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      Single cell transcriptomic landscape of diabetic foot ulcers

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          Abstract

          Diabetic foot ulceration (DFU) is a devastating complication of diabetes whose pathogenesis remains incompletely understood. Here, we profile 174,962 single cells from the foot, forearm, and peripheral blood mononuclear cells using single-cell RNA sequencing. Our analysis shows enrichment of a unique population of fibroblasts overexpressing MMP1, MMP3, MMP11, HIF1A, CHI3L1, and TNFAIP6 and increased M1 macrophage polarization in the DFU patients with healing wounds. Further, analysis of spatially separated samples from the same patient and spatial transcriptomics reveal preferential localization of these healing associated fibroblasts toward the wound bed as compared to the wound edge or unwounded skin. Spatial transcriptomics also validates our findings of higher abundance of M1 macrophages in healers and M2 macrophages in non-healers. Our analysis provides deep insights into the wound healing microenvironment, identifying cell types that could be critical in promoting DFU healing, and may inform novel therapeutic approaches for DFU treatment.

          Abstract

          Diabetic foot ulcers (DFUs) remain a complication of diabetes that are difficult to heal and lead to disability. Here the authors use single-cell RNA-sequencing and spatial transcriptomics to characterize the DFU cellular landscape and identify a population of fibroblasts that is associated with successful wound closure.

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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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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              FLASH: fast length adjustment of short reads to improve genome assemblies.

              Next-generation sequencing technologies generate very large numbers of short reads. Even with very deep genome coverage, short read lengths cause problems in de novo assemblies. The use of paired-end libraries with a fragment size shorter than twice the read length provides an opportunity to generate much longer reads by overlapping and merging read pairs before assembling a genome. We present FLASH, a fast computational tool to extend the length of short reads by overlapping paired-end reads from fragment libraries that are sufficiently short. We tested the correctness of the tool on one million simulated read pairs, and we then applied it as a pre-processor for genome assemblies of Illumina reads from the bacterium Staphylococcus aureus and human chromosome 14. FLASH correctly extended and merged reads >99% of the time on simulated reads with an error rate of <1%. With adequately set parameters, FLASH correctly merged reads over 90% of the time even when the reads contained up to 5% errors. When FLASH was used to extend reads prior to assembly, the resulting assemblies had substantially greater N50 lengths for both contigs and scaffolds. The FLASH system is implemented in C and is freely available as open-source code at http://www.cbcb.umd.edu/software/flash. t.magoc@gmail.com.
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                Author and article information

                Contributors
                aveves@bidmc.harvard.edu
                manoj.bhasin@emory.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                10 January 2022
                10 January 2022
                2022
                : 13
                : 181
                Affiliations
                [1 ]GRID grid.38142.3c, ISNI 000000041936754X, The Rongxiang Xu, MD, Center for Regenerative Therapeutics and Joslin-Beth Israel Deaconess Foot Center, , Beth Israel Deaconess Medical Center and Harvard Medical School, ; Boston, MA USA
                [2 ]GRID grid.189967.8, ISNI 0000 0001 0941 6502, Aflac Cancer and Blood Disorders Center, Children Healthcare of Atlanta, Department of Pediatrics and Biomedical Informatics, , Emory University, ; Atlanta, GA USA
                [3 ]GRID grid.189967.8, ISNI 0000 0001 0941 6502, Winship Cancer Institute, , Emory University, ; Atlanta, GA USA
                [4 ]GRID grid.47100.32, ISNI 0000000419368710, Molecular, Cellular and Developmental Biology, , Yale University, ; New Haven, CT USA
                [5 ]GRID grid.38142.3c, ISNI 000000041936754X, Vaccine and Immunotherapy Center, Department of Medicine, Massachusetts General Hospital, , Harvard Medical School, ; Boston, MA USA
                [6 ]GRID grid.413203.7, ISNI 0000 0000 8489 2368, Lincoln County Hospital, , Northern Lincolnshire and Goole NHS Foundation Trust, ; Scunthorpe, UK
                [7 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Pathology, , Beth Israel Deaconess Medical Center, and Harvard Medical School, ; Boston, MA USA
                [8 ]GRID grid.47100.32, ISNI 0000000419368710, Yale Plastic and Reconstructive Surgery-Wound Center, , Yale School of Medicine, ; New Haven, CT USA
                Author information
                http://orcid.org/0000-0002-3228-9131
                http://orcid.org/0000-0002-4171-1736
                http://orcid.org/0000-0001-7905-1713
                http://orcid.org/0000-0002-1254-5839
                http://orcid.org/0000-0001-8661-1459
                http://orcid.org/0000-0002-3901-4405
                Article
                27801
                10.1038/s41467-021-27801-8
                8748704
                35013299
                4f762a8e-6b7a-4261-9bd0-543750ed604f
                © The Author(s) 2022

                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
                : 18 March 2021
                : 29 November 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000062, U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases);
                Award ID: 5U24DK115255-04
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                diabetes complications,bioinformatics,transcriptomics
                Uncategorized
                diabetes complications, bioinformatics, transcriptomics

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