![]() ![]() To precisely understand the transcriptomic status of such heterogeneous cell populations, we can use scRNA-seq techniques (Table 1). For example, in the RNA-seq of cancer tissue, transcripts from various types of cells, including tumor cells, immune cells, fibroblasts, and endothelial cells, are analyzed. Bulk RNA-seq analysis allows the measurement of only the average transcript expression in a cell population. RNA-seq analysis conventionally measures transcripts in a mixture of cells (called a “bulk”). Single-cell RNA sequencing (scRNA-seq) 2 has been widely utilized worldwide. In this review, we introduce basic information and describe several applications of single-cell sequencing techniques. To study all types of cells and omics layers, we should consider single-cell sequencing methods from both laboratory and clinical perspectives. The HCA platform utilizes single-cell sequencing techniques to obtain single-cell genomic information from healthy and diseased cells. Under an international approach, the Human Cell Atlas (HCA ) has been generating comprehensive molecular maps of all human cells 1. A large number of reports on this topic have been published worldwide from various regions. Single-cell sequencing has the power to elucidate genomic, epigenomic, and transcriptomic heterogeneity in cellular populations, and the changes at these levels. Recently, single-cell sequencing technologies have been rapidly developed for observing the multilayered status of single cells. Introduction: single-cell sequencing analysis ![]()
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