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High-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury
Recovery from acute kidney injury can vary widely in patients and in animal models. Immunofluorescence staining can provide spatial information about heterogeneous injury responses, but often only a fraction of stained tissue is analyzed. Deep learning can expand analysis to larger areas and sample...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10115810/ https://www.ncbi.nlm.nih.gov/pubmed/37076596 http://dx.doi.org/10.1038/s41598-023-33433-3 |
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author | McElliott, Madison C. Al-Suraimi, Anas Telang, Asha C. Ference-Salo, Jenna T. Chowdhury, Mahboob Soofi, Abdul Dressler, Gregory R. Beamish, Jeffrey A. |
author_facet | McElliott, Madison C. Al-Suraimi, Anas Telang, Asha C. Ference-Salo, Jenna T. Chowdhury, Mahboob Soofi, Abdul Dressler, Gregory R. Beamish, Jeffrey A. |
author_sort | McElliott, Madison C. |
collection | PubMed |
description | Recovery from acute kidney injury can vary widely in patients and in animal models. Immunofluorescence staining can provide spatial information about heterogeneous injury responses, but often only a fraction of stained tissue is analyzed. Deep learning can expand analysis to larger areas and sample numbers by substituting for time-intensive manual or semi-automated quantification techniques. Here we report one approach to leverage deep learning tools to quantify heterogenous responses to kidney injury that can be deployed without specialized equipment or programming expertise. We first demonstrated that deep learning models generated from small training sets accurately identified a range of stains and structures with performance similar to that of trained human observers. We then showed this approach accurately tracks the evolution of folic acid induced kidney injury in mice and highlights spatially clustered tubules that fail to repair. We then demonstrated that this approach captures the variation in recovery across a robust sample of kidneys after ischemic injury. Finally, we showed markers of failed repair after ischemic injury were correlated both spatially within and between animals and that failed repair was inversely correlated with peritubular capillary density. Combined, we demonstrate the utility and versatility of our approach to capture spatially heterogenous responses to kidney injury. |
format | Online Article Text |
id | pubmed-10115810 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101158102023-04-21 High-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury McElliott, Madison C. Al-Suraimi, Anas Telang, Asha C. Ference-Salo, Jenna T. Chowdhury, Mahboob Soofi, Abdul Dressler, Gregory R. Beamish, Jeffrey A. Sci Rep Article Recovery from acute kidney injury can vary widely in patients and in animal models. Immunofluorescence staining can provide spatial information about heterogeneous injury responses, but often only a fraction of stained tissue is analyzed. Deep learning can expand analysis to larger areas and sample numbers by substituting for time-intensive manual or semi-automated quantification techniques. Here we report one approach to leverage deep learning tools to quantify heterogenous responses to kidney injury that can be deployed without specialized equipment or programming expertise. We first demonstrated that deep learning models generated from small training sets accurately identified a range of stains and structures with performance similar to that of trained human observers. We then showed this approach accurately tracks the evolution of folic acid induced kidney injury in mice and highlights spatially clustered tubules that fail to repair. We then demonstrated that this approach captures the variation in recovery across a robust sample of kidneys after ischemic injury. Finally, we showed markers of failed repair after ischemic injury were correlated both spatially within and between animals and that failed repair was inversely correlated with peritubular capillary density. Combined, we demonstrate the utility and versatility of our approach to capture spatially heterogenous responses to kidney injury. Nature Publishing Group UK 2023-04-19 /pmc/articles/PMC10115810/ /pubmed/37076596 http://dx.doi.org/10.1038/s41598-023-33433-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article McElliott, Madison C. Al-Suraimi, Anas Telang, Asha C. Ference-Salo, Jenna T. Chowdhury, Mahboob Soofi, Abdul Dressler, Gregory R. Beamish, Jeffrey A. High-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury |
title | High-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury |
title_full | High-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury |
title_fullStr | High-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury |
title_full_unstemmed | High-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury |
title_short | High-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury |
title_sort | high-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10115810/ https://www.ncbi.nlm.nih.gov/pubmed/37076596 http://dx.doi.org/10.1038/s41598-023-33433-3 |
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