Balance of activity during a critical period tunes a developing network.

Developing neural circuits are influenced by activity and are especially sensitive to changes in activity during critical periods (CPs) of development. Changes occurring during a CP often become 'locked in' so that they affect the mature network. Indeed, several neurodevelopmental disorders have been linked to excessive activity during such periods. It is, therefore, important to identify those aspects of neural circuit development that are influenced by neural activity during a CP. In this study, we take advantage of the genetic tractability of Drosophila to show that activity perturbation during an embryonic CP permanently alters properties of the locomotor circuit. Specific changes we identify include increased synchronicity of motoneuron activity and greater strengthening of excitatory over inhibitory synaptic drive to motoneurons. These changes are sufficient to reduce network robustness, evidenced by increased sensitivity to induced seizure. We also show that we can rescue these changes when increased activity is mitigated by inhibition provided by mechanosensory neurons. Similarly, we demonstrate a dose-dependent relationship between inhibition experienced during the CP and the extent to which it is possible to rescue the hyperexcitable phenotype characteristic of the parabss mutation. This suggests that developing circuits must be exposed to a properly balanced sum of excitation and inhibition during the CP to achieve normal mature network function. Our results, therefore, provide novel insight into how activity during a CP shapes specific elements of a circuit, and how activity during this period is integrated to tune neural circuits to the environment in which they will likely function.

Electrophysiological validation of monosynaptic connectivity between premotor interneurons and the aCC motoneuron in the Drosophila larval CNS.

The Drosophila connectome project aims to map the synaptic connectivity of entire larval and adult fly neural networks, which is essential for understanding nervous system development and function. So far, the project has produced an impressive amount of electron microscopy data that has facilitated reconstructions of specific synapses, including many in the larval locomotor circuit. While this breakthrough represents a technical tour-de-force, the data remain under-utilised, partly due to a lack of functional validation of reconstructions. Attempts to validate connectivity posited by the connectome project, have mostly relied on behavioural assays and/or GRASP or GCaMP imaging. While these techniques are useful, they have limited spatial or temporal resolution. Electrophysiological assays of synaptic connectivity overcome these limitations. Here, we combine patch clamp recordings with optogenetic stimulation in male and female larvae, to test synaptic connectivity proposed by connectome reconstructions. Specifically, we use multiple driver lines to confirm that several connections between premotor interneurons and the anterior corner cell (aCC) motoneuron are, as the connectome project suggests, monosynaptic. In contrast, our results also show that conclusions based on GRASP imaging may provide false positive results regarding connectivity between cells. We also present a novel imaging tool, based on the same technology as our electrophysiology, as a favourable alternative to GRASP. Finally, of eight Gal4 lines tested, five are reliably expressed in the premotors they are targeted to. Thus, our work highlights the need to confirm functional synaptic connectivity, driver line specificity, and use of appropriate genetic tools to support connectome projects.SIGNIFICANCE STATEMENTThe Drosophila connectome project aims to provide a complete description of connectivity between neurons in an organism that presents experimental advantages over other models. It has reconstructed over 80 percent of the fly larva's synaptic connections by manual identification of anatomical landmarks present in serial section transmission electron microscopy (ssTEM) volumes of the larval CNS. We use a highly reliable electrophysiological approach to verify these connections, so provide useful insight into the accuracy of work based on ssTEM. We also present a novel imaging tool for validating excitatory monosynaptic connections between cells, and show that several genetic driver lines designed to target neurons of the larval connectome exhibit non-specific and/or unreliable expression.

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.

Differential phase register of Hes1 oscillations with mitoses underlies cell-cycle heterogeneity in ER+ breast cancer cells.

Here, we study the dynamical expression of endogenously labeled Hes1, a transcriptional repressor implicated in controlling cell proliferation, to understand how cell-cycle length heterogeneity is generated in estrogen receptor (ER)+ breast cancer cells. We find that Hes1 shows oscillatory expression with ∼25 h periodicity and during each cell cycle has a variable peak in G1, a trough around G1-S transition, and a less variable second peak in G2/M. Compared to other subpopulations, the cell cycle in CD44HighCD24Low cancer stem cells is longest and most variable. Most cells divide around the peak of the Hes1 expression wave, but preceding mitoses in slow dividing CD44HighCD24Low cells appear phase-shifted, resulting in a late-onset Hes1 peak in G1. The position, duration, and shape of this peak, rather than the Hes1 expression levels, are good predictors of cell-cycle length. Diminishing Hes1 oscillations by enforcing sustained expression slows down the cell cycle, impairs proliferation, abolishes the dynamic expression of p21, and increases the percentage of CD44HighCD24Low cells. Reciprocally, blocking the cell cycle causes an elongation of Hes1 periodicity, suggesting a bidirectional interaction of the Hes1 oscillator and the cell cycle. We propose that Hes1 oscillations are functionally important for the efficient progression of the cell cycle and that the position of mitosis in relation to the Hes1 wave underlies cell-cycle length heterogeneity in cancer cell subpopulations.

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.

Visualizing gene expression during zebrafish pronephros development and regeneration.

The vertebrate kidney is comprised of functional units known as nephrons. Defects in nephron development or activity are a common feature of kidney disease. Current medical treatments are unable to ameliorate the dire consequences of nephron deficit or injury. Although there have been tremendous advancements in our understanding of nephron ontogeny and the response to damage, many significant knowledge gaps still remain. The zebrafish embryo kidney, or pronephros, is an ideal model for many renal development and regeneration studies because it is comprised of nephrons that share conserved features with the nephron units that comprise the mammalian metanephric kidney. In this chapter, we provide an overview about the benefits of using the zebrafish pronephros to study the mechanisms underlying nephrogenesis as well as epithelial repair and regeneration. We subsequently detail methods for the spatiotemporal assessment of gene and protein expression in zebrafish embryos that can be used to extend the understanding of nephron development and disease, and thereby create new opportunities to identify therapeutic strategies for regenerative medicine.

Stochasticity in the miR-9/Hes1 oscillatory network can account for clonal heterogeneity in the timing of differentiation.

Recent studies suggest that cells make stochastic choices with respect to differentiation or division. However, the molecular mechanism underlying such stochasticity is unknown. We previously proposed that the timing of vertebrate neuronal differentiation is regulated by molecular oscillations of a transcriptional repressor, HES1, tuned by a post-transcriptional repressor, miR-9. Here, we computationally model the effects of intrinsic noise on the Hes1/miR-9 oscillator as a consequence of low molecular numbers of interacting species, determined experimentally. We report that increased stochasticity spreads the timing of differentiation in a population, such that initially equivalent cells differentiate over a period of time. Surprisingly, inherent stochasticity also increases the robustness of the progenitor state and lessens the impact of unequal, random distribution of molecules at cell division on the temporal spread of differentiation at the population level. This advantageous use of biological noise contrasts with the view that noise needs to be counteracted.