The evolution of mushroom body and telencephalic cell types, studied by single cell expression profiling of Platynereis dumerilii larvae

One of the many ways to understand the evolution of elaborate organs such as the brain is to investigate the different cell types that constitute that organ. Cell types are defined by a unique combination of genes (molecular fingerprint) that specify the distinct morphological and physiological features that are characteristic of that cell type. In order to study the cell types in the brain of the developing annelid Platynereis dumerilii I have investigated the co-expression of several genes at cellular resolution. For this, I have developed a protocol, the so-called Whole Mount In Silico Expression Profiling (WMISEP), utilizing advanced image processing algorithms, whole mount in situ hybridization, immunostaining against acetylated tubulin and whole mount reflection confocal microscopy. The basic idea of the protocol is to acquire two color confocal image stacks, with one channel containing expression information for gene and the other channel containing the information of the axonal scaffold. The information in the axonal scaffold channel is then used to align several such images to a common reference average axonal scaffold image, and thus bringing the expression patterns into the same coordinate system. I conducted several experiments to illustrate the cellular resolution sensitivity and specificity of the protocol. WMISEP has been used to generate cell resolution expression of 72 genes. I also developed a cellular model of the 48 hour old Platynereis larval brain, which facilitated the generation of cellular gene expression profiles. Subsequently, I used several clustering techniques to cluster the larval brain cells and genes based on their expression profiles and spatial patterns respectively. As an example application of WMISEP, I investigated the evolution of mushroom bodies (MBs) and telencephalic cell types. Firstly, I investigated the anatomy, development and molecular fingerprint of Platynereis MB cells. Subsequently, I compared the anatomy and molecular fingerprint of Platynereis and insect’s MBs to test for deep homology. Furthermore, I investigated the expression of early telencephalon regionalization genes in Platynereis and showed that the vertebrate telencephalon patterning genes are expressed in a similar spatial orientation in the Platynereis larval brain, suggesting that the telencephalon patterning gene network already existed in the last common ancestor of all bilaterian animals. Finally, the Platynereis MB and vertebrate cortex/hippocampus develop from the same molecular regions with respect to the conserved molecular topography

Uneven balance of power between hypothalamic peptidergic neurons in the control of feeding

Two classes of peptide-producing neurons in the arcuate nucleus (Arc) of the hypothalamus are known to exert opposing actions on feeding: the anorexigenic neurons that express proopiomelanocortin (POMC) and the orexigenic neurons that express agouti-related protein (AgRP) and neuropeptide Y (NPY). These neurons are thought to arise from a common embryonic progenitor, but our anatomical and functional understanding of the interplay of these two peptidergic systems that contribute to the control of feeding remains incomplete. The present study uses a combination of optogenetic stimulation with viral and transgenic approaches, coupled with neural activity mapping and brain transparency visualization to demonstrate the following: (i) selective activation of Arc POMC neurons inhibits food consumption rapidly in unsated animals; (ii) activation of Arc neurons arising from POMC-expressing progenitors, including POMC and a subset of AgRP neurons, triggers robust feeding behavior, even in the face of satiety signals from POMC neurons; (iii) the opposing effects on food intake are associated with distinct neuronal projection and activation patterns of adult hypothalamic POMC neurons versus Arc neurons derived from POMC-expressing lineages; and (iv) the increased food intake following the activation of orexigenic neurons derived from POMC-expressing progenitors engages an extensive neural network that involves the endogenous opioid system. Together, these findings shed further light on the dynamic balance between two peptidergic systems in the moment-to-moment regulation of feeding behavior

Focused ultrasound-mediated brain genome editing.

Gene editing in the brain has been challenging because of the restricted transport imposed by the blood-brain barrier (BBB). Current approaches mainly rely on local injection to bypass the BBB. However, such administration is highly invasive and not amenable to treating certain delicate regions of the brain. We demonstrate a safe and effective gene editing technique by using focused ultrasound (FUS) to transiently open the BBB for the transport of intravenously delivered CRISPR/Cas9 machinery to the brain

tomerlab/suiteWB: v1.0.0

<p>A set of tools for whole mouse brain phenotyping.</p&gt

Profiling by Image Registration Reveals Common Origin of Annelid Mushroom Bodies and Vertebrate Pallium

SummaryThe evolution of the highest-order human brain center, the “pallium” or “cortex,” remains enigmatic. To elucidate its origins, we set out to identify related brain parts in phylogenetically distant animals, to then unravel common aspects in cellular composition and molecular architecture. Here, we compare vertebrate pallium development to that of the mushroom bodies, sensory-associative brain centers, in an annelid. Using a newly developed protocol for cellular profiling by image registration (PrImR), we obtain a high-resolution gene expression map for the developing annelid brain. Comparison to the vertebrate pallium reveals that the annelid mushroom bodies develop from similar molecular coordinates within a conserved overall molecular brain topology and that their development involves conserved patterning mechanisms and produces conserved neuron types that existed already in the protostome-deuterostome ancestors. These data indicate deep homology of pallium and mushroom bodies and date back the origin of higher brain centers to prebilaterian times

Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets

<div><p>Complex tissues, such as the brain, are composed of multiple different cell types, each of which have distinct and important roles, for example in neural function. Moreover, it has recently been appreciated that the cells that make up these sub-cell types themselves harbour significant cell-to-cell heterogeneity, in particular at the level of gene expression. The ability to study this heterogeneity has been revolutionised by advances in experimental technology, such as Wholemount in Situ Hybridizations (WiSH) and single-cell RNA-sequencing. Consequently, it is now possible to study gene expression levels in thousands of cells from the same tissue type. After generating such data one of the key goals is to cluster the cells into groups that correspond to both known and putatively novel cell types. Whilst many clustering algorithms exist, they are typically unable to incorporate information about the spatial dependence between cells within the tissue under study. When such information exists it provides important insights that should be directly included in the clustering scheme. To this end we have developed a clustering method that uses a Hidden Markov Random Field (HMRF) model to exploit both quantitative measures of expression and spatial information. To accurately reflect the underlying biology, we extend current HMRF approaches by allowing the degree of spatial coherency to differ between clusters. We demonstrate the utility of our method using simulated data before applying it to cluster single cell gene expression data generated by applying WiSH to study expression patterns in the brain of the marine annelid <i>Platynereis dumereilii</i>. Our approach allows known cell types to be identified as well as revealing new, previously unexplored cell types within the brain of this important model system.</p></div

Validating the estimation of beta.

<p>This figure shows the evolution for of the mean value of across all the clusters. The red dots represent the biological data clustering (i.e the reference in our simulations scheme). The green dots represent the results obtained after clustering simulated data, which shows an underestimation of . To confirm that this underestimation come from the simulation scheme and not the clustering method, we used the simulated data as the reference to generate a "second generation" of simulated data, suppressing the simulation scheme bias (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003824#pcbi-1003824-g007" target="_blank">Figure 7</a>). The results of this re-simulation are shown by the blue dots, which exhibit no underestimation of . Finally the brown dots represent the mean value of on the same simulated data but spatially randomized, as expected the are now estimated to .</p

Decrease in spatial coherency due to the simulation scheme.

<p>For an example cluster , gene may only be expressed in half of the voxels. This will yield . However, in the biological data, the voxels expressing gene may be spatially coherent (i.e., located close to one another), leading to a reduced area of expression discontinuity (the green line). By contrast, in the simulated data the expression of such a gene will lose its spatial coherency, leading to an increased area of expression discontinuity. The number of voxels having a neighbour with some differences in the gene expression pattern is directly linked to the value of through the energy function (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003824#s4" target="_blank">Methods</a>). This explains the underestimation of observed in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003824#pcbi-1003824-g006" target="_blank">Figure 6</a>.</p

In-situ hybridization image for rOpsin and rOpsin3 in the full brain at 48hpf (Apical view).

<p>Z-projection of the expression of rOpsin (red) in both the adult eyes and the larval eyes, rOpsin3 (green) specifically in the larval eyes and co-expression areas in some areas of the larval eyes in the full brain of <i>Platynereis</i> at 48hpf. This image been obtained directly from the data obtained in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003824#pcbi.1003824-Tomer1" target="_blank">[3]</a></p

Densities of log luminescence values for two genes (rOpsin, PRDM8) over the voxels.

<p>For <i>rOpsin</i>, the density exhibits two clear peaks making the choice of a binarizing threshold easy. By contrast, for <i>PRDM8</i> there is no such clear threshold, making an automated binarization method hard to implement.</p