Single cell genomics and developmental biology: moving beyond the generation of cell type catalogues

Single Cell Sequencing Provides Clues about the Developmental Genetic Basis of Evolutionary Adaptations in Syngnathid Fishes

Seahorses, pipefishes, and seadragons are fishes from the family Syngnathidae that have evolved extraordinary traits including male pregnancy, elongated snouts, loss of teeth, and dermal bony armor. The developmental genetic and cellular changes that led to the evolution of these traits are largely unknown. Recent syngnathid genome assemblies revealed suggestive gene content differences and provide the opportunity for detailed genetic analyses. We created a single cell RNA sequencing atlas of Gulf pipefish embryos to understand the developmental basis of four traits: derived head shape, toothlessness, dermal armor, and male pregnancy. We completed marker gene analyses, built genetic networks, and examined spatial expression of select genes. We identified osteochondrogenic mesenchymal cells in the elongating face that express regulatory genes bmp4, sfrp1a, and prdm16. We found no evidence for tooth primordia cells, and we observed re-deployment of osteoblast genetic networks in developing dermal armor. Finally, we found that epidermal cells expressed nutrient processing and environmental sensing genes, potentially relevant for the brooding environment. The examined pipefish evolutionary innovations are composed of recognizable cell types, suggesting derived features originate from changes within existing gene networks. Future work addressing syngnathid gene networks across multiple stages and species is essential for understanding how their novelties evolved.

Spatiotemporal analysis of mRNA-protein relationships enhances transcriptome-based developmental inference

Elucidating the complex relationships between mRNA and protein expression at high spatiotemporal resolution is critical for unraveling multilevel gene regulation and enhancing mRNA-based developmental analyses. In this study, we conduct a single-cell analysis of mRNA and protein expression of transcription factors throughout C. elegans embryogenesis. Initially, cellular co-presence of mRNA and protein is low, increasing to a medium-high level (73%) upon factoring in delayed protein synthesis and long-term protein persistence. These factors substantially affect mRNA-protein concordance, leading to potential inaccuracies in mRNA-reliant gene detection and specificity characterization. Building on the learned relationship, we infer protein presence from mRNA expression and demonstrate its utility in identifying tissue-specific genes and elucidating relationships between genes and cells. This approach facilitates identifying the role of sptf-1/SP7 in neuronal lineage development. Collectively, this study provides insights into gene expression dynamics during rapid embryogenesis and approaches for improving the efficacy of transcriptome-based developmental analyses.

Coordinated changes in gene expression kinetics underlie both mouse and human erythroid maturation

BACKGROUND

Single-cell technologies are transforming biomedical research, including the recent demonstration that unspliced pre-mRNA present in single-cell RNA-Seq permits prediction of future expression states. Here we apply this RNA velocity concept to an extended timecourse dataset covering mouse gastrulation and early organogenesis.

RESULTS

Intriguingly, RNA velocity correctly identifies epiblast cells as the starting point, but several trajectory predictions at later stages are inconsistent with both real-time ordering and existing knowledge. The most striking discrepancy concerns red blood cell maturation, with velocity-inferred trajectories opposing the true differentiation path. Investigating the underlying causes reveals a group of genes with a coordinated step-change in transcription, thus violating the assumptions behind current velocity analysis suites, which do not accommodate time-dependent changes in expression dynamics. Using scRNA-Seq analysis of chimeric mouse embryos lacking the major erythroid regulator Gata1, we show that genes with the step-changes in expression dynamics during erythroid differentiation fail to be upregulated in the mutant cells, thus underscoring the coordination of modulating transcription rate along a differentiation trajectory. In addition to the expected block in erythroid maturation, the Gata1-chimera dataset reveals induction of PU.1 and expansion of megakaryocyte progenitors. Finally, we show that erythropoiesis in human fetal liver is similarly characterized by a coordinated step-change in gene expression.

CONCLUSIONS

By identifying a limitation of the current velocity framework coupled with in vivo analysis of mutant cells, we reveal a coordinated step-change in gene expression kinetics during erythropoiesis, with likely implications for many other differentiation processes.

Discriminating the single-cell gene regulatory networks of human pancreatic islets: A novel deep learning application

Analysis of single-cell pancreatic data can play an important role in understanding various metabolic diseases and health conditions. Due to the sparsity and noise present in such single-cell gene expression data, inference of single-cell gene regulatory networks remains a challenge. Since recent studies have reported the reliable inference of single-cell gene regulatory networks (SCGRNs), the current study focused on discriminating the SCGRNs of T2D patients from those of healthy controls. By accurately distinguishing SCGRNs of healthy pancreas from those of T2D pancreas, it would be possible to annotate, organize, visualize, and identify common patterns of SCGRNs in metabolic diseases. Such annotated SCGRNs could play an important role in accelerating the process of building large data repositories. This study aimed to contribute to the development of a novel deep learning (DL) application. First, we generated a dataset consisting of 224 SCGRNs belonging to both T2D and healthy pancreas and made it freely available. Next, we chose seven DL architectures, including VGG16, VGG19, Xception, ResNet50, ResNet101, DenseNet121, and DenseNet169, trained each of them on the dataset, and checked their prediction based on a test set. Of note, we evaluated the DL architectures on a single NVIDIA GeForce RTX 2080Ti GPU. Experimental results on the whole dataset, using several performance measures, demonstrated the superiority of VGG19 DL model in the automatic classification of SCGRNs, derived from the single-cell pancreatic data.

The transcriptional regulation of normal and malignant blood cell development

Development of multicellular organisms requires the differential usage of our genetic information to change one cell fate into another. This process drives the appearance of different cell types that come together to form specialized tissues sustaining a healthy organism. In the last decade, by moving away from studying single genes toward a global view of gene expression control, a revolution has taken place in our understanding of how genes work together and how cells communicate to translate the information encoded in the genome into a body plan. The development of hematopoietic cells has long served as a paradigm of development in general. In this review, we highlight how transcription factors and chromatin components work together to shape the gene regulatory networks controlling gene expression in the hematopoietic system and to drive blood cell differentiation. In addition, we outline how this process goes astray in blood cancers. We also touch upon emerging concepts that place these processes firmly into their associated subnuclear structures adding another layer of the control of differential gene expression.

Post-transcriptional control of cellular differentiation by the RNA exosome complex

Given the complexity of intracellular RNA ensembles and vast phenotypic remodeling intrinsic to cellular differentiation, it is instructive to consider the role of RNA regulatory machinery in controlling differentiation. Dynamic post-transcriptional regulation of protein-coding and non-coding transcripts is vital for establishing and maintaining proteomes that enable or oppose differentiation. By contrast to extensively studied transcriptional mechanisms governing differentiation, many questions remain unanswered regarding the involvement of post-transcriptional mechanisms. Through its catalytic activity to selectively process or degrade RNAs, the RNA exosome complex dictates the levels of RNAs comprising multiple RNA classes, thereby regulating chromatin structure, gene expression and differentiation. Although the RNA exosome would be expected to control diverse biological processes, studies to elucidate its biological functions and how it integrates into, or functions in parallel with, cell type-specific transcriptional mechanisms are in their infancy. Mechanistic analyses have demonstrated that the RNA exosome confers expression of a differentiation regulatory receptor tyrosine kinase, downregulates the telomerase RNA component TERC, confers genomic stability and promotes DNA repair, which have considerable physiological and pathological implications. In this review, we address how a broadly operational RNA regulatory complex interfaces with cell type-specific machinery to control cellular differentiation.