{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T06:10:07Z","timestamp":1758867007384,"version":"3.44.0"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:00:00Z","timestamp":1758844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007709","name":"Michigan State University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007709","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Assessing the quality of bovine satellite cells (BSCs) is vital for advancing tissue engineered muscle constructs with applications in sustainable protein research. In this study, we present a non-invasive deep learning approach for optical imaging that predicts fluorescent markers directly from brightfield microscopy images of BSC cultures. Using a convolutional neural network based on the U-Net architecture, our method simultaneously predicts two key fluorescent signals, specifically DAPI and Pax7, which serve as biomarkers for cell abundance and differentiation status. An image preprocessing pipeline featuring fluorescent signal denoising was implemented to enhance prediction performance and consistency. A dataset comprising 48 biological replicates was evaluated using statistical metrics such as the Pearson <jats:italic>r<\/jats:italic> (correlation coefficient), the mean squared error (MSE), and the structural similarity Index (SSIM). For DAPI, denoising improved the Pearson <jats:italic>r<\/jats:italic> from 0.065 to 0.212 and SSIM from 0.047 to 0.761 (with MSE increasing from 9.507 to 41.571). For Pax7, the Pearson <jats:italic>r<\/jats:italic> increased from 0.020 to 0.124 and MSE decreased from 44.753 to 18.793, while SSIM remained low, reflecting inherent biological heterogeneity. Furthermore, enhanced visualization techniques, including color mapping and image overlay, improved the interpretability of the predicted outputs. These findings underscore the importance of optimized data preprocessing and demonstrate the potential of AI to advance non-invasive optical imaging for cellular quality assessment in tissue biology. This work also contributes to the broader integration of machine learning and computer vision methods in biological and agricultural applications.<\/jats:p>","DOI":"10.3389\/frai.2025.1577027","type":"journal-article","created":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T05:31:23Z","timestamp":1758864683000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Fluorescent marker prediction for non-invasive optical imaging in bovine satellite cells using deep learning"],"prefix":"10.3389","volume":"8","author":[{"given":"Sania","family":"Sinha","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aarham","family":"Wasit","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Won Seob","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jongkyoo","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiyoon","family":"Yi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,9,26]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1038\/s42256-022-00472-w","article-title":"Microscopy analysis neural network to solve detection, enumeration and segmentation from image-level annotations","volume":"4","author":"Bilodeau","year":"2022","journal-title":"Nat. Mach. Intell"},{"key":"B2","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1038\/s42256-021-00303-4","article-title":"Morphological and molecular breast cancer profiling through explainable machine learning","volume":"3","author":"Binder","year":"2021","journal-title":"Nat. Mach. Intell"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1488","DOI":"10.1109\/TIP.2011.2173206","article-title":"On the mathematical properties of the structural similarity index","volume":"21","author":"Brunet","year":"2012","journal-title":"IEEE Trans. Image Proc"},{"key":"B4","doi-asserted-by":"publisher","first-page":"eabe0431","DOI":"10.1126\/sciadv.abe0431","article-title":"Single-cell cytometry via multiplexed fluorescence prediction by label-free reflectance microscopy","volume":"7","author":"Cheng","year":"2021","journal-title":"Sci. Adv"},{"key":"B5","doi-asserted-by":"publisher","first-page":"6610","DOI":"10.1038\/s41598-022-10643-9","article-title":"Numerical learning of deep features from drug-exposed cell images to calculate IC50 without staining","volume":"12","author":"Cho","year":"2022","journal-title":"Sci. Rep"},{"key":"B6","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1016\/j.cell.2018.03.040","article-title":"In silico labeling: predicting fluorescent labels in unlabeled images","volume":"173","author":"Christiansen","year":"2018","journal-title":"Cell"},{"key":"B7","doi-asserted-by":"publisher","first-page":"10808","DOI":"10.1038\/s41598-018-28746-7","article-title":"Maintaining bovine satellite cells stemness through p38 pathway","volume":"8","author":"Ding","year":"2018","journal-title":"Sci. Rep"},{"key":"B8","first-page":"43","article-title":"\u201cA survey of color mapping and its applications,\u201d","author":"Faridul","year":"2014","journal-title":"Eurographics"},{"key":"B9","doi-asserted-by":"publisher","first-page":"skad303","DOI":"10.1093\/jas\/skad303","article-title":"Heat shock protein 27 regulates myogenic and self-renewal potential of bovine satellite cells under heat stress","volume":"101","author":"Kim","year":"2023","journal-title":"J. Animal Sci"},{"key":"B10","doi-asserted-by":"publisher","first-page":"103684","DOI":"10.1016\/j.jtherbio.2023.103684","article-title":"Exploring the impact of temporal heat stress on skeletal muscle hypertrophy in bovine myocytes","volume":"117","author":"Kim","year":"2023","journal-title":"J. Therm. Biol"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1524","DOI":"10.3390\/cells12111524","article-title":"Evaluating differentiation status of mesenchymal stem cells by label-free microscopy system and machine learning","volume":"12","author":"Kong","year":"2023","journal-title":"Cells"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1934","DOI":"10.1109\/TMI.2021.3069558","article-title":"Evaluation of deep learning architectures for complex immunofluorescence nuclear image segmentation","volume":"40","author":"Kromp","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"B13","doi-asserted-by":"publisher","first-page":"673","DOI":"10.5187\/jast.2021.e40","article-title":"Principal protocols for the processing of cultured meat","volume":"63","author":"Lee","year":"2021","journal-title":"J. Animal Sci. Technol"},{"key":"B14","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1038\/s43016-021-00419-1","article-title":"A serum-free media formulation for cultured meat production supports bovine satellite cell differentiation in the absence of serum starvation","volume":"3","author":"Messmer","year":"2022","journal-title":"Nat. Food"},{"key":"B15","doi-asserted-by":"publisher","first-page":"1233","DOI":"10.1038\/s41592-019-0403-1","article-title":"Deep learning for cellular image analysis","volume":"16","author":"Moen","year":"2019","journal-title":"Nat. Methods"},{"key":"B16","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/B978-0-12-416602-8.00006-6","article-title":"\u201cChapter 6 - selection of variables and factor derivation,\u201d","author":"Nettleton","year":"2014","journal-title":"Commercial Data Mining"},{"key":"B17","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1038\/s41592-018-0111-2","article-title":"Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy","volume":"15","author":"Ounkomol","year":"2018","journal-title":"Nat. Methods"},{"key":"B18","doi-asserted-by":"publisher","first-page":"1398","DOI":"10.1109\/JBHI.2023.3348436","article-title":"Is attention all you need in medical image analysis? A review","volume":"28","author":"Papanastasiou","year":"2024","journal-title":"IEEE J. Biomed. Health Inf"},{"key":"B19","article-title":"\u201cPytorch: an imperative style, high-performance deep learning library,\u201d","author":"Paszke","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"B20","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","article-title":"\u201cU-net: convolutional networks for biomedical image segmentation,\u201d","author":"Ronneberger","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention MICCAI 2015"},{"key":"B21","article-title":"Explainable artificial intelligence: understanding, visualizing and interpreting deep learning models","author":"Samek","year":"2017","journal-title":"arXiv preprint arXiv:1708.08296"},{"key":"B22","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1016\/S0092-8674(00)00066-0","article-title":"Pax7 is required for the specification of myogenic satellite cells","volume":"102","author":"Seale","year":"2000","journal-title":"Cell"},{"key":"B23","doi-asserted-by":"publisher","first-page":"1567","DOI":"10.1021\/acssynbio.3c00216","article-title":"Immortalized bovine satellite cells for cultured meat applications","volume":"12","author":"Stout","year":"2023","journal-title":"ACS Synth. Biol"},{"key":"B24","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","article-title":"Convolutional neural networks for medical image analysis: full training or fine tuning?","volume":"35","author":"Tajbakhsh","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"B25","doi-asserted-by":"publisher","first-page":"16474","DOI":"10.1073\/pnas.1307680110","article-title":"Pax7 is critical for the normal function of satellite cells in adult skeletal muscle","volume":"110","author":"von Maltzahn","year":"2013","journal-title":"Proc. Nat. Acad. Sci"},{"key":"B26","doi-asserted-by":"publisher","first-page":"e12","DOI":"10.1017\/S2633903X23000120","article-title":"Bright-field to fluorescence microscopy image translation for cell nuclei health quantification","volume":"3","author":"Wang","year":"2023","journal-title":"Biol. Imag"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1101\/2022.06.10.494732","article-title":"A single cell spatial temporal atlas of skeletal muscle reveals cellular neighborhoods that orchestrate regeneration and become disrupted in aging","author":"Wang","year":"2022","journal-title":"bioRxiv preprint 2022.06.10.494732"},{"key":"B28","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1109\/97.995823","article-title":"A universal image quality index","volume":"9","author":"Wang","year":"2002","journal-title":"IEEE Signal Process. Lett"},{"key":"B29","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: from error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Proc"},{"key":"B30","doi-asserted-by":"publisher","first-page":"582218","DOI":"10.3389\/fnana.2020.582218","article-title":"Immunofluorescence staining of paraffin sections step by step","volume":"14","author":"Zaqout","year":"2020","journal-title":"Front. Neuroanat"},{"key":"B31","doi-asserted-by":"publisher","first-page":"2614","DOI":"10.1038\/s41467-021-22758-0","article-title":"Deep learning-based predictive identification of neural stem cell differentiation","volume":"12","author":"Zhu","year":"2021","journal-title":"Nat. Commun"}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1577027\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T05:31:24Z","timestamp":1758864684000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1577027\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,26]]},"references-count":31,"alternative-id":["10.3389\/frai.2025.1577027"],"URL":"https:\/\/doi.org\/10.3389\/frai.2025.1577027","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,26]]},"article-number":"1577027"}}