Development of a deep learning-based 1D convolutional neural network model for cross-species natural killer T cell identification using peripheral blood mononuclear cell single-cell RNA sequencing data
Background and Aim: Natural killer T (NKT) cells exhibit the traits of both T and NK cells. Although their roles have been well studied in humans and mice, limited knowledge is available regarding their roles in dogs and pigs, which serve as models for human immunology. Single-cell RNA sequencing (s...
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Veterinary World
2024-12-01
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| Series: | Veterinary World |
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| Online Access: | https://www.veterinaryworld.org/Vol.17/December-2024/14.pdf |
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| author | Kaj Chokeshaiusaha Thanida Sananmuang Denis Puthier Roongtham Kedkovid |
| author_facet | Kaj Chokeshaiusaha Thanida Sananmuang Denis Puthier Roongtham Kedkovid |
| author_sort | Kaj Chokeshaiusaha |
| collection | DOAJ |
| description | Background and Aim: Natural killer T (NKT) cells exhibit the traits of both T and NK cells. Although their roles have been well studied in humans and mice, limited knowledge is available regarding their roles in dogs and pigs, which serve as models for human immunology. Single-cell RNA sequencing (scRNA-Seq) can elucidate NKT cell functions. However, identifying cells in mixed populations, like peripheral blood mononuclear cells (PBMCs) is challenging using this technique. This study presented the application of one-dimensional convolutional neural network (1DCNN) for the identification of NKT cells within scRNA-seq data derived from PBMCs.
Materials and Methods: We used human scRNA-Seq data to train a 1DCNN model for cross-species identification of NKT cells in canine and porcine PBMC datasets. K-means clustering was used to isolate human NKT cells for training the 1DCNN model. The trained model predicted NKT cell subpopulations in PBMCs from all species. We performed Differential gene expression and Gene Ontology (GO) enrichment analyses to assess shared gene functions across species.
Results: We successfully trained the 1DCNN model on human scRNA-Seq data, achieving 99.3% accuracy, and successfully identified NKT cell candidates in human, canine, and porcine PBMC datasets using the model. Across species, these NKT cells shared 344 genes with significantly elevated expression (FDR ≤ 0.001). GO term enrichment analyses confirmed the association of these genes with the immunoactivity of NKT cells.
Conclusion: This study developed a 1DCNN model for cross-species NKT cell identification and identified conserved immune function genes. The approach has broad implications for identifying other cell types in comparative immunology, and future studies are needed to validate these findings. |
| format | Article |
| id | doaj-art-f7d6ff2bd7d04ea5a863bdb59b74ba8d |
| institution | Kabale University |
| issn | 0972-8988 2231-0916 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Veterinary World |
| record_format | Article |
| series | Veterinary World |
| spelling | doaj-art-f7d6ff2bd7d04ea5a863bdb59b74ba8d2024-12-20T14:14:13ZengVeterinary WorldVeterinary World0972-89882231-09162024-12-0117122846285710.14202/vetworld.2024.2846-2857Development of a deep learning-based 1D convolutional neural network model for cross-species natural killer T cell identification using peripheral blood mononuclear cell single-cell RNA sequencing dataKaj Chokeshaiusaha0https://orcid.org/0000-0002-2953-9101Thanida Sananmuang1https://orcid.org/0000-0002-8653-3558Denis Puthier2https://orcid.org/0000-0002-7240-5280Roongtham Kedkovid3https://orcid.org/0000-0001-8595-6156Department of Veterinary Science, Faculty of Veterinary Medicine, Rajamangala University of Technology, Tawan-OK, Chon Buri, Thailand.Department of Veterinary Science, Faculty of Veterinary Medicine, Rajamangala University of Technology, Tawan-OK, Chon Buri, Thailand.Aix-Marseille Université, INSERM UMR 1090, TAGC, Marseille, France.Department of Veterinary Medicine, Faculty of Veterinary Science, Chulalongkorn University, Bangkok, Thailand.Background and Aim: Natural killer T (NKT) cells exhibit the traits of both T and NK cells. Although their roles have been well studied in humans and mice, limited knowledge is available regarding their roles in dogs and pigs, which serve as models for human immunology. Single-cell RNA sequencing (scRNA-Seq) can elucidate NKT cell functions. However, identifying cells in mixed populations, like peripheral blood mononuclear cells (PBMCs) is challenging using this technique. This study presented the application of one-dimensional convolutional neural network (1DCNN) for the identification of NKT cells within scRNA-seq data derived from PBMCs. Materials and Methods: We used human scRNA-Seq data to train a 1DCNN model for cross-species identification of NKT cells in canine and porcine PBMC datasets. K-means clustering was used to isolate human NKT cells for training the 1DCNN model. The trained model predicted NKT cell subpopulations in PBMCs from all species. We performed Differential gene expression and Gene Ontology (GO) enrichment analyses to assess shared gene functions across species. Results: We successfully trained the 1DCNN model on human scRNA-Seq data, achieving 99.3% accuracy, and successfully identified NKT cell candidates in human, canine, and porcine PBMC datasets using the model. Across species, these NKT cells shared 344 genes with significantly elevated expression (FDR ≤ 0.001). GO term enrichment analyses confirmed the association of these genes with the immunoactivity of NKT cells. Conclusion: This study developed a 1DCNN model for cross-species NKT cell identification and identified conserved immune function genes. The approach has broad implications for identifying other cell types in comparative immunology, and future studies are needed to validate these findings.https://www.veterinaryworld.org/Vol.17/December-2024/14.pdf1d convolutional neural networkk-means clusteringnatural killer t cellperipheral blood mononuclear cellssingle-cell rna sequencing |
| spellingShingle | Kaj Chokeshaiusaha Thanida Sananmuang Denis Puthier Roongtham Kedkovid Development of a deep learning-based 1D convolutional neural network model for cross-species natural killer T cell identification using peripheral blood mononuclear cell single-cell RNA sequencing data Veterinary World 1d convolutional neural network k-means clustering natural killer t cell peripheral blood mononuclear cells single-cell rna sequencing |
| title | Development of a deep learning-based 1D convolutional neural network model for cross-species natural killer T cell identification using peripheral blood mononuclear cell single-cell RNA sequencing data |
| title_full | Development of a deep learning-based 1D convolutional neural network model for cross-species natural killer T cell identification using peripheral blood mononuclear cell single-cell RNA sequencing data |
| title_fullStr | Development of a deep learning-based 1D convolutional neural network model for cross-species natural killer T cell identification using peripheral blood mononuclear cell single-cell RNA sequencing data |
| title_full_unstemmed | Development of a deep learning-based 1D convolutional neural network model for cross-species natural killer T cell identification using peripheral blood mononuclear cell single-cell RNA sequencing data |
| title_short | Development of a deep learning-based 1D convolutional neural network model for cross-species natural killer T cell identification using peripheral blood mononuclear cell single-cell RNA sequencing data |
| title_sort | development of a deep learning based 1d convolutional neural network model for cross species natural killer t cell identification using peripheral blood mononuclear cell single cell rna sequencing data |
| topic | 1d convolutional neural network k-means clustering natural killer t cell peripheral blood mononuclear cells single-cell rna sequencing |
| url | https://www.veterinaryworld.org/Vol.17/December-2024/14.pdf |
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