基于电感耦合等离子体质谱的单细胞分析

单细胞分析可以获得细胞在微环境中准确的个体信息,对于研究细胞的信号传导、生理病理和疾病的早期诊断等具有十分重要的意义.近年来,基于电感耦合等离子体质谱(ICP-MS)的单细胞分析方法开始得到越来越多的应用.本文综述了基于ICP-MS的单细胞分析方法及其在免疫分析、疾病诊断、药物筛选、纳米分析等方面的部分应用,并对基于ICP-MS的单细胞分析方法做出总结和展望.     

生物质谱技术在蛋白质组学研究中的应用

随着技术的进步,蛋白质组学的研究重心由最初旨在鉴定细胞或组织内基因组所表达的全部蛋白质转移到从整个蛋白质组水平上阐述包括蛋白翻译后修饰、生物大分子相互作用等反映蛋白质功能的层次。多种质谱离子化技术的突破使质谱技术成为蛋白质组学研究必不可少的手段。质谱技术联合蛋白质组学多角度、深层次探索生命系统分子本质成为现阶段生命科学研究领域的主旋律之一。本文简要综述了肽和蛋白质等生物大分子质谱分析的原理、方式和应用,并对其发展前景做出展望。     

微生物流式分选的单细胞样品制备及存活率提高的方法优化

【背景】以流式细胞技术为代表的高通量筛选技术能够高效筛选具有目标性状的微生物工程菌株。在流式分选中微生物的粘连会造成分析数据不准确，分选纯度降低，因此快速简便的单细胞样品制备是流式检测的关键。优势菌大多是通过筛选偶联荧光蛋白的随机突变库获得，阳性率低，杂质和死细胞的自发荧光较强，容易混入分选门内造成存活率降低，亟须提高分选存活率的方法。【目的】建立一种简便的微生物流式分选的单细胞样品制备方法，并通过碘化丙啶(propidium iodide, PI)染色提高分选样品存活率。【方法】分别在大肠杆菌、枯草芽孢杆菌、谷氨酸棒状杆菌和酵母菌4种底盘细胞中探索超声波、消化酶、表面活性剂及超声-表面活性剂联合作用4种方式对单细胞制备效率的影响。提高微生物流式分选存活率，用常压室温等离子诱变(atmospheric and room temperature plasma, ARTP)技术处理含有绿色荧光蛋白(green fluorescent protein, GFP)的酿酒酵母HZ848 (简称HZ848-GFP)，形成不同强度GFP文库后，按照GFP强度分选全细胞和PI染色阴性细胞的前0.5%，统计单细胞存活率。【结果】酵母细胞分散条件为：0.01% Tween-80联合超声1 min，单细胞率达到88%以上，PI染色细胞破损率＜1.4%。谷氨酸棒状杆菌单细胞分散条件为：0.01% Tween-80联合超声5 min，单细胞率达到97%以上，PI染色细胞破损率＜1%。分选存活率结果表明，未用PI染色的酿酒酵母分选后单细胞存活率是4.3%，用PI染色去除死细胞后再分选单细胞存活率是18.3%，后者是前者的4.3倍，且具有显著性差异。【结论】本研究为微生物流式分选建立了一套简单快捷的单细胞样品制备方法，证实了PI染色法能够显著提高分选样品存活率。     

基于稀疏表示算法的蛋白质质谱数据特征选择

高维、小样本数据的特征选择方法在蛋白质质谱数据处理分析领域有着广泛应用。本文针对蛋白质质谱特征选择问题,结合稀疏表示这一新理论框架,提出了一种基于稀疏表示的特征选择算法(sparse representation based feature selection,SRFS)。该方法将稀疏表示分类的结果作为评定某一个特征子空间特征相对重要性的度量,然后通过对大量随机采样子空间计算结果的统计,得到特征空间中每个特征的排序,并进一步分析提炼出与肿瘤疾病相关的若干谱峰。通过在卵巢癌公共数据集OC-WCX2a和浙江省肿瘤医院乳腺癌数据集BC-WCX2a上的实验结果表明,SRFS算法可以有效应用于本文所使用的SELDI-TOF蛋白质质谱数据的分析。     

植物单细胞转录组测序研究进展

单细胞转录组测序是一种在单细胞水平上研究基因表达的技术.多孔板法和液滴法是目前应用于植物研究的两类主要的单细胞转录组技术.首先概述了植物单细胞转录组测序的技术原理和数据分析流程,然后介绍了植物单细胞转录组的研究进展,重点阐述了单细胞转录组测序技术在鉴定植物细胞类型、揭示细胞演化轨迹和构建细胞间调控网络中的应用.单细胞转...     

单细胞测序方法研究进展

近年来,高通量测序技术(Next-generation sequencing,NGS)快速发展,已广泛应用于生命科学各个领域,但传统的混合细胞测序(Bulk cell sequencing)检测的是细胞群体的总平均反应,无法反应每个细胞的真实情况,这会影响研究者对细胞功能认知的准确性。单细胞测序技术(Single cell sequencing,sc-Seq)的出现,从一定程度上解决了传统测序固有的缺陷。单细胞测序是针对单个细胞的RNA或DNA进行测序,能够准确测出单个细胞的基因结构和表达状态,从而分析相同表型细胞的异质性。本文首先介绍单细胞测序的原理、测序类型和测序平台,有助于理解单细胞测序和在进行科研项目时设计合适的项目方案。进一步介绍单细胞转录组测序的分析流程和各种常用的分析工具或软件,并重点阐述单细胞转录组测序分析中的细胞聚类和拟时序分析的原理和研究进展,为进行单细胞转录组测序数据分析提供参考。最后,本文简述了单细胞测序研究热度、单细胞测序的应用、挑战和展望等,有助于更全面地认识单细胞测序。     

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。     

纳米二次离子质谱技术(NanoSIMS)在微生物生态学研究中的应用

超高分辨率显微镜成像技术与同位素示踪技术相结合的纳米二次离子质谱技术(NanoSIMS)具有较高的灵敏度和离子传输效率、极高的质量分辨率和空间分辨率(＜ 50 nm),代表着当今离子探针成像技术的最高水平.利用稳定性或者放射性同位素在原位或者微宇宙条件下示踪目标微生物,然后将样品进行固定、脱水、树脂包埋或者导电镀膜处理,制备成可供二次离子质谱分析的薄片,进一步通过NanoSIMS成像分析,不仅能够在单细胞水平上提供微生物的生理生态特征信息,而且能够准确识别复杂环境样品中的代谢活跃的微生物细胞及其系统分类信息,对于认识微生物介导的元素生物地球化学循环机制具有重要意义.介绍了纳米二次离子质谱技术的工作原理和技术路线,及其与同位素示踪技术、透射电子显微镜(TEM)、扫描电子显微镜(SEM)、荧光原位杂交技术(FISH)、催化报告沉积荧光原位杂交技术(CARD-FISH)、卤素原位杂交技术(Halogen In Situ Hybridization,HISH)等联合使用在微生物生态学研究方面的应用.     

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.     

生物质谱分析的研究进展及临床应用

质谱分析技术已应用于化学、化工、环境、能源、医药、运动医学、刑侦科学、生命科学、材料科学等各个领域。阐述目前生物质谱技术的类型、原理以及在医学领域中的应用,进而分析质谱技术在未来发展的前景。     

Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE

The ability to analyze multiple single-cell parameters is critical for understanding cellular heterogeneity. Despite recent advances in measurement technology, methods for analyzing high-dimensional single-cell data are often subjective, labor intensive and require prior knowledge of the biological system. To objectively uncover cellular heterogeneity from single-cell measurements, we present a versatile computational approach, spanning-tree progression analysis of density-normalized events (SPADE). We applied SPADE to flow cytometry data of mouse bone marrow and to mass cytometry data of human bone marrow. In both cases, SPADE organized cells in a hierarchy of related phenotypes that partially recapitulated well-described patterns of hematopoiesis. We demonstrate that SPADE is robust to measurement noise and to the choice of cellular markers. SPADE facilitates the analysis of cellular heterogeneity, the identification of cell types and comparison of functional markers in response to perturbations.     

Identification of biomarkers from mass spectrometry data using a "common" peak approach

Background  

Proteomic data obtained from mass spectrometry have attracted great interest for the detection of early-stage cancer. However, as mass spectrometry data are high-dimensional, identification of biomarkers is a key problem.     

Applying single-cell highly multiplexed secretome proteomics to characterize immunotherapeutic products and predict clinical responses

Genetically and phenotypically identical immune cell populations can be highly heterogenous in terms of their immune functions and protein secretion profiles. The microfluidic chip-based single-cell highly multiplexed secretome proteomics enables characterization of cellular heterogeneity of immune responses at different cellular and molecular layers. Increasing evidence has demonstrated that polyfunctional T cells that simultaneously produce 2+ proteins per cell at the single-cell level are key effector cells that contribute to the development of potent and durable cellular immunity against pathogens and cancers. The functional proteomic technology offers a wide spectrum of cellular function assessment and can uniquely define highly polyfunctional cell subsets with cytokine signatures from live individual cells. This high-dimensional single-cell analysis provides deep dissection into functional heterogeneity and helps identify predictive biomarkers and potential correlates that are crucial for immunotherapeutic product design optimization and personalized immunotherapy development to achieve better clinical outcomes.     

Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data

Clustering high-dimensional data, such as images or biological measurements, is a long-standing problem and has been studied extensively. Recently, Deep Clustering has gained popularity due to its flexibility in fitting the specific peculiarities of complex data. Here we introduce the Mixture-of-Experts Similarity Variational Autoencoder (MoE-Sim-VAE), a novel generative clustering model. The model can learn multi-modal distributions of high-dimensional data and use these to generate realistic data with high efficacy and efficiency. MoE-Sim-VAE is based on a Variational Autoencoder (VAE), where the decoder consists of a Mixture-of-Experts (MoE) architecture. This specific architecture allows for various modes of the data to be automatically learned by means of the experts. Additionally, we encourage the lower dimensional latent representation of our model to follow a Gaussian mixture distribution and to accurately represent the similarities between the data points. We assess the performance of our model on the MNIST benchmark data set and challenging real-world tasks of clustering mouse organs from single-cell RNA-sequencing measurements and defining cell subpopulations from mass cytometry (CyTOF) measurements on hundreds of different datasets. MoE-Sim-VAE exhibits superior clustering performance on all these tasks in comparison to the baselines as well as competitor methods.     

高维蛋白质波谱癌症数据特征提取

高维蛋白质波谱癌症数据分析,一直面临着高维数据的困扰。针对高维蛋白质波谱癌症数据在降维过程中的问题,提出基于小波分析技术和主成分分析技术的高维蛋白质波谱癌症数据特征提取的方法,并在特征提取之后,使用支持向量机进行分类。对8-7-02数据集进行2层小波分解时,分别使用db1、db3、db4、db6、db8、db10、haar小波基,并使用支持向量机进行分类,正确率分别达到98.18%、98.35%、98.04%、98.36%、97.89%、97.96%、98.20%。在进一步提高分类识别正确率的同时,提高了时间率。     

Cell population-based framework of genetic epidemiology in the single-cell omics era

Genetic epidemiology is a rapidly advancing field due to the recent availability of large amounts of omics data. In recent years, it has become possible to obtain omics information at the single-cell level, so genetic epidemiological models need to be updated to integrate with single-cell expression data. In this perspective paper, we propose a cell population-based framework for genetic epidemiology in the single-cell era. In this framework, genetic diversity influences phenotypic diversity through the diversity of cell population profiles, which are defined as high-dimensional probability distributions of the state spaces of biomolecules of each omics layer. We discuss how biomolecular experimental measurement data can capture the different properties of this distribution. In particular, single-cell data constitute a sample from this population distribution where only some coordinate values are observable. From a data analysis standpoint, we introduce methodology for feature extraction from cell population profiles. Finally, we discuss how this framework can be applied not only to genetic epidemiology but also to systems biology.     

An introduction to mass cytometry: fundamentals and applications

Mass cytometry addresses the analytical challenges of polychromatic flow cytometry by using metal atoms as tags rather than fluorophores and atomic mass spectrometry as the detector rather than photon optics. The many available enriched stable isotopes of the transition elements can provide up to 100 distinguishable reporting tags, which can be measured simultaneously because of the essential independence of detection provided by the mass spectrometer. We discuss the adaptation of traditional inductively coupled plasma mass spectrometry to cytometry applications. We focus on the generation of cytometry-compatible data and on approaches to unsupervised multivariate clustering analysis. Finally, we provide a high-level review of some recent benchmark reports that highlight the potential for massively multi-parameter mass cytometry.     

Charging it up: global analysis of protein phosphorylation

Protein phosphorylation affects most, if not all, cellular activities in eukaryotes and is essential for cell proliferation and development. An estimated 30% of cellular proteins are phosphorylated, representing the phosphoproteome, and phosphorylation can alter a protein's function, activity, localization and stability. Recent studies for large-scale identification of phosphosites using mass spectrometry are revealing the components of the phosphoproteome. The development of new tools, such as kinase assays using modified kinases or protein microarrays, enables rapid kinase substrate identification. The dynamics of specific phosphorylation events can now be monitored using mass spectrometry, single-cell analysis of flow cytometry, or fluorescent reporters. Together, these techniques are beginning to elucidate cellular processes and pathways regulated by phosphorylation, in addition to global regulatory networks.     

Molecular imaging of c-Met tyrosine kinase activity

Fluorescent flow cytometry has become the method of choice for interrogation of bacterial populations at the single-cell level. However, limitations of this technique include issues of dynamic range, spectral overlap, photobleaching, and overall low signal intensity due to the small size of bacteria. The recent development of mass cytometry allows single-cell analysis with the resolution of inductively coupled plasma mass spectrometry, facilitating multiparametric analysis. Using a combination of a metal-based membrane stain and lectins conjugated to lanthanide-chelating polymers, we demonstrate that individual Escherichia coli cells can be differentiated based on their cell surface polysaccharides using mass cytometry. The model E. coli system involves evaluation of three different surface polysaccharides using element-tagged concanavalin A and wheat germ agglutinin lectins. Finally, this technique enabled experiments designed to follow the export of O-antigen substituted lipopolysaccharide in a conditional mutant. These studies revealed that the culture responds as a uniform population and that lipopolysaccharide export is approximately 10 times faster than the logarithmic bacterial doubling time.