Variability of Gene Expression Identifies Transcriptional Regulators of Early Human Embryonic Development

<div><p>An analysis of gene expression variability can provide an insightful window into how regulatory control is distributed across the transcriptome. In a single cell analysis, the inter-cellular variability of gene expression measures the consistency of transcript copy numbers observed between cells in the same population. Application of these ideas to the study of early human embryonic development may reveal important insights into the transcriptional programs controlling this process, based on which components are most tightly regulated. Using a published single cell RNA-seq data set of human embryos collected at four-cell, eight-cell, morula and blastocyst stages, we identified genes with the most stable, invariant expression across all four developmental stages. Stably-expressed genes were found to be enriched for those sharing indispensable features, including essentiality, haploinsufficiency, and ubiquitous expression. The stable genes were less likely to be associated with loss-of-function variant genes or human recessive disease genes affected by a DNA copy number variant deletion, suggesting that stable genes have a functional impact on the regulation of some of the basic cellular processes. Genes with low expression variability at early stages of development are involved in regulation of DNA methylation, responses to hypoxia and telomerase activity, whereas by the blastocyst stage, low-variability genes are enriched for metabolic processes as well as telomerase signaling. Based on changes in expression variability, we identified a putative set of gene expression markers of morulae and blastocyst stages. Experimental validation of a blastocyst-expressed variability marker demonstrated that <i>HDDC2</i> plays a role in the maintenance of pluripotency in human ES and iPS cells. Collectively our analyses identified new regulators involved in human embryonic development that would have otherwise been missed using methods that focus on assessment of the average expression levels; in doing so, we highlight the value of studying expression variability for single cell RNA-seq data.</p></div

Identifying regulatory control states.

<p>Genes were assigned to clusters using a mixture model algorithm for each developmental stage based on levels of inter-cellular variability. The goal was to use this algorithm to quantify how many different clusters, or regulatory control states were present for each stage. The mixture models identified <b>(A)</b> four clusters for the 4-cell stage, (<b>B)</b> three clusters for the 8-cell and <b>(C)</b> morulae stages, and <b>(D)</b> two clusters for the blastocyst stage. In <b>(B-D)</b> the distribution of the inter-cellular expression variability are represented for each cluster or control state per stage.</p

Stage-specific variability markers are based on changes in inter-cellular variability.

<p><b>(A)</b> A schematic illustrating how a narrower distribution corresponds to greater homogeneity in the expression of a variability marker in a cell population (the shading indicates level of expression of a gene). <b>(B-D)</b> The distribution of expression for the variability markers identified for each developmental stage.</p

Putative variability markers identified for each stage based on changes in gene expression variability.

<p>Gene names in bold and underlined indicate that these genes that were also identified by a standard ANOVA model.</p

Effect of <i>HDDC2</i> shRNA-mediated knock-down.

<p><b>(A)</b> Decrease in <i>HDDC2</i> mRNA levels after 2 days of constitutive expression of shRNAs (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005428#pgen.1005428.s026" target="_blank">S11</a>–<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005428#pgen.1005428.s028" target="_blank">S13</a> Tables). <b>(B)</b> Corresponding drop in the expression of pluripotency markers <i>NANOG</i> and <b>(C)</b><i>DNMT3B</i> suggests a role for <i>HDDC2</i> in the maintenance of pluripotency.</p

A snapshot of cellular heterogeneity across embryonic development.

<p><b>(A)</b> The variability profile for a group of cells identifies both the regions and proportions of the transcriptome that have different levels of expression variability. <b>(B)</b> A schematic diagram showing how different densities can arise depending on how heterogeneous the underlying cell population is. (<b>C)</b> Observed densities for the 8-cell, morula and blastocyst stages where each line corresponds to a 4-cell combination selected from the total cells available in each developmental stage.</p

Overview of the analysis performed.

<p>The questions that our study seeks to address, and the main results obtained are highlighted.</p

Activation of the endogenous <i>HDDC2</i> locus using an inducible Cas9-VP64 system attenuates neural differentiation of human pluripotent stem cells.

<p><b>(A)</b> Upregulation of the <i>HDDC2</i> mRNA levels after 2 days of induction of activation in pluripotent hES cells. <b>(B-D)</b> Effect of Cas9-VP64-driven <i>HDDC2</i> up-regulation on gene expression during the early stages (day 3) of neural differentiation. We observed that artificially-maintained levels of <i>HDDC2</i> expression <b>(B)</b> resulted in more sustained <i>NANOG</i> expression <b>(C)</b> and lower induction of <i>PAX6</i><b>(D)</b>, a definitive neuroectodermal marker.</p

Stable genes can be classified into three distinct modes of expression activity.

<p>For each developmental stage, inspection of the absolute expression of the stable genes we identified, reveals that these genes fall into three expression modes spanning low, medium and high levels of expression. <b>(A)</b> The black lines denote the smoothed density of gene expression levels that have been averaged across all cells from a specific developmental stage. The yellow lines indicate the boundaries defining the three expression modes, these boundaries were calculated using a mixture model that clustered the genes into these three groups. <b>(B)</b> Single cell gene expression profiles for the stable genes for each developmental stage. These profiles have been plotted based on which expression mode they have been clustered into using the mixture model. The black dots represent the expression level of the stable genes in a single cell. The colored dot represents the average expression per expression mode.</p

Synthetic vaccine particles for durable cytolytic T lymphocyte responses and anti-tumor immunotherapy

<div><p>We previously reported that synthetic vaccine particles (SVP) encapsulating antigens and TLR agonists resulted in augmentation of immune responses with minimal production of systemic inflammatory cytokines. Here we evaluated two different polymer formulations of SVP-encapsulated antigens and tested their ability to induce cytolytic T lymphocytes (CTL) in combination with SVP-encapsulated adjuvants. One formulation led to efficient antigen processing and cross-presentation, rapid and sustained CTL activity, and expansion of CD8⁺ T cell effector memory cells locally and centrally, which persisted for at least 1–2 years after a single immunization. SVP therapeutic dosing resulted in suppression of tumor growth and a substantial delay in mortality in several syngeneic mouse cancer models. Treatment with checkpoint inhibitors and/or cytotoxic drugs, while suboptimal on their own, showed considerable synergy with SVP immunization. SVP encapsulation of endosomal TLR agonists provided superior CTL induction, therapeutic benefit and/or improved safety profile compared to free adjuvants. SVP vaccines encapsulating mutated HPV-16 E7 and E6/E7 recombinant proteins led to induction of broad CTL activity and strong inhibition of TC-1 tumor growth, even when administered therapeutically 13–14 days after tumor inoculation in animals bearing palpable tumors. A pilot study in non-human primates showed that SVP-encapsulated E7/E6 adjuvanted with SVP-encapsulated poly(I:C) led to robust induction of antigen-specific T and B cell responses.</p></div