单细胞技术进展及其在植物研究中的应用

多细胞生物体的生存依赖于不同类型细胞特异性的功能分工,不同类型的细胞尽管基因组相同,但有其独特的发育过程和应对环境变化的能力。生物学的一大挑战就是揭示基因如何在正确的位置、正确的时间表达到正确的水平,最近出现了很多通过细胞类型特异性方法研究单细胞组学的工具,这些新技术使我们能通过空前分辨率,理解多细胞生物体内不同类型的单个细胞基因表达特点及其适应环境变化的机制。单细胞样品的获取一直是单细胞研究的一大技术瓶颈,因此本文将以如何获得起始材料为重点,探讨单细胞研究的样品标记、单细胞分离及获取、组学数据分析和结果验证等技术方法及其在植物研究中的应用。     

空间转录物组学技术进展及其在神经科学领域中的应用

空间转录物组学是在单细胞RNA测序技术基础上实现细胞空间位置信息测定的组学技术。该技术克服了单细胞转录物组学在单细胞分离建库过程中丢失细胞在组织中空间信息的问题,可同时提供研究对象的转录物组数据信息和在组织中的空间位置信息。空间转录物组学技术对研究细胞谱系的发生过程、细胞间的调控机制和相互作用等具有重要作用,是组学技术研究的重要发展方向和热点。近年来,空间转录物组学技术发展迅速,新的检测方法不断产生,检测灵敏度、分辨率和检测通量等技术指标不断提升。本文根据获取空间信息的原理不同,将较为常用的空间转录物组学技术进行了分类,总结了各类方法的检测原理、代表性技术手段及其相应的技术指标。随后,从脑细胞类型区分与细胞层图谱构建、神经系统相关疾病特征分析与标志物研究两个方面举例论述了空间转录物组学技术在神经科学中的应用。最后,对空间转录物组学技术目前存在的问题进行了总结,并对其未来的发展方向进行了展望。     

单细胞多组学技术新进展及其在发育生物学研究中的应用

单细胞多组学技术基于单细胞测序技术和高通量组学方法发展而来,能在单细胞分辨率下同时整合转录组、基因组、表观遗传组、蛋白质组或空间状态等两种或多种组学信息.相比单细胞单组学技术,单细胞多组学技术能多层次、多角度地描绘细胞间的异质性,挖掘各层组学之间的直接和潜在联系,从而更加全面和系统地描绘细胞的状态和命运,在生命科学和医学多领域有广泛的应用前景.本文总结了现有的单细胞多组学技术,预测了其发展趋势,讨论了其在发育生物学中的应用实例和潜力.     

单细胞转录组测序技术新进展及其在造血系统研究中的应用

单细胞测序技术凭借其能全面反映细胞群体异质性这一优势,近年来发展迅速.其中,单细胞转录组测序技术提供了在分子水平上对细胞作分类或表征的替代方法,在发育生物学、神经科学、血液学、免疫及癌症等研究领域均展示出了广泛的应用前景.本文总结了近年来单细胞转录组测序技术的主要发展趋势,并列举了该技术在造血系统中的应用.     

单细胞测序技术及应用进展

同一组织中的细胞往往被认为是具有相同状态的功能单位,传统的检测手段分析的是细胞群体的总体平均反应。然而通过对单个细胞的DNA或RNA进行测序,表明组织系统层面的功能是由异质性细胞构成的。单细胞测序以单个细胞为单位,通过全基因组或转录组扩增,进行高通量测序,能够揭示单个细胞的基因结构和基因表达状态,反映细胞间的异质性,在肿瘤、发育生物学、微生物学、神经科学等领域发挥重要作用,正成为生命科学研究的焦点。单细胞测序的难点是单个细胞的分离、单细胞基因组和转录组的扩增。本文主要介绍和分析了单细胞测序技术中常用的单细胞分离技术、单细胞基因组扩增技术和转录组扩增技术及其优缺点,并对当前已经取得成果的应用领域进行了阐述,为单细胞测序技术的研究与应用提供参考。     

空间分辨代谢组学进展和挑战

空间分辨代谢组学即整合质谱成像和代谢组学技术,对动/植物组织和细胞中内/外源性代谢物的种类、含量和差异性空间分布进行精准测定。质谱成像技术因其具有无标记、非特异、高灵敏度、高化学覆盖、元素/分子同时检测等优势,被广泛应用于动/植物组织中各类代谢物、多肽和蛋白的时空分布研究。首先介绍了代谢组学和质谱成像技术的研究现状,然后重点综述了空间分辨代谢组学在动物组织、植物组织和单细胞水平上的前沿应用。最后展望了空间分辨代谢组学技术的现有瓶颈和未来发展方向。空间分辨代谢组学是继代谢组学之后又一门新兴的分子成像组学技术,能够无标记、可视化检测动物组织中外源性药物的吸收、分布、代谢和排泄,以及植物组织中多种代谢产物的生物合成、转运途径和积累规律。该技术将推动靶向药物发现、病理机制解析和动植物生长发育密切关联的空间代谢网络调控等前沿应用研究。     

单细胞转录组测序在药物成瘾研究中的应用

药物成瘾是复杂的中枢神经系统疾病，相关基础与临床研究均证实药物成瘾的神经机制及神经环路在成瘾行为形成的不同阶段逐渐发生改变。利用全基因组关联研究、全基因组测序、全外显子测序或高通量转录组测序等技术的组学研究对包括药物成瘾在内的精神疾病遗传的脆弱性进行了深入研究。上述单核苷酸多态性检测技术或测序技术主要预测疾病的遗传风险位点。然而，许多中枢神经系统疾病的发生与环境因素密切相关，而且在疾病发展的不同阶段，相关基因的表达存在脑区特异性的细胞异质性信息。因此，传统研究对发病机制的解释存在一定的局限性。单细胞转录组测序技术是针对单个细胞进行转录水平的测定，规避了传统测序对细胞群体平均转录水平检测的缺点，可以定量描述细胞异质性。近年来，单细胞转录测序技术在神经精神科学研究中的应用逐渐受到关注，本文总结了该技术在神经科学研究中的重要应用，并以药物成瘾为例，重点阐述说明其在中枢神经系统疾病中的应用价值。     

单细胞技术在干细胞研究中的应用

干细胞是具有自我更新和分化潜能的异质性细胞群体。基于细胞群体水平的干细胞研究不能满足深入认识干细胞生物学本质及实际应用的需要。近年来,单细胞相关技术不断发展和成熟,并正在干细胞基础研究及其相关领域中获得迅速应用。该文以造血干细胞为主要例举,就实验研究中常用的单细胞分离、单细胞克隆分析、单细胞移植、单细胞实时定量PCR及单细胞测序等技术原理及其应用进行综述。     

单细胞转录组技术及其在人类细胞图谱构建中的应用

单细胞转录组技术在单细胞水平上进行转录组测序,提供了单个细胞的基因表达差异信息,使在单细胞尺度下研究个体细胞、相关环境细胞及其相互作用的机理成为可能.近年来,单细胞转录组技术在c DNA扩增原理上经历了从末端加尾、体外逆转录到模板置换的方法发展,大大提高了基因检测的数量、基因表达的准确性等.同时,在单细胞选取方式上进行了从96/384孔板到油包水液滴以及纳米微孔的创新,在提高通量和重复性的同时降低了整体实验成本.单细胞转录组技术广泛应用于细胞群体分类和异质性研究,推动了从发育生物学到正常、病态组织细胞图谱的构建.本文对单细胞转录组技术近年的技术进展以及在人类细胞图谱构建中的应用进行了综述.     

单细胞基因表达分析技术在神经科学研究中的应用

单细胞的分子生物学是神经科学中较新的领域,研究对象包括单细胞DNA、RNA、蛋白质和线粒体DNA。单细胞基因表达分析技术具有传统技术难以相比的优势,正成为神经科学研究的重要工具。本文将介绍单细胞基因表达分析技术的操作流程、技术和方法的特点,概述其在神经科学研究中的应用,并展望其应用前景。     

Application of single-cell technology in cancer research

In this review, we have outlined the application of single-cell technology in cancer research. Single-cell technology has made encouraging progress in recent years and now provides the means to detect rare cancer cells such as circulating tumor cells and cancer stem cells. We reveal how this technology has advanced the analysis of intratumor heterogeneity and tumor epigenetics, and guided individualized treatment strategies. The future prospects now are to bring single-cell technology into the clinical arena. We believe that the clinical application of single-cell technology will be beneficial in cancer diagnostics and treatment, and ultimately improve survival in cancer patients.     

单细胞全基因组扩增技术与应用

同一组织中的细胞往往具有类似的结构和功能,然而通过对单个细胞进行测序分析后,发现每个细胞都具有一定异质性.单细胞全基因组扩增技术是进行单细胞测序的前提,该技术可用于揭示单细胞基因组结构差异,同时在肿瘤研究、发育生物学、微生物学等研究中发挥重要作用,并成为生命科学研究技术的热点之一.单细胞全基因组扩增技术的难点在于单细胞的分离和全基因组的扩增.本文介绍了单细胞全基因组扩增技术中常用的单细胞分离技术和单细胞全基因组扩增技术,并对各技术间的优缺点进行比较,同时着重讨论该技术在肿瘤研究、发育生物学和微生物学研究中的应用.     

A statistical framework for multiparameter analysis at the single-cell level

Phenotypic characterization of individual cells provides crucial insights into intercellular heterogeneity and enables access to information that is unavailable from ensemble averaged, bulk cell analyses. Single-cell studies have attracted significant interest in recent years and spurred the development of a variety of commercially available and research-grade technologies. To quantify cell-to-cell variability of cell populations, we have developed an experimental platform for real-time measurements of oxygen consumption (OC) kinetics at the single-cell level. Unique challenges inherent to these single-cell measurements arise, and no existing data analysis methodology is available to address them. Here we present a data processing and analysis method that addresses challenges encountered with this unique type of data in order to extract biologically relevant information. We applied the method to analyze OC profiles obtained with single cells of two different cell lines derived from metaplastic and dysplastic human Barrett's esophageal epithelium. In terms of method development, three main challenges were considered for this heterogeneous dynamic system: (i) high levels of noise, (ii) the lack of a priori knowledge of single-cell dynamics, and (iii) the role of intercellular variability within and across cell types. Several strategies and solutions to address each of these three challenges are presented. The features such as slopes, intercepts, breakpoint or change-point were extracted for every OC profile and compared across individual cells and cell types. The results demonstrated that the extracted features facilitated exposition of subtle differences between individual cells and their responses to cell-cell interactions. With minor modifications, this method can be used to process and analyze data from other acquisition and experimental modalities at the single-cell level, providing a valuable statistical framework for single-cell analysis.     

Ramanome technology platform for label-free screening and sorting of microbial cell factories at single-cell resolution

Phenotypic profiling of natural, engineered or synthetic cells has increasingly become a bottleneck in the mining and engineering of cell factories. Single-cell phenotyping technologies are highly promising for tackling this hurdle, yet ideally they should allow non-invasive live-cell probing, be label-free, provide landscape-like phenotyping capability, distinguish complex functions, operate with high speed, sufficient throughput and low cost, and finally, couple with cell sorting so as to enable downstream omics analysis. This review focuses on recent progress in Ramanome Technology Platform (RTP), which consists of Raman spectroscopy based phenotyping, sorting and sequencing of single cells, and discuss the key challenges and emerging trends. In addition, we propose ramanome, a collection of single-cell Raman spectra (SCRS) acquired from individual cells within a cellular population or consortium, as a new type of biological phenome datatype at the single-cell resolution. By establishing the phenome-genome links in a label-free, single-cell manner, RTP should find wide applications in functional screening and strain development of live microbial, plant and animal cell factories.     

Breaking the silence: three bHLH proteins direct cell-fate decisions during stomatal development

Stomata are microscopic pores on the surface of land plants used for gas and water vapor exchange. A pair of highly specialized guard cells surround the pore and adjust pore size. Studies in Arabidopsis have revealed that cell-cell communication is essential to coordinate the asymmetric cell divisions required for proper stomatal patterning. Initial research in this area identified signaling molecules that negatively regulate stomatal differentiation. However, genes promoting cell-fate transition leading to mature guard cells remained elusive. Now, three closely related basic helix-loop-helix (bHLH) proteins, SPEECHLESS, MUTE and FAMA have been identified as positive regulators that direct three consecutive cell-fate decisions during stomatal development. The identification of these genes opens a new direction to investigate the evolution of stomatal development and the conserved functions of bHLH proteins in cell type differentiation adopted by plants and animals.     

scHiCTools: A computational toolbox for analyzing single-cell Hi-C data

Single-cell Hi-C (scHi-C) sequencing technologies allow us to investigate three-dimensional chromatin organization at the single-cell level. However, we still need computational tools to deal with the sparsity of the contact maps from single cells and embed single cells in a lower-dimensional Euclidean space. This embedding helps us understand relationships between the cells in different dimensions, such as cell-cycle dynamics and cell differentiation. We present an open-source computational toolbox, scHiCTools, for analyzing single-cell Hi-C data comprehensively and efficiently. The toolbox provides two methods for screening single cells, three common methods for smoothing scHi-C data, three efficient methods for calculating the pairwise similarity of cells, three methods for embedding single cells, three methods for clustering cells, and a build-in function to visualize the cells embedding in a two-dimensional or three-dimensional plot. scHiCTools, written in Python3, is compatible with different platforms, including Linux, macOS, and Windows.     

Adult mouse hematopoietic stem cells: purification and single-cell assays

Mouse hematopoietic stem cells (HSCs) are the best-studied stem cells because functional assays for mouse HSCs were established earliest and purification techniques for mouse HSCs have progressed furthest. Here we describe our current protocols for the purification of CD34-/lowc-Kit+Sca-1+lineage marker- (CD34-KSL) cells, the HSC population making up approximately 0.005% of bone marrow cells in adult C557BL/6 mice. Purified HSCs have been characterized at cellular and molecular levels. Since clonal analysis is essential for the study of self-renewal and lineage commitment in HSCs, here we present our single-cell colony assay and single-cell transplantation procedures. We also introduce our immunostaining procedures for small numbers of HSCs, which are useful for signal transduction analysis. The purification of CD34-KSL cells requires approximately 6 h. Initialization of single-cell culture requires approximately 1 h. Single-cell transplantation requires approximately 6 h. Single-cell immunostaining requires approximately 2 d.     

单细胞转录组测序技术在心脏发育、疾病以及医学中的应用

单细胞转录组测序(Single-cell RNA sequencing,scRNA-seq)可以在单细胞水平描绘出每个细胞同一基因的表达量在不同细胞间的表达水平差异,使得在单细胞水平重新认识各种组织器官成为可能。目前对心脏的测序研究正从传统的普通转录组水平过渡到单细胞水平,对小鼠和人的心脏的测序陆续地发表出来。概述了scRNA-seq在心脏发育、疾病以及医学中的应用,讨论了scRNA-seq技术在胚胎心脏发育、心脏细胞的异质性以及在心脏血管方面、多能干细胞分化心血管细胞模型和先天性心脏畸形的进展和存在问题,对scRNA-seq技术对心脏发育、心脏再生、心脏病和单细胞个性化医疗等方面作出展望。