Single-cell RNA sequencing reveals species-specific time spans of cell cycle transitions in early oogenesis

2021 ◽  
Author(s):  
Zheng-Hui Zhao ◽  
Tie-Gang Meng ◽  
Hong-Yong Zhang ◽  
Yi Hou ◽  
Heide Schatten ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Germ Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Germ Cells ◽  
Cell Cycle Regulators ◽  
Sequencing Data ◽  
Interspecies Differences ◽  
Single Cell Rna Sequencing ◽  
Female Germ Cell

Abstract Oogenesis is a highly regulated process and its basic cellular events are evolutionarily conserved. However, the time spans of oogenesis differ substantially among species. To explore these interspecies differences in oogenesis, we performed single-cell RNA-sequencing on mouse and monkey female germ cells and downloaded the single-cell RNA-sequencing data of human female germ cells. The cell cycle analyses indicate that the period and extent of cell cycle transitions are significantly different between the species. Moreover, hierarchical clustering of critical cell cycle genes and the interacting network of cell cycle regulators also exhibit distinguished patterns across species. We propose that differences in the regulation of cell cycle transitions may underlie female germ cell developmental allochrony between species. A better understanding of the cell cycle transition machinery will provide new insights into the interspecies differences in female germ cell developmental time spans.

scholarly journals Single cell RNA sequencing reveals regulation of fetal ovary development in the monkey (Macaca fascicularis)

2020 ◽  
Author(s):  
Zheng-Hui Zhao ◽  
Chun-Yang Li ◽  
Tie-Gang Meng ◽  
Yan Wang ◽  
Wen-Bo Liu ◽  
...  
Keyword(s):  
Germ Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Germ Cells ◽  
Leydig Cells ◽  
Macaca Fascicularis ◽  
Ovary Development ◽  
Single Cell Rna Sequencing ◽  
Female Germ Cell ◽  
Fetal Ovary

ABSTRACTGerm cells are vital for reproduction and heredity. However, the mechanisms underlying female germ cell development in primates, especially in late embryonic stages, remain elusive. Here, we performed single-cell RNA sequencing of 12471 cells from whole fetal ovaries, and explored the communications between germ cells and niche cells. We depicted the two waves of oogenesis at single cell resolution and demonstrated that progenitor theca cells exhibit similar characteristics to Leydig cells in fetal monkey ovaries. Notably, we found that ZGLP1 displays differentially expressed patterns between mouse and monkey, which is not overlapped with NANOG in monkey germ cells, suggesting its role in meiosis entry but not in activating oogenic program in primates. Furthermore, the majority of germ cell clusters that highly expressed PRDM9 and SPO11 might undergo apoptosis after cyst breakdown, leading to germ cell attrition. Overall, our work provides new insights into the molecular and cellular basis of primate fetal ovary development at single-cell resolution.

scholarly journals Single-cell RNA sequencing reveals regulation of fetal ovary development in the monkey (Macaca fascicularis)

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Zheng-Hui Zhao ◽  
Chun-Yang Li ◽  
Tie-Gang Meng ◽  
Yan Wang ◽  
Wen-Bo Liu ◽  
...  
Keyword(s):  
Germ Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Germ Cells ◽  
Leydig Cells ◽  
Macaca Fascicularis ◽  
Ovary Development ◽  
Single Cell Rna Sequencing ◽  
Female Germ Cell ◽  
Fetal Ovary

AbstractGerm cells are vital for reproduction and heredity. However, the mechanisms underlying female germ cell development in primates, especially in late embryonic stages, remain elusive. Here, we performed single-cell RNA sequencing of 12,471 cells from whole fetal ovaries, and explored the communications between germ cells and niche cells. We depicted the two waves of oogenesis at single-cell resolution and demonstrated that progenitor theca cells exhibit similar characteristics to Leydig cells in fetal monkey ovaries. Notably, we found that ZGLP1 displays differentially expressed patterns between mouse and monkey, which is not overlapped with NANOG in monkey germ cells, suggesting its role in meiosis entry but not in activating oogenic program in primates. Furthermore, the majority of germ cell clusters that sharply express PRDM9 and SPO11 might undergo apoptosis after cyst breakdown, leading to germ cell attrition. Overall, our work provides new insights into the molecular and cellular basis of primate fetal ovary development at single-cell resolution.

scholarly journals Single‐cell RNA sequencing reveals the landscape of early female germ cell development

2020 ◽  
Vol 34 (9) ◽  
pp. 12634-12645
Author(s):  
Zheng‐Hui Zhao ◽  
Jun‐Yu Ma ◽  
Tie‐Gang Meng ◽  
Zhen‐Bo Wang ◽  
Wei Yue ◽  
...  
Keyword(s):  
Germ Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Development ◽  
Germ Cell Development ◽  
Single Cell Rna Sequencing ◽  
Female Germ Cell

scholarly journals Analysis of single-cell RNA-sequencing data to identify quiescent and proliferating neural cell populations in Glioblastoma

2021 ◽  
Author(s):  
Rajeev Vikram ◽  
Wen□Cheng Chou ◽  
Pei-Ei Wu ◽  
Wei-Ting Chen ◽  
Chen-Yang Shen
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
High Grade ◽  
Sequencing Data ◽  
Quiescent Cells ◽  
Single Cell Rna Sequencing ◽  
Tumorigenic Cells

ABSTRACTBackgroundDiffuse Glioblastoma (GBM) has high mortality and remains one of the most challenging type of cancer to treat. Identifying and characterizing the cells populations driving tumor growth and therapy resistance has been particularly difficult owing to marked inter and intra tumoral heterogeneity observed in these tumors. These tumorigenic populations contain long lived cells associated with latency, immune evasion and metastasis.MethodsHere, we analyzed the single-cell RNA-sequencing data of high grade glioblastomas from four different studies using integrated analysis of gene expression patterns, cell cycle stages and copy number variation to identify gene expression signatures associated with quiescent and cycling neuronal tumorigenic cells.ResultsThe results show that while cycling and quiescent cells are present in GBM of all age groups, they exist in a much larger proportion in pediatric glioblastomas. These cells show similarities in their expression patterns of a number of pluripotency and proliferation related genes. Upon unbiased clustering, these cells explicitly clustered on their cell cycle stage. Quiescent cells in both the groups specifically overexpressed a number of genes for ribosomal protein, while the cycling cells were enriched in the expression of high-mobility group and heterogeneous nuclear ribonucleoprotein group genes. A number of well-known markers of quiescence and proliferation in neurogenesis showed preferential expression in the quiescent and cycling populations identified in our analysis. Through our analysis, we identify ribosomal proteins as key constituents of quiescence in glioblastoma stem cells.ConclusionsThis study identifies gene signatures common to adult and pediatric glioblastoma quiescent and cycling stem cell niches. Further research elucidating their role in controlling quiescence and proliferation in tumorigenic cells in high grade glioblastoma will open avenues in more effective treatment strategies for glioblastoma patients.

scholarly journals IMMU-27. SINGLE CELL RNA-SEQUENCING IDENTIFIES NOVEL BONE MARROW DERIVED MYELOID CELLS IN GLIOBLASTOMA ASSOCIATED WITH TUMOR AGGRESSION

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii110-ii110
Author(s):  
Christina Jackson ◽  
Christopher Cherry ◽  
Sadhana Bom ◽  
Hao Zhang ◽  
John Choi ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Single Cell ◽  
Tumor Cells ◽  
Rna Sequencing ◽  
Metabolic Pathways ◽  
Myeloid Cells ◽  
Tumor Grade ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Two Populations

Abstract BACKGROUND Glioma associated myeloid cells (GAMs) can be induced to adopt an immunosuppressive phenotype that can lead to inhibition of anti-tumor responses in glioblastoma (GBM). Understanding the composition and phenotypes of GAMs is essential to modulating the myeloid compartment as a therapeutic adjunct to improve anti-tumor immune response. METHODS We performed single-cell RNA-sequencing (sc-RNAseq) of 435,400 myeloid and tumor cells to identify transcriptomic and phenotypic differences in GAMs across glioma grades. We further correlated the heterogeneity of the GAM landscape with tumor cell transcriptomics to investigate interactions between GAMs and tumor cells. RESULTS sc-RNAseq revealed a diverse landscape of myeloid-lineage cells in gliomas with an increase in preponderance of bone marrow derived myeloid cells (BMDMs) with increasing tumor grade. We identified two populations of BMDMs unique to GBMs; Mac-1and Mac-2. Mac-1 demonstrates upregulation of immature myeloid gene signature and altered metabolic pathways. Mac-2 is characterized by expression of scavenger receptor MARCO. Pseudotime and RNA velocity analysis revealed the ability of Mac-1 to transition and differentiate to Mac-2 and other GAM subtypes. We further found that the presence of these two populations of BMDMs are associated with the presence of tumor cells with stem cell and mesenchymal features. Bulk RNA-sequencing data demonstrates that gene signatures of these populations are associated with worse survival in GBM. CONCLUSION We used sc-RNAseq to identify a novel population of immature BMDMs that is associated with higher glioma grades. This population exhibited altered metabolic pathways and stem-like potentials to differentiate into other GAM populations including GAMs with upregulation of immunosuppressive pathways. Our results elucidate unique interactions between BMDMs and GBM tumor cells that potentially drives GBM progression and the more aggressive mesenchymal subtype. Our discovery of these novel BMDMs have implications in new therapeutic targets in improving the efficacy of immune-based therapies in GBM.

scholarly journals Software Benchmark—Classification Tree Algorithms for Cell Atlases Annotation Using Single-Cell RNA-Sequencing Data

2021 ◽  
Vol 12 (2) ◽  
pp. 317-334
Author(s):  
Omar Alaqeeli ◽  
Li Xing ◽  
Xuekui Zhang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Classification Tree ◽  
Area Under The Curve ◽  
Data Sets ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Tree Algorithms ◽  
R Packages

Classification tree is a widely used machine learning method. It has multiple implementations as R packages; rpart, ctree, evtree, tree and C5.0. The details of these implementations are not the same, and hence their performances differ from one application to another. We are interested in their performance in the classification of cells using the single-cell RNA-Sequencing data. In this paper, we conducted a benchmark study using 22 Single-Cell RNA-sequencing data sets. Using cross-validation, we compare packages’ prediction performances based on their Precision, Recall, F1-score, Area Under the Curve (AUC). We also compared the Complexity and Run-time of these R packages. Our study shows that rpart and evtree have the best Precision; evtree is the best in Recall, F1-score and AUC; C5.0 prefers more complex trees; tree is consistently much faster than others, although its complexity is often higher than others.

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.

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