Single Cell Data Enables Dissecting Cell Types Present in Bulk Transcriptome Data

Area of Science:

• Stem cell biology
• Genomics
• Bioinformatics

Background:

• Organoid models are valuable research tools, but assessing their cellular composition can be challenging.
• Single-cell RNA sequencing (scRNA-seq) provides high-resolution cellular data, but bulk transcriptome data is often more accessible.
• Bridging the gap between these data types is crucial for robust organoid characterization.

Purpose of the Study:

• To develop and validate a computational pipeline for deconvolving bulk transcriptome data using scRNA-seq reference profiles.
• To enable the estimation of cell type fractions in induced pluripotent stem cell (iPSC)-derived kidney and brain organoids.
• To provide a method for assessing organoid quality and cellular heterogeneity.

Main Methods:

• A bioinformatics pipeline was developed for analyzing scRNA-seq data.
• The pipeline performs deconvolution to estimate cell type proportions from bulk transcriptome data.
• The method was applied to iPSC-derived kidney and brain organoid transcriptome data, using existing scRNA-seq datasets for reference.

Main Results:

• The pipeline successfully identified key cell types in kidney organoids, including proximal tubules, distal convoluted tubules, and podocytes.
• Relevant cell populations in brain organoids, such as neurons, astrocytes, oligodendrocytes, and microglia, were also identified.
• Essential endothelial and immune-related cell types were detected in both organoid types.

Conclusions:

• The developed pipeline effectively deconvolves bulk transcriptome data to reveal cellular composition in organoids.
• This approach facilitates the assessment of cell type fractions and quality control for iPSC-derived kidney and brain organoids.
• The methodology is anticipated to be applicable to organoids derived from other tissues, broadening its utility in regenerative medicine and developmental biology research.