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      Efficient genome editing in erythroid cells unveils novel MYB target genes and regulatory functions

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          Summary

          Targeted genome editing holds great promise in biology. However, efficient genome modification, including gene knock-in (KI), remains an unattained goal in multiple cell types and loci due to poor transfection efficiencies and low target genes expression, impeding the positive selection of recombined cells. Here, we describe a genome editing approach to achieve efficient gene targeting using hard to transfect erythroid cell lines. We demonstrate robust fluorescent protein KI efficiency in low expressed transcription factor (TF) genes (e.g., Myb or Zeb1). We further show the ability to target two independent loci in individual cells, exemplified by MYB-GFP and NuMA-Cherry double KI, allowing multicolor labeling of regulatory factors at physiological endogenous levels. Our KI tagging approach allowed us to perform genome-wide TF analysis at increased signal-to-noise ratios, and highlighted previously unidentified MYB target genes and pathways. Overall, we establish a versatile CRISPR-Cas9-based platform, offering attractive opportunities for the dissection of the erythroid differentiation process.

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          Highlights

          • Efficient genome editing in hard-to-transfect erythroid cell lines

          • High-efficiency in-frame GFP insertion in low expressed gene loci Myb and Zeb1

          • Multi-gene targeting in individual cells: Myb-Gfp/ Numa1-Cherry double KI

          • MYB-GFP CUT&RUN analysis uncovers previously unidentified MYB target genes

          Abstract

          Cell biology; Genetic engineering; Molecular biology

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          Most cited references60

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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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              clusterProfiler: an R package for comparing biological themes among gene clusters.

              Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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                Author and article information

                Contributors
                Journal
                iScience
                iScience
                iScience
                Elsevier
                2589-0042
                15 August 2023
                15 September 2023
                15 August 2023
                : 26
                : 9
                : 107641
                Affiliations
                [1 ]IGMM, University Montpellier, CNRS, Montpellier, France
                [2 ]Laboratory of Excellence GR-Ex, Université de Paris, Paris, France
                Author notes
                []Corresponding author eric.soler@ 123456igmm.cnrs.fr
                [3]

                Senior author

                [4]

                These authors contributed equally

                [5]

                Present address: Institut de Génomique Fonctionnelle, INSERM U 1191, CNRS UMR 5203, Montpellier, France

                [6]

                Lead contact

                Article
                S2589-0042(23)01718-2 107641
                10.1016/j.isci.2023.107641
                10475484
                37670779
                6b26a2e3-faf1-4dec-8026-5e527f564794
                © 2023 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
                : 27 March 2023
                : 9 July 2023
                : 11 August 2023
                Categories
                Article

                cell biology,genetic engineering,molecular biology
                cell biology, genetic engineering, molecular biology

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