scholarly journals A single cell RNA sequencing resource for early sea urchin development

Development ◽  
2020 ◽  
Vol 147 (17) ◽  
pp. dev191528 ◽  
Author(s):  
Stephany Foster ◽  
Nathalie Oulhen ◽  
Gary Wessel
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Network ◽  
Sea Urchin ◽  
Germ Cells ◽  
Regulatory Network ◽  
Germ Line ◽  
Primordial Germ Cells ◽  
Single Cell Rna Sequencing ◽  
Gene Regulatory

ABSTRACTIdentifying cell states during development from their mRNA profiles provides insight into their gene regulatory network. Here, we leverage the sea urchin embryo for its well-established gene regulatory network to interrogate the embryo using single cell RNA sequencing. We tested eight developmental stages in Strongylocentrotus purpuratus, from the eight-cell stage to late in gastrulation. We used these datasets to parse out 22 major cell states of the embryo, focusing on key transition stages for cell type specification of each germ layer. Subclustering of these major embryonic domains revealed over 50 cell states with distinct transcript profiles. Furthermore, we identified the transcript profile of two cell states expressing germ cell factors, one we conclude represents the primordial germ cells and the other state is transiently present during gastrulation. We hypothesize that these cells of the Veg2 tier of the early embryo represent a lineage that converts to the germ line when the primordial germ cells are deleted. This broad resource will hopefully enable the community to identify other cell states and genes of interest to expose the underpinning of developmental mechanisms.

scholarly journals Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Christopher A Jackson ◽  
Dayanne M Castro ◽  
Giuseppe-Antonio Saldi ◽  
Richard Bonneau ◽  
David Gresham
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Target Genes ◽  
Single Cell Rna Sequencing ◽  
Gene Regulatory

Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse transcriptionally barcoded gene deletion mutants in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We benchmarked a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,228 interactions.

scholarly journals Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

2019 ◽  
Author(s):  
Christopher A Jackson ◽  
Dayanne M Castro ◽  
Giuseppe-Antonio Saldi ◽  
Richard Bonneau ◽  
David Gresham
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Network Reconstruction ◽  
Single Cell Rna Sequencing ◽  
Gene Regulatory

AbstractUnderstanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for transcriptionally barcoding gene deletion mutants and performing scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse genotypes in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We developed, and benchmarked, a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,018 interactions. Our study establishes a general approach to gene regulatory network reconstruction from scRNAseq data that can be employed in any organism.

scholarly journals Identifying strengths and weaknesses of methods for computational network inference from single cell RNA-seq data

2021 ◽  
Author(s):  
Matthew Stone ◽  
Sunnie Grace McCalla ◽  
Alireza Fotuhi Siahpirani ◽  
Viswesh Periyasamy ◽  
Junha Shin ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Network Inference ◽  
Cost Effective ◽  
Gene Regulatory Network Inference ◽  
Single Cell Rna Sequencing ◽  
Gene Regulatory ◽  
Inference Methods

Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional pro- grams of different cellular states by measuring the transcriptome of thousands individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory net- works and a number of methods with different learning frameworks have been developed. Here we present a expanded benchmarking study of eleven recent network inference methods on six published single-cell RNA-sequencing datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that while no method is a universal winner and most methods have a modest recovery of experimentally derived interactions based on global metrics such as AUPR, methods are able to capture targets of regulators that are relevant to the system under study. Based on overall performance we grouped the methods into three main categories and found a combination of information-theoretic and regression-based methods to have a generally high perfor- mance. We also evaluate the utility of imputation for gene regulatory network inference and find that a small number of methods benefit from imputation, which further depends upon the dataset. Finally, comparisons to inferred networks for comparable bulk conditions showed that networks inferred from scRNA-seq datasets are often better or at par to those from bulk suggesting that scRNA-seq datasets can be a cost-effective way for gene regulatory network inference. Our analysis should be beneficial in selecting algorithms for performing network inference but also argues for improved methods and better gold standards for accurate assessment of regulatory network inference methods for mammalian systems.

scholarly journals Single-Cell RNA Sequencing Revealed the Heterogeneity of Gonadal Primordial Germ Cells in Zebra Finch (Taeniopygia guttata)

2021 ◽  
Vol 9 ◽  
Author(s):  
Kyung Min Jung ◽  
Minseok Seo ◽  
Young Min Kim ◽  
Jin Lee Kim ◽  
Jae Yong Han
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Zebra Finch ◽  
Germ Cells ◽  
Expression Patterns ◽  
Primordial Germ Cells ◽  
Cell Types ◽  
Avian Species ◽  
Single Population ◽  
Single Cell Rna Sequencing

Primordial germ cells (PGCs) are undifferentiated gametes with heterogeneity, an evolutionarily conserved characteristic across various organisms. Although dynamic selection at the level of early germ cell populations is an important biological feature linked to fertility, the heterogeneity of PGCs in avian species has not been characterized. In this study, we sought to evaluate PGC heterogeneity in zebra finch using a single-cell RNA sequencing (scRNA-seq) approach. Using scRNA-seq of embryonic gonadal cells from male and female zebra finches at Hamburger and Hamilton (HH) stage 28, we annotated nine cell types from 20 cell clusters. We found that PGCs previously considered a single population can be separated into three subtypes showing differences in apoptosis, proliferation, and other biological processes. The three PGC subtypes were specifically enriched for genes showing expression patterns related to germness or pluripotency, suggesting functional differences in PGCs according to the three subtypes. Additionally, we discovered a novel biomarker, SMC1B, for gonadal PGCs in zebra finch. The results provide the first evidence of substantial heterogeneity in PGCs previously considered a single population in birds. This discovery expands our understanding of PGCs to avian species, and provides a basis for further research.

Monolayer platform to generate and purify human primordial germ cells in vitro provides new insights into germline specification

2021 ◽  
Author(s):  
Vittorio Sebastiano ◽  
Gugene Kang ◽  
Sivakamasundari Vijayakumar ◽  
Roberta Sala ◽  
Angela Chen ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Germ Cells ◽  
Primordial Germ Cells ◽  
Primitive Streak ◽  
Human Reproduction ◽  
Pluripotency Factors ◽  
Single Cell Rna Sequencing ◽  
Marker Profile

Abstract Generating primordial germ cells (PGCs) from human pluripotent stem cells (hPSCs) advances studies of human reproduction and development of infertility treatments, but currently entails complex 3D aggregates. Here we develop a simplified, monolayer method to differentiate hPSCs into PGCs within 3.5 days. We used our simplified differentiation platform and single-cell RNA-sequencing to uncover new insights into PGC specification. Transient WNT activation for 12 hours followed by WNT inhibition specified PGCs; by contrast, sustained WNT instead induced primitive streak. Thus, somatic (primitive streak) and PGCs are related—yet distinct—lineages segregated by temporally-dynamic signaling. Pluripotency factors including NANOG are continuously expressed during the transition from pluripotency to posterior epiblast to PGCs, thus bridging pluripotent and germline states. Finally, hPSC-derived PGCs can be easily purified by virtue of their CXCR4+PDGFRA−GARP− surface-marker profile and single-cell RNA-sequencing reveals that they harbor strong transcriptional similarities with fetal PGCs.

scholarly journals Endogenous retrovirus rewired the gene regulatory network shared between primordial germ cells and naïve pluripotent cells in hominoids

2021 ◽  
Author(s):  
Jumpei Ito ◽  
Yasunari Seita ◽  
Shohei Kojima ◽  
Nicholas F. Parrish ◽  
Kotaro Sasaki ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Germ Cells ◽  
Regulatory Network ◽  
Primordial Germ Cells ◽  
Cell Types ◽  
Endogenous Retrovirus ◽  
Mammalian Evolution ◽  
Comparative Transcriptome ◽  
Pluripotent Cells ◽  
Gene Regulatory

AbstractAlthough the gene regulatory network controlling germ cell development is critical for gamete integrity, this network has been substantially diversified during mammalian evolution. Here, we show that several hundred loci of LTR5_Hs, a hominoid-specific endogenous retrovirus (ERV), function as enhancers in both human primordial germ cells (PGCs) and naïve pluripotent cells. PGCs and naïve pluripotent cells exhibit a similar transcriptome signature, and the enhancers derived from LTR5_Hs contribute to establishing such similarity. LTR5_Hs appears to be activated by transcription factors critical in both cell types (KLF4, TFAP2C, NANOG, and CBFA2T2). Comparative transcriptome analysis between humans and macaques suggested that the expression of many genes in PGCs and naïve pluripotent cells has been upregulated by LTR5_Hs insertions in the hominoid lineage. Together, this study suggests that LTR5_Hs insertions have rewired and finetuned the gene regulatory network shared between PGCs and naïve pluripotent cells during hominoid evolution.TeaserA hominoid-specific ERV has rewired the gene regulatory network shared between PGCs and naïve pluripotent cells.

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