转录组
计算生物学
生物
电池类型
基因表达
基因组
基因
基因表达谱
系统生物学
细胞
细胞生物学
生物信息学
遗传学
作者
Eryn E. Dixon,Hao Wu,Elizabeth Sulvarán-Guel,Juanru Guo,Benjamin D. Humphreys
标识
DOI:10.1016/j.kint.2022.06.011
摘要
Defining changes in gene expression during health and disease is critical for the understanding of human physiology. In recent years, single-cell/nuclei RNA sequencing (sc/snRNAseq) has revolutionized the definition and discovery of cell types and states as well as the interpretation of organ- and cell-type–specific signaling pathways. However, these advances require tissue dissociation to the level of the single cell or single nuclei level. Spatially resolved transcriptomics (SrT) now provides a platform to overcome this barrier in understanding the physiological contexts of gene expression and cellular microenvironment changes in development and disease. Some of these transcriptomic tools allow for high-resolution mapping of hundreds of genes simultaneously in cellular and subcellular compartments. Other tools offer genome depth mapping but at lower resolution. We review advances in SrT, considerations for using SrT in your own research, and applications for kidney biology. Defining changes in gene expression during health and disease is critical for the understanding of human physiology. In recent years, single-cell/nuclei RNA sequencing (sc/snRNAseq) has revolutionized the definition and discovery of cell types and states as well as the interpretation of organ- and cell-type–specific signaling pathways. However, these advances require tissue dissociation to the level of the single cell or single nuclei level. Spatially resolved transcriptomics (SrT) now provides a platform to overcome this barrier in understanding the physiological contexts of gene expression and cellular microenvironment changes in development and disease. Some of these transcriptomic tools allow for high-resolution mapping of hundreds of genes simultaneously in cellular and subcellular compartments. Other tools offer genome depth mapping but at lower resolution. We review advances in SrT, considerations for using SrT in your own research, and applications for kidney biology.
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