Single-cell gene expression analyses hold promise for characterizing cellular heterogeneity, but current methods compromise on either the coverage, the sensitivity or the throughput. Here, we introduce Smart-seq2 with improved reverse transcription, template switching and preamplification to increase both yield and length of cDNA libraries generated from individual cells. Smart-seq2 transcriptome libraries have improved detection, coverage, bias and accuracy compared to Smart-seq libraries and are generated with off-the-shelf reagents at lower cost.

Smart-seq2 for sensitive full-length transcriptome profiling in single cells

Individual cell heterogeneity within a population can be critical to its peculiar function and fate. Subpopulations studies with mixed mutants and wild types may not be as informative regarding which cell responds to which drugs or clinical treatments. Cell to cell differences in RNA transcripts and protein expression can be key to answering questions in cancer, neurobiology, stem cell biology, immunology, and developmental biology. Conventional cell-based assays mainly analyze the average responses from a population of cells, without regarding individual cell phenotypes. To better understand the variations from cell to cell, scientists need to use single cell analyses to provide more detailed information for therapeutic decision making in precision medicine. In this review, we focus on the recent developments in single cell isolation and analysis, which include technologies, analyses and main applications. Here, we summarize the historical background, limitations, applications, and potential of single cell isolation technologies.

/pdf/single-cell-isolation-and-analysis-eo044gt0h9.pdf

Single Cell Isolation and Analysis.

Molecular heterogeneity in breast cancer: State of the science and implications for patient care.

Mechanisms that drive inflammatory tumor microenvironment, tumor heterogeneity, and metastatic progression

The development of new drugs is multidisciplinary and systematic work. High-throughput techniques based on "-omics" have driven the discovery of biomarkers in diseases and therapeutic targets of drugs. A transcriptome is the complete set of all RNAs transcribed by certain tissues or cells at a specific stage of development or physiological condition. Transcriptome research can demonstrate gene functions and structures from the whole level and reveal the molecular mechanism of specific biological processes in diseases. Currently, gene expression microarray and high-throughput RNA-sequencing have been widely used in biological, medical, clinical, and drug research. The former has been applied in drug screening and biomarker detection of drugs due to its high throughput, fast detection speed, simple analysis, and relatively low price. With the further development of detection technology and the improvement of analytical methods, the detection flux of RNA-seq is much higher but the price is lower, hence it has powerful advantages in detecting biomarkers and drug discovery. Compared with the traditional RNA-seq, scRNA-seq has higher accuracy and efficiency, especially the single-cell level of gene expression pattern analysis can provide more information for drug and biomarker discovery. Therefore, (sc)RNA-seq has broader application prospects, especially in the field of drug discovery. In this overview, we will review the application of these technologies in drug, especially in natural drug and biomarker discovery and development. Emerging applications of scRNA-seq and the third generation RNA-sequencing tools are also discussed.

High-Throughput Transcriptome Profiling in Drug and Biomarker Discovery

Advances in genomic technology have enabled the faithful detection and measurement of mutations and the gene expression profile of cancer cells at the single-cell level. Recently, several single-cell sequencing methods have been developed that permit the comprehensive and precise analysis of the cancer-cell genome, transcriptome, and epigenome. The use of these methods to analyze cancer cells has led to a series of unanticipated discoveries, such as the high heterogeneity and stochastic changes in cancer-cell populations, the new driver mutations and the complicated clonal evolution mechanisms, and the novel identification of biomarkers of variant tumors. These methods and the knowledge gained from their utilization could potentially improve the early detection and monitoring of rare cancer cells, such as circulating tumor cells and disseminated tumor cells, and promote the development of personalized and highly precise cancer therapy. Here, we discuss the current methods for single cancer-cell sequencing, with a strong focus on those practically used or potentially valuable in cancer research, including single-cell isolation, whole genome and transcriptome amplification, epigenome profiling, multi-dimensional sequencing, and next-generation sequencing and analysis. We also examine the current applications, challenges, and prospects of single cancer-cell sequencing.

https://cancerres.aacrjournals.org/content/canres/76/6/1305.full.pdf

Zhaoyang Hu

Papers

Single-Cell Sequencing for Precise Cancer Research: Progress and Prospects