Mass spectrometry techniques for imaging and detection of metallodrugs

Undoubtedly, metallomic approaches based on mass spectrometry have evolved into essential tools supporting the drug development of novel metal-based anticancer drugs. This article will comment on the state-of-the-art instrumentation and highlight some of the recent analytical advances beyond routine, especially focusing on the latest developments in inductively coupled plasma-mass spectrometry (ICP-MS). Mass spectrometry-based bioimaging and single-cell methods will be presented, paving the way to exciting investigations of metal-based anticancer drugs in heterogeneous and structurally, as well as functionally complex solid tumor tissues.     

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.     

Single-cell analysis of tumors: Creating new value for molecular biomarker discovery of cancer stem cells and tumor-infiltrating immune cells

Biomarker-driven individualized treatment in oncology has made tremendous progress through technological developments, new therapeutic modalities and a deeper understanding of the molecular biology for tumors, cancer stem cells and tumor-infiltrating immune cells. Recent technical developments have led to the establishment of a variety of cancer-related diagnostic, prognostic and predictive biomarkers. In this regard, different modern OMICs approaches were assessed in order to categorize and classify prognostically different forms of neoplasia. Despite those technical advancements, the extent of molecular heterogeneity at the individual cell level in human tumors remains largely uncharacterized. Each tumor consists of a mixture of heterogeneous cell types. Therefore, it is important to quantify the dynamic cellular variations in order to predict clinical parameters, such as a response to treatment and or potential for disease recurrence. Recently, single-cell based methods have been developed to characterize the heterogeneity in seemingly homogenous cancer cell populations prior to and during treatment. In this review, we highlight the recent advances for single-cell analysis and discuss the challenges and prospects for molecular characterization of cancer cells, cancer stem cells and tumor-infiltrating immune cells.     

Mass spectrometry imaging and profiling of single cells

Mass spectrometry imaging and profiling of individual cells and subcellular structures provide unique analytical capabilities for biological and biomedical research, including determination of the biochemical heterogeneity of cellular populations and intracellular localization of pharmaceuticals. Two mass spectrometry technologies-secondary ion mass spectrometry (SIMS) and matrix assisted laser desorption/ionization mass spectrometry (MALDI MS)-are most often used in micro-bioanalytical investigations. Recent advances in ion probe technologies have increased the dynamic range and sensitivity of analyte detection by SIMS, allowing two- and three-dimensional localization of analytes in a variety of cells. SIMS operating in the mass spectrometry imaging (MSI) mode can routinely reach spatial resolutions at the submicron level; therefore, it is frequently used in studies of the chemical composition of subcellular structures. MALDI MS offers a large mass range and high sensitivity of analyte detection. It has been successfully applied in a variety of single-cell and organelle profiling studies. Innovative instrumentation such as scanning microprobe MALDI and mass microscope spectrometers enables new subcellular MSI measurements. Other approaches for MS-based chemical imaging and profiling include those based on near-field laser ablation and inductively-coupled plasma MS analysis, which offer complementary capabilities for subcellular chemical imaging and profiling.     

Recent advances of single-cell RNA sequencing technology in mesenchymal stem cell research

Mesenchymal stem cells (MSCs) are multipotent stromal cells with great potential for clinical applications. However, little is known about their cell heterogeneity at a single-cell resolution, which severely impedes the development of MSC therapy. In this review, we focus on advances in the identification of novel surface markers and functional subpopulations of MSCs made by single-cell RNA sequencing and discuss their participation in the pathophysiology of stem cells and related diseases. The challenges and future directions of single-cell RNA sequencing in MSCs are also addressed in this review.     

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.     

Recent advances in mammalian reproductive biology

Reproductive biology is a uniquely important topic since it is about germ cells, which are central for transmitting genetic information from generation to generation. In this review, we discuss recent advances in mammalian germ cell development,including preimplantation development, fetal germ cell development and postnatal development of oocytes and sperm. We also discuss the etiologies of female and male infertility and describe the emerging technologies for studying reproductive biology such as gene editing and single-cell technologies.     

Single-Cell Gene Expression Analysis: Implications for Neurodegenerative and Neuropsychiatric Disorders

Technical and experimental advances in microaspiration techniques, RNA amplification, quantitative real-time polymerase chain reaction (qPCR), and cDNA microarray analysis have led to an increase in the number of studies of single-cell gene expression. In particular, the central nervous system (CNS) is an ideal structure to apply single-cell gene expression paradigms. Unlike an organ that is composed of one principal cell type, the brain contains a constellation of neuronal and noneuronal populations of cells. A goal is to sample gene expression from similar cell types within a defined region without potential contamination by expression profiles of adjacent neuronal subpopulations and noneuronal cells. The unprecedented resolution afforded by single-cell RNA analysis in combination with cDNA microarrays and qPCR-based analyses allows for relative gene expression level comparisons across cell types under different experimental conditions and disease states. The ability to analyze single cells is an important distinction from global and regional assessments of mRNA expression and can be applied to optimally prepared tissues from animal models as well as postmortem human brain tissues. This focused review illustrates the potential power of single-cell gene expression studies within the CNS in relation to neurodegenerative and neuropsychiatric disorders such as Alzheimer's disease (AD) and schizophrenia, respectively.     

Advances in higher-order chromatin architecture: the move towards 4D genome

In eukaryotes, the genome is hierarchically packed inside the nucleus, which facilitates physical contact between cis-regulatory elements (CREs), such as enhancers and promoters. Accumulating evidence highlights the critical role of higher-order chromatin structure in precise regulation of spatiotemporal gene expression under diverse biological contexts including lineage commitment and cell activation by external stimulus. Genomics and imaging-based technologies, such as Hi-C and DNA fluorescence in situ hybridization (FISH), have revealed the key principles of genome folding, while newly developed tools focus on improvement in resolution, throughput and modality at single-cell and population levels, and challenge the knowledge obtained through conventional approaches. In this review, we discuss recent advances in our understanding of principles of higher-order chromosome conformation and technologies to investigate 4D chromatin interactions.     

Toward mechanical manipulations of cell membranes and membrane proteins using an atomic force microscope

Recent advances in the use of the atomic force microscope (AFM) for manipulating cell membranes and membrane proteins are reviewed. Early pioneering work on measurements of the magnitude of the force required to create indentations with defined depth on their surfaces and to separate interacting pairs of avidin-biotin, antigen-antibody, and complementary DNA pairs formed the basis of this field. The method has subsequently been applied to map the presence of cell surface receptors and polysaccharides on live cell membranes by force measurement, with promising results. Attempts to extract phospholipids and proteins from lipid bilayers and live cell surfaces have been reported, providing a new tool for the manipulation of cellular activities and biochemical analysis at the single-cell level. An increasing awareness of the effect of the pulling speed (nm/s or microm/s), or more accurately, the force loading rate (pN/s or nN/s) on the magnitude of the rupture force, has led researchers to construct energy diagrams of rupture events based on the parameters available from such studies. Information on such nature of the interplay of force and loading rate is vital for nanomanipulation of living cells and cell membranes. Some relevant work for membrane manipulation using other methods is also reviewed in relation to AFM-based methodology.     

Flexible copula model for integrating correlated multi-omics data from single-cell experiments

With recent advances in technologies to profile multi-omics data at the single-cell level, integrative multi-omics data analysis has been increasingly popular. It is increasingly common that information such as methylation changes, chromatin accessibility, and gene expression are jointly collected in a single-cell experiment. In biomedical studies, it is often of interest to study the associations between various data types and to examine how these associations might change according to other factors such as cell types and gene regulatory components. However, since each data type usually has a distinct marginal distribution, joint analysis of these changes of associations using multi-omics data is statistically challenging. In this paper, we propose a flexible copula-based framework to model covariate-dependent correlation structures independent of their marginals. In addition, the proposed approach could jointly combine a wide variety of univariate marginal distributions, either discrete or continuous, including the class of zero-inflated distributions. The performance of the proposed framework is demonstrated through a series of simulation studies. Finally, it is applied to a set of experimental data to investigate the dynamic relationship between single-cell RNA sequencing, chromatin accessibility, and DNA methylation at different germ layers during mouse gastrulation.     

Neue Verfahren für Einzelzellanalysen in Forschung und Diagnostik

Traditional cytogenetics is a paradigm for single-cell diagnostics; after a banding procedure, each metaphase examined represents the analysis of an entire genome of a cell, albeit at a low resolution. For several decades, this single-cell character has represented an important distinction in molecular genetics technologies, which are mostly based on DNA or RNA extracted from hundreds or thousands of cells. However, many essential questions can be addressed only by analyzing cells on the level of fewer or single cells. In the last few years, new single-cell techniques have been developed with the aim to simultaneously examine more regions with improved resolution. In this overview we summarize the most important recent developments and changes.     

Population Model of Quorum Sensing with Multiple Parallel Pathways

Quorum sensing (QS) is a bacterial communication mechanism that uses signal-receptor binding to regulate gene expression based on cell density, resulting in group behaviors such as biofilm formation, bioluminescence and stress response. In certain bacterial species such as Vibrio harveyi, several parallel QS signaling pathways drive a single phosphorylation–dephosphorylation cycle, which in turn regulates QS target genes. In this paper, we investigate the possible role of parallel signaling pathways by developing a mathematical model of QS in V. harveyi at both the single-cell and population levels. First we explore how signal integration may be achieved at the single-cell level, and how different model parameters influence the process. We then consider two examples of signal integration at the population level: a one-population model responding to two environmental cues (cell density and mass transfer), and a two-population model with distinct cell densities. In each case, we use contraction analysis to reduce the population model to an effective single-cell model.     

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.     

Clean Chemistry for Elemental Impurities Analysis of Pharmaceuticals in Compliance with USP 232

United States Pharmacopeia updated its 100 years old metal analysis method with inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma optical emission spectrometry (ICP-OES). These sensitive instruments require that sample preparation be at least as sophisticated as the instrumentation used in the analysis. Sample contamination during sample preparation has to be controlled to an acceptable level given the low detection limit of these instruments and the ubiquitous presence of elements. This article focused on sample contamination during sample preparation. Contaminations from environment, reagents, and lab apparatus were investigated for their impact on trace element analysis. Advice on clean lab practice was offered to the pharmaceutical industry in regard to contamination control in elemental analysis labs at a time when the industry is preparing for compliance with elemental impurities in drug products.     

Characterizing peptides in individual mammalian cells using mass spectrometry

Cell-to-cell chemical signaling plays multiple roles in coordinating the activity of the functional elements of an organism, with these elements ranging from a three-neuron reflex circuit to the entire animal. In recent years, single-cell mass spectrometry (MS) has enabled the discovery of cell-to-cell signaling molecules from the nervous system of a number of invertebrates. We describe a protocol for analyzing individual cells from rat pituitary using matrix-assisted laser desorption/ionization MS. Each step in the sample preparation process, including cell stabilization, isolation, sample preparation, signal acquisition and data interpretation, is detailed here. Although we employ this method to investigate peptides in individual pituitary cells, it can be adapted to other cell types and even subcellular sections from a range of animals. This protocol allows one to obtain 20-30 individual cell samples and acquire mass spectra from them in a single day.     

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.