SenPred: a single-cell RNA sequencing-based machine learning pipeline to classify deeply senescent dermal fibroblast cells for the detection of an in vivo senescent cell burden
Abstract Background Senescence classification is an acknowledged challenge within the field, as markers are cell-type and context dependent. Currently, multiple morphological and immunofluorescence markers are required. However, emerging scRNA-seq datasets have enabled an increased understanding of...
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2025-01-01
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| Series: | Genome Medicine |
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| Online Access: | https://doi.org/10.1186/s13073-024-01418-0 |
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| author | Bethany K. Hughes Andrew Davis Deborah Milligan Ryan Wallis Federica Mossa Michael P. Philpott Linda J. Wainwright David A. Gunn Cleo L. Bishop |
| author_facet | Bethany K. Hughes Andrew Davis Deborah Milligan Ryan Wallis Federica Mossa Michael P. Philpott Linda J. Wainwright David A. Gunn Cleo L. Bishop |
| author_sort | Bethany K. Hughes |
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| description | Abstract Background Senescence classification is an acknowledged challenge within the field, as markers are cell-type and context dependent. Currently, multiple morphological and immunofluorescence markers are required. However, emerging scRNA-seq datasets have enabled an increased understanding of senescent cell heterogeneity. Methods Here we present SenPred, a machine-learning pipeline which identifies fibroblast senescence based on single-cell transcriptomics from fibroblasts grown in 2D and 3D. Results Using scRNA-seq of both 2D and 3D deeply senescent fibroblasts, the model predicts intra-experimental fibroblast senescence to a high degree of accuracy (> 99% true positives). Applying SenPred to in vivo whole skin scRNA-seq datasets reveals that cells grown in 2D cannot accurately detect fibroblast senescence in vivo. Importantly, utilising scRNA-seq from 3D deeply senescent fibroblasts refines our ML model leading to improved detection of senescent cells in vivo. This is context specific, with the SenPred pipeline proving effective when detecting senescent human dermal fibroblasts in vivo, but not the senescence of lung fibroblasts or whole skin. Conclusions We position this as a proof-of-concept study based on currently available scRNA-seq datasets, with the intention to build a holistic model to detect multiple senescent triggers using future emerging datasets. The development of SenPred has allowed for the detection of an in vivo senescent fibroblast burden in human skin, which could have broader implications for the treatment of age-related morbidities. All code for the SenPred pipeline is available at the following URL: https://github.com/bethk-h/SenPred_HDF . |
| format | Article |
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| institution | Kabale University |
| issn | 1756-994X |
| language | English |
| publishDate | 2025-01-01 |
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| series | Genome Medicine |
| spelling | doaj-art-5b1165020aba49cf902cac10cb1c0fec2025-01-19T12:33:57ZengBMCGenome Medicine1756-994X2025-01-0117111610.1186/s13073-024-01418-0SenPred: a single-cell RNA sequencing-based machine learning pipeline to classify deeply senescent dermal fibroblast cells for the detection of an in vivo senescent cell burdenBethany K. Hughes0Andrew Davis1Deborah Milligan2Ryan Wallis3Federica Mossa4Michael P. Philpott5Linda J. Wainwright6David A. Gunn7Cleo L. Bishop8Blizard Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of LondonUnilever R&DBlizard Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of LondonBlizard Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of LondonBlizard Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of LondonBlizard Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of LondonUnilever R&DUnilever R&DBlizard Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of LondonAbstract Background Senescence classification is an acknowledged challenge within the field, as markers are cell-type and context dependent. Currently, multiple morphological and immunofluorescence markers are required. However, emerging scRNA-seq datasets have enabled an increased understanding of senescent cell heterogeneity. Methods Here we present SenPred, a machine-learning pipeline which identifies fibroblast senescence based on single-cell transcriptomics from fibroblasts grown in 2D and 3D. Results Using scRNA-seq of both 2D and 3D deeply senescent fibroblasts, the model predicts intra-experimental fibroblast senescence to a high degree of accuracy (> 99% true positives). Applying SenPred to in vivo whole skin scRNA-seq datasets reveals that cells grown in 2D cannot accurately detect fibroblast senescence in vivo. Importantly, utilising scRNA-seq from 3D deeply senescent fibroblasts refines our ML model leading to improved detection of senescent cells in vivo. This is context specific, with the SenPred pipeline proving effective when detecting senescent human dermal fibroblasts in vivo, but not the senescence of lung fibroblasts or whole skin. Conclusions We position this as a proof-of-concept study based on currently available scRNA-seq datasets, with the intention to build a holistic model to detect multiple senescent triggers using future emerging datasets. The development of SenPred has allowed for the detection of an in vivo senescent fibroblast burden in human skin, which could have broader implications for the treatment of age-related morbidities. All code for the SenPred pipeline is available at the following URL: https://github.com/bethk-h/SenPred_HDF .https://doi.org/10.1186/s13073-024-01418-03D organotypic cultureIn vivo senescence detectionLiving skin equivalentMachine learningscRNA-seqSenescence |
| spellingShingle | Bethany K. Hughes Andrew Davis Deborah Milligan Ryan Wallis Federica Mossa Michael P. Philpott Linda J. Wainwright David A. Gunn Cleo L. Bishop SenPred: a single-cell RNA sequencing-based machine learning pipeline to classify deeply senescent dermal fibroblast cells for the detection of an in vivo senescent cell burden Genome Medicine 3D organotypic culture In vivo senescence detection Living skin equivalent Machine learning scRNA-seq Senescence |
| title | SenPred: a single-cell RNA sequencing-based machine learning pipeline to classify deeply senescent dermal fibroblast cells for the detection of an in vivo senescent cell burden |
| title_full | SenPred: a single-cell RNA sequencing-based machine learning pipeline to classify deeply senescent dermal fibroblast cells for the detection of an in vivo senescent cell burden |
| title_fullStr | SenPred: a single-cell RNA sequencing-based machine learning pipeline to classify deeply senescent dermal fibroblast cells for the detection of an in vivo senescent cell burden |
| title_full_unstemmed | SenPred: a single-cell RNA sequencing-based machine learning pipeline to classify deeply senescent dermal fibroblast cells for the detection of an in vivo senescent cell burden |
| title_short | SenPred: a single-cell RNA sequencing-based machine learning pipeline to classify deeply senescent dermal fibroblast cells for the detection of an in vivo senescent cell burden |
| title_sort | senpred a single cell rna sequencing based machine learning pipeline to classify deeply senescent dermal fibroblast cells for the detection of an in vivo senescent cell burden |
| topic | 3D organotypic culture In vivo senescence detection Living skin equivalent Machine learning scRNA-seq Senescence |
| url | https://doi.org/10.1186/s13073-024-01418-0 |
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