Single Cell Omics

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Background

CONTENTS:

1. Theoretical background, introduction, and significance
2. Technology progress on molecular methodology
3. Development of platform and instrument
4. Revolution in algorithm and data analysis
5. Applications in biological studies
6. Applications in clinics: cancer and genetic diseases

DESCRIPTION:

Single-cell genome-wide analysis is an emerging discipline that stems from the sequencing of the human genome and the development of “omics” technologies, particularly genomics, transcriptomics, and epigenomics, but the sensitivity is improved to single cell level. The new generation of methodologies, especially the next generation sequencing (NGS) technology, plays a leading role in genomics related fields. However, the conventional techniques of “omics” need a large number of cells, usually on the order of millions of cells, which is hardly accessible in many cases, and so genomics is unable to address questions at the single-cell level. More importantly, harnessing the power of “omics” technologies and applying them at the single-cell level is crucial since every cell is specific and unique; almost every cell population, derived in vivo or in vitro, is heterogeneous. A massive analysis of cell populations would not be complete and representative without an extensive examination of a significant number of individual cells. Single-cell analysis or single-cell biology, as a new frontier, seeks to study a number of individual cells, providing unprecedented resolution for the understanding of the omics structure and function previously unknown of an organ or tissue or system, and the interaction of single cells on a global scale. This ability greatly promotes the understanding of life at a fundamental level and has vast applications in medicine.

Single-cell analysis enables the identification of minor subpopulation of cells that may play a critical role in a biological process. It provides an ultra-sensitive tool to clarify specific molecular mechanisms and pathways and reveal the nature of cell heterogeneity. It also facilitates the clinical investigation of patients when a very low quantity or a single cell is available for analysis, such as noninvasive prenatal diagnosis and noninvasive cancer screening, and genetic evaluation for in vitro fertilization. Within only a few years, single-cell analysis, especially whole genomic and exome sequencing, and transcriptomic sequencing, is becoming robust and broadly accessible. Besides the original core technologies, recently, multiplexing barcode design in combination of microfluidic platform, exampled by Fluidigm and Drop-seq, enables a large scale of single cell analysis, switched from one single cell to hundreds or thousands of single cell in a single test. The unique molecular identifiers (UMIs) allows the amplification bias among the original molecules to be corrected faithfully, leading to a quantitative measurement. Single cell epigenomics analysis is emerging, such as single cell accessibility and CpG methylation profiling, and opens a new era. Localized (in situ) and in vivo RNA-seq emphasize the native, and dynamic relationship of the individual cells and the spatial and temporal interactions of these cells. Multidimensional analysis that allows a simultaneous analysis of DNA (sequence mutation or sequence epi-modification), RNA, and probably protein from a single cell are the target of a significant effort. The technology and biological noise, together with the complicated interaction of the multiple elements bring a huge challenge for the bioinformatics, and progress in the data analysis has been made since then. Single cell analysis impacts on the wide range of biological studies, pharmaceutic RD, and clinical diagnosis and monitor and precise medicine is tremendous.

This topic will describe single cell analysis technologies, their ongoing development, applications, and prospects. The technology part will give a detail description of the major technologies strategies from single cell isolation/capture to genomics, transcriptomics and epigenomics analysis, their advantages and pitfalls; it will also introduce the advanced instruments and specific mathematical models for the data analysis. The application part will include an extensive review on the specific and influencing discoveries in varieties of fields, such as cancer, neuron and neural system, stem cell, embryo development, and immune cells, from laboratory research to clinics and pharmaceutical industrial application. Some discoveries may be significant not only to the particular system studied but also to other systems. The topic will also discuss the current problems, effort directions and prospects. This topic will be of significant interest to scientists working in or affiliated with this field.

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