Cross-species imputation and comparison of single-cell transcriptomic profiles

Area of Science:

• Genomics
• Computational Biology
• Evolutionary Biology

Background:

• Cross-species gene expression comparison aids understanding of evolutionary regulatory changes and knowledge transfer from model organisms to humans.
• Single-cell RNA sequencing (scRNA-seq) provides high-resolution gene expression data but faces challenges in cross-species analysis due to data sparsity, batch effects, and cell matching issues.
• Obtaining single-cell profiles can be difficult in certain biological contexts, limiting research scope.

Purpose of the Study:

• To develop a computational framework for accurate cross-species prediction and comparison of single-cell gene expression profiles.
• To address challenges in scRNA-seq data analysis, including data sparsity, batch effects, and cell type identification across species.
• To enable high-resolution cell type and disease profiling in under-characterized biological contexts and facilitate evolutionary studies.

Main Methods:

• Developed Icebear, a neural network framework that decomposes single-cell measurements into distinct factors (cell identity, species, batch).
• Utilized Icebear to predict single-cell gene expression profiles across different species.
• Applied Icebear to compare conserved gene expression patterns, specifically focusing on X-chromosome genes in mammals versus autosomal genes in chickens.

Main Results:

• Icebear accurately predicts single-cell gene expression profiles across species.
• The framework provides high-resolution cell type and disease profiles, even in data-limited contexts.
• Direct cross-species comparison revealed novel insights into the evolutionary adaptations of X-chromosome gene regulation in mammals.

Conclusions:

• Icebear offers a robust solution for cross-species single-cell gene expression analysis and prediction.
• The tool enhances our ability to study conserved biological mechanisms and evolutionary adaptations.
• This work provides a foundation for deeper understanding of gene regulation evolution and facilitates research in understudied species and contexts.