scCamAge: A context-aware prediction engine for cellular age, aging-associated bioactivities, and morphometrics
Summary: Current deep-learning-based image-analysis solutions exhibit limitations in holistically capturing spatiotemporal cellular changes, particularly during aging. We present scCamAge, an advanced context-aware multimodal prediction engine that co-leverages image-based cellular spatiotemporal fe...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Cell Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211124725000415 |
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| author | Vishakha Gautam Subhadeep Duari Saveena Solanki Mudit Gupta Aayushi Mittal Sakshi Arora Anmol Aggarwal Anmol Kumar Sharma Sarthak Tyagi Rathod Kunal Pankajbhai Arushi Sharma Sonam Chauhan Shiva Satija Suvendu Kumar Sanjay Kumar Mohanty Juhi Tayal Nilesh Kumar Dixit Debarka Sengupta Anurag Mehta Gaurav Ahuja |
| author_facet | Vishakha Gautam Subhadeep Duari Saveena Solanki Mudit Gupta Aayushi Mittal Sakshi Arora Anmol Aggarwal Anmol Kumar Sharma Sarthak Tyagi Rathod Kunal Pankajbhai Arushi Sharma Sonam Chauhan Shiva Satija Suvendu Kumar Sanjay Kumar Mohanty Juhi Tayal Nilesh Kumar Dixit Debarka Sengupta Anurag Mehta Gaurav Ahuja |
| author_sort | Vishakha Gautam |
| collection | DOAJ |
| description | Summary: Current deep-learning-based image-analysis solutions exhibit limitations in holistically capturing spatiotemporal cellular changes, particularly during aging. We present scCamAge, an advanced context-aware multimodal prediction engine that co-leverages image-based cellular spatiotemporal features at single-cell resolution alongside cellular morphometrics and aging-associated bioactivities such as genomic instability, mitochondrial dysfunction, vacuolar dynamics, reactive oxygen species levels, and epigenetic and proteasomal dysfunctions. scCamAge employed heterogeneous datasets comprising ∼1 million single yeast cells and was validated using pro-longevity drugs, genetic mutants, and stress-induced models. scCamAge also predicted a pro-longevity response in yeast cells under iterative thermal stress, confirmed using integrative omics analyses. Interestingly, scCamAge, trained solely on yeast images, without additional learning, surpasses generic models in predicting chemical and replication-induced senescence in human fibroblasts, indicating evolutionary conservation of aging-related morphometrics. Finally, we enhanced the generalizability of scCamAge by retraining it on human fibroblast senescence datasets, which improved its ability to predict senescent cells. |
| format | Article |
| id | doaj-art-0659a21300644d61acbd3a1bd224f64e |
| institution | Kabale University |
| issn | 2211-1247 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Cell Reports |
| spelling | doaj-art-0659a21300644d61acbd3a1bd224f64e2025-02-07T04:47:28ZengElsevierCell Reports2211-12472025-02-01442115270scCamAge: A context-aware prediction engine for cellular age, aging-associated bioactivities, and morphometricsVishakha Gautam0Subhadeep Duari1Saveena Solanki2Mudit Gupta3Aayushi Mittal4Sakshi Arora5Anmol Aggarwal6Anmol Kumar Sharma7Sarthak Tyagi8Rathod Kunal Pankajbhai9Arushi Sharma10Sonam Chauhan11Shiva Satija12Suvendu Kumar13Sanjay Kumar Mohanty14Juhi Tayal15Nilesh Kumar Dixit16Debarka Sengupta17Anurag Mehta18Gaurav Ahuja19Department of Computational Biology, Indraprastha Institute of Information Technology - Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, India; Corresponding authorDepartment of Computational Biology, Indraprastha Institute of Information Technology - Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, IndiaDepartment of Computational Biology, Indraprastha Institute of Information Technology - Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, IndiaDepartment of Computational Biology, Indraprastha Institute of Information Technology - Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, IndiaDepartment of Computational Biology, Indraprastha Institute of Information Technology - Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, IndiaDepartment of Computational Biology, Indraprastha Institute of Information Technology - Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, IndiaDepartment of Computational Biology, Indraprastha Institute of Information Technology - Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, IndiaDepartment of Computational Biology, Indraprastha Institute of Information Technology - Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, IndiaDepartment of Computational Biology, Indraprastha Institute of Information Technology - Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, IndiaDepartment of Computational Biology, Indraprastha Institute of Information Technology - Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, IndiaDepartment of Computational Biology, Indraprastha Institute of Information Technology - Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, IndiaDepartment of Computational Biology, Indraprastha Institute of Information Technology - Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, IndiaDepartment of Computational Biology, Indraprastha Institute of Information Technology - Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, IndiaDepartment of Computational Biology, Indraprastha Institute of Information Technology - Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, IndiaDepartment of Computational Biology, Indraprastha Institute of Information Technology - Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, IndiaRajiv Gandhi Cancer Institute & Research Centre, Sir Chotu Ram Marg, Rohini Institutional Area, Sector 5, Rohini, New Delhi 110085, IndiaDepartment of Computational Biology, Indraprastha Institute of Information Technology - Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, IndiaDepartment of Computational Biology, Indraprastha Institute of Information Technology - Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, India; Infosys Centre for AI, Indraprastha Institute of Information Technology - Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, IndiaRajiv Gandhi Cancer Institute & Research Centre, Sir Chotu Ram Marg, Rohini Institutional Area, Sector 5, Rohini, New Delhi 110085, IndiaDepartment of Computational Biology, Indraprastha Institute of Information Technology - Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, India; Infosys Centre for AI, Indraprastha Institute of Information Technology - Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, India; Corresponding authorSummary: Current deep-learning-based image-analysis solutions exhibit limitations in holistically capturing spatiotemporal cellular changes, particularly during aging. We present scCamAge, an advanced context-aware multimodal prediction engine that co-leverages image-based cellular spatiotemporal features at single-cell resolution alongside cellular morphometrics and aging-associated bioactivities such as genomic instability, mitochondrial dysfunction, vacuolar dynamics, reactive oxygen species levels, and epigenetic and proteasomal dysfunctions. scCamAge employed heterogeneous datasets comprising ∼1 million single yeast cells and was validated using pro-longevity drugs, genetic mutants, and stress-induced models. scCamAge also predicted a pro-longevity response in yeast cells under iterative thermal stress, confirmed using integrative omics analyses. Interestingly, scCamAge, trained solely on yeast images, without additional learning, surpasses generic models in predicting chemical and replication-induced senescence in human fibroblasts, indicating evolutionary conservation of aging-related morphometrics. Finally, we enhanced the generalizability of scCamAge by retraining it on human fibroblast senescence datasets, which improved its ability to predict senescent cells.http://www.sciencedirect.com/science/article/pii/S2211124725000415CP: Cell biology |
| spellingShingle | Vishakha Gautam Subhadeep Duari Saveena Solanki Mudit Gupta Aayushi Mittal Sakshi Arora Anmol Aggarwal Anmol Kumar Sharma Sarthak Tyagi Rathod Kunal Pankajbhai Arushi Sharma Sonam Chauhan Shiva Satija Suvendu Kumar Sanjay Kumar Mohanty Juhi Tayal Nilesh Kumar Dixit Debarka Sengupta Anurag Mehta Gaurav Ahuja scCamAge: A context-aware prediction engine for cellular age, aging-associated bioactivities, and morphometrics Cell Reports CP: Cell biology |
| title | scCamAge: A context-aware prediction engine for cellular age, aging-associated bioactivities, and morphometrics |
| title_full | scCamAge: A context-aware prediction engine for cellular age, aging-associated bioactivities, and morphometrics |
| title_fullStr | scCamAge: A context-aware prediction engine for cellular age, aging-associated bioactivities, and morphometrics |
| title_full_unstemmed | scCamAge: A context-aware prediction engine for cellular age, aging-associated bioactivities, and morphometrics |
| title_short | scCamAge: A context-aware prediction engine for cellular age, aging-associated bioactivities, and morphometrics |
| title_sort | sccamage a context aware prediction engine for cellular age aging associated bioactivities and morphometrics |
| topic | CP: Cell biology |
| url | http://www.sciencedirect.com/science/article/pii/S2211124725000415 |
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