The embryonic transcriptome of Parhyale hawaiensis reveals different dynamics of microRNAs and mRNAs during the maternal-zygotic transition.

Parhyale hawaiensis has emerged as the crustacean model of choice due to its tractability, ease of imaging, sequenced genome, and development of CRISPR/Cas9 genome editing tools. However, transcriptomic datasets spanning embryonic development are lacking, and there is almost no annotation of non-protein-coding RNAs, including microRNAs. We have sequenced microRNAs, together with mRNAs and long non-coding RNAs, in Parhyale using paired size-selected RNA-seq libraries at seven time-points covering important transitions in embryonic development. Focussing on microRNAs, we annotate 175 loci in Parhyale, 88 of which have no known homologs. We use these data to annotate the microRNAome of 37 crustacean genomes, and suggest a core crustacean microRNA set of around 61 sequence families. We examine the dynamic expression of microRNAs and mRNAs during the maternal-zygotic transition. Our data suggest that zygotic genome activation occurs in two waves in Parhyale with microRNAs transcribed almost exclusively in the second wave. Contrary to findings in other arthropods, we do not predict a general role for microRNAs in clearing maternal transcripts. These data significantly expand the available transcriptomics resources for Parhyale, and facilitate its use as a model organism for the study of small RNAs in processes ranging from embryonic development to regeneration.

smiFISH and embryo segmentation for single-cell multi-gene RNA quantification in arthropods.

Recently, advances in fluorescent in-situ hybridization techniques and in imaging technology have enabled visualization and counting of individual RNA molecules in single cells. This has greatly enhanced the resolution in our understanding of transcriptional processes. Here, we adapt a recently published smiFISH protocol (single-molecule inexpensive fluorescent in-situ hybridization) to whole embryos across a range of arthropod model species, and also to non-embryonic tissues. Using multiple fluorophores with distinct spectra and white light laser confocal imaging, we simultaneously detect and separate single RNAs from up to eight different genes in a whole embryo. We also combine smiFISH with cell membrane immunofluorescence, and present an imaging and analysis pipeline for 3D cell segmentation and single-cell RNA counting in whole blastoderm embryos. Finally, using whole embryo single-cell RNA count data, we propose two alternative single-cell variability measures to the commonly used Fano factor, and compare the capacity of these three measures to address different aspects of single-cell expression variability.