Single Cell mass spectrometry: Towards quantification of small molecules in individual cells.

Studying cell heterogeneity can provide a deeper understanding of biological activities, but appropriate studies cannot be performed using traditional bulk analysis methods. The development of diverse single cell bioanalysis methods is in urgent need and of great significance. Mass spectrometry (MS) has been recognized as a powerful technique for bioanalysis for its high sensitivity, wide applicability, label-free detection, and capability for quantitative analysis. In this review, the general development of single cell mass spectrometry (SCMS) field is covered. First, multiple existing SCMS techniques are described and compared. Next, the development of SCMS field is discussed in a chronological order. Last, the latest quantification studies on small molecules using SCMS have been described in detail.

Anticancer Drug Affects Metabolomic Profiles in Multicellular Spheroids: Studies Using Mass Spectrometry Imaging Combined with Machine Learning.

Multicellular spheroids (hereinafter referred to as spheroids) are 3D biological models. The metabolomic profiles inside spheroids provide crucial information reflecting the molecular phenotypes and microenvironment of cells. To study the influence of an anticancer drug on the spatially resolved metabolites, spheroids were cultured using HCT-116 colorectal cancer cells, treated with the anticancer drug Irinotecan under a series of time- and concentration-dependent conditions. The Single-probe mass spectrometry imaging (MSI) technique was utilized to conduct the experiments. The MSI data were analyzed using advanced data analysis methods to efficiently extract metabolomic information. Multivariate curve resolution alternating least square (MCR-ALS) was used to decompose each MS image into different components with grouped species. To improve the efficiency of data analysis, both supervised (Random Forest) and unsupervised (cluster large applications (CLARA)) machine learning (ML) methods were employed to cluster MS images according to their metabolomic features. Our results indicate that anticancer drug significantly affected the abundances of a variety of metabolites in different regions of spheroids. This integrated experiment and data analysis approach can facilitate the studies of metabolites in different types of 3D tumor models and tissues and potentially benefit the drug discovery, therapeutic resistance, and other biological research fields.

Multimodal Imaging of Amyloid Plaques: Fusion of the Single-Probe Mass Spectrometry Image and Fluorescence Microscopy Image.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. The formation of amyloid plaques by aggregated amyloid beta (Aß) peptides is a primary event in AD pathology. Understanding the metabolomic features and related pathways is critical for studying plaque-related pathological events (e.g., cell death and neuron dysfunction). Mass spectrometry imaging (MSI), due to its high sensitivity and ability to obtain the spatial distribution of metabolites, has been applied to AD studies. However, limited studies of metabolites in amyloid plaques have been performed due to the drawbacks of the commonly used techniques such as matrix-assisted laser desorption/ionization MSI. In the current study, we obtained high spatial resolution (∼17 µm) MS images of the AD mouse brain using the Single-probe, a microscale sampling and ionization device, coupled to a mass spectrometer under ambient conditions. The adjacent slices were used to obtain fluorescence microscopy images to locate amyloid plaques. The MS image and the fluorescence microscopy image were fused to spatially correlate histological protein hallmarks with metabolomic features. The fused images produced significantly improved spatial resolution (∼5 µm), allowing for the determination of fine structures in MS images and metabolomic biomarkers representing amyloid plaques.

MassLite: An integrated python platform for single cell mass spectrometry metabolomics data pretreatment with graphical user interface and advanced peak alignment method.

Mass spectrometry (MS) has been one of the most widely used tools for bioanalytical analysis due to its high sensitivity, capability of quantitative analysis, and compatibility with biomolecules. Among various MS techniques, single cell mass spectrometry (SCMS) is an advanced approach to molecular analysis of cellular contents in individual cells. In tandem with the creation of novel experimental techniques, the development of new SCMS data analysis tools is equally important. As most published software packages are not specifically designed for pretreatment of SCMS data, including peak alignment and background removal, their applicability on processing SCMS data is generally limited. Hereby we introduce a Python platform, MassLite, specifically designed for rapid SCMS metabolomics data pretreatment. This platform is made user-friendly with graphical user interface (GUI) and exports data in the forms of each individual cell for further analysis. A core function of this tool is to use a novel peak alignment method that avoids the intrinsic drawbacks of traditional binning method, allowing for more effective handling of MS data obtained from high resolution mass spectrometers. Other functions, such as void scan filtering, dynamic grouping, and advanced background removal, are also implemented in this tool to improve pretreatment efficiency.

Extract Metabolomic Information from Mass Spectrometry Images Using Advanced Data Analysis.

Mass spectrometry imaging (MSI) data generally contains large sizes and high-dimensional structures due to their inherent complex chemical and spatial information. A variety of data analysis methods have been developed to comprehensively analyze the MSI experimental results and extract essential information. Here, we describe the protocols of data preprocessing and emerging methods for data analyses, including multivariate analysis, machine learning, and image fusion, that have been applied to the data generated from the Single-probe MSI technique. These strategies and methods can be potentially applied to handling data produced from other MSI techniques.

Constructing Spin-Adiabatic States for the Modeling of Spin-Crossing Reactions. I. A Shared-Orbital Implementation.

In the modeling of spin-crossing reactions, it has become popular to directly explore the spin-adiabatic surfaces. Specifically, through constructing spin-adiabatic states from a two-state Hamiltonian (with spin-orbit coupling matrix elements) at each geometry, one can readily employ advanced geometry optimization algorithms to acquire a "transition state" structure, where the spin crossing occurs. In this work, we report the implementation of a fully-variational spin-adiabatic approach based on Kohn-Sham density functional theory spin states (sharing the same set of molecular orbitals) and the Breit-Pauli one-electron spin-orbit operator. For three model spin-crossing reactions [predissociation of N2O, singlet-triplet conversion in CH2, and CO addition to Fe(CO)4], the spin-crossing points were obtained. Our results also indicated the Breit-Pauli one-electron spin-orbit coupling can vary significantly along the reaction pathway on the spin-adiabatic energy surface. On the other hand, due to the restriction that low-spin and high-spin states share the same set of molecular orbitals, the acquired spin-adiabatic energy surface shows a cusp (i.e. a first-order discontinuity) at the crossing point, which prevents the use of standard geometry optimization algorithms to pinpoint the crossing point. An extension with this restriction removed is being developed to achieve the smoothness of spin-adiabatic surfaces.

Forward-Osmosis Desalination with Poly(Ionic Liquid) Hydrogels as Smart Draw Agents.

The combination of high desalination efficiency, negligible draw-solute leakage, nontoxicity, ease of regeneration, and effective separation to produce liquid water makes the smart draw agents developed here highly suited for forward-osmosis desalination.

[Experimental study on action of acupoint embedding thread on ANCA in the rat of ulcerative colitis].

OBJECTIVE: To observe therapeutic effect of acupoint embedding thread therapy on ulcerative colitis and to study the mechanism. METHODS: The rat model of ulcerative colitis was developed with 2,4-dinitrochlorobenzene method. The 20th day after the end of model development, they were randomly divided into a model group, a salicylazosulfapyridine (SASP) treatment group and an acupoint embedding thread therapy group. The SASP treatment group were treated with intragastrical perfusion of 4.5% SASP suspension, and the acupoint embedding thread therapy group were treated with embedding thread at "Zusanli" (ST 36) and "Shangjuxu" (ST 37). Anti-neutrophil cytoplasmic antibody (ANCA) was detected with indirect immunofluorescence, and the pathological changes were investigated with pathological tissue section technique. RESULTS: The positive rate of ANCA reached to 62.5% in the rat with ulcerative colitis with significant differences among the 3 groups. After treatment, the positive rate of ANCA was decreased in the two treatment groups (P < 0.05, P < 0.01). CONCLUSION: Acupoint embedding thread therapy has definite therapeutic effect on ulcerative colitis and the mechanism is possibly related with the decrease of ANCA.