Literature DB >> 35125393

Spatial transcriptomics and the kidney.

Ricardo Melo Ferreira1, Debora L Gisch, Michael T Eadon.   

Abstract

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.
Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.

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Year:  2022        PMID: 35125393      PMCID: PMC9035033          DOI: 10.1097/MNH.0000000000000781

Source DB:  PubMed          Journal:  Curr Opin Nephrol Hypertens        ISSN: 1062-4821            Impact factor:   3.416


  41 in total

1.  Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution.

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

2.  Single-cell in situ RNA profiling by sequential hybridization.

Authors:  Eric Lubeck; Ahmet F Coskun; Timur Zhiyentayev; Mubhij Ahmad; Long Cai
Journal:  Nat Methods       Date:  2014-04       Impact factor: 28.547

3.  Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer's Disease.

Authors:  Wei-Ting Chen; Ashley Lu; Katleen Craessaerts; Benjamin Pavie; Carlo Sala Frigerio; Nikky Corthout; Xiaoyan Qian; Jana Laláková; Malte Kühnemund; Iryna Voytyuk; Leen Wolfs; Renzo Mancuso; Evgenia Salta; Sriram Balusu; An Snellinx; Sebastian Munck; Aleksandra Jurek; Jose Fernandez Navarro; Takaomi C Saido; Inge Huitinga; Joakim Lundeberg; Mark Fiers; Bart De Strooper
Journal:  Cell       Date:  2020-07-22       Impact factor: 41.582

4.  The circadian clock regulates rhythmic erythropoietin expression in the murine kidney.

Authors:  Lina K Sciesielski; Matthias Felten; Laura Michalick; Karin M Kirschner; Georgia Lattanzi; Charlotte Lj Jacobi; Thomas Wallach; Veronika Lang; Dominic Landgraf; Achim Kramer; Christof Dame
Journal:  Kidney Int       Date:  2021-07-29       Impact factor: 10.612

5.  Spatial mapping reveals human adipocyte subpopulations with distinct sensitivities to insulin.

Authors:  Jesper Bäckdahl; Lovisa Franzén; Lucas Massier; Qian Li; Jutta Jalkanen; Hui Gao; Alma Andersson; Nayanika Bhalla; Anders Thorell; Mikael Rydén; Patrik L Ståhl; Niklas Mejhert
Journal:  Cell Metab       Date:  2021-08-10       Impact factor: 27.287

6.  A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys.

Authors:  Blue B Lake; Song Chen; Masato Hoshi; Nongluk Plongthongkum; Diane Salamon; Amanda Knoten; Anitha Vijayan; Ramakrishna Venkatesh; Eric H Kim; Derek Gao; Joseph Gaut; Kun Zhang; Sanjay Jain
Journal:  Nat Commun       Date:  2019-06-27       Impact factor: 14.919

7.  Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography.

Authors:  Alma Andersson; Joseph Bergenstråhle; Michaela Asp; Ludvig Bergenstråhle; Aleksandra Jurek; José Fernández Navarro; Joakim Lundeberg
Journal:  Commun Biol       Date:  2020-10-09

8.  Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram.

Authors:  Tommaso Biancalani; Gabriele Scalia; Lorenzo Buffoni; Raghav Avasthi; Ziqing Lu; Aman Sanger; Neriman Tokcan; Charles R Vanderburg; Åsa Segerstolpe; Meng Zhang; Inbal Avraham-Davidi; Sanja Vickovic; Mor Nitzan; Sai Ma; Ayshwarya Subramanian; Michal Lipinski; Jason Buenrostro; Nik Bear Brown; Duccio Fanelli; Xiaowei Zhuang; Evan Z Macosko; Aviv Regev
Journal:  Nat Methods       Date:  2021-10-28       Impact factor: 28.547

9.  Three-dimensional intact-tissue sequencing of single-cell transcriptional states.

Authors:  Xiao Wang; William E Allen; Matthew A Wright; Emily L Sylwestrak; Nikolay Samusik; Sam Vesuna; Kathryn Evans; Cindy Liu; Charu Ramakrishnan; Jia Liu; Garry P Nolan; Felice-Alessio Bava; Karl Deisseroth
Journal:  Science       Date:  2018-06-21       Impact factor: 47.728

10.  A multimodal and integrated approach to interrogate human kidney biopsies with rigor and reproducibility: guidelines from the Kidney Precision Medicine Project.

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

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