scholarly journals Single-cell sequencing reveals suppressive transcriptional programs regulated by MIS/AMH in neonatal ovaries

2021 ◽  
Vol 118 (20) ◽  
pp. e2100920118
Author(s):  
Marie-Charlotte Meinsohn ◽  
Hatice D. Saatcioglu ◽  
Lina Wei ◽  
Yi Li ◽  
Heiko Horn ◽  
...  
Keyword(s):  
Single Cell ◽  
Germ Cells ◽  
Granulosa Cells ◽  
Cell Types ◽  
Follicle Development ◽  
Follicle Growth ◽  
Single Cell Sequencing ◽  
Single Cell Rna Sequencing ◽  
Important Regulator ◽  
Unique Signature

Müllerian inhibiting substance (MIS/AMH), produced by granulosa cells of growing follicles, is an important regulator of folliculogenesis and follicle development. Treatment with exogenous MIS in mice suppresses follicle development and prevents ovulation. To investigate the mechanisms by which MIS inhibits follicle development, we performed single-cell RNA sequencing of whole neonatal ovaries treated with MIS at birth and analyzed at postnatal day 6, coinciding with the first wave of follicle growth. We identified distinct transcriptional signatures associated with MIS responses in the ovarian cell types. MIS treatment inhibited proliferation in granulosa, surface epithelial, and stromal cell types of the ovary and elicited a unique signature of quiescence in granulosa cells. In addition to decreasing the number of growing preantral follicles, we found that MIS treatment uncoupled the maturation of germ cells and granulosa cells. In conclusion, MIS suppressed neonatal follicle development by inhibiting proliferation, imposing a quiescent cell state, and preventing granulosa cell differentiation.

scholarly journals Single Cell Gene Transcriptome Analysis of Ovarian Mature Teratomas

2021 ◽  
Vol 27 ◽  
Author(s):  
Sun Shin ◽  
Youn Jin Choi ◽  
Seung-Hyun Jung ◽  
Yeun-Jun Chung ◽  
Sug Hyung Lee
Keyword(s):  
Single Cell ◽  
Germ Cells ◽  
Expression Profiles ◽  
Cell Types ◽  
Cell Tumor ◽  
Single Cell Level ◽  
Cell Level ◽  
Normal Cells ◽  
Single Cell Rna Sequencing ◽  
Cell Gene

Teratoma is a type of germ cell tumor that originates from totipotential germ cells that are present in gonads, which can differentiate into any of the cell types found in adult tissues. Ovarian teratomas are usually mature cystic teratomas (OMCTs, also known as dermoid cysts). Chromosome studies in OMCTs show that the chromosomes are uniformly homozygous with karyotype of 46, XX, indicating that they may be parthenogenic tumors that arise from a single ovum after thefirst meiotic division. However, the tissues in OMCTs have been known to be morphologically and immunophenotypically identical to the orthotopic tissues. Currently, expression profiles of tissue components in OMCTs are not known. To identify whether OMCT tissues are expressionally similar to or different from the orthotopic tissues, we adopted single-cell RNA-sequencing (scRNA-seq), and analyzed transcriptomes of individual cells in heterogenous tissues of two OMCTs. We found that transcriptome profiles of the OMCTs at single cell level were not significantly different from those of normal cells in orthotopic locations. The present data suggest that parthenogeneticlly altered OMCTs may not alter expression profiles of inrivirual tissue components in OMCTs.

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

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.

scholarly journals Single-cell transcriptome landscape of ovarian cells during primordial follicle assembly in mice

PLoS Biology ◽  
2020 ◽  
Vol 18 (12) ◽  
pp. e3001025
Author(s):  
Jun-Jie Wang ◽  
Wei Ge ◽  
Qiu-Yue Zhai ◽  
Jing-Cai Liu ◽  
Xiao-Wen Sun ◽  
...  
Keyword(s):  
Single Cell ◽  
Germ Cells ◽  
Granulosa Cells ◽  
Network Inference ◽  
Primordial Follicle ◽  
Cell Types ◽  
Bioinformatics Analyses ◽  
Primordial Follicles ◽  
Ovarian Cells ◽  
New Genes

Primordial follicle assembly in the mouse occurs during perinatal ages and largely determines the ovarian reserve that will be available to support the reproductive life span. The development of primordial follicles is controlled by a complex network of interactions between oocytes and ovarian somatic cells that remain poorly understood. In the present research, using single-cell RNA sequencing performed over a time series on murine ovaries, coupled with several bioinformatics analyses, the complete dynamic genetic programs of germ and granulosa cells from E16.5 to postnatal day (PD) 3 were reported. Along with confirming the previously reported expression of genes by germ cells and granulosa cells, our analyses identified 5 distinct cell clusters associated with germ cells and 6 with granulosa cells. Consequently, several new genes expressed at significant levels at each investigated stage were assigned. By building single-cell pseudotemporal trajectories, 3 states and 1 branch point of fate transition for the germ cells were revealed, as well as for the granulosa cells. Moreover, Gene Ontology (GO) term enrichment enabled identification of the biological process most represented in germ cells and granulosa cells or common to both cell types at each specific stage, and the interactions of germ cells and granulosa cells basing on known and novel pathway were presented. Finally, by using single-cell regulatory network inference and clustering (SCENIC) algorithm, we were able to establish a network of regulons that can be postulated as likely candidates for sustaining germ cell-specific transcription programs throughout the period of investigation. Above all, this study provides the whole transcriptome landscape of ovarian cells and unearths new insights during primordial follicle assembly in mice.

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.

scholarly journals Single-cell transcriptomic analysis of mIHC images via antigen mapping

2021 ◽  
Vol 7 (10) ◽  
pp. eabc5464
Author(s):  
Kiya W. Govek ◽  
Emma C. Troisi ◽  
Zhen Miao ◽  
Rachael G. Aubin ◽  
Steven Woodhouse ◽  
...  
Keyword(s):  
Single Cell ◽  
Spatial Patterns ◽  
Cell Types ◽  
Level Of Detail ◽  
Cell Populations ◽  
Sequencing Data ◽  
Spatially Resolved ◽  
Murine Spleen ◽  
Single Cell Rna Sequencing ◽  
Antibody Panel

Highly multiplexed immunohistochemistry (mIHC) enables the staining and quantification of dozens of antigens in a tissue section with single-cell resolution. However, annotating cell populations that differ little in the profiled antigens or for which the antibody panel does not include specific markers is challenging. To overcome this obstacle, we have developed an approach for enriching mIHC images with single-cell RNA sequencing data, building upon recent experimental procedures for augmenting single-cell transcriptomes with concurrent antigen measurements. Spatially-resolved Transcriptomics via Epitope Anchoring (STvEA) performs transcriptome-guided annotation of highly multiplexed cytometry datasets. It increases the level of detail in histological analyses by enabling the systematic annotation of nuanced cell populations, spatial patterns of transcription, and interactions between cell types. We demonstrate the utility of STvEA by uncovering the architecture of poorly characterized cell types in the murine spleen using published cytometry and mIHC data of this organ.

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

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.

484 Bioturing browser: interactively explore public single cell sequencing data

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A520-A520
Author(s):  
Son Pham ◽  
Tri Le ◽  
Tan Phan ◽  
Minh Pham ◽  
Huy Nguyen ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Cell Types ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Data Formats ◽  
Cancer Types ◽  
Cell Data

BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual datasets and meta-analysis.MethodsN/AResultsWe present BioTuring Browser, an interactive platform for accessing and reanalyzing published single-cell omics data. The platform is currently hosting a curated database of more than 10 million cells from 247 projects, covering more than 120 immune cell types and subtypes, and 15 different cancer types. All data are processed and annotated with standardized labels of cell types, diseases, therapeutic responses, etc. to be instantly accessed and explored in a uniform visualization and analytics interface. Based on this massive curated database, BioTuring Browser supports searching similar expression profiles, querying a target across datasets and automatic cell type annotation. The platform supports single-cell RNA-seq, CITE-seq and TCR-seq data. BioTuring Browser is now available for download at www.bioturing.com.ConclusionsN/A

Multi tissue processing for single cell sequencing of human immune cells v1

2021 ◽  
Author(s):  
Daniel Rainbow ◽  
Sarah Howlett ◽  
Lorna Jarvis ◽  
Joanne Jones
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cells ◽  
Human Tissues ◽  
Single Cell Sequencing ◽  
Tissue Processing ◽  
Simultaneous Processing ◽  
Single Cell Rna Sequencing ◽  
Human Immune

This protocol has been developed for the simultaneous processing of multiple human tissues to extract immune cells for single cell RNA sequencing using the 10X platform, and ideal for atlasing projects. Included in this protocol are the steps needed to go from tissue to loading the 10X Chromium for single cell RNA sequencing and includes the hashtag and CiteSeq labelling of cells as well as the details needed to stimulate cells with PMA+I.

scholarly journals Identifying common and novel cell types in single-cell RNA-sequencing data using FR-Match

2021 ◽  
Author(s):  
Yun Zhang ◽  
Brian Aevermann ◽  
Rohan Gala ◽  
Richard H. Scheuermann
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Sample Type ◽  
Cell Type ◽  
Sequencing Data ◽  
Excellent Performance ◽  
Single Cell Rna Sequencing ◽  
Accurate Performance ◽  
Cross Platform ◽  
Tissue Region

Reference cell type atlases powered by single cell transcriptomic profiling technologies have become available to study cellular diversity at a granular level. We present FR-Match for matching query datasets to reference atlases with robust and accurate performance for identifying novel cell types and non-optimally clustered cell types in the query data. This approach shows excellent performance for cross-platform, cross-sample type, cross-tissue region, and cross-data modality cell type matching.

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