Proteomic Exploration of Porcine Oocytes During Meiotic Maturation in vitro Using an Accurate TMT-Based Quantitative Approach

Jia, Baoyu; Xiang, Decai; Shao, Qingyong; Hong, Qionghua; Quan, Guobo; Wu, Guoquan

doi:10.3389/fvets.2021.792869

Cited by 5 publications

(2 citation statements)

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“…TMOD3 plays a crucial role in oocyte maturation by controlling the density of the cytoplasmic actin mesh ( Jo et al, 2016 ). MYO5B may play an important role in actin cytoskeleton remodeling during oocyte maturation ( Jia et al, 2022 ). TMEFF2 was studied for its upregulation in early oocyte development in the primordial and primary follicle stages ( Yu et al, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud

Choi,

Park,

Chae

2024

PeerJ

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Single-cell omics sequencing has rapidly advanced, enabling the quantification of diverse omics profiles at a single-cell resolution. To facilitate comprehensive biological insights, such as cellular differentiation trajectories, precise annotation of cell subtypes is essential. Conventional methods involve clustering cells and manually assigning subtypes based on canonical markers, a labor-intensive and expert-dependent process. Hence, an automated computational prediction framework is crucial. While several classification frameworks for predicting cell subtypes from single-cell RNA sequencing datasets exist, these methods solely rely on single-omics data, offering insights at a single molecular level. They often miss inter-omic correlations and a holistic understanding of cellular processes. To address this, the integration of multi-omics datasets from individual cells is essential for accurate subtype annotation. This article introduces moSCminer, a novel framework for classifying cell subtypes that harnesses the power of single-cell multi-omics sequencing datasets through an attention-based neural network operating at the omics level. By integrating three distinct omics datasets—gene expression, DNA methylation, and DNA accessibility—while accounting for their biological relationships, moSCminer excels at learning the relative significance of each omics feature. It then transforms this knowledge into a novel representation for cell subtype classification. Comparative evaluations against standard machine learning-based classifiers demonstrate moSCminer’s superior performance, consistently achieving the highest average performance on real datasets. The efficacy of multi-omics integration is further corroborated through an in-depth analysis of the omics-level attention module, which identifies potential markers for cell subtype annotation. To enhance accessibility and scalability, moSCminer is accessible as a user-friendly web-based platform seamlessly connected to a cloud system, publicly accessible at http://203.252.206.118:5568. Notably, this study marks the pioneering integration of three single-cell multi-omics datasets for cell subtype identification.

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Section: Resultsmentioning

confidence: 99%

moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud

Choi,

Park,

Chae

2024

PeerJ

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“…Genetic information is stored in the genomes of organisms. mRNA transcription is the basic process of gene expression, while the protein product carries out the molecular function of a gene [ 35 ]. To verify that the changes in transcript levels were consistent with those in protein levels, we detected changes in the mRNA expression levels of the upregulated protein-coding genes PELO, SLC27A4, SLC2A1, and ITGAL and the downregulated protein-coding genes GFAP, OLFML3, TUBA4A, and TOMM20 in the uterine horns of does in the NAC and control groups by qRT–PCR ( Figure 5 ).…”

Section: Resultsmentioning

confidence: 99%

A Proteomic Study of the Effect of N-acetylcysteine on the Regulation of Early Pregnancy in Goats

Yang

Chen

Tian

et al. 2022

Animals

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Dietary supplementation with N-acetyl-L-cysteine (NAC) may support early pregnancy regulation and fertility in female animals. The purpose of this study was to investigate the effect of supplementation with 0.07% NAC on the expression of the uterine keratin gene and protein in Qianbei-pockmarked goats during early pregnancy using tandem mass spectrometry (TMT) relative quantitative proteomics. The results showed that there were significant differences in uterine keratin expression between the experimental group (NAC group) and the control group on day 35 of gestation. A total of 6271 proteins were identified, 6258 of which were quantified by mass spectrometry. There were 125 differentially expressed proteins (DEPs), including 47 upregulated and 78 downregulated proteins, in the NAC group. Bioinformatic analysis showed that these DEPs were mainly involved in the transport and biosynthesis of organic matter and were related to the binding of transition metal ions, DNA and proteins and the catalytic activity of enzymes. They were enriched in the Jak-STAT signalling pathway, RNA monitoring pathway, amino acid biosynthesis, steroid biosynthesis and other pathways that may affect the early pregnancy status of does through different pathways and thus influence early embryonic development. Immunohistochemistry, real-time quantitative PCR and Western blotting were used to verify the expression and localization of glial fibrillary acidic protein (GFAP) and pelota mRNA surveillance and ribosomal rescue factor (PELO) in uterine horn tissue. The results showed that both PELO and GFAP were localized to endometrial and stromal cells, consistent with the mass spectrometry data at the transcriptional and translational levels. Moreover, NAC supplementation increased the levels of the reproductive hormones follicle-stimulating hormone (FSH), luteinizing hormone (LH), oestradiol (E2), progesterone (P4), superoxide dismutase (SOD), glutamate peroxidase (GSH-Px) and nitric oxide (NO) in the serum of does. These findings provide new insight into the mechanism by which NAC regulates early pregnancy and embryonic development in goats.

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The establishment of goat semen protein profile using a tandem mass tag-based proteomics approach

Liang

Xiang

et al. 2022

Research in Veterinary Science

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Proteomic Exploration of Porcine Oocytes During Meiotic Maturation in vitro Using an Accurate TMT-Based Quantitative Approach

Cited by 5 publications

References 68 publications

moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud

moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud

A Proteomic Study of the Effect of N-acetylcysteine on the Regulation of Early Pregnancy in Goats

The establishment of goat semen protein profile using a tandem mass tag-based proteomics approach

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