An integrated single-cell transcriptomic dataset for non-small cell lung cancer.

Area of Science:

• Genomics
• Oncology
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity.
• Existing scRNA-seq datasets often suffer from small cohort sizes and limited cell type information, hindering data reuse.
• Non-small cell lung cancer (NSCLC) research requires robust single-cell data for detailed analysis.

Purpose of the Study:

• To create a large, integrated scRNA-seq dataset of human primary NSCLC tumors.
• To improve cell type classification and annotation within NSCLC single-cell data.
• To provide a valuable resource for future NSCLC transcriptome studies.

Main Methods:

• Integration of seven independent scRNA-seq datasets comprising 224,611 cells using an anchor-based approach.
• Utilized five datasets as a reference and two for validation.
• Developed two-level annotation based on conserved cell type-specific markers.

Main Results:

• Successfully integrated multiple scRNA-seq datasets into a large NSCLC single-cell resource.
• Demonstrated the usability of the integrated dataset by accurately predicting annotations in validation datasets.
• Performed trajectory analysis on T cells and lung cancer cells within the integrated data.

Conclusions:

• The integrated dataset provides a robust resource for single-cell level NSCLC transcriptome analysis.
• This resource addresses limitations of previous scRNA-seq studies in terms of cohort size and cell type information.
• Facilitates deeper insights into NSCLC cellular heterogeneity and potential therapeutic targets.