Infertile ovotestis (mixture of ovary and testis) often occurs in intersex individuals under certain pathological and physiological conditions. However, how ovotestis is formed remains largely unknown. Here, we report the first comprehensive single-cell developmental atlas of the model ovotestis. We provide an overview of cell identities and a roadmap of germline, niche, and stem cell development in ovotestis by cell lineage reconstruction and a uniform manifold approximation and projection. We identify common progenitors of germline stem cells with two states, which reveal their bipotential nature to differentiate into both spermatogonial stem cells and female germline stem cells. Moreover, we found that ovotestis infertility was caused by degradation of female germline cells via liquid–liquid phase separation of the proteasomes in the nucleus, and impaired histone-to-protamine replacement in spermatid differentiation. Notably, signaling pathways in gonadal niche cells and their interaction with germlines synergistically determined distinct cell fate of both male and female germlines. Overall, we reveal a cellular fate map of germline and niche cell development that shapes cell differentiation direction of ovotestis, and provide novel insights into ovotestis development.
Single-cell mRNA sequencing has been adopted as a powerful technique for understanding gene expression profiles at the single-cell level. However, challenges remain due to factors such as the inefficiency of mRNA molecular capture, technical noises and separate sequencing of cells in different batches. Normalization methods have been developed to ensure a relatively accurate analysis. This work presents a survey on 10 tools specifically designed for single-cell mRNA sequencing data preprocessing steps, among which 6 tools are used for dropout normalization and 4 tools are for batch effect correction. In this survey, we outline the main methodology for each of these tools, and we also compare these tools to evaluate their normalization performance on datasets which are simulated under the constraints of dropout inefficiency, batch effect or their combined effects. We found that Saver and Baynorm performed better than other methods in dropout normalization, in most cases. Beer and Batchelor performed better in the batch effect normalization, and the Saver–Beer tool combination and the Baynorm–Beer combination performed better in the mixed dropout-and-batch effect normalization. Over-normalization is a common issue occurred to these dropout normalization tools that is worth of future investigation. For the batch normalization tools, the capability of retaining heterogeneity between different groups of cells after normalization can be another direction for future improvement.
The COVID-19 pandemic caused by SARS-CoV-2 has had a severe impact on people worldwide. The reference genome of the virus has been widely used as a template for designing mRNA vaccines to combat the disease. In this study, we present a computational method aimed at identifying co-existing intra-host strains of the virus from RNA-sequencing data of short reads that were used to assemble the original reference genome. Our method consisted of five key steps: extraction of relevant reads, error correction for the reads, identification of within-host diversity, phylogenetic study, and protein binding affinity analysis. Our study revealed that multiple strains of SARS-CoV-2 can coexist in both the viral sample used to produce the reference sequence and a wastewater sample from California. Additionally, our workflow demonstrated its capability to identify within-host diversity in foot-and-mouth disease virus (FMDV). Through our research, we were able to shed light on the binding affinity and phylogenetic relationships of these strains with the published SARS-CoV-2 reference genome, SARS-CoV, variants of concern (VOC) of SARS-CoV-2, and some closely related coronaviruses. These insights have important implications for future research efforts aimed at identifying within-host diversity, understanding the evolution and spread of these viruses, as well as the development of effective treatments and vaccines against them.
Tree- and linear-shaped cell differentiation trajectories have been widely observed in developmental biologies and can be also inferred through computational methods from single-cell RNA-sequencing datasets. However, trajectories with complicated topologies such as loops, disparate lineages and bifurcating hierarchy remain difficult to infer accurately. Here, we introduce a density-based trajectory inference method capable of constructing diverse shapes of topological patterns including the most intriguing bifurcations. The novelty of our method is a step to exploit overlapping probability distributions to identify transition states of cells for determining connectability between cell clusters, and another step to infer a stable trajectory through a base-topology guided iterative fitting. Our method precisely re-constructed various benchmark reference trajectories. As a case study to demonstrate practical usefulness, our method was tested on single-cell RNA sequencing profiles of blood cells of SARS-CoV-2-infected patients. We not only re-discovered the linear trajectory bridging the transition from IgM plasmablast cells to developing neutrophils, and also found a previously-undiscovered lineage which can be rigorously supported by differentially expressed gene analysis.
Background Neurocognitive disorders and psychosocial difficulties are common in patients with Turner syndrome and multiple neurodegenerative diseases, yet there is no effective cure. Human primordial germ cells (hPGCs) are pluripotent germline stem cells in early embryo, which pass genetic information from one generation to the next, whereas all somatic cells will die along with the end of life. However, it is not known whether patient hPGCs with Turner syndrome contain information of neurocognitive and psychosocial illness. Results In this report, we used a high-density of culture system of embryoids derived from iPSCs of a patient with Turner syndrome to ask how pathogenetic pathways are associated with onset of neurocognitive and psychosocial disorders. The hPGC-Like Cells (hPGCLCs) were in vitro specified from iPSCs of 45,XO, 46,XX and 46,XY by the high-density induction of embryoids. Amazingly, we found that the specification process of the hPGCLCs in 45,XO, compared to those in 46,XX and 46,XY, enriched several common pathogenetic pathways regulating neurocognitive and psychosocial disorders, that shared among multiple neurodegenerative diseases and Turner syndrome. The downregulated chemical synaptic transmission pathways, including glutamatergic, GABAergic, and nicotine cholinergic synapses, indicated synaptic dysfunctions, while upregulated pathways that were associated with imbalance of mitochondrial respiratory chain complexes and apoptosis, may contribute to neuronal dysfunctions. Notably, downregulation of three types of ubiquitin ligases E1-E2-E3 and lysosome-associated sulfatases and RAB9A, owing to haploinsufficiency and parental preference of the X chromosome expression, indicated that two pathways of cellular degradation, lysosome and ubiquitin–proteasome, were impaired in the specification process of 45,XO hPGCLCs. This would lead to accumulation of undesired proteins and aggregates, which is a typically pathological hallmark in neurodegenerative diseases. Conclusions Our data suggest that the specification process of the hPGCLCs in 45,XO, compared to those in 46,XX and 46,XY, enriched pathogenetic pathways that are associated with the onset of neurocognitive and psychosocial disorders.
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