Metabolic Noise and Distinct Subpopulations Observed by Single Cell LAESI Mass Spectrometry of Plant Cells in situ

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Bringing CAM photosynthesis to the table: Paving the way for resilient and productive agricultural systems in a changing climate

Modern agricultural systems are directly threatened by global climate change and the resulting freshwater crisis. A considerable challenge in the coming years will be to develop crops that can cope with the consequences of declining freshwater resources and changing temperatures. One approach to meeting this challenge may lie in our understanding of plant photosynthetic adaptations and water use efficiency. Plants from various taxa have evolved crassulacean acid metabolism (CAM), a water-conserving adaptation of photosynthetic carbon dioxide fixation that enables plants to thrive under semi-arid or seasonally drought-prone conditions. Although past research on CAM has led to a better understanding of the inner workings of plant resilience and adaptation to stress, successful introduction of this pathway into C3 or C4 plants has not been reported. The recent revolution in molecular, systems, and synthetic biology, as well as innovations in high-throughput data generation and mining, creates new opportunities to uncover the minimum genetic tool kit required to introduce CAM traits into drought-sensitive crops. Here, we propose four complementary research avenues to uncover this tool kit. First, genomes and computational methods should be used to improve understanding of the nature of variations that drive CAM evolution. Second, single-cell 'omics technologies offer the possibility for in-depth characterization of the mechanisms that trigger environmentally controlled CAM induction. Third, the rapid increase in new 'omics data enables a comprehensive, multimodal exploration of CAM. Finally, the expansion of functional genomics methods is paving the way for integration of CAM into farming systems.

Recent Progress in Mass Spectrometry-Based Single-Cell Metabolic Analysis

Single-cell analysis enables the measurement of biomolecules at the level of individual cells, facilitating in-depth investigations into cellular heterogeneity and precise interpretation of the related biological mechanisms. Among these biomolecules, cellular metabolites exhibit remarkable sensitivity to environmental and biochemical changes, unveiling a hidden world underlying cellular heterogeneity and allowing for the determination of cell physiological states. However, the metabolic analysis of single cells is challenging due to the extremely low concentrations, substantial content variations, and rapid turnover rates of cellular metabolites. Mass spectrometry (MS), characterized by its high sensitivity, wide dynamic range, and excellent selectivity, is employed in single-cell metabolic analysis. This review focuses on recent advances and applications of MS-based single-cell metabolic analysis, encompassing three key steps of single-cell isolation, detection, and application. It is anticipated that MS will bring profound implications in biomedical practices, serving as advanced tools to depict the single-cell metabolic landscape.

Quantification of Nitric Oxide in Single Cells Using the Single-Probe Mass Spectrometry Technique

Nitric oxide (NO) is a small molecule that plays important roles in biological systems and human diseases. The abundance of intracellular NO is tightly related to numerous biological processes. Due to cell heterogeneity, the intracellular NO amounts significantly vary from cell to cell, and therefore, any meaningful studies need to be conducted at the single-cell level. However, measuring NO in single cells is very challenging, primarily due to the extremely small size of single cells and reactive nature of NO. In the current studies, the quantitative reaction between NO and amlodipine, a compound containing the Hantzsch ester group, was performed in live cells. The product dehydro amlodipine was then detected by the Single-probe single-cell mass spectrometry technique to quantify NO in single cells. The experimental results indicated heterogeneous distributions of intracellular NO amounts in single cells with the existence of subpopulations.

High-Throughput f-LAESI-IMS-MS for Mapping Biological Nitrogen Fixation One Cell at a Time

For the characterization of the metabolic heterogeneity of cell populations, high-throughput single-cell analysis platforms are needed. In this study, we utilized mass spectrometry (MS) enhanced with ion mobility separation (IMS) and coupled with an automated sampling platform, fiber-based laser ablation electrospray ionization (f-LAESI), for in situ high-throughput single-cell metabolomics in soybean (Glycine max) root nodules. By fully automating the in situ sampling platform, an overall sampling rate of 804 cells/h was achieved for high numbers (>500) of tissue-embedded plant cells. This is an improvement by a factor of 13 compared to the previous f-LAESI-MS configuration. By introducing IMS, the molecular coverage improved, and structural isomers were separated on a millisecond time scale. The enhanced f-LAESI-IMS-MS platform produced 259 sample-related peaks/cell, almost twice as much as the 131 sample-related peaks/cell produced by f-LAESI-MS without IMS. Using the upgraded system, two types of metabolic heterogeneity characterization methods became possible. For unimodal metabolite abundance distributions, the metabolic noise reported on the metabolite level variations within the cell population. For bimodal distributions, the presence of metabolically distinct subpopulations was established. Discovering these latent cellular phenotypes could be linked to the presence of different cell states, e.g., proliferating bacteria in partially occupied plant cells and quiescent bacteroids in fully occupied cells in biological nitrogen fixation, or spatial heterogeneity due to altered local environments.

Single-Cell Proteomics with Spatial Attributes: Tools and Techniques

Now-a-days, the single-cell proteomics (SCP) concept is attracting interest, especially in clinical research, because it can identify the proteomic signature specific to diseased cells. This information is very essential when dealing with the progression of certain diseases, such as cancer, diabetes, Alzheimer's, etc. One of the major drawbacks of conventional destructive proteomics is that it gives an average idea about the protein expression profile in the disease condition. During the extraction of the protein from a biopsy or blood sample, proteins may come from both diseased cells and adjacent normal cells or any other cells from the disease environment. Again, SCP along with spatial attributes is utilized to learn about the heterogeneous function of a single protein. Before performing SCP, it is necessary to isolate single cells. This can be done by various techniques, including fluorescence-activated cell sorting (FACS), magnetic-activated cell sorting (MACS), laser capture microdissection (LCM), microfluidics, manual cell picking/micromanipulation, etc. Among the different approaches for proteomics, mass spectrometry-based proteomics tools are widely used for their high resolution as well as sensitivity. This Review mainly focuses on the mass spectrometry-based approaches for the study of single-cell proteomics.

Impact of wavelength and spot size on laser depth of focus: Considerations for mass spectrometry imaging of non-flat samples

Biospecimens with nearly flat surfaces on a flat stage are typically required for laser-based mass spectrometry imaging (MSI) techniques. However, sampling stages are rarely perfectly level, and accounting for this and the need to accommodate non-flat samples requires a deeper understanding of the laser beam depth of focus. In ablation-based MSI methods, a laser is focused on top of the sample surface, ensuring that the sample is at the focal point or remains within depth of focus. In general, the depth of focus of a given laser is related to the beam quality (M2 ) and the wavelength (λ). However, because laser is applied on a biological sample, other variables can also impact the depth of focus, which could affect the robustness of the MSI techniques for diverse sample types. When the height of a sample ranges outside of the depth of focus, ablated area and the corresponding ion abundances may vary as well, increasing the variability of results. In this tutorial, we examine the parameters and equations that describe the depth of focus of a Gaussian laser beam. Additionally, we describe several approaches that account for surface roughness exceeding the depth of focus of the laser.

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.

Advances in Mass Spectrometry-Based Single Cell Analysis

Technological developments and improvements in single-cell isolation and analytical platforms allow for advanced molecular profiling at the single-cell level, which reveals cell-to-cell variation within the admixture cells in complex biological or clinical systems. This helps to understand the cellular heterogeneity of normal or diseased tissues and organs. However, most studies focused on the analysis of nucleic acids (e.g., DNA and RNA) and mass spectrometry (MS)-based analysis for proteins and metabolites of a single cell lagged until recently. Undoubtedly, MS-based single-cell analysis will provide a deeper insight into cellular mechanisms related to health and disease. This review summarizes recent advances in MS-based single-cell analysis methods and their applications in biology and medicine.

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.

The Biological Hierarchy, Time, and Temporal 'Omics in Evolutionary Biology: A Perspective

Sequencing data-genomics, transcriptomics, epigenomics, proteomics, and metabolomics-have revolutionized biological research, enabling a more detailed study of processes, ranging from subcellular to evolutionary, that drive biological organization. These processes, collectively, are responsible for generating patterns of phenotypic variation and can operate over dramatically different timescales (milliseconds to billions of years). While researchers often study phenotypic variation at specific levels of biological organization to isolate processes operating at that particular scale, the varying types of sequence data, or 'omics, can also provide complementary inferences to link molecular and phenotypic variation to produce an integrated view of evolutionary biology, ranging from molecular pathways to speciation. We briefly describe how 'omics has been used across biological levels and then demonstrate the utility of integrating different types of sequencing data across multiple biological levels within the same study to better understand biological phenomena. However, single-time-point studies cannot evaluate the temporal dynamics of these biological processes. Therefore, we put forward temporal 'omics as a framework that can better enable researchers to study the temporal dynamics of target processes. Temporal 'omics is not infallible, as the temporal sampling regime directly impacts inferential ability. Thus, we also discuss the role the temporal sampling regime plays in deriving inferences about the environmental conditions driving biological processes and provide examples that demonstrate the impact of the sampling regime on biological inference. Finally, we forecast the future of temporal 'omics by highlighting current methodological advancements that will enable temporal 'omics to be extended across species and timescales. We extend this discussion to using temporal multi-omics to integrate across the biological hierarchy to evaluate and link the temporal dynamics of processes that generate phenotypic variation.

Improved spatial resolution of infrared matrix-assisted laser desorption electrospray ionization mass spectrometry imaging using a reflective objective

RATIONALE

The level of visual detail of a mass spectrometry image is dependent on the spatial resolution with which it is acquired, which is largely determined by the focal diameter in infrared laser ablation-based techniques. While the use of mid-IR light for mass spectrometry imaging (MSI) has advantages, it results in a relatively large focal diameter and spatial resolution. The continual advancement of infrared matrix-assisted electrospray ionization (IR-MALDESI) for MSI warranted novel methods to decrease laser ablation areas and thus improve spatial resolution.

METHODS

In this work, a Schwarzschild-like reflective objective was incorporated into the novel NextGen IR-MALDESI source and characterized on both burn paper and mammalian tissue using an ice matrix. Ablation areas, mass spectra, and annotations obtained using the objective were compared against the current optical train on the NextGen system without modification.

RESULTS

The effective resolution was determined to be 55 μm by decreasing the step size until oversampling was observed. Use of the objective improved the spatial resolution by a factor of three as compared against the focus lens.

CONCLUSIONS

A Schwarzschild-like reflective objective was successfully incorporated into the NextGen source and characterized on mammalian tissue using an ice matrix. The corresponding improvement in spatial resolution facilitates the future expansion of IR-MALDESI applications to include those that require fine structural detail.

Mass Spectrometry Imaging technology in metabolomics: a systematic review

Mass spectrometry imaging (MSI) is a powerful label-free analysis technique that can provide simultaneous spatial distribution of multiple compounds in a single experiment. By combining the sensitive and rapid screening of high-throughput mass spectrometry with spatial chemical information, metabolite analysis and morphological characteristics are presented in a single image. MSI can be used for qualitative and quantitative analysis of metabolic profiles and it can provide visual analysis of spatial distribution information of complex biological and microbial systems. Matrix assisted laser desorption ionization, laser ablation electrospray ionization and desorption electrospray ionization are commonly used in MSI. Here, we summarize and compare these three technologies, as well as the applications and prospects of MSI in metabolomics.

Recent advances in proteomics and metabolomics in plants

Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.

Image to insight: exploring natural products through mass spectrometry imaging

Covering: 2017 to 2022Mass spectrometry imaging (MSI) has become a mature molecular imaging technique that is well-matched for natural product (NP) discovery. Here we present a brief overview of MSI, followed by a thorough discussion of different MSI applications in NP research. This review will mainly focus on the recent progress of MSI in plants and microorganisms as they are the main producers of NPs. Specifically, the opportunity and potential of combining MSI with other imaging modalities and stable isotope labeling are discussed. Throughout, we focus on both the strengths and weaknesses of MSI, with an eye on future improvements that are necessary for the progression of MSI toward routine NP studies. Finally, we discuss new areas of research, future perspectives, and the overall direction that the field may take in the years to come.

Influence of Ion Source Geometry on the Repeatability of Topographically Guided LAESI-MSI

Spatially resolving the relative distribution of analyte molecules in biological matter holds great promise in the life sciences. Mass spectrometry imaging (MSI) is a technique that can provide such spatial resolution but remains underused in fields such as chemical ecology, as traditional MSI sample preparation is often chemically or morphologically invasive. Laser ablation electrospray ionization (LAESI)-MSI is a variation of MSI particularly well-suited for situations where chemical sample preparation is too invasive but provides new challenges related to the repeatability of measurement outcomes. We assess the repeatability of LAESI-MSI by sampling a droplet of [ring-¹³C₆]l-phenylalanine with known concentration and expressing the resulting variability as a coefficient of variation, c_v. In doing so, we entirely eliminate variability caused by surface morphology or underlying true sample gradients. We determine the limit of detection (LOD) for¹³C₆-Phe by sampling from droplets with successively decreasing but known concentration. We assess the influence of source geometry on the LOD and repeatability by performing these experiments using four distinct variations of sources: one commercial and three custom-built ones. Finally, we extend our study to leaf and stem samples Arabidopsis thaliana and Gossypium hirsutum. We overcome the challenges of LAESI associated with three-dimensional surface morphology by relying on work previously published. Our measurements on both controlled standard and realistic samples give strong evidence that LAESI-MSI's repeatability in current implementations is insufficient for MSI in chemical ecology.

High-Throughput Single-Cell Mass Spectrometry Reveals Abnormal Lipid Metabolism in Pancreatic Ductal Adenocarcinoma

Even populations of clonal cells are heterogeneous, which requires high-throughput analysis methods with single-cell sensitivity. Here, we propose a rapid, label-free single-cell analytical method based on active capillary dielectric barrier discharge ionization mass spectrometry, which can analyze multiple metabolites in single cells at a rate of 38 cells/minute. Multiple cell types (HEK-293T, PANC-1, CFPAC-1, H6c7, HeLa and iBAs) were discriminated successfully. We found evidence for abnormal lipid metabolism in pancreatic cancer cells. We also analyzed gene expression in a cancer genome atlas dataset and found that the mRNA level of a critical enzyme of lipid synthesis (ATP citrate lyase, ACLY) was upregulated in human pancreatic ductal adenocarcinoma (PDAC). Moreover, both an ACLY chemical inhibitor and a siRNA approach targeting ACLY could suppress the viability of PDAC cells. A significant reduction in lipid content in treated cells indicates that ACLY could be a potential target for treating pancreatic cancer.

Ambient Single-Cell Analysis and Native Tissue Imaging Using Laser-Ablation Electrospray Ionization Mass Spectrometry with Increased Spatial Resolution

Laser-ablation electrospray ionization mass spectrometry (LAESI-MS) is an emerging method that has the potential to transform the field of metabolomics. This is in part due to LAESI-MS being an ambient ionization method that requires minimal sample preparation and uses (endogenous) water for in situ analysis. This application note details the employment of the "LAESI microscope" source to perform spatially resolved MS analysis of cells and MS imaging (MSI) of tissues at high spatial resolution. This source configuration utilizes a long-working-distance reflective objective that permits both visualization of the sample and a smaller LAESI laser beam profile than conventional LAESI setups. Here, we analyzed 200 single cells of Allium cepa (red onion) and imaged Fittonia argyroneura (nerve plant) in high spatial resolution using this source coupled to a Fourier transform mass spectrometer for high-mass-resolution and high-mass-accuracy metabolomics.

High-Throughput Analysis of Tissue-Embedded Single Cells by Mass Spectrometry with Bimodal Imaging and Object Recognition

In biological tissues, cell-to-cell variations stem from the stochastic and modulated expression of genes and the varying abundances of corresponding proteins. These variations are then propagated to downstream metabolite products and result in cellular heterogeneity. Mass spectrometry imaging (MSI) is a promising tool to simultaneously provide spatial distributions for hundreds of biomolecules without the need for labels or stains. Technological advances in MSI instrumentation for the direct analysis of tissue-embedded single cells are dominated by improvements in sensitivity, sample pretreatment, and increased spatial resolution but are limited by low throughput. Herein, we introduce a bimodal microscopy imaging system combined with fiber-based laser ablation electrospray ionization (f-LAESI) MSI with improved throughput ambient analysis of tissue-embedded single cells (n > 1000) to provide insight into cellular heterogeneity. Based on automated image analysis, accurate single-cell sampling is achieved by f-LAESI leading to the discovery of cellular phenotypes characterized by differing metabolite levels.

Phosphorus deprivation affects composition and spatial distribution of membrane lipids in legume nodules

In legumes, symbiotic nitrogen (N) fixation (SNF) occurs in specialized organs called nodules after successful interactions between legume hosts and rhizobia. In a nodule, N-fixing rhizobia are surrounded by symbiosome membranes, through which the exchange of nutrients and ammonium occurs between bacteria and the host legume. Phosphorus (P) is an essential macronutrient, and N2-fixing legumes have a higher requirement for P than legumes grown on mineral N. As in the previous studies, in P deficiency, barrel medic (Medicago truncatula) plants had impaired SNF activity, reduced growth, and accumulated less phosphate in leaves, roots, and nodules compared with the plants grown in P sufficient conditions. Membrane lipids in M. truncatula tissues were assessed using electrospray ionization-mass spectrometry. Galactolipids were found to increase in P deficiency, with declines in phospholipids (PL), especially in leaves. Lower PL losses were found in roots and nodules. Subsequently, matrix-assisted laser desorption/ionization-mass spectrometry imaging was used to spatially map the distribution of the positively charged phosphatidylcholine (PC) species in nodules in both P-replete and P-deficient conditions. Our results reveal heterogeneous distribution of several PC species in nodules, with homogeneous distribution of other PC classes. In P poor conditions, some PC species distributions were observed to change. The results suggest that specific PC species may be differentially important in diverse nodule zones and cell types, and that membrane lipid remodeling during P stress is not uniform across the nodule.

Mass Spectrometry Imaging of Bio-oligomer Polydispersity in Plant Tissues by Laser Desorption Ionization from Silicon Nanopost Arrays

Mass spectrometry imaging (MSI) enables simultaneous spatial mapping for diverse molecules in biological tissues. Matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS) has been a mainstream MSI method for a wide range of biomolecules. However, MALDI-MSI of biological homopolymers used for energy storage and molecular feedstock is limited by, e.g., preferential ionization for certain molecular classes. Matrix-free nanophotonic ionization from silicon nanopost arrays (NAPAs) is an emerging laser desorption ionization (LDI) platform with ultra-trace sensitivity and molecular imaging capabilities. Here, we show complementary analysis and MSI of polyhydroxybutyric acid (PHB), polyglutamic acid (PGA), and polysaccharide oligomers in soybean root nodule sections by NAPA-LDI and MALDI. For PHB, number and weight average molar mass, polydispersity, and oligomer size distributions across the tissue section and in regions of interest were characterized by NAPA-LDI-MSI.

Spatially Resolved Mass Spectrometry at the Single Cell: Recent Innovations in Proteomics and Metabolomics

Biological systems are composed of heterogeneous populations of cells that intercommunicate to form a functional living tissue. Biological function varies greatly across populations of cells, as each single cell has a unique transcriptome, proteome, and metabolome that translates to functional differences within single species and across kingdoms. Over the past decade, substantial advancements in our ability to characterize omic profiles on a single cell level have occurred, including in multiple spectroscopic and mass spectrometry (MS)-based techniques. Of these technologies, spatially resolved mass spectrometry approaches, including mass spectrometry imaging (MSI), have shown the most progress for single cell proteomics and metabolomics. For example, reporter-based methods using heavy metal tags have allowed for targeted MS investigation of the proteome at the subcellular level, and development of technologies such as laser ablation electrospray ionization mass spectrometry (LAESI-MS) now mean that dynamic metabolomics can be performed in situ. In this Perspective, we showcase advancements in single cell spatial metabolomics and proteomics over the past decade and highlight important aspects related to high-throughput screening, data analysis, and more which are vital to the success of achieving proteomic and metabolomic profiling at the single cell scale. Finally, using this broad literature summary, we provide a perspective on how the next decade may unfold in the area of single cell MS-based proteomics and metabolomics.

Optical Microscopy-Guided Laser Ablation Electrospray Ionization Ion Mobility Mass Spectrometry: Ambient Single Cell Metabolomics with Increased Confidence in Molecular Identification

Single cell analysis is a field of increasing interest as new tools are continually being developed to understand intercellular differences within large cell populations. Laser-ablation electrospray ionization mass spectrometry (LAESI-MS) is an emerging technique for single cell metabolomics. Over the years, it has been validated that this ionization technique is advantageous for probing the molecular content of individual cells in situ. Here, we report the integration of a microscope into the optical train of the LAESI source to allow for visually informed ambient in situ single cell analysis. Additionally, we have coupled this ‘LAESI microscope’ to a drift-tube ion mobility mass spectrometer to enable separation of isobaric species and allow for the determination of ion collision cross sections in conjunction with accurate mass measurements. This combined information helps provide higher confidence for structural assignment of molecules ablated from single cells. Here, we show that this system enables the analysis of the metabolite content of Allium cepa epidermal cells with high confidence structural identification together with their spatial locations within a tissue.

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.

Microbial metabolic noise

From the time a cell was first placed under the microscope, it became apparent that identifying two clonal cells that "look" identical is extremely challenging. Since then, cell-to-cell differences in shape, size, and protein content have been carefully examined, informing us of the ultimate limits that hinder two cells from occupying an identical phenotypic state. Here, we present recent experimental and computational evidence that similar limits emerge also in cellular metabolism. These limits pertain to stochastic metabolic dynamics and, thus, cell-to-cell metabolic variability, including the resulting adapting benefits. We review these phenomena with a focus on microbial metabolism and conclude with a brief outlook on the potential relationship between metabolic noise and adaptive evolution. This article is categorized under: Metabolic Diseases > Computational Models Metabolic Diseases > Biomedical Engineering.

Metabolomic profiling of wild-type and mutant soybean root nodules using laser-ablation electrospray ionization mass spectrometry reveals altered metabolism

The establishment of the nitrogen-fixing symbiosis between soybean and Bradyrhizobium japonicum is a complex process. To document the changes in plant metabolism as a result of symbiosis, we utilized laser ablation electrospray ionization-mass spectrometry (LAESI-MS) for in situ metabolic profiling of wild-type nodules, nodules infected with a B. japonicum nifH mutant unable to fix nitrogen, nodules doubly infected by both strains, and nodules formed on plants mutated in the stearoyl-acyl carrier protein desaturase (sacpd-c) gene, which were previously shown to have an altered nodule ultrastructure. The results showed that the relative abundance of fatty acids, purines, and lipids was significantly changed in response to the symbiosis. The nifH mutant nodules had elevated levels of jasmonic acid, correlating with signs of nitrogen deprivation. Nodules resulting from the mixed inoculant displayed similar, overlapping metabolic distributions within the sectors of effective (fix+ ) and ineffective (nifH mutant, fix- ) endosymbionts. These data are inconsistent with the notion that plant sanctioning is cell autonomous. Nodules lacking sacpd-c displayed an elevation of soyasaponins and organic acids in the central necrotic regions. The present study demonstrates the utility of LAESI-MS for high-throughput screening of plant phenotypes. Overall, nodules disrupted in the symbiosis were elevated in metabolites related to plant defense.

Single-Cell Metabolic Profiling: Metabolite Formulas from Isotopic Fine Structures in Heterogeneous Plant Cell Populations

Characterization of the metabolic heterogeneity in cell populations requires the analysis of single cells. Most current methods in single-cell analysis rely on cell manipulation, potentially altering the abundance of metabolites in individual cells. A small sample volume and the chemical diversity of metabolites are additional challenges in single-cell metabolomics. Here, we describe the combination of fiber-based laser ablation electrospray ionization (f-LAESI) with 21 T Fourier transform ion cyclotron resonance mass spectrometry (21TFTICR-MS) for in situ single-cell metabolic profiling in plant tissue. Single plant cells infected by bacteria were selected and sampled directly from the tissue without cell manipulation through mid-infrared ablation with a fine optical fiber tip for ionization by f-LAESI. Ultrahigh performance 21T-FTICR-MS enabled the simultaneous capture of isotopic fine structures (IFSs) for 47 known and 11 unknown compounds, thus elucidating their elemental compositions from single cells and providing information on metabolic heterogeneity in the cell population.

An approach for visualizing the spatial metabolome of an entire plant root system inspired by the Swiss-rolling technique

The spatial configuration and morphology of roots are commonly monitored for a better understanding of plant health and development. However, this approach provides minimal details about the biochemistry regulating the observable traits. Therefore, the ability to metabolically map the entire root structure would be of major value. Here, we developed a sample preparation approach that enables imaging of the entire root within a restricted space (width of microscope slide), which was influenced by the Swiss-rolling technique. We were able to image and confidently identify molecules along the entire root structure from rolled-root tissue sections using multiple spatially resolved mass spectrometry approaches.

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.

Dual cationic-anionic profiling of metabolites in a single identified cell in a live Xenopus laevis embryo by microprobe CE-ESI-MS

In situ capillary microsampling with capillary electrophoresis (CE) electrospray ionization (ESI) mass spectrometry (MS) enabled the characterization of cationic metabolites in single cells in complex tissues and organisms. For deeper coverage of the metabolome and metabolic networks, analytical approaches are needed that provide complementary detection for anionic metabolites, ideally using the same instrumentation. Described here is one such approach that enables sequential cationic and anionic (dual) analysis of metabolites in the same identified cell in a live vertebrate embryo. A calibrated volume was microaspirated from the animal-ventral cell in a live 8-cell embryo of Xenopus laevis, and cationic and anionic metabolites were one-pot microextracted from the aspirate, followed by CE-ESI-MS analysis of the same extract. A laboratory-built CE-ESI interface was reconfigured to enable dual cationic-anionic analysis with ∼5-10 nM (50-100 amol) lower limit of detection and a capability for quantification. To provide robust separation and efficient ion generation, the CE-ESI interface was enclosed in a nitrogen gas filled chamber, and the operational parameters were optimized for the cone-jet spraying regime in both the positive and negative ion mode. A total of ∼250 cationic and ∼200 anionic molecular features were detected from the cell between m/z 50-550, including 60 and 24 identified metabolites, respectively. With only 11 metabolites identified mutually, the duplexed approach yielded complementary information on metabolites produced in the cell, which in turn deepened network coverage for several metabolic pathways. With scalability to smaller cells and adaptability to other types of tissues and organisms, dual cationic-anionic detection with in situ microprobe CE-ESI-MS opens a door to better understand cell metabolism.