A wealth of single-cell expression data is now available for a range of phylogenetically diverse species. Comparative analysis of such data can offer valuable insight into the fundamental features of individual cell types and tissues as well as the relationship between changes in gene expression and the generation of novel phenotypes. Here, we discuss challenges associated with cross-species transcriptomic analysis and highlight molecular mechanisms of developmental evolution which are masked in conventional dataset integration techniques. We present a novel framework for mapping gene expression across species by generating coupled yet independent genewise alignments, which we formalize as an optimization problem. We apply this method to detect evolutionarily conserved and divergent features associated with erythropoietic differentiation.