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In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
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Identification of Marker Genes in Infectious Diseases from ScRNA-seq Data Using Interpretable Machine Learning
Gustavo Sganzerla Martinez1,2,3, Alexis Garduno4,5, Ali Toloue Ostadgavahi1,2,3
1Microbiology and Immunology, Dalhousie University, Halifax, NS B3H 4H7, Canada.
International Journal of Molecular Sciences
|June 19, 2024
Summary
Artificial intelligence and single-cell RNA sequencing can detect infection severity. Key gene expression in immune cells helps differentiate between mild and severe sepsis and COVID-19, aiding early diagnosis.
Area of Science:
- Immunology
- Computational Biology
- Genomics
Background:
- Infections can trigger abnormal immune responses, leading to inflammation, tissue damage, and organ failure.
- Immune dysregulation manifests as altered immune cell populations and biomarker concentrations.
- Early diagnosis and severity assessment of immune-dysregulated syndromes are critical.
Purpose of the Study:
- To develop an artificial intelligence (AI) tool for early diagnosis and severity differentiation of immune-dysregulated syndromes.
- To utilize single-cell RNA sequencing (scRNA-seq) data for immune response analysis.
- To identify key genes and immune cell markers for infection classification.
Main Methods:
- Built an AI classification system using scRNA-seq data.
- Analyzed gene expression patterns in immune cells.
- Interpreted AI decision patterns to identify significant genes and cell types.
Main Results:
- Single-cell transcriptomics successfully distinguished between mild and severe sepsis and COVID-19.
- Specific gene expression changes (CD3, CD14, CD16, FOSB, S100A12, TCRɣδ) in immune cells accurately differentiated infection severity.
- Identified immune cell populations crucial for distinguishing infection degrees.
Conclusions:
- AI combined with scRNA-seq offers a promising approach for early diagnosis of infection severity.
- Identified genes serve as potential diagnostic markers for sepsis and COVID-19.
- These findings may guide the development of targeted immunotherapeutic interventions.
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