Metabolomic Fingerprints of Individual Algal Cells Using the Single-Probe Mass Spectrometry Technique

Competition in the Periphytic Algal Community during the Colonization Process: Evidence from the World's Largest Water Diversion Project

Periphytic algal colonization is common in aquatic systems, but its interspecific competition remains poorly understood. In order to fill the gap, the process of periphytic algal colonization in the Middle Route of the South to North Water Diversion Project was studied. The results showed that the process was divided into three stages: the initial colonization stage (T1, 3-6 days), community formation stage (T2, 12-18 days) and primary succession stage (T3, 24-27 days). In T1, the dominant species were Diatoma vulgaris (Bory), Navicula phyllepta (Kützing) and Fragilaria amphicephaloides (Lange-Bertalot) belonging to Heterokontophyta; these species boasted wide niche widths (NWs), low niche overlap (NO) and low ecological response rates (ERRs). In T2, the dominant species were Diatoma vulgaris, Cymbella affinis (Kützing), Navicula phyllepta, Fragilaria amphicephaloides, Gogorevia exilis (Kützing), Melosira varians (C.Agardh), Phormidium willei (N.L.Gardner) and Cladophora rivularis (Kuntze). These species displayed wider NWs, lower NO, and lower ERRs than those in T1. In T3, the dominant species were Diatoma vulgaris, Cymbella affinis, Navicula phyllepta, Fragilaria amphicephaloides, Achnanthes exigu (Grunow), etc. Among them, Heterokontophyta such as Diatoma vulgaris and Cymbella affinis had a competitive advantage based on NWs and ERRs. Cyanobacteria like Phormidium willei lost their dominant status due to the narrower NW and the increased NO. It could be concluded the interspecific competition became fiercer and shaped the colonization process; this study will be helpful in understanding the colonization of periphytic algal communities.

A critical review of machine-learning for "multi-omics" marine metabolite datasets

During the last decade, genomic, transcriptomic, proteomic, metabolomic, and other omics datasets have been generated for a wide range of marine organisms, and even more are still on the way. Marine organisms possess unique and diverse biosynthetic pathways contributing to the synthesis of novel secondary metabolites with significant bioactivities. As marine organisms have a greater tendency to adapt to stressed environmental conditions, the chance to identify novel bioactive metabolites with potential biotechnological application is very high. This review presents a comprehensive overview of the available "-omics" and "multi-omics" approaches employed for characterizing marine metabolites along with novel data integration tools. The need for the development of machine-learning algorithms for "multi-omics" approaches is briefly discussed. In addition, the challenges involved in the analysis of "multi-omics" data and recommendations for conducting "multi-omics" study were discussed.

Quantifying Cell Heterogeneity and Subpopulations Using Single Cell Metabolomics

Mass spectrometry (MS) has become an indispensable tool for metabolomics studies. However, due to the lack of applicable experimental platforms, suitable algorithm, software, and quantitative analyses of cell heterogeneity and subpopulations, investigating global metabolomics profiling at the single cell level remains challenging. We combined the Single-probe single cell MS (SCMS) experimental technique with a bioinformatics software package, SinCHet-MS (Single Cell Heterogeneity for Mass Spectrometry), to characterize changes of tumor heterogeneity, quantify cell subpopulations, and prioritize the metabolite biomarkers of each subpopulation. As proof of principle studies, two melanoma cancer cell lines, the primary (WM115; with a lower drug resistance) and the metastatic (WM266-4; with a higher drug resistance), were used as models. Our results indicate that after the treatment of the anticancer drug vemurafenib, a new subpopulation emerged in WM115 cells, while the proportion of the existing subpopulations was changed in the WM266-4 cells. In addition, metabolites for each subpopulation can be prioritized. Combining the SCMS experimental technique with a bioinformatics tool, our label-free approach can be applied to quantitatively study cell heterogeneity, prioritize markers for further investigation, and improve the understanding of cell metabolism in human diseases and response to therapy.

FACS-assisted single-cell lipidome analysis of phosphatidylcholines and sphingomyelins in cells of different lineages

Recent advances in single-cell genomics and transcriptomics technologies have transformed our understanding of cellular heterogeneity in growth, development, ageing, and disease; however, methods for single-cell lipidomics have comparatively lagged behind in development. We have developed a method for the detection and quantification of a wide range of phosphatidylcholine and sphingomyelin species from single cells that combines fluorescence-assisted cell sorting with automated chip-based nanoESI and shotgun lipidomics. We show herein that our method is capable of quantifying more than 50 different phosphatidylcholine and sphingomyelin species from single cells and can easily distinguish between cells of different lineages or cells treated with exogenous fatty acids. Moreover, our method can detect more subtle differences in the lipidome between cell lines of the same cancer type. Our approach can be run in parallel with other single-cell technologies to deliver near-complete, high-throughput multi-omics data on cells with a similar phenotype and has the capacity to significantly advance our current knowledge on cellular heterogeneity.

Mass spectrometry-based strategies for single-cell metabolomics

Single cell analysis has drawn increasing interest from the research community due to its capability to interrogate cellular heterogeneity, allowing refined tissue classification and facilitating novel biomarker discovery. With the advancement of relevant instruments and techniques, it is now possible to perform multiple omics including genomics, transcriptomics, metabolomics or even proteomics at single cell level. In comparison with other omics studies, single-cell metabolomics (SCM) represents a significant challenge since it involves many types of dynamically changing compounds with a wide range of concentrations. In addition, metabolites cannot be amplified. Although difficult, considerable progress has been made over the past decade in mass spectrometry (MS)-based SCM in terms of processing technologies and biochemical applications. In this review, we will summarize recent progress in the development of promising MS platforms, sample preparation methods and SCM analysis of various cell types (including plant cell, cancer cell, neuron, embryo cell, and yeast cell). Current limitations and future research directions in the field of SCM will also be discussed.

Microalgal Carotenoids: Therapeutic Application and Latest Approaches to Enhance the Production

Microalgae are microscopic photosynthetic organisms frequently found in fresh and marine water ecosystems. Various microalgal species have been considered a reservoir of diverse health-value products, including vitamins, proteins, lipids, and polysaccharides, and are broadly utilized as food and for the treatment of human ailments such as cancer, cardiovascular diseases, allergies, and immunodeficiency. Microalgae-derived carotenoids are the type of accessory pigment that possess light-absorbing potential and play a significant role in metabolic functions. To date, nearly a thousand carotenoids have been reported, but a very less number of microalgae have been used for the commercial production of carotenoids. This review article briefly discussed the carotenoids of microalgal origin and their therapeutic application. In addition, we have briefly compiled the optimization of culture parameters used to enhance microalgal carotenoid production. In addition, the latest biotechnological approaches used to improve the yields of carotenoid has also been discussed.

Single-Cell Mass Spectrometry Enables Insight into Heterogeneity in Infectious Disease

Cellular heterogeneity is generally overlooked in infectious diseases. In this study, we investigated host cell heterogeneity during infection with Trypanosoma cruzi (T. cruzi) parasites, causative agents of Chagas disease (CD). In chronic-stage CD, only a few host cells are infected with a large load of parasites and symptoms may appear at sites distal to parasite colonization. Furthermore, recent work has revealed T. cruzi heterogeneity with regard to replication rates and drug susceptibility. However, the role of cellular-level metabolic heterogeneity in these processes has yet to be assessed. To fill this knowledge gap, we developed a Single-probe SCMS (single-cell mass spectrometry) method compatible with biosafety protocols, to acquire metabolomics data from individual cells during T. cruzi infection. This study revealed heterogeneity in the metabolic response of the host cells to T. cruzi infection in vitro. Our results showed that parasite-infected cells possessed divergent metabolism compared to control cells. Strikingly, some uninfected cells adjacent to infected cells showed metabolic impacts as well. Specific metabolic changes include increases in glycerophospholipids with infection. These results provide novel insight into the pathogenesis of CD. Furthermore, they represent the first application of bioanalytical SCMS to the study of mammalian-infectious agents, with the potential for broad applications to study infectious diseases.

Single-cell sequencing: expansion, integration and translation

With the rapid advancement in sequencing technologies, the concept of omics has revolutionized our understanding of cellular behaviors. Conventional omics investigation approaches measure the averaged behaviors of multiple cells, which may easily hide signals represented by a small-cell cohort, urging for the development of techniques with enhanced resolution. Single-cell RNA sequencing, investigating cell transcriptomics at the resolution of a single cell, has been rapidly expanded to investigate other omics such as genomics, proteomics and metabolomics since its invention. The requirement for comprehensive understanding of complex cellular behavior has led to the integration of multi-omics and single-cell sequencing data with other layers of information such as spatial data and the CRISPR screening technique towards gained knowledge or innovative functionalities. The development of single-cell sequencing in both dimensions has rendered it a unique field that offers us a versatile toolbox to delineate complex diseases, including cancers.

Single cell mass spectrometry studies reveal metabolomic features and potential mechanisms of drug-resistant cancer cell lines

Irinotecan (Iri) is a key drug to treat metastatic colorectal cancer, but its clinical activity is often limited by de novo and acquired drug resistance. Studying the underlying mechanisms of drug resistance is necessary for developing novel therapeutic strategies. In this study, we used both regular and irinotecan-resistant (Iri-resistant) colorectal cell lines as models, and performed single cell mass spectrometry (SCMS) metabolomics studies combined with analyses from cytotoxicity assay, western blot, flow cytometry, quantitative real-time polymerase chain reaction (qPCR), and reactive oxygen species (ROS). Our SCMS results indicate that Iri-resistant cancer cells possess higher levels of unsaturated lipids compared with the regular cancer cells. In addition, multiple protein biomarkers and their corresponding mRNAs of colon cancer stem cells are overexpressed in Iri-resistance cells. Particularly, stearoyl-CoA desaturase 1 (SCD1) is upregulated with the development of drug resistance in Iri-resistant cells, whereas inhibiting the activity of SCD1 efficiently increase their sensitivity to Iri treatment. In addition, we demonstrated that SCD1 directly regulates the expression of ALDH1A1, which contributes to the cancer stemness and ROS level in Iri-resistant cell lines.

Global metabolome analysis of Dunaliella tertiolecta, Phaeobacter italicus R11 Co-cultures using thermal desorption - Comprehensive two-dimensional gas chromatography - Time-of-flight mass spectrometry (TD-GC×GC-TOFMS)

Dunaliella tertiolecta is a marine microalgae that has been studied extensively as a potential carbon-neutral biofuel source (Tang et al., 2011). Microalgae oil contains high quantities of energy-rich fatty acids and lipids, but is not yet commercially viable as an alternative fuel. Carefully optimised growth conditions, and more recently, algal-bacterial co-cultures have been explored as a way of improving the yield of D. tertiolecta microalgae oils. The relationship between the host microalgae and bacterial co-cultures is currently poorly understood. Here, a complete workflow is proposed to analyse the global metabolomic profile of co-cultured D. tertiolectra and Phaeobacter italicus R11, which will enable researchers to explore the chemical nature of this relationship in more detail. To the best of the authors' knowledge this study is one of the first of its kind, in which a pipeline for an entirely untargeted analysis of the algal metabolome is proposed using a practical sample preparation, introduction, and data analysis routine.

The liquid micro junction-surface sampling probe (LMJ-SSP); a versatile ambient mass spectrometry interface

Ambient ionization methods have become important tools in mass spectrometry. The LMJ-SSP can significantly simplify/reduce lengthy sample preparation requirements associated with mass spectrometry analysis. Samples may be introduced through direct contact, insertion and droplet injection, enabling applications from drug discovery and surface analysis to tissue profiling and metabolic mapping. This review examines the underlying principles associated with the LMJ-SSP interface and highlights modifications of the original design that have extended its capability. We summarize different application areas that have exploited the method and describe potential future directions for the adaptable ambient ionization source.

Omics for the Improvement of Abiotic, Biotic, and Agronomic Traits in Major Cereal Crops: Applications, Challenges, and Prospects

Omics technologies, namely genomics, transcriptomics, proteomics, metabolomics, and phenomics, are becoming an integral part of virtually every commercial cereal crop breeding program, as they provide substantial dividends per unit time in both pre-breeding and breeding phases. Continuous advances in omics assure time efficiency and cost benefits to improve cereal crops. This review provides a comprehensive overview of the established omics methods in five major cereals, namely rice, sorghum, maize, barley, and bread wheat. We cover the evolution of technologies in each omics section independently and concentrate on their use to improve economically important agronomic as well as biotic and abiotic stress-related traits. Advancements in the (1) identification, mapping, and sequencing of molecular/structural variants; (2) high-density transcriptomics data to study gene expression patterns; (3) global and targeted proteome profiling to study protein structure and interaction; (4) metabolomic profiling to quantify organ-level, small-density metabolites, and their composition; and (5) high-resolution, high-throughput, image-based phenomics approaches are surveyed in this review.

The limitless applications of single-cell metabolomics

Single-cell metabolomics (SCM) is currently one of the most powerful tools for performing high-throughput metabolic analysis at the cellular level. The power of single-cell metabolomics to determine the metabolic profiles of individual cells makes it very suitable for decoding cell heterogeneity. SCM bears great potential in cell type identification and differentiation within cell colonies. With the development of various equipment and techniques, SCM analysis has become possible for a wide range of biological samples. Many fields have incorporated this cutting-edge analytic tool to generate fruitful findings. This review article pays close attention to the prevalent techniques utilized in SCM and the exciting new findings and applications developed by studies in phytology, neurology, and oncology using SCM.

Transcriptome and metabolome analyses of cold and darkness-induced pellicle cysts of Scrippsiella trochoidea

BACKGROUND

Dinoflagellates are a group of unicellular organisms that are a major component of aquatic eukaryotes and important contributors to marine primary production. Nevertheless, many dinoflagellates are considered harmful algal bloom (HAB) species due to their detrimental environmental and human health impacts. Cyst formation is widely perceived as an adaptive strategy of cyst-forming dinoflagellates in response to adverse environmental conditions. Dinoflagellate cysts play critical roles in bloom dynamics. However, our insight into the underlying molecular basis of encystment is still limited. To investigate the molecular processes regulating encystment in dinoflagellates, transcriptome and metabolome investigations were performed on cold and darkness-induced pellicle cysts of Scrippsiella trochoidea.

RESULTS

No significant transcriptional response was observed at 2 h; however, massive transcriptome and metabolome reprogramming occurred at 5 h and in pellicle cysts. The gene-to-metabolite network demonstrated that the initial transformation from vegetative cells into pellicle cysts was highly energy demanding through the activation of catabolism, including glycolysis, β-oxidation, TCA cycle and oxidative phosphorylation, to cope with cold-darkness-induced stress. However, after transformation into pellicle cysts, the metabolism was greatly reduced, and various sugars, polyunsaturated fatty acids and amino acids accumulated to prolong survival. The identification of 56 differentially expressed genes (DEGs) related to signal transduction indicated that S. trochoidea received a cold-darkness signal that activated multiple signal transduction pathways, leading to encystment. The elevated expression of genes encoding enzymes involved in ROS stress suggested that pellicle cysts respond to increased oxidative stress. Several cell cycle-related genes were repressed. Intriguingly, 11 DEGs associated with sexual reproduction suggested that pellicle cysts (or some portion thereof) may be a product of sexual reproduction.

CONCLUSIONS

This study provides the first transcriptome and metabolome analyses conducted during the encystment of S. trochoidea, an event that requires complex regulatory mechanisms and impacts on population dynamics. The results reveal comprehensive molecular regulatory processes underlying life cycle regulation in dinoflagellates involving signal transduction, gene expression and metabolite profile, which will improve our ability to understand and monitor dinoflagellate blooms.

Susceptibility of phytoplankton to the increasing presence of active pharmaceutical ingredients (APIs) in the aquatic environment: A review

Human and veterinary pharmaceuticals either in the form of un-metabolized, incompletely metabolized, and metabolized drugs are increasingly present in aquatic ecosystems. These active pharmaceutical ingredients from pharmaceutical industries, hospitals, agricultural, and domestic discharges find their way into water systems - where they adversely affect non-target organisms like phytoplankton. Different aspects of phytoplankton life; ranging from growth, reproduction, morphology, physiology, biochemical composition, oxidative response, proteomics, and transcriptomics are altered by pharmaceuticals. This review discusses the currently available information on the susceptibility of phytoplankton to the ever-increasing presence of pharmaceutical products in the aquatic environment by focusing on the effect of APIs on the physiology, metabolome, and proteome profiles of phytoplankton. We also highlight gaps in literature concerning the salient underlining biochemical interactions between phytoplankton communities and pharmaceuticals that require an in-depth investigation. This is all in a bid to understand the imminent dangers of the contamination of water bodies with pharmaceutical products and how this process unfavorably affects aquatic food webs.

Single cell metabolomics using mass spectrometry: Techniques and data analysis

Mass spectrometry (MS) based techniques are gaining popularity for metabolomics research due to their high sensitivity, wide detection range, and capability of molecular identification. Utilizing such powerful technique to explore the cellular metabolism at the single cell level not only appreciates the subtle cell-to-cell difference (i.e., cell heterogeneity), but also gains biological merits corresponding to individual cells or small cell subpopulations. In this review article, we first briefly summarize recent advances in single cell MS experimental techniques, and then emphasize on the single cell metabolomics data analysis approaches. Through implementation of statistical analysis and more advanced data analysis methods, single cell metabolomics is expected to find more potential applications in the translational and clinical fields in the future.

Plant Single-Cell Metabolomics-Challenges and Perspectives

Omics approaches for investigating biological systems were introduced in the mid-1990s and quickly consolidated to become a fundamental pillar of modern biology. The idea of measuring the whole complement of genes, transcripts, proteins, and metabolites has since become widespread and routinely adopted in the pursuit of an infinity of scientific questions. Incremental improvements over technical aspects such as sampling, sensitivity, cost, and throughput pushed even further the boundaries of what these techniques can achieve. In this context, single-cell genomics and transcriptomics quickly became a well-established tool to answer fundamental questions challenging to assess at a whole tissue level. Following a similar trend as the original development of these techniques, proteomics alternatives for single-cell exploration have become more accessible and reliable, whilst metabolomics lag behind the rest. This review summarizes state-of-the-art technologies for spatially resolved metabolomics analysis, as well as the challenges hindering the achievement of sensu stricto metabolome coverage at the single-cell level. Furthermore, we discuss several essential contributions to understanding plant single-cell metabolism, finishing with our opinion on near-future developments and relevant scientific questions that will hopefully be tackled by incorporating these new exciting technologies.

Bioengineering of Microalgae: Recent Advances, Perspectives, and Regulatory Challenges for Industrial Application

Microalgae, due to their complex metabolic capacity, are being continuously explored for nutraceuticals, pharmaceuticals, and other industrially important bioactives. However, suboptimal yield and productivity of the bioactive of interest in local and robust wild-type strains are of perennial concerns for their industrial applications. To overcome such limitations, strain improvement through genetic engineering could play a decisive role. Though the advanced tools for genetic engineering have emerged at a greater pace, they still remain underused for microalgae as compared to other microorganisms. Pertaining to this, we reviewed the progress made so far in the development of molecular tools and techniques, and their deployment for microalgae strain improvement through genetic engineering. The recent availability of genome sequences and other omics datasets form diverse microalgae species have remarkable potential to guide strategic momentum in microalgae strain improvement program. This review focuses on the recent and significant improvements in the omics resources, mutant libraries, and high throughput screening methodologies helpful to augment research in the model and non-model microalgae. Authors have also summarized the case studies on genetically engineered microalgae and highlight the opportunities and challenges that are emerging from the current progress in the application of genome-editing to facilitate microalgal strain improvement. Toward the end, the regulatory and biosafety issues in the use of genetically engineered microalgae in commercial applications are described.

Combining Mass Spectrometry with Paternò-Büchi Reaction to Determine Double-Bond Positions in Lipids at the Single-Cell Level

Single cell MS (SCMS) techniques are under rapid development for molecular analysis of individual cells among heterogeneous populations. Lipids are basic cellular constituents playing essential functions in energy storage and the cellular signaling processes of cells. Unsaturated lipids are characterized with one or multiple carbon-carbon double (C═C) bonds, and they are critical for cell functions and human diseases. Characterizing unsaturated lipids in single cells allows for better understanding of metabolomic biomarkers and therapeutic targets of rare cells (e.g., cancer stem cells); however, these studies remain challenging. We developed a new technique using a micropipette needle, in which Paternò-Büchi (PB) reactions at C═C bond can be induced, to determine locations of C═C bonds in unsaturated lipids at the single-cell level. The micropipette needle is produced by combining a pulled glass capillary needle with a fused silica capillary. Cell lysis solvent and PB reagent (acetone or benzophenone) are delivered into the micropipette needle (tip size ≈ 15 um) through a fused silica capillary. The capillary needle plays multiple functions (i.e., single cell sampling probe, cell lysis container, microreactor, and nano-ESI emitter) in the experiments. Both regular (no reaction) and reactive (with PB reaction) SCMS analyses of the same cell can be achieved. C═C bond locations were determined from MS scan and MS/MS of PB products assisted by Python programs. This technique can potentially be used for other reactive SCMS studies to enhance molecular analysis for broad ranges of single cells.

Comprehensive analysis of metabolic alterations in Schizochytrium sp. strains with different DHA content

Along with the daily growth of the market requirements for docosahexaenoic acid (DHA) algae oil, a large DHA ingredients are needed to ensure worldwide supply. Undoubtedly a high-productive strain would be the prerequisite for high quality and yield. A comprehensive understanding of the processes of DHA synthesis from glycolysis to the lipid accumulation would be benefit to achieve the final optimization of DHA production. In this study, we comprehensively characterized the metabolic profiles of a Schizochytrium sp. strain, which has higher DHA content and different biomass amino acid composition compared with the wild type to explore the affected pathways and underlying mechanism. Combined with the multivariate statistical analysis, twenty-two differential metabolites were screened as relevant to the discrepancy between two strains. The results showed relatively downregulated glycolysis and saturated fatty acids (SFA) synthesis, and upregulated TCA cycle, amino acids and polyunsaturated fatty acids (PUFA) synthesis in DHA high yield strain. The current study provide a terminal picture of gene regulation from downstream metabolism and demonstrate the advantage of metabolomics in characterizing metabolic status which in turn could provide effective information for the metabolic engineering.

Towards early monitoring of chemotherapy-induced drug resistance based on single cell metabolomics: Combining single-probe mass spectrometry with machine learning

Despite the presence of methods evaluating drug resistance during chemotherapies, techniques, which allow for monitoring the degree of drug resistance in early chemotherapeutic stage from single cells in their native microenvironment, are still absent. Herein, we report an analytical approach that combines single cell mass spectrometry (SCMS) based metabolomics with machine learning (ML) models to address the existing challenges. Metabolomic profiles of live cancer cells (HCT-116) with different levels (i.e., no, low, and high) of chemotherapy-induced drug resistance were measured using the Single-probe SCMS technique. A series of ML models, including random forest (RF), artificial neural network (ANN), and penalized logistic regression (LR), were constructed to predict the degrees of drug resistance of individual cells. A systematic comparison of performance was conducted among multiple models, and the method validation was carried out experimentally. Our results indicate that these ML models, especially the RF model constructed on the obtained SCMS datasets, can rapidly and accurately predict different degrees of drug resistance of live single cells. With such rapid and reliable assessment of drug resistance demonstrated at the single cell level, our method can be potentially employed to evaluate chemotherapeutic efficacy in the clinic.

Redesigning the T-probe for mass spectrometry analysis of online lysis of non-adherent single cells

Single cell mass spectrometry (SCMS) allows for molecular analysis of individual cells while avoiding the inevitable drawbacks of using cell lysate prepared from populations of cells. Based on our previous design of the T-probe, a microscale sampling and ionization device for SCMS analysis, we further developed the device to perform online, and real time lysis of non-adherent live single cells for mass spectrometry (MS) analysis at ambient conditions. This redesigned T-probe includes three parts: a sampling probe with a small tip to withdraw a whole cell, a solvent-providing capillary to deliver lysis solution (i.e., acetonitrile), and a nano-ESI emitter in which rapid cell lysis and ionization occur followed by MS analysis. These three components are embedded between two polycarbonate slides and are jointed through a T-junction to form an integrated device. Colon cancer cells (HCT-116) under control and treatment (using anticancer drug irinotecan) conditions were analyzed. We detected a variety of intracellular species, and structural identification of selected ions was conducted using tandem MS (MS2). We further conducted statistical analysis (e.g., PLS-DA and t-test) to gain biological insights of cellular metabolism. Our results indicate that the influence of anticancer drugs on cellular metabolism of live non-adherent cells can be obtained using the SCMS experiments combined with statistical data analysis.

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 (Aβ) 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.

Omics Analysis for Dinoflagellates Biology Research

Dinoflagellates are important primary producers for marine ecosystems and are also responsible for certain essential components in human foods. However, they are also notorious for their ability to form harmful algal blooms, and cause shellfish poisoning. Although much work has been devoted to dinoflagellates in recent decades, our understanding of them at a molecular level is still limited owing to some of their challenging biological properties, such as large genome size, permanently condensed liquid-crystalline chromosomes, and the 10-fold lower ratio of protein to DNA than other eukaryotic species. In recent years, omics technologies, such as genomics, transcriptomics, proteomics, and metabolomics, have been applied to the study of marine dinoflagellates and have uncovered many new physiological and metabolic characteristics of dinoflagellates. In this article, we review recent application of omics technologies in revealing some of the unusual features of dinoflagellate genomes and molecular mechanisms relevant to their biology, including the mechanism of harmful algal bloom formations, toxin biosynthesis, symbiosis, lipid biosynthesis, as well as species identification and evolution. We also discuss the challenges and provide prospective further study directions and applications of dinoflagellates.

Integrating a generalized data analysis workflow with the Single-probe mass spectrometry experiment for single cell metabolomics

We conducted single cell metabolomics studies of live cancer cells through online single cell mass spectrometry (SCMS) experiments combined with a generalized comprehensive data analysis workflow. The SCMS experiments were carried out using the Single-probe device coupled with a mass spectrometer to measure molecular profiles of cells in response to two mitotic inhibitors, taxol and vinblastine, under a series of treatment conditions. SCMS metabolomic data were analyzed using a comprehensive approach, including data pre-treatment, visualization, statistical analysis, machine learning, and pathway enrichment analysis. For comparative studies, traditional liquid chromatography-MS (LC-MS) experiments were conducted using lysates prepared from bulk cell samples. Metabolomic profiles of single cells were visualized through Partial Least Square-Discriminant Analysis (PLS-DA), and the phenotypic biomarkers associated with emerging phenotypes induced by drug treatment were discovered and compared through a series of rigorous statistical analysis. Species of interest were further identified at both the single cell and population levels. In addition, four biological pathways potentially involved in the drug treatment were determined through pathway enrichment analysis. Our work demonstrated the capability of comprehensive pipeline studies of single cell metabolomics. This method can be potentially applied to broader types of SCMS datasets for future pharmaceutical and chemotherapeutic research.

Quantification of Drug Molecules in Live Single Cells Using the Single-Probe Mass Spectrometry Technique

Analyzing cellular constituents on the single-cell level through mass spectrometry (MS) allows for a wide range of compounds to be studied simultaneously. However, there is a need for quantitative single-cell mass spectrometry (qSCMS) methods to fully characterize drug efficacy from individual cells within cell populations. In this study, qSCMS experiments were carried out using the Single-probe MS technique. The method was successfully used to perform rapid absolute quantifications of the anticancer drug irinotecan in individual mammalian cancer cells under ambient conditions in real time. Traditional liquid chromatography/mass spectrometry (LC/MS) quantifications of irinotecan in cell lysate samples were used to compare the results from Single-probe qSCMS. This technique showcases heterogeneity of drug efficacy on the single-cell level.

Marine Natural Products from Microalgae: An -Omics Overview

Over the last decade, genome sequences and other -omics datasets have been produced for a wide range of microalgae, and several others are on the way. Marine microalgae possess distinct and unique metabolic pathways, and can potentially produce specific secondary metabolites with biological activity (e.g., antipredator, allelopathic, antiproliferative, cytotoxic, anticancer, photoprotective, as well as anti-infective and antifouling activities). Because microalgae are very diverse, and adapted to a broad variety of environmental conditions, the chances to find novel and unexplored bioactive metabolites with properties of interest for biotechnological and biomedical applications are high. This review presents a comprehensive overview of the current efforts and of the available solutions to produce, explore and exploit -omics datasets, with the aim of identifying species and strains with the highest potential for the identification of novel marine natural products. In addition, funding efforts for the implementation of marine microalgal -omics resources and future perspectives are presented as well.

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.

Live Single-Cell Metabolomics With Matrix-Free Laser/Desorption Ionization Mass Spectrometry to Address Microalgal Physiology

Unicellular phototrophic algae can form massive blooms with up to millions of individual cells per milliliter in freshwater and marine ecosystems. Despite the temporal dominance of bloom formers many algal species can co-exist and compete for nutrients and space, creating a complex and diverse community. While microscopy and single cell genomics can address the taxonomic inventory, the cellular metabolome has yet to be thoroughly explored to determine the physiological status of microalgae. This might, however, provide a key to understand the observed species diversity in the homogeneous environment. Here, we introduce an effective, rapid and versatile method to analyze living single cells from aqueous substrata with laser-desorption/ionization mass spectrometry (LDI-MS) using a simple and inexpensive matrix-free support. The cells deposited on a cultivation-medium wetted support are analyzed with minimal disturbance as they remain in their natural viable state until their disruption during LDI-MS. Metabolites desorbed from single cells are analyzed on High-Resolution Mass Spectrometry (HR-MS) using the Orbitrap FT-MS technology to fingerprint cellular chemistry. This live single-cell mass spectrometry (LSC-MS) allows assessing the physiological status and strain-specifics of different microalgae, including marine diatoms and freshwater chlorophytes, at the single-cell level. We further report a reliable and robust data treatment pipeline to perform multivariate statistics on the replicated LSC-MS data. Comparing single cell MS spectra from natural phytoplankton samples and from laboratory strains allows the identification and discrimination of inter and intra-specific metabolic variability and thereby has promising applications in addressing highly complex phytoplankton communities. Notably, the herein described matrix-free live-single-cell LDI-HR-MS approach enables monitoring dynamics of the plankton and might explain why key-players survive, thrive, avoid selective feeding or pathogenic virus and bacteria, while others are overcome and die.

Metabolomic Studies of Live Single Cancer Stem Cells Using Mass Spectrometry

Cancer stem cells (CSCs) are rare types of cells responsible for tumor development, relapse, and metastasis. However, current research in CSC biology is largely limited by the difficulty of obtaining sufficient CSCs. Single-cell analysis techniques are promising tools for CSC-related studies. Here, we used the Single-probe mass spectrometry (MS) technique to investigate the metabolic features of live colorectal CSCs at the single-cell level. Experimental data were analyzed using statistical analysis methods, including the t-test and partial least squares discriminant analysis. Our results indicate that the overall metabolic profiles of CSCs are distinct from non-stem cancer cells (NSCCs). Specifically, we demonstrated that tricarboxylic acid (TCA) cycle metabolites are more abundant in CSCs compared to NSCCs, indicating their major energy production pathways are different. Moreover, CSCs have relatively higher levels of unsaturated lipids. Inhibiting the activities of stearoyl-CoA desaturase-1 (SCD1), nuclear factor κB (NF-κB), and aldehyde dehydrogenases (ALDH1A1) in CSCs significantly reduced the abundances of unsaturated lipids and hindered the formation of spheroids, resulting in reduced stemness of CSCs. Our techniques and experimental protocols can be potentially used for metabolomic studies of other CSCs and rare types of cells and provide a new approach to discovering functional biomarkers as therapeutic targets.

On the intersection of electrochemistry and mass spectrometry

The application of nanopipettes, developed first as a tool for electrochemistry and electrophysiology, as tools for mass spectrometry is considered. Recent examples of advances in electrospray ionization and sampling for mass spectrometry with nanopipettes is discussed. These examples show a scientific intersection that is ripe for further development.

Advances in mass spectrometry based single-cell metabolomics

Single cell metabolomics using mass spectrometry can contribute to understanding biological activities in health and disease.

Recent Advances and New Perspectives in Capillary Electrophoresis-Mass Spectrometry for Single Cell "Omics"

Accurate clinical therapeutics rely on understanding the metabolic responses of individual cells. However, the high level of heterogeneity between cells means that simply sampling from large populations of cells is not necessarily a reliable approximation of an individual cell's response. As a result, there have been numerous developments in the field of single-cell analysis to address this lack of knowledge. Many of these developments have focused on the coupling of capillary electrophoresis (CE), a separation technique with low sample consumption and high resolving power, and mass spectrometry (MS), a sensitive detection method for interrogating all ions in a sample in a single analysis. In recent years, there have been many notable advancements at each step of the single-cell CE-MS analysis workflow, including sampling, manipulation, separation, and MS analysis. In each of these areas, the combined improvements in analytical instrumentation and achievements of numerous researchers have served to drive the field forward to new frontiers. Consequently, notable biological discoveries have been made possible by the implementation of these methods. Although there is still room in the field for numerous further advances, researchers have effectively minimized various limitations in detection of analytes, and it is expected that there will be many more developments in the near future.

Crop metabolomics: from diagnostics to assisted breeding

BACKGROUND

Until recently, plant metabolomics have provided a deep understanding on the metabolic regulation in individual plants as experimental units. The application of these techniques to agricultural systems subjected to more complex interactions is a step towards the implementation of translational metabolomics in crop breeding.

AIM OF REVIEW

We present here a review paper discussing advances in the knowledge reached in the last years derived from the application of metabolomic techniques that evolved from biomarker discovery to improve crop yield and quality.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Translational metabolomics applied to crop breeding programs.

T-Probe: An Integrated Microscale Device for Online In Situ Single Cell Analysis and Metabolic Profiling Using Mass Spectrometry

The exploration of single cells reveals cell heterogeneity and biological principle of cellular metabolism. Although a number of mass spectrometry (MS) based single cell MS (SCMS) techniques have been dedicatedly developed with high efficiency and sensitivity, limitations still exist. In this work, we introduced a microscale multifunctional device, the T-probe, which integrates cellular contents extraction and immediate ionization, to implement online in situ SCMS analysis at ambient conditions with minimal sample preparation. With high sensitivity and reproducibility, the T-probe was employed for MS analysis of single HeLa cells under control and anticancer drug treatment conditions. Intracellular species and xenobiotic metabolites were detected, and changes of cellular metabolic profiles induced by drug treatment were measured. Combining SCMS experiments with statistical data analyses, including Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) and two-sample t-test, we provided biological insights into cellular metabolic response to drug treatment. Online MS/MS analysis was conducted at single cell level to identify species of interest, including endogenous metabolites and the drug compound. Using the T-probe SCMS technique combined with comprehensive data analyses, we provide an approach to understanding cellular metabolism and evaluate chemotherapies at the single cell level.