An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

Animals are grouped into ~35 'phyla' based upon the notion of distinct body plans. Morphological and molecular analyses have revealed that a stage in the middle of development--known as the phylotypic period--is conserved among species within some phyla. Although these analyses provide evidence for their existence, phyla have also been criticized as lacking an objective definition, and consequently based on arbitrary groupings of animals. Here we compare the developmental transcriptomes of ten species, each annotated to a different phylum, with a wide range of life histories and embryonic forms. We find that in all ten species, development comprises the coupling of early and late phases of conserved gene expression. These phases are linked by a divergent 'mid-developmental transition' that uses species-specific suites of signalling pathways and transcription factors. This mid-developmental transition overlaps with the phylotypic period that has been defined previously for three of the ten phyla, suggesting that transcriptional circuits and signalling mechanisms active during this transition are crucial for defining the phyletic body plan and that the mid-developmental transition may be used to define phylotypic periods in other phyla. Placing these observations alongside the reported conservation of mid-development within phyla, we propose that a phylum may be defined as a collection of species whose gene expression at the mid-developmental transition is both highly conserved among them, yet divergent relative to other species.

Single-cell transcriptomics requires a method that is sensitive, accurate, and reproducible. Here, we present CEL-Seq2, a modified version of our CEL-Seq method, with threefold higher sensitivity, lower costs, and less hands-on time. We implemented CEL-Seq2 on Fluidigm’s C1 system, providing its first single-cell, on-chip barcoding method, and we detected gene expression changes accompanying the progression through the cell cycle in mouse fibroblast cells. We also compare with Smart-Seq to demonstrate CEL-Seq2’s increased sensitivity relative to other available methods. Collectively, the improvements make CEL-Seq2 uniquely suited to single-cell RNA-Seq analysis in terms of economics, resolution, and ease of use.