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
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...
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| Published in | Genome medicine Vol. 17; no. 1; pp. 2 - 16 |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
14.01.2025
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1756-994X 1756-994X |
| DOI | 10.1186/s13073-024-01418-0 |
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| 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
. |
|---|---|
| AbstractList | 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.BACKGROUNDSenescence 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.Here we present SenPred, a machine-learning pipeline which identifies fibroblast senescence based on single-cell transcriptomics from fibroblasts grown in 2D and 3D.METHODSHere we present SenPred, a machine-learning pipeline which identifies fibroblast senescence based on single-cell transcriptomics from fibroblasts grown in 2D and 3D.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.RESULTSUsing 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.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 .CONCLUSIONSWe 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 . 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 . 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. Here we present SenPred, a machine-learning pipeline which identifies fibroblast senescence based on single-cell transcriptomics from fibroblasts grown in 2D and 3D. 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. 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. 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: Keywords: 3D organotypic culture, In vivo senescence detection, Living skin equivalent, Machine learning, scRNA-seq, Senescence 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 . BackgroundSenescence 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.MethodsHere we present SenPred, a machine-learning pipeline which identifies fibroblast senescence based on single-cell transcriptomics from fibroblasts grown in 2D and 3D.ResultsUsing 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.ConclusionsWe 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. 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. Here we present SenPred, a machine-learning pipeline which identifies fibroblast senescence based on single-cell transcriptomics from fibroblasts grown in 2D and 3D. 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. 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 . |
| ArticleNumber | 2 |
| Audience | Academic |
| Author | Mossa, Federica Philpott, Michael P. Gunn, David A. Wallis, Ryan Wainwright, Linda J. Davis, Andrew Hughes, Bethany K. Bishop, Cleo L. Milligan, Deborah |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39810225$$D View this record in MEDLINE/PubMed |
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| Keywords | In vivo senescence detection Living skin equivalent 3D organotypic culture scRNA-seq Senescence Machine learning |
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| Snippet | Background
Senescence classification is an acknowledged challenge within the field, as markers are cell-type and context dependent. Currently, multiple... Senescence classification is an acknowledged challenge within the field, as markers are cell-type and context dependent. Currently, multiple morphological and... Background Senescence classification is an acknowledged challenge within the field, as markers are cell-type and context dependent. Currently, multiple... BackgroundSenescence classification is an acknowledged challenge within the field, as markers are cell-type and context dependent. Currently, multiple... Abstract Background Senescence classification is an acknowledged challenge within the field, as markers are cell-type and context dependent. Currently,... |
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| SubjectTerms | 3D organotypic culture Age Analysis Antibiotics Bioinformatics Biomedical and Life Sciences Biomedicine Cancer Research Cell cycle Cellular Senescence - genetics Classification Datasets Fibroblasts Fibroblasts - cytology Fibroblasts - metabolism Genomics Human Genetics Humans Humidity Immunofluorescence In vivo senescence detection Learning algorithms Living skin equivalent Machine Learning Medicine/Public Health Metabolomics Morphology Pipe lines Pipelines RNA RNA sequencing RNA-Seq scRNA-seq Senescence Sequence Analysis, RNA - methods Single-Cell Analysis - methods Skin Skin - cytology Systems Biology Transcriptome Transcriptomics |
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| 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 |
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