Introduction
Skeletal muscle carries a unique ability for adaptation as well as regeneration that highly depends on a supportive cellular microenvironment. Here we show that spatial and single-cell transcriptomic analysis in human skeletal muscle is a unique approach to illuminate cellular interactions in the microenvironment. Using a model of electrically induced eccentric muscle injury, we obtain substantial regeneration in elderly human subjects. Barcode-labelling techniques, such as single-cell transcriptomics, have transformed our understanding of the cellular heterogeneity of many human organs including skeletal muscle but has only been performed under homeostatic conditions. Combining this technique with spatial transcriptome analysis can identify cellular arrangement and heterogeneity and offers a more mechanistic approach to human studies. The technique has recently been used explorative in CNS, adipose tissue, and solid tumors etc. In this study, we show the dynamics of human skeletal muscle regeneration by the utilization of spatial and single-cell transcriptome analysis.
Please cite the HuMdb spatial database as:
Jonas Brorson, Lin Lin, Jakob Wang, Amanda Baek, Tine Borum Billeskov, Frederik Forsberg Thybo, Jesper Just, Janos Hasko, Christen Ravn, Rehne L. Hansen, Mats Bue, Jens Otto Lunde Jorgensen, Yonglun Luo, Niels Jessen, Jean Farup. Nature Communications volume 16, Article number: 5233 (2025)
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Copyright 2022 Steno Diabetes Center Aarhus (SDCA); Aarhus University
Created by DREAMlab, Department of Biomedicine, Aarhus University
Get in touch with us: lin.lin@biomed.au.dk
Gene Summary
Differential expressed genes in different time points compared to sham
* Wilcoxon Rank Sum test were used to identify differentially expressed genes between two groups of Spots
* pct: The percentage of Spots where the gene detected in the group
* avg_log2FC: log fold-chage of the average expression between the two groups.
* p_val_adj: Adjusted p-value, based on bonferroni correction using all genes in the dataset
Spatial gene expression: Spatial plot
Spatial geneset score: Spatial plot
Spot information vs gene expression on reduced dimensions
In this tab, users can visualise both Spot information and gene expression side-by-side on low-dimensional representions.Dimension Reduction
Spot information vs Spot information on dimension reduction
In this tab, users can visualise two Spot informations side-by-side on low-dimensional representions.Dimension Reduction
Gene expression vs gene expression on dimension reduction
In this tab, users can visualise two gene expressions side-by-side on low-dimensional representions.Dimension Reduction
Coexpression of two genes on reduced dimensions
In this tab, users can visualise the coexpression of two genes on low-dimensional representions.Dimension Reduction
Spot information / gene expression violin plot / box plot
In this tab, users can visualise the gene expression or continuous Spot information (e.g. Number of UMIs / module score) across groups of Spots (e.g. libary / clusters).Proportion / Spot numbers across different Spot information
In this tab, users can visualise the composition of single Spots based on one discrete Spot information across another discrete Spot information. Usage examples include the library or Spotcycle composition across clusters.Gene expression bubbleplot / heatmap
In this tab, users can visualise the gene expression patterns of multiple genes grouped by categorical Spot information (e.g. library / cluster).The normalised expression are averaged, log-transformed and then plotted.
Geneset score violin plot / box plot
In this tab, users can visualise the geneset score or continuous Spot information (e.g. Number of UMIs / module score) across groups of Spots (e.g. libary / clusters).This webpage was modified from ShinyCell