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

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.

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A single cell RNA sequencing resource for early sea urchin development

Development ◽

10.1242/dev.191528 ◽

2020 ◽

Vol 147 (17) ◽

pp. dev191528 ◽

Cited By ~ 3

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.

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Aging-associated alterations in the mammary gland revealed by single-cell RNA sequencing

10.1101/773408 ◽

2019 ◽

Cited By ~ 2

Author(s):

Carman Man-Chung Li ◽

Hana Shapiro ◽

Christina Tsiobikas ◽

Laura Selfors ◽

Huidong Chen ◽

...

Keyword(s):

Mammary Gland ◽

Single Cell ◽

Rna Sequencing ◽

Expression Patterns ◽

Cell Types ◽

Single Cell Rna Sequencing ◽

Numerous Cell ◽

Luminal Cells ◽

Increased Susceptibility ◽

Mammary Tissues

AbstractAging of the mammary gland is closely associated with increased susceptibility to diseases such as cancer, but there have been limited systematic studies of aging-induced alterations within this organ. We performed high-throughput single-cell RNA-sequencing (scRNA-seq) profiling of mammary tissues from young and old nulliparous mice, including both epithelial and stromal cell types. Our analysis identified altered proportions and distinct gene expression patterns in numerous cell populations as a consequence of the aging process, independent of parity and lactation. In addition, we detected a subset of luminal cells that express both hormone-sensing and alveolar markers and decrease in relative abundance with age. These data provide a high-resolution landscape of aging mammary tissues, with potential implications for normal tissue functions and cancer predisposition.

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Single-cell RNA-sequencing reveals the dynamic process and novel markers in porcine spermatogenesis

Journal of Animal Science and Biotechnology ◽

10.1186/s40104-021-00638-3 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Lingkai Zhang ◽

Fuyuan Li ◽

Peipei Lei ◽

Ming Guo ◽

Ruifang Liu ◽

...

Keyword(s):

Cell Surface ◽

Single Cell ◽

Rna Sequencing ◽

Germ Cells ◽

Cell Types ◽

Functional Enrichment ◽

Surface Markers ◽

Cell Surface Markers ◽

Male Germ Cells ◽

Single Cell Rna Sequencing

Abstract Background Spermatogenesis is the process by which male gametes are formed from spermatogonial stem cells and it is essential for the reliable transmission of genetic information between generations. To date, the dynamic transcriptional changes of defined populations of male germ cells in pigs have not been reported. Results To characterize the atlas of porcine spermatogenesis, we profiled the transcriptomes of ~ 16,966 testicular cells from a 150-day-old pig testis through single-cell RNA-sequencing (scRNA-seq). The scRNA-seq analysis identified spermatogonia, spermatocytes, spermatids and three somatic cell types in porcine testes. The functional enrichment analysis demonstrated that these cell types played diverse roles in porcine spermatogenesis. The accuracy of the defined porcine germ cell types was further validated by comparing the data from scRNA-seq with those from bulk RNA-seq. Since we delineated four distinct spermatogonial subsets, we further identified CD99 and PODXL2 as novel cell surface markers for undifferentiated and differentiating spermatogonia, respectively. Conclusions The present study has for the first time analyzed the transcriptome of male germ cells and somatic cells in porcine testes through scRNA-seq. Four subsets of spermatogonia were identified and two novel cell surface markers were discovered, which would be helpful for studies on spermatogonial differentiation in pigs. The datasets offer valuable information on porcine spermatogenesis, and pave the way for identification of key molecular markers involved in development of male germ cells.

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Comprehensive analysis of a mouse model of spontaneous uveoretinitis using single-cell RNA sequencing

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1915571116 ◽

2019 ◽

Vol 116 (52) ◽

pp. 26734-26744 ◽

Cited By ~ 3

Author(s):

Jacob S. Heng ◽

Sean F. Hackett ◽

Genevieve L. Stein-O’Brien ◽

Briana L. Winer ◽

John Williams ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Expression Patterns ◽

Cell Types ◽

Retinal Cell ◽

Central Tolerance ◽

Single Cell Rna Sequencing ◽

Unbiased Manner ◽

Lymphoid Structures

Autoimmune uveoretinitis is a significant cause of visual loss, and mouse models offer unique opportunities to study its disease mechanisms.Aire−/−mice fail to express self-antigens in the thymus, exhibit reduced central tolerance, and develop a spontaneous, chronic, and progressive uveoretinitis. Using single-cell RNA sequencing (scRNA-seq), we characterized wild-type andAire−/−retinas to define, in a comprehensive and unbiased manner, the cell populations and gene expression patterns associated with disease. Based on scRNA-seq, immunostaining, and in situ hybridization, we infer that 1) the dominant effector response inAire−/−retinas is Th1-driven, 2) a subset of monocytes convert to either a macrophage/microglia state or a dendritic cell state, 3) the development of tertiary lymphoid structures constitutes part of theAire−/−retinal phenotype, 4) all major resident retinal cell types respond to interferon gamma (IFNG) by changing their patterns of gene expression, and 5) Muller glia up-regulate specific genes in response to IFN gamma and may act as antigen-presenting cells.

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FIRM: Fast Integration of single-cell RNA-sequencing data across Multiple platforms

10.1101/2020.06.02.129031 ◽

2020 ◽

Author(s):

Jingsi Ming ◽

Zhixiang Lin ◽

Xiang Wan ◽

Can Yang ◽

Angela Ruohao Wu

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Biology ◽

Developmental Trajectories ◽

Expression Patterns ◽

Cell Types ◽

Original Structure ◽

Cell Type ◽

Sequencing Data ◽

Single Cell Rna Sequencing

AbstractSingle-cell RNA-sequencing (scRNA-seq) has now been used extensively to discover novel cell types and reconstruct developmental trajectories by measuring mRNA expression patterns of individual cells. However, datasets collected using different scRNA-seq technology platforms, including the popular SMART-Seq2 (SS2) and 10X platforms, are difficult to compare because of their heterogeneity. Each platform has unique advantages, and integration of these datasets would provide deeper insights into cell biology and gene regulation. Through comprehensive data exploration, we found that accurate integration is often hampered by differences in cell-type compositions. Herein we describe FIRM, an algorithm that addresses this problem and achieves efficient and accurate integration of heterogeneous scRNA-seq datasets across multiple platforms. We applied FIRM to numerous scRNA-seq datasets generated using SS2 and 10X from mouse, mouse lemur, and human, comparing its performance in dataset integration with other state-of-the-art methods. The integrated datasets generated using FIRM show accurate mixing of shared cell type identities and superior preservation of original structure for each dataset. FIRM not only generates robust integrated datasets for downstream analysis, but is also a facile way to transfer cell type labels and annotations from one dataset to another, making it a versatile and indispensable tool for scRNA-seq analysis.

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

10.21203/rs.3.rs-113078/v1 ◽

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.

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Simultaneous Identification of Brain Cell Type and Lineage via Single Cell RNA Sequencing

10.1101/2020.12.31.425016 ◽

2021 ◽

Author(s):

Donovan J. Anderson ◽

Florian M. Pauler ◽

Aaron McKenna ◽

Jay Shendure ◽

Simon Hippenmeyer ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Expression Patterns ◽

Cortical Cell ◽

Cell Types ◽

F1 Hybrids ◽

Mouse Strains ◽

Monoallelic Expression ◽

Cell Divisions ◽

Single Cell Rna Sequencing

ABSTRACTAcquired mutations are sufficiently frequent such that the genome of a single cell offers a record of its history of cell divisions. Among more common somatic genomic alterations are loss of heterozygosity (LOH). Large LOH events are potentially detectable in single cell RNA sequencing (scRNA-seq) datasets as tracts of monoallelic expression for constitutionally heterozygous single nucleotide variants (SNVs) located among contiguous genes. We identified runs of monoallelic expression, consistent with LOH, uniquely distributed throughout the genome in single cell brain cortex transcriptomes of F1 hybrids involving different inbred mouse strains. We then phylogenetically reconstructed single cell lineages and simultaneously identified cell types by corresponding gene expression patterns. Our results are consistent with progenitor cells giving rise to multiple cortical cell types through stereotyped expansion and distinct waves of neurogenesis. Compared to engineered recording systems, LOH events accumulate throughout the genome and across the lifetime of an organism, affording tremendous capacity for encoding lineage information and increasing resolution for later cell divisions. This approach can conceivably be computationally incorporated into scRNA-seq analysis and may be useful for organisms where genetic engineering is prohibitive, such as humans.

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Single-cell data clustering based on sparse optimization and low-rank matrix factorization

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab098 ◽

2021 ◽

Author(s):

Yinlei Hu ◽

Bin Li ◽

Falai Chen ◽

Kun Qu

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Matrix Factorization ◽

Data Clustering ◽

Cell Types ◽

Low Rank ◽

Sequencing Data ◽

Rank Matrix ◽

Single Cell Rna Sequencing ◽

Low Rank Matrix

Abstract Unsupervised clustering is a fundamental step of single-cell RNA sequencing data analysis. This issue has inspired several clustering methods to classify cells in single-cell RNA sequencing data. However, accurate prediction of the cell clusters remains a substantial challenge. In this study, we propose a new algorithm for single-cell RNA sequencing data clustering based on Sparse Optimization and low-rank matrix factorization (scSO). We applied our scSO algorithm to analyze multiple benchmark datasets and showed that the cluster number predicted by scSO was close to the number of reference cell types and that most cells were correctly classified. Our scSO algorithm is available at https://github.com/QuKunLab/scSO. Overall, this study demonstrates a potent cell clustering approach that can help researchers distinguish cell types in single-cell RNA sequencing data.

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