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      Integrative analysis of single-cell RNA-seq and ATAC-seq reveals heterogeneity of induced pluripotent stem cell-derived hepatic organoids

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          Summary

          To gain deeper insights into transcriptomes and epigenomes of organoids, liver organoids from two states (expandable and more differentiated) were subjected to single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) analyses. Mitochondrial gene expression was higher in differentiated than in non-differentiated hepatocytes, with ATAC-seq peaks increasing near the mitochondrial control region. Differentiation of liver organoids resulted in the expression of transcription factors that act as enhancers and repressors. In addition, epigenetic mechanisms regulating the expression of alpha-fetoprotein (AFP) and albumin (ALB) differed in liver organoids and adult liver. Knockdown of PDX1, an essential transcription factor for pancreas development, led to the hepatic maturation of liver organoids through regulation of AFP and ALB expression. This integrative analysis of the transcriptomes and epigenomes of liver organoids at the single-cell level may contribute to a better understanding of the regulatory networks during liver development and the further development of mature in vitro human liver models.

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          Highlights

          • Liver organoids exhibited cell compositions similar to human liver tissue

          • Mitochondrial gene expression increased in the mature hepatocytes of liver organoid

          • Differences between liver organoid and tissue were revealed by single-cell data

          • Knockdown of PDX1 led to the hepatic maturation of liver organoids

          Abstract

          Molecular mechanism of gene regulation; Integrative aspects of cell biology; Experimental models in systems biology; Transcriptomics

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          Most cited references76

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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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            Fast gapped-read alignment with Bowtie 2.

            As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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              Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

              Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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                Author and article information

                Contributors
                Journal
                iScience
                iScience
                iScience
                Elsevier
                2589-0042
                18 August 2023
                15 September 2023
                18 August 2023
                : 26
                : 9
                : 107675
                Affiliations
                [1 ]Korean Bioinformation Center, Daejeon, Korea
                [2 ]Stem Cell Convergence Research Center, Daejeon, Korea
                [3 ]Personalized Genomic Medicine Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Korea
                [4 ]Department of Functional Genomics, University of Science and Technology (UST), Daejeon, Korea
                [5 ]School of Pharmacy, Sungkyunkwan University, Suwon 16419, Republic of Korea
                Author notes
                []Corresponding author mjson@ 123456kribb.re.kr
                [∗∗ ]Corresponding author kimsy@ 123456kribb.re.kr
                [6]

                These authors contributed equally

                [7]

                Lead contact

                Article
                S2589-0042(23)01752-2 107675
                10.1016/j.isci.2023.107675
                10481365
                37680467
                80606933-613c-40d7-9626-14e11d919b75
                © 2023 The Author(s)

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

                History
                : 23 November 2022
                : 30 March 2023
                : 14 August 2023
                Categories
                Article

                molecular mechanism of gene regulation,integrative aspects of cell biology,experimental models in systems biology,transcriptomics

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