Phenotyping senescent mesenchymal stromal cells using AI image translation
Mesenchymal stromal cells (MSCs) offer promising potential in biomedical research, clinical therapeutics, and immunomodulatory therapies due to their ease of isolation and multipotent, immunoprivileged, and immunosuppersive properties. Extensive efforts have focused on optimizing the cell isolation...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Current Research in Biotechnology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590262823000023 |
| _version_ | 1797797106574950400 |
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| author | Leya Weber Brandon S. Lee Sara Imboden Cho-Jui Hsieh Neil Y.C. Lin |
| author_facet | Leya Weber Brandon S. Lee Sara Imboden Cho-Jui Hsieh Neil Y.C. Lin |
| author_sort | Leya Weber |
| collection | DOAJ |
| description | Mesenchymal stromal cells (MSCs) offer promising potential in biomedical research, clinical therapeutics, and immunomodulatory therapies due to their ease of isolation and multipotent, immunoprivileged, and immunosuppersive properties. Extensive efforts have focused on optimizing the cell isolation and culture methods to generate scalable, therapeutically-relevant MSCs for clinical applications. However, MSC-based therapies are often hindered by cell heterogeneity and inconsistency of therapeutic function caused, in part, by MSC senescence. As such, noninvasive and molecular-based MSC characterizations play an essential role in assuring the consistency of MSC functions. Here, we demonstrated that AI image translation algorithms can effectively predict immunofluorescence images of MSC senescence markers from phase contrast images. We showed that the expression level of senescence markers including senescence-associated beta-galactosidase (SABG), p16, p21, and p38 are accurately predicted by deep-learning models for Doxorubicin-induced MSC senescence, irradiation-induced MSC senescence, and replicative MSC senescence. Our AI model distinguished the non-senescent and senescent MSC populations and simultaneously captured the cell-to-cell variability within a population. Our microscopy-based phenotyping platform can be integrated with cell culture routines making it an easily accessible tool for MSC engineering and manufacturing. |
| first_indexed | 2024-03-13T03:43:10Z |
| format | Article |
| id | doaj.art-06801a897bcf4ea0a554ca3f033617e7 |
| institution | Directory Open Access Journal |
| issn | 2590-2628 |
| language | English |
| last_indexed | 2024-03-13T03:43:10Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Current Research in Biotechnology |
| spelling | doaj.art-06801a897bcf4ea0a554ca3f033617e72023-06-23T04:44:01ZengElsevierCurrent Research in Biotechnology2590-26282023-01-015100120Phenotyping senescent mesenchymal stromal cells using AI image translationLeya Weber0Brandon S. Lee1Sara Imboden2Cho-Jui Hsieh3Neil Y.C. Lin4Department of Mechanical and Aerospace Engineering, University of California, Los Angeles 90095, CA, United StatesDepartment of Bioengineering, University of California, Los Angeles 90095, CA, United StatesDepartment of Mechanical and Aerospace Engineering, University of California, Los Angeles 90095, CA, United StatesDepartment of Computer Science, University of California, Los Angeles 90095, CA, United StatesDepartment of Mechanical and Aerospace Engineering, University of California, Los Angeles 90095, CA, United States; Department of Bioengineering, University of California, Los Angeles 90095, CA, United States; California NanoSystems Institute, University of California, Los Angeles 90095, CA, United States; Jonsson Comprehensive Cancer Center, University of California, Los Angeles 90095, CA, United States; Institute for Quantitative and Computational Biosciences, University of California, Los Angeles 90095, CA, United States; Broad Stem Cell Center, University of California, Los Angeles 90095, CA, United States; Corresponding author at: Department of Mechanical and Aerospace Engineering, University of California, Los Angeles 90095, CA, United States.Mesenchymal stromal cells (MSCs) offer promising potential in biomedical research, clinical therapeutics, and immunomodulatory therapies due to their ease of isolation and multipotent, immunoprivileged, and immunosuppersive properties. Extensive efforts have focused on optimizing the cell isolation and culture methods to generate scalable, therapeutically-relevant MSCs for clinical applications. However, MSC-based therapies are often hindered by cell heterogeneity and inconsistency of therapeutic function caused, in part, by MSC senescence. As such, noninvasive and molecular-based MSC characterizations play an essential role in assuring the consistency of MSC functions. Here, we demonstrated that AI image translation algorithms can effectively predict immunofluorescence images of MSC senescence markers from phase contrast images. We showed that the expression level of senescence markers including senescence-associated beta-galactosidase (SABG), p16, p21, and p38 are accurately predicted by deep-learning models for Doxorubicin-induced MSC senescence, irradiation-induced MSC senescence, and replicative MSC senescence. Our AI model distinguished the non-senescent and senescent MSC populations and simultaneously captured the cell-to-cell variability within a population. Our microscopy-based phenotyping platform can be integrated with cell culture routines making it an easily accessible tool for MSC engineering and manufacturing.http://www.sciencedirect.com/science/article/pii/S2590262823000023MSC phenotypingSenescenceAI image translationCell manufacturing |
| spellingShingle | Leya Weber Brandon S. Lee Sara Imboden Cho-Jui Hsieh Neil Y.C. Lin Phenotyping senescent mesenchymal stromal cells using AI image translation Current Research in Biotechnology MSC phenotyping Senescence AI image translation Cell manufacturing |
| title | Phenotyping senescent mesenchymal stromal cells using AI image translation |
| title_full | Phenotyping senescent mesenchymal stromal cells using AI image translation |
| title_fullStr | Phenotyping senescent mesenchymal stromal cells using AI image translation |
| title_full_unstemmed | Phenotyping senescent mesenchymal stromal cells using AI image translation |
| title_short | Phenotyping senescent mesenchymal stromal cells using AI image translation |
| title_sort | phenotyping senescent mesenchymal stromal cells using ai image translation |
| topic | MSC phenotyping Senescence AI image translation Cell manufacturing |
| url | http://www.sciencedirect.com/science/article/pii/S2590262823000023 |
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