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      Heterogeneity of human corneal endothelium implicates lncRNA NEAT1 in Fuchs endothelial corneal dystrophy

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          Abstract

          The corneal endothelium is critical for maintaining corneal clarity by mediating hydration through barrier and pump functions. Progressive loss of corneal endothelial cells during aging has been associated with the development of Fuchs endothelial corneal dystrophy (FECD), one of the main causes of cornea-related vision loss. The mechanisms underlying FECD development remain elusive. Single-cell RNA sequencing of isolated healthy human corneas discovered 4 subpopulations of corneal endothelial cells with distinctive signatures. Unsupervised clustering analysis uncovered nuclear enriched abundant transcript 1 ( NEAT1), a long non-coding RNA (lncRNA), as the top expressed gene in the C0-endothelial subpopulation, but markedly downregulated in FECD. Consistent with human corneas, a UVA-induced mouse FECD model validated the loss of NEAT1 expression. Loss of NEAT1 function by an in vivo genetic approach reproduced the exacerbated phenotype of FECD by ablating corneal endothelial cells. Conversely, gain of function by a CRISPR-activated adenoviral delivery system protected corneas from UVA-induced FECD. Our findings provide novel mechanistic insights into the development of FECD, and targeting NEAT1 offers an attractive approach for treating FECD.

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          Abstract

          No curative therapy is available for effectively treating FECD, an age-related complex genetic disorder. Wang et al. identified NEAT1, a lncRNA strongly downregulated in FECD, as a key regulator in development of FECD, whose overexpression by the rAAV CRISPR system delayed the onset and progression of FECD.

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          Most cited references78

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          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.
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            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/.
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              Comprehensive Integration of Single-Cell Data

              Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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                Author and article information

                Contributors
                Journal
                Mol Ther Nucleic Acids
                Mol Ther Nucleic Acids
                Molecular Therapy. Nucleic Acids
                American Society of Gene & Cell Therapy
                2162-2531
                10 January 2022
                08 March 2022
                10 January 2022
                : 27
                : 880-893
                Affiliations
                [1 ]State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Eye Institute of Shandong First Medical University, China
                [2 ]Qingdao Eye Hospital of Shandong First Medical University, Qingdao, China
                [3 ]Department of Microbiology, Tumor, and Cell Biology, Karolinska Institute, Stockholm, Sweden
                [4 ]Eye Hospital of Shandong First Medical University, Jinan, China
                Author notes
                []Corresponding author Qingjun Zhou, Shandong First Medical University, No. 5 Yanerdao Road, Qingdao 266071, China. qjzhou2000@ 123456hotmail.com
                [∗∗ ]Corresponding author Weiyun Shi, Shandong First Medical University, No. 5 Yanerdao Road, Qingdao 266071, China. weiyunshi@ 123456163.com
                [5]

                These authors contributed equally

                Article
                S2162-2531(22)00010-5
                10.1016/j.omtn.2022.01.005
                8807987
                35141048
                f1c05748-a20f-4ed6-b21c-6997666c5b80
                © 2022 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 19 August 2021
                : 7 January 2022
                Categories
                Original Article

                Molecular medicine
                single-cell transcriptome,corneal endothelium,lncrna neat1,fuchs endothelial corneal dystrophy,crispr-activated delivery system

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