Integrative analysis of spatial transcriptome with single-cell transcriptome and single-cell epigenome in mouse lungs...

Area of Science:

• Immunology
• Genomics
• Computational Biology

Background:

• Understanding lung immunity necessitates precise anatomical profiling of tissue-resident T cells.
• Such detailed spatial information has been lacking in immunized mouse models.
• Current methods do not fully capture the spatial distribution of immune cells within the lung microenvironment.

Purpose of the Study:

• To perform an unbiased, spatial profiling of lung immune cells in an immunized mouse model.
• To investigate the anatomical locations and cell-type compositions of T cells within the lung.
• To explore the potential of single-cell multi-omics for uncovering spatial-dependent mechanisms of lung immunity.

Main Methods:

• Integrative analysis of spatial transcriptome (10x Visium) with single-cell RNA-seq and single-cell ATAC-seq.
• Utilized the 10x Genomics Chromium and Visium platforms for high-resolution data acquisition.
• Developed an optimized deconvolution pipeline for accurate cell-type identification by anatomical location.

Main Results:

• Demonstrated that combining scATAC-seq and scRNA-seq enhances cell-type identification, particularly for T helper cells.
• Observed dynamic changes in T helper cell locations and their associated chemokines using multi-modal single-cell analysis.
• Successfully deciphered specific cell-type compositions based on anatomical location within the lung.

Conclusions:

• Single-cell multi-omics analysis is a powerful approach for studying lung immunity.
• This study provides a proof-of-principle for uncovering spatial and cell-type-dependent immune mechanisms.
• The findings highlight the importance of anatomical context in understanding immune responses within the lung.