single cell genomics

[What is the main focus of the review article by Hwang et al. (2018)?]

[The review article focuses on single-cell RNA sequencing (scRNA-seq) technologies and bioinformatics pipelines, discussing technical challenges in single-cell isolation, library preparation, and computational analysis tools for scRNA-seq data.]

[Why is single-cell RNA sequencing (scRNA-seq) important in biological research?]

[scRNA-seq is important because it allows researchers to uncover complex and rare cell populations, regulatory relationships between genes, and track the trajectories of distinct cell lineages in development, which cannot be achieved with traditional bulk population sequencing methods.]

[What are some of the challenges associated with single-cell isolation techniques?]

[Challenges include low efficiency in limiting dilution, time-consuming and low-throughput micromanipulation, the need for large starting volumes and monoclonal antibodies in FACS, and the requirement for homogeneous cell sizes in microfluidic platforms like Fluidigm C1.]

[What is the role of unique molecular identifiers (UMIs) in scRNA-seq?]

[UMIs are used to tag individual mRNA molecules during reverse transcription, allowing researchers to distinguish between original molecules and PCR-amplified copies. This helps in reducing PCR bias and improving the accuracy of gene expression quantification.]

[What are some common steps in scRNA-seq library preparation?]

[Common steps include cell lysis, reverse transcription into first-strand cDNA, second-strand synthesis, and cDNA amplification. These steps are crucial for generating libraries that can be sequenced to analyze gene expression at the single-cell level.]

[What are some computational challenges in analyzing scRNA-seq data?]

[Computational challenges include pre-processing the data (quality control, read alignment, and normalization), addressing technical and biological variations, and performing downstream analyses such as clustering, differential expression analysis, and inferring regulatory networks.]

[What is the significance of normalization in scRNA-seq data analysis?]

[Normalization is crucial to remove cell-specific biases, such as differences in capture efficiency and amplification bias, which can affect downstream analyses like differential gene expression and clustering.]

[What are some methods used for dimensionality reduction in scRNA-seq data analysis?]

[Methods include Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), Multidimensional Scaling, Locally Linear Embedding (LLE), and Isomap. These methods help in visualizing and interpreting high-dimensional scRNA-seq data.]

[How can scRNA-seq be used to infer gene regulatory networks (GRNs)?]

[scRNA-seq data can be used to infer GRNs by identifying co-expression patterns, using machine learning-based approaches, model-based inference, and information theory-based methods. These networks help in understanding the regulatory interactions between genes and proteins.]

[What is the concept of "pseudotime" in scRNA-seq analysis?]

[Pseudotime is a computational concept used to order cells along a trajectory based on their gene expression profiles, representing a cell's progression through a biological process such as differentiation or cell cycle, even though the cells are sampled at a single time point.]

[What are some potential applications of scRNA-seq in medical research?]

[Potential applications include studying tumor heterogeneity, identifying drug-resistant cancer cells, analyzing circulating tumor cells (CTCs) for liquid biopsies, and reconstructing cell lineage relationships during development and disease progression.]

[What is the Human Cell Atlas project mentioned in the review?]

[The Human Cell Atlas is an ambitious project aimed at mapping all 35 trillion cells in the human body using scRNA-seq. It seeks to classify and identify new cell types based on gene expression profiles, providing insights into cellular heterogeneity and function.]

[What are some future prospects for scRNA-seq technology?]

[Future prospects include the ability to routinely analyze millions of cells, deeper understanding of immune cell heterogeneity, identification of novel pathways in neuro-related diseases, and the development of standardized bioinformatics pipelines for more accurate and reproducible analyse