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Genetics at the Cell Level
Published in Carlos Simón, Carmen Rubio, Handbook of Genetic Diagnostic Technologies in Reproductive Medicine, 2022
Valentina Lorenzi, Roser Vento-Tormo
Single-cell sequencing is a relatively recent technological development which was named Method of the Year in 2013 by the journal Nature Methods (Nawy, 2014). The earliest single-cell sequencing method applicable on a large scale was transcriptome analysis via RNA-Seq (single-cell RNA sequencing, scRNA-seq) (Tang et al., 2009). scRNA-seq marked an enormous progression in the definition of cell identities because it offered a comprehensive and unbiased characterization of the cell at the molecular level (Trapnell, 2015).
Introduction to Genomics
Published in Altuna Akalin, Computational Genomics with R, 2020
Another recent development is single-cell sequencing. Current technologies usually work on genetic material from thousands to millions of cells. This means that the results you receive represent the population of cells that were used in the experiment. However, there is a lot of variation between the same type of cells, but this variation is not observed at all. Newer sequencing techniques can work on single cells and give quantitative information on each cell.
Laboratory Molecular Methodologies to Analyze DNA Methylation
Published in Cristina Camprubí, Joan Blanco, Epigenetics and Assisted Reproduction, 2018
Single-cell sequencing technologies have greatly helped dissect the heterogeneity of different cells present within a sample. In addition to scPBAT-seq and scRRBS, single-cell epigenome methods such as scHi-Seq, scChIP-seq, scDNAse1-seq, and scATAC-seq have revealed the wider epigenetic and chromatin heterogeneity in cell populations (41). All of these methods currently suffer from problems of spare coverage and poor signal-to-noise ratios. The simultaneous measurement of different layers of epigenetic information from the same cell remains challenging. One multi-omics protocol, Chromatin Overall Omics-scale Landscape sequencing (COOL-seq) allows for the assessment of chromatin accessibility, nucleosome positioning and DNA methylation (42). This method is essentially a scaled-down combination of Nucleosome Occupancy and Methylome sequencing (NOMe-seq), by which accessible chromatin is probes by sensitivity to methylation by the bacterial M.CviP1 methyltransferase, and scPBAT-seq, which has been shown to be sensitive and robust at the single-cell level.
The intellectual base and global trends in contrast-induced acute kidney injury: a bibliometric analysis
Published in Renal Failure, 2023
Heng Wang, Tingting Gao, Ruijing Zhang, Jie Hu, Yuwen Wang, Jianing Wei, Yun Zhou, Honglin Dong
Currently, most of the research on CI-AKI is done in clinical trials, while animal models also play an important role in the study of the molecular mechanisms of CI-AKI. Clinical trials play an important role in investigating risk factors, prevention strategies, and treatment options for CI-AKI and should be a hot spot for future research. We believe that research on CI-AKI should adopt a multicentric, multi-institutional model, increase the sample sizes, do longer patient follow-up, and collect the most comprehensive clinical data and laboratory and imaging data possible. In addition, there is a paucity of basic research on clinical specimens. Multiomics analysis of human specimens or single-cell sequencing should be performed in compliance with ethical requirements, which is expected to yield more information on the mechanism of injury.
Screening target genes for the treatment of PCOS via analysis of single-cell sequencing data
Published in Annals of Medicine, 2022
Zhenzhen Lu, Chunyan Chen, Ying Gao, Yanhui Li, Xiaojie Zhao, Hanke Zhang, Qiongqiong Wei, Xinliu Zeng, Yajie Li, Min Wan
Single cell sequencing data were filtered and discriminated for characteristics. Violin diagram construction revealed gene and cell quantities and mitochondrial gene percentages (Figure 2(A)). Gene quantities and mitochondrial gene percentages in relation to cell quantity were shown in Figure 2(B). The 10 most highly variable genes were shown in Figure 2(C). JackStraw plotting was used to detail available data dimensions of the data as shown in Figure 2(D). A heatmap of PC1 was shown in Figure 2(E), it represents the new variables obtained by transforming the variables in the original data. Nonlinear dimensionality reduction clustering via t-SNE was shown in Figure 2(F) and mRNAs of the two groups Figure 2(G), the top 4 DEGs in each cluster were shown in Figure 2(H). Pseudotime analysis plots and total mRNA content of the 2 clusters were shown in Figure 2(I).
The omics strategy: the use of systems vaccinology to characterize immune responses to childhood immunization
Published in Expert Review of Vaccines, 2022
Technological advances continue to present new and improved approaches to characterize the immune responses underlying vaccine responses. There has been a recent explosion in the use of single cell sequencing methods within the field of immunological research. While the high costs associated with single cell RNA-sequencing (scRNA-seq) have prohibited its widespread adoption, decreasing prices and an increasingly appreciation of the granularity these approaches provide, appear to indicate that scRNA-seq will likely soon dominate transcriptomic research. Moreover, cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) allows detection and quantification of multiplexed protein markers simultaneously with transcriptomic data, enriching the data that can be gleaned from these single cell experiments [96].