PURPOSE OF REVIEW: The application of spatial transcriptomics technologies to the interrogation of kidney tissue is a burgeoning effort. These technologies share a common purpose in mapping both the expression of individual molecules and entire transcriptomic signatures of kidney cell types and structures. Such information is often superimposed upon a histologic image. The resulting datasets are readily merged with other imaging and transcriptomic techniques to establish a spatially anchored atlas of the kidney. This review provides an overview of the various spatial transcriptomic technologies and recent studies in kidney disease. Potential applications gleaned from the interrogation of other organ systems, but relative to the kidney, are also discussed. RECENT FINDINGS: Spatial transcriptomic technologies have enabled localization of whole transcriptome mRNA expression, correlation of mRNA to histology, measurement of in situ changes in expression across time, and even subcellular localization of transcripts within the kidney. These innovations continue to aid in the development of human cellular atlases of the kidney, the reclassification of disease, and the identification of important therapeutic targets. SUMMARY: Spatial localization of gene expression will complement our current understanding of disease derived from single cell RNA sequencing, histopathology, protein immunofluorescence, and electron microscopy. Although spatial technologies continue to evolve rapidly, their importance in the localization of disease signatures is already apparent. Further efforts are required to integrate whole transcriptome and subcellular expression signatures into the individualized assessment of human kidney disease.
PURPOSE OF REVIEW: The application of spatial transcriptomics technologies to the interrogation of kidney tissue is a burgeoning effort. These technologies share a common purpose in mapping both the expression of individual molecules and entire transcriptomic signatures of kidney cell types and structures. Such information is often superimposed upon a histologic image. The resulting datasets are readily merged with other imaging and transcriptomic techniques to establish a spatially anchored atlas of the kidney. This review provides an overview of the various spatial transcriptomic technologies and recent studies in kidney disease. Potential applications gleaned from the interrogation of other organ systems, but relative to the kidney, are also discussed. RECENT FINDINGS: Spatial transcriptomic technologies have enabled localization of whole transcriptome mRNA expression, correlation of mRNA to histology, measurement of in situ changes in expression across time, and even subcellular localization of transcripts within the kidney. These innovations continue to aid in the development of human cellular atlases of the kidney, the reclassification of disease, and the identification of important therapeutic targets. SUMMARY: Spatial localization of gene expression will complement our current understanding of disease derived from single cell RNA sequencing, histopathology, protein immunofluorescence, and electron microscopy. Although spatial technologies continue to evolve rapidly, their importance in the localization of disease signatures is already apparent. Further efforts are required to integrate whole transcriptome and subcellular expression signatures into the individualized assessment of human kidney disease.
Authors: Samuel G Rodriques; Robert R Stickels; Aleksandrina Goeva; Carly A Martin; Evan Murray; Charles R Vanderburg; Joshua Welch; Linlin M Chen; Fei Chen; Evan Z Macosko Journal: Science Date: 2019-03-28 Impact factor: 47.728
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Authors: Alma Andersson; Joseph Bergenstråhle; Michaela Asp; Ludvig Bergenstråhle; Aleksandra Jurek; José Fernández Navarro; Joakim Lundeberg Journal: Commun Biol Date: 2020-10-09
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Authors: Tarek M El-Achkar; Michael T Eadon; Rajasree Menon; Blue B Lake; Tara K Sigdel; Theodore Alexandrov; Samir Parikh; Guanshi Zhang; Dejan Dobi; Kenneth W Dunn; Edgar A Otto; Christopher R Anderton; Jonas M Carson; Jinghui Luo; Chris Park; Habib Hamidi; Jian Zhou; Paul Hoover; Andrew Schroeder; Marianinha Joanes; Evren U Azeloglu; Rachel Sealfon; Seth Winfree; Becky Steck; Yongqun He; Vivette D'Agati; Ravi Iyengar; Olga G Troyanskaya; Laura Barisoni; Joseph Gaut; Kun Zhang; Zoltan Laszik; Brad H Rovin; Pierre C Dagher; Kumar Sharma; Minnie M Sarwal; Jeffrey B Hodgin; Charles E Alpers; Matthias Kretzler; Sanjay Jain Journal: Physiol Genomics Date: 2020-11-16 Impact factor: 3.107