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10.1111/jcmm.17392 http://scihub22266oqcxt.onion/10.1111/jcmm.17392 35593050!9189342!35593050     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=35593050&cmd=llinks): Failed to open stream: HTTP request failed! 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ScoMorphoFISH: A deep learning enabled toolbox for single-cell single-mRNA quantification and correlative (ultra-)morphometry |pdf=|usr=}}{{35593050}} gab.com Text ScoMorphoFISH: A deep learning enabled toolbox for single-cell single-mRNA quantification and correlative (ultra-)morphometry JO: J Cell Mol Med http://www.kidney.de/pget.php?&tw=1&pmid=35593050 #FOAvip Increasing the information depth of single kidney biopsies can improve diagnostic precision, personalized medicine and accelerate basic kidney research. Until now, information on mRNA abundance and morphologic analysis has been obtained from different samples, missing out on the spatial context and single-cell correlation of findings. Herein, we present scoMorphoFISH, a modular toolbox to obtain spatial single-cell single-mRNA expression data from routinely generated kidney biopsies. Deep learning was used to virtually dissect tissue sections in tissue compartments and cell types to which single-cell expression data were assigned. Furthermore, we show correlative and spatial single-cell expression quantification with super-resolved podocyte foot process morphometry. In contrast to bulk analysis methods, this approach will help to identify local transcription changes even in less frequent kidney cell types on a spatial single-cell level with single-mRNA resolution. Using this method, we demonstrate that ACE2 can be locally upregulated in podocytes upon injury. In a patient suffering from COVID-19-associated collapsing FSGS, ACE2 expression levels were correlated with intracellular SARS-CoV-2 abundance. As this method performs well with standard formalin-fixed paraffin-embedded samples and we provide pretrained deep learning networks embedded in a comprehensive image analysis workflow, this method can be applied immediately in a variety of settings. Twit Text FOAVip ScoMorphoFISH: A deep learning enabled toolbox for single-cell single-mRNA quantification and correlative (ultra-)morphometry ????J Cell Mol Med http://www.kidney.de/pget.php?&tw=1&pmid=35593050 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35593050 #FOAvip English Wikipedia <ref>{{Cite pmid|35593050}}</ref> ScoMorphoFISH: A deep learning enabled toolbox for single-cell single-mRNA quantification and correlative (ultra-)morphometry #MMPMID35593050 Siegerist F; Hay E; Dikou JS; Pollheimer M; Buscher A; Oh J; Ribback S; Zimmermann U; Brasen JH; Lenoir O; Drenic V; Eller K; Tharaux PL; Endlich N J Cell Mol Med 2022[Jun]; 26 (12): 3513-3526 PMID35593050show ga Increasing the information depth of single kidney biopsies can improve diagnostic precision, personalized medicine and accelerate basic kidney research. Until now, information on mRNA abundance and morphologic analysis has been obtained from different samples, missing out on the spatial context and single-cell correlation of findings. Herein, we present scoMorphoFISH, a modular toolbox to obtain spatial single-cell single-mRNA expression data from routinely generated kidney biopsies. Deep learning was used to virtually dissect tissue sections in tissue compartments and cell types to which single-cell expression data were assigned. Furthermore, we show correlative and spatial single-cell expression quantification with super-resolved podocyte foot process morphometry. In contrast to bulk analysis methods, this approach will help to identify local transcription changes even in less frequent kidney cell types on a spatial single-cell level with single-mRNA resolution. Using this method, we demonstrate that ACE2 can be locally upregulated in podocytes upon injury. In a patient suffering from COVID-19-associated collapsing FSGS, ACE2 expression levels were correlated with intracellular SARS-CoV-2 abundance. As this method performs well with standard formalin-fixed paraffin-embedded samples and we provide pretrained deep learning networks embedded in a comprehensive image analysis workflow, this method can be applied immediately in a variety of settings. |*COVID-19/genetics[MESH] |*Deep Learning[MESH] |Angiotensin-Converting Enzyme 2[MESH] |Humans[MESH] |RNA, Messenger/genetics/metabolism[MESH]ScoMorphoFISH: A deep learning enabled toolbox for single-cell single-(epmc).pdf DeepDyve Pubget Overpricing