Inferring post-transcriptional regulation within and across cell types in human testis

Area of Science:

• Proteogenomics
• Single-cell biology
• Biostatistics

Background:

• Single-cell atlases commonly use RNA abundance as a proxy for protein abundance.
• Protein levels are influenced by synthesis and degradation rates, indicating post-transcriptional regulation.
• Distinguishing biological variation from technical noise is crucial in single-cell studies.

Purpose of the Study:

• To estimate the contribution of post-transcriptional regulation to protein abundance.
• To develop a robust computational model for joint analysis of single-cell RNA and protein data.
• To investigate cell-type specific protein regulation in human testis.

Main Methods:

• Quantified proteomes of 5,883 single human testis cells using SCoPE2, pSCoPE, and plexDIA mass spectrometry.
• Developed BayesPG, a Bayesian model, to integrate transcriptomic and proteomic data while accounting for technical variation.
• Jointly modeled RNA and protein data from 29,709 single cells across multiple datasets and methods.

Main Results:

• BayesPG estimated consensus mRNA and protein levels for 3,861 gene products and quantified the relative protein-to-mRNA ratio (rPTR).
• Approximately 28% of gene products showed significant differences between protein and RNA levels, impacting ~1,500 Gene Ontology (GO) groups.
• Spermatogenesis-related functions and post-translationally modified peptides exhibited significant cell-type specific regulation.

Conclusions:

• Post-transcriptional regulation plays a significant role in specialized cellular functions, such as spermatogenesis.
• The BayesPG model provides a generalizable framework for inferring protein regulation from single-cell proteogenomic data.
• Cell-type specific regulation of protein abundance, including via phosphorylation, is evident in human testis.