung diseases. Employing genomic analysis and live lung imaging, this study reveals a profile of increased cellular senescence, telomere shortening, and fibrosis-induced impaired alveolar function in the natural history of human lung aging.

          Related collections

          Most cited references63

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Integrating single-cell transcriptomic data across different conditions, technologies, and species

              Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
                Bookmark

                Author and article information

                Contributors
                jb39@columbia.edu
                paul.wolters@ucsf.edu
                mallar.bhattacharya@ucsf.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                2 November 2021
                2 November 2021
                2021
                : 12
                : 6309
                Affiliations
                [1 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Department of Medicine, Division of Pulmonary, Critical Care, Allergy, and Sleep, , University of California, ; San Francisco, CA USA
                [2 ]GRID grid.21729.3f, ISNI 0000000419368729, Lung Biology Laboratory, Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, , Vagelos College of Physicians and Surgeons of Columbia University, ; New York, NY USA
                [3 ]GRID grid.6451.6, ISNI 0000000121102151, Lorry I. Lokey Interdisciplinary Center for Life Sciences & Engineering, , Technion Israel Institute of Technology, ; Haifa, Israel
                [4 ]GRID grid.418158.1, ISNI 0000 0004 0534 4718, Genentech Research and Early Development, , Genentech, Inc., ; South San Francisco, CA USA
                [5 ]GRID grid.21729.3f, ISNI 0000000419368729, Department of Pediatrics, , Vagelos College of Physicians and Surgeons of Columbia University, ; New York, NY USA
                [6 ]GRID grid.21729.3f, ISNI 0000000419368729, Department of Surgery, , Vagelos College of Physicians and Surgeons of Columbia University, ; New York, NY USA
                [7 ]GRID grid.21729.3f, ISNI 0000000419368729, Department of Microbiology and Immunology, , Columbia University, ; New York, NY USA
                Author information
                http://orcid.org/0000-0003-3158-9666
                http://orcid.org/0000-0001-7929-9444
                http://orcid.org/0000-0001-6334-5039
                http://orcid.org/0000-0003-3039-8155
                http://orcid.org/0000-0001-7677-9979
                http://orcid.org/0000-0001-8236-9183
                http://orcid.org/0000-0002-5588-6211
                http://orcid.org/0000-0002-3108-6164
                http://orcid.org/0000-0002-5439-547X
                Article
                26603
                10.1038/s41467-021-26603-2
                8563941
                34728633
                1c1ba456-d9be-465f-830c-0d60eac6c3db
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 11 January 2021
                : 14 October 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000050, U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI);
                Award ID: HL131560
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                gene expression,ageing,respiratory tract diseases
                Uncategorized
                gene expression, ageing, respiratory tract diseases

                Comments

                Comment on this article