SkewC: Identifying cells with skewed gene body coverage in single-cell RNA sequencing data

Application of omics technologies in studies on antitumor effects of Traditional Chinese Medicine

Traditional Chinese medicine (TCM) is considered to be one of the most comprehensive and influential form of traditional medicine. It plays an important role in clinical treatment and adjuvant therapy for cancer. However, the complex composition of TCM presents challenges to the comprehensive and systematic understanding of its antitumor mechanisms, which hinders further development of TCM with antitumor effects. Omics technologies can immensely help in elucidating the mechanism of action of drugs. They utilize high-throughput sequencing and detection techniques to provide deeper insights into biological systems, revealing the intricate mechanisms through which TCM combats tumors. Multi-omics approaches can be used to elucidate the interrelationships among different omics layers by integrating data from various omics disciplines. By analyzing a large amount of data, these approaches further unravel the complex network of mechanisms underlying the antitumor effects of TCM and explain the mutual regulations across different molecular levels. In this study, we presented a comprehensive overview of the recent progress in single-omics and multi-omics research focused on elucidating the mechanisms underlying the antitumor effects of TCM. We discussed the significance of omics technologies in advancing research on the antitumor properties of TCM and also provided novel research perspectives and methodologies for further advancing this research field.

Optimized single-cell RNA sequencing protocol to study early genome activation in mammalian preimplantation development

Here, we present a modification of single-cell tagged reverse transcription protocol to study gene expression on a single-cell level or with limited RNA input. We describe different enzymes for reverse transcription and cDNA amplification, modified lysis buffer, and additional clean-up steps before cDNA amplification. We also detail an optimized single-cell RNA sequencing method for handpicked single cells, or tens to hundreds of cells, as input material to study mammalian preimplantation development. For complete details on the use and execution of this protocol, please refer to Ezer et al.1.

Computational approach to evaluate scRNA-seq data quality and gene body coverage with SkewC

SkewC is a single-cell RNA sequencing (scRNA-seq) data quality evaluation tool. The approach is based on determining gene body coverage, and its skewness, as a quality metric for each individual cell. SkewC distinguishes between two types of single cells: typical cells with prototypical gene body coverage profiles and skewed cells with skewed gene body coverage profiles. SkewC can be used on any scRNA-seq data as it is independent from the underlying technology used to generate the data. For complete details on the use and execution of this protocol, please refer to Abugessaisa et al. (2022).1.

Microfluidic cell sorter sample preparation for genomic assays

Single-cell RNA-Sequencing has led to many novel discoveries such as the detection of rare cell populations, microbial populations, and cancer mutations. The quality of single-cell transcriptomics relies heavily on sample preparation and cell sorting techniques that best preserve RNA quality while removing dead cells or debris prior to cDNA generation and library preparation. Magnetic bead cell enrichment is a simple process of cleaning up a sample but can only separate on a single-criterion. Droplet-based cell sorters, on the other hand, allows for higher purity of sorted cells gated on several fluorescent and scatter properties. The downside of traditional droplet-based sorters is their operational complexity, accessibility, and potential stress on cells due to their high-pressure pumps. The WOLF® Cell Sorter, and WOLF G2®, developed by NanoCellect Biomedical, are novel microfluidic-based cell sorters that use gentle sorting technology compatible with several RNA-sequencing platforms. The experiments highlighted here demonstrate how microfluidic sorting can be successfully used to remove debris and unwanted cells prior to genomic sample preparation resulting in more data per cell and improved library complexity.