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      Single-Cell RNA-Seq Analysis of Cells from Degenerating and Non-Degenerating Intervertebral Discs from the Same Individual Reveals New Biomarkers for Intervertebral Disc Degeneration

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          Abstract

          In this study, we used single-cell transcriptomic analysis to identify new specific biomarkers for nucleus pulposus (NP) and inner annulus fibrosis (iAF) cells, and to define cell populations within non-degenerating (nD) and degenerating (D) human intervertebral discs (IVD) of the same individual. Cluster analysis based on differential gene expression delineated 14 cell clusters. Gene expression profiles at single-cell resolution revealed the potential functional differences linked to degeneration, and among NP and iAF subpopulations. GO and KEGG analyses discovered molecular functions, biological processes, and transcription factors linked to cell type and degeneration state. We propose two lists of biomarkers, one as specific cell type, including C2orf40, MGP, MSMP, CD44, EIF1, LGALS1, RGCC, EPYC, HILPDA, ACAN, MT1F, CHI3L1, ID1, ID3 and TMED2. The second list proposes predictive IVD degeneration genes, including MT1G, SPP1, HMGA1, FN1, FBXO2, SPARC, VIM, CTGF, MGST1, TAF1D, CAPS, SPTSSB, S100A1, CHI3L2, PLA2G2A, TNRSF11B, FGFBP2, MGP, SLPI, DCN, MT-ND2, MTCYB, ADIRF, FRZB, CLEC3A, UPP1, S100A2, PRG4, COL2A1, SOD2 and MT2A. Protein and mRNA expression of MGST1, vimentin, SOD2 and SYF2 (p29) genes validated our scRNA-seq findings. Our data provide new insights into disc cells phenotypes and biomarkers of IVD degeneration that could improve diagnostic and therapeutic options.

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            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.
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              Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

              Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
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                Author and article information

                Contributors
                Journal
                IJMCFK
                International Journal of Molecular Sciences
                IJMS
                MDPI AG
                1422-0067
                April 2022
                April 03 2022
                : 23
                : 7
                : 3993
                Article
                10.3390/ijms23073993
                35409356
                4be26cdb-c287-44b5-bb23-43edb037aa62
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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