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Deep learning strategy for small dataset from atomic force microscopy mechano-imaging on macrophages phenotypes
The cytoskeleton is involved during movement, shaping, resilience, and functionality in immune system cells. Biomarkers such as elasticity and adhesion can be promising alternatives to detect the status of cells upon phenotype activation in correlation with functionality. For instance, professional...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582561/ https://www.ncbi.nlm.nih.gov/pubmed/37860624 http://dx.doi.org/10.3389/fbioe.2023.1259979 |
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author | Wu, Hao Zhang, Lei Zhao, Banglei Yang, Wenjie Galluzzi, Massimiliano |
author_facet | Wu, Hao Zhang, Lei Zhao, Banglei Yang, Wenjie Galluzzi, Massimiliano |
author_sort | Wu, Hao |
collection | PubMed |
description | The cytoskeleton is involved during movement, shaping, resilience, and functionality in immune system cells. Biomarkers such as elasticity and adhesion can be promising alternatives to detect the status of cells upon phenotype activation in correlation with functionality. For instance, professional immune cells such as macrophages undergo phenotype functional polarization, and their biomechanical behaviors can be used as indicators for early diagnostics. For this purpose, combining the biomechanical sensitivity of atomic force microscopy (AFM) with the automation and performance of a deep neural network (DNN) is a promising strategy to distinguish and classify different activation states. To resolve the issue of small datasets in AFM-typical experiments, nanomechanical maps were divided into pixels with additional localization data. On such an enlarged dataset, a DNN was trained by multimodal fusion, and the prediction was obtained by voting classification. Without using conventional biomarkers, our algorithm demonstrated high performance in predicting the phenotype of macrophages. Moreover, permutation feature importance was employed to interpret the results and unveil the importance of different biophysical properties and, in turn, correlated this with the local density of the cytoskeleton. While our results were demonstrated on the RAW264.7 model cell line, we expect that our methodology could be opportunely customized and applied to distinguish different cell systems and correlate feature importance with biophysical properties to unveil innovative markers for diagnostics. |
format | Online Article Text |
id | pubmed-10582561 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105825612023-10-19 Deep learning strategy for small dataset from atomic force microscopy mechano-imaging on macrophages phenotypes Wu, Hao Zhang, Lei Zhao, Banglei Yang, Wenjie Galluzzi, Massimiliano Front Bioeng Biotechnol Bioengineering and Biotechnology The cytoskeleton is involved during movement, shaping, resilience, and functionality in immune system cells. Biomarkers such as elasticity and adhesion can be promising alternatives to detect the status of cells upon phenotype activation in correlation with functionality. For instance, professional immune cells such as macrophages undergo phenotype functional polarization, and their biomechanical behaviors can be used as indicators for early diagnostics. For this purpose, combining the biomechanical sensitivity of atomic force microscopy (AFM) with the automation and performance of a deep neural network (DNN) is a promising strategy to distinguish and classify different activation states. To resolve the issue of small datasets in AFM-typical experiments, nanomechanical maps were divided into pixels with additional localization data. On such an enlarged dataset, a DNN was trained by multimodal fusion, and the prediction was obtained by voting classification. Without using conventional biomarkers, our algorithm demonstrated high performance in predicting the phenotype of macrophages. Moreover, permutation feature importance was employed to interpret the results and unveil the importance of different biophysical properties and, in turn, correlated this with the local density of the cytoskeleton. While our results were demonstrated on the RAW264.7 model cell line, we expect that our methodology could be opportunely customized and applied to distinguish different cell systems and correlate feature importance with biophysical properties to unveil innovative markers for diagnostics. Frontiers Media S.A. 2023-10-04 /pmc/articles/PMC10582561/ /pubmed/37860624 http://dx.doi.org/10.3389/fbioe.2023.1259979 Text en Copyright © 2023 Wu, Zhang, Zhao, Yang and Galluzzi. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioengineering and Biotechnology Wu, Hao Zhang, Lei Zhao, Banglei Yang, Wenjie Galluzzi, Massimiliano Deep learning strategy for small dataset from atomic force microscopy mechano-imaging on macrophages phenotypes |
title | Deep learning strategy for small dataset from atomic force microscopy mechano-imaging on macrophages phenotypes |
title_full | Deep learning strategy for small dataset from atomic force microscopy mechano-imaging on macrophages phenotypes |
title_fullStr | Deep learning strategy for small dataset from atomic force microscopy mechano-imaging on macrophages phenotypes |
title_full_unstemmed | Deep learning strategy for small dataset from atomic force microscopy mechano-imaging on macrophages phenotypes |
title_short | Deep learning strategy for small dataset from atomic force microscopy mechano-imaging on macrophages phenotypes |
title_sort | deep learning strategy for small dataset from atomic force microscopy mechano-imaging on macrophages phenotypes |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582561/ https://www.ncbi.nlm.nih.gov/pubmed/37860624 http://dx.doi.org/10.3389/fbioe.2023.1259979 |
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