A quantitative computational framework for allopolyploid single-cell data integration and core gene ranking in development [scRNA-seq]

Polyploidization drives regulatory and phenotypic innovation. How the merger of different genomes contributes to polyploid development is a fundamental issue in evolutionary developmental biology and breeding research. Clarifying this issue is challenging because of genome complexity and the difficulty in tracking stochastic subgenome divergence during development. Recent single-cell sequencing techniques enabled probing subgenome divergent regulation in the context of cellular differentiation. However, analyzing single-cell data suffers from high error rates due to high-dimensionality, noise, and sparsity, and the errors stack up in polyploid analysis due to the increased dimensionality of comparisons between subgenomes of each cell, hindering deeper mechanistic understandings. Here, we developed a quantitative computational framework, pseudo-genome divergence quantification (pgDQ), for quantifying and tracking subgenome divergence directly at the cellular level. Further comparing with cellular differentiation trajectories derived from scRNA-seq data allowed for an examination of the relationship between subgenome divergence and the progression of development. pgDQ produces robust results and is insensitive to data dropout and noise, avoiding high error rates due to multiple comparisons of genes, cells, and subgenomes. A statistical diagonostic approach is proposed to identify genes that are central to subgenome divergence during development, which facilitates the integration of different data modalities, enabling the identification of factors and pathways that mediate subgenome-divergent activity during development. Case studies demonstrated that applying pgDQ to single cell and bulk tissue transcriptome data promotes a systematic and deeper understanding of how dynamic subgenome divergence contributes to developmental trajectories in polyploid evolution. Overall design: Common wheat (Triticum aestivum cultivar 'Chinese Spring') seeds were surface-sterilized via a 10-min incubation in 30% H2O2 and then thoroughly washed five times with distilled water. The seeds were germinated in water for 3 days at 22 °C.The germinated seeds with residual endosperm were transferred to Hoagland solution and grown under 16 h light/ 8 h dark condition at 22 °C in greenhouse.The root tips (0.5 cm) of the seedlings were harvested for scRNA-seq experiment. The root tips were harvested and digested for 2 h at room temperature in digestion buffer (1.5% cellulase R10, 1.5% macerozyme R10, 0.3 M mannitol, 3 mM ß-mercaptoethanol, 10 mM CaCl2, and 0.3% BSA). Single-cell suspensions were loaded onto 10× GENOMICS Chromium to capture approximately 6000 single cells following the manufacturer's instructions for the 10× Genomics Chromium Single-Cell 3' kit (V3). cDNA amplification, library construction, and sequencing on the Illumina NovaSeq 6000 platform (paired-end multiplexing run, 150 bp) were performed by LC-Bio Technology (HangZhou, China)