Advances in Mass Spectrometry-Based Single Cell Analysis

Exploring Single-Probe Single-Cell Mass Spectrometry: Current Trends and Future Directions

The Single-probe single-cell mass spectrometry (SCMS) is an innovative analytical technique designed for metabolomic profiling, offering a miniaturized, multifunctional device capable of direct coupling to mass spectrometers. It is an ambient technique leveraging microscale sampling and nanoelectrospray ionization (nanoESI), enabling the analysis of cells in their native environments without the need for extensive sample preparation. Due to its miniaturized design and versatility, this device allows for applications in diverse research areas, including single-cell metabolomics, quantification of target molecules in single cell, MS imaging (MSI) of tissue sections, and investigation of extracellular molecules in live single spheroids. This review explores recent advancements in Single-probe-based techniques and their applications, emphasizing their potential utility in advancing MS methodologies in microscale bioanalysis.

A laboratory-friendly protocol for freeze-drying sample preparation in ToF-SIMS single-cell imaging

ToF-SIMS is a high spatial resolution imaging technique for cellular or subcellular analysis of biological samples. Accurate molecular data in single-cell studies depend on proper cell morphology and chemical integrity, highlighting the importance of sample preparation. In this work, we standardized a more efficient freeze-drying method using standard lab materials and improved the sample preparation process. Our comprehensive freeze-drying protocol for cellular samples, encompassing washing, fixation, and drying steps, facilitates the acquisition of enhanced cellular information and ensures high reproducibility. These improvements are poised to significantly advance single-cell mass spectrometry imaging research.

The autophagy proteome in the brain

As one of the most important cellular housekeepers, autophagy directly affects cellular health, homeostasis, and function. Even though the mechanisms behind autophagy are well described, how molecular alterations and dysfunctions can lead to pathology in disease contexts still demands deeper investigation. Proteomics is a widely employed tool used to investigate molecular alterations associated with pathological states and has proven useful in identifying alterations in protein expression levels and post-translational modifications in autophagy. In this narrative review, we expand on the molecular mechanisms behind autophagy and its regulation, and further compile recent literature associating autophagy disturbances in context of brain disorders, utilizing discoveries from varying models and species from rodents and cellular models to human post-mortem brain samples. To outline, the canonical pathways of autophagy, the effects of post-translational modifications on regulating each step of autophagy, and the future directions of proteomics in autophagy will be discussed. We further aim to suggest how advancing proteomics can help further unveil molecular mechanisms with regard to neurological disorders.

Single-Cell Simultaneous Metabolome and Transcriptome Profiling Revealing Metabolite-Gene Correlation Network

Metabolic studies at the single cell level can directly define the cellular phenotype closest to physiological or disease states. However, the current single cell metabolome (SCM) study using mass spectroscopy has difficulty giving a complete view of the metabolic activity in the cell, and the prediction of the metabolism-phenotype relationship is limited by the potential inconsistency between transcriptomic and metabolic levels. Here, the single-cell simultaneous metabolome and transcriptome profiling method (scMeT-seq) is developed at one single cell, based on sub-picoliter sampling from the cell for the initial metabolome profiling followed by single cell transcriptome sequencing. This design not only provides sufficient cytoplasm for SCM but also nicely keeps the cellular viability for the accurate transcriptomic analysis in the same cell. Integrative analysis of scMeT-seq reveals both dynamical and cell state-specific associations between metabolome and transcriptome in the macrophages with defined metabolic perturbations. Moreover, metabolite signatures are mapped to the single-cell trajectory and gene correlation network of macrophage transition, which allows the unsupervised functional interpretation of metabolome. Thus, the established scMeT-seq should lead to a new perspective in metabolic research by transforming metabolomics from a metabolite snapshot to a functional approach.

Advancements in Single-Cell Proteomics and Mass Spectrometry-Based Techniques for Unmasking Cellular Diversity in Triple Negative Breast Cancer

BACKGROUND

Triple-negative breast cancer (TNBC) is an aggressive and complex subtype of breast cancer characterized by a lack of targeted treatment options. Intratumoral heterogeneity significantly drives disease progression and complicates therapeutic responses, necessitating advanced analytical approaches to understand its underlying biology. This review aims to explore the advancements in single-cell proteomics and their application in uncovering cellular diversity in TNBC. It highlights innovations in sample preparation, mass spectrometry-based techniques, and the potential for integrating proteomics into multi-omics platforms.

METHODS

The review discusses the combination of improved sample preparation methods and cutting-edge mass spectrometry techniques in single-cell proteomics. It emphasizes the challenges associated with protein analysis, such as the inability to amplify proteins akin to transcripts, and examines strategies to overcome these limitations.

RESULTS

Single-cell proteomics provides a direct link to phenotype and cell behavior, complementing transcriptomic approaches and offering new insights into the mechanisms driving TNBC. The integration of advanced techniques has enabled deeper exploration of cellular heterogeneity and disease mechanisms.

CONCLUSION

Despite the challenges, single-cell proteomics holds immense potential to evolve into a high-throughput and scalable multi-omics platform. Addressing existing hurdles will enable deeper biological insights, ultimately enhancing the diagnosis and treatment of TNBC.

NK/DC crosstalk-modulating antitumor activity via Sema3E/PlexinD1 axis for enhanced cancer immunotherapy

The complex relationship between natural killer (NK) cells and dendritic cells (DCs) within the tumor microenvironment significantly impacts the success of cancer immunotherapy. Recent advancements in cancer treatment have sought to bolster innate and adaptive immune responses through diverse modalities, aiming to tilt the immune equilibrium toward tumor elimination. Optimal antitumor immunity entails a multifaceted interplay involving NK cells, T cells and DCs, orchestrating immune effector functions. Although DC-based vaccines and NK cells' cytotoxic capabilities hold substantial therapeutic potential, their interaction is frequently hindered by immunosuppressive elements such as myeloid-derived suppressor cells (MDSCs) and regulatory T cells. Chemokines and cytokines, such as CXCL12, CCL2, interferons, and interleukins, play crucial roles in modulating NK/DC interactions and enhancing immune responses. This review elucidates the mechanisms underlying NK/DC interaction, emphasizing their pivotal roles in augmenting antitumor immune responses and the impediments posed by tumor-induced immunosuppression. Furthermore, it explores the therapeutic prospects of restoring NK/DC crosstalk, highlighting the significance of molecules like Sema3E/PlexinD1 in this context, offering potential avenues for enhancing the effectiveness of current immunotherapeutic strategies and advancing cancer treatment paradigms. Harnessing the dynamic interplay between NK and DC cells, including the modulation of Sema3E/PlexinD1 signaling, holds promise for developing more potent therapies that harness the immune system's full potential in combating cancer.

Harnessing Variability Signatures and Biological Noise May Enhance Immunotherapies' Efficacy and Act as Novel Biomarkers for Diagnosing and Monitoring Immune-Associated Disorders

Lack of response to immunotherapies poses a significant challenge in treating immune-mediated disorders and cancers. While the mechanisms associated with poor responsiveness are not well defined and change between and among subjects, the current methods for overcoming the loss of response are insufficient. The Constrained Disorder Principle (CDP) explains biological systems based on their inherent variability, bounded by dynamic boundaries that change in response to internal and external perturbations. Inter and intra-subject variability characterize the immune system, making it difficult to provide a single therapeutic regimen to all patients and even the same patients over time. The dynamicity of the immune variability is also a significant challenge for personalizing immunotherapies. The CDP-based second-generation artificial intelligence system is an outcome-based dynamic platform that incorporates personalized variability signatures into the therapeutic regimen and may provide methods for improving the response and overcoming the loss of response to treatments. The signatures of immune variability may also offer a method for identifying new biomarkers for early diagnosis, monitoring immune-related disorders, and evaluating the response to treatments.

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.

A Tutorial Review of Labeling Methods in Mass Spectrometry-Based Quantitative Proteomics

Recent advancements in mass spectrometry (MS) have revolutionized quantitative proteomics, with multiplex isotope labeling emerging as a key strategy for enhancing accuracy, precision, and throughput. This tutorial review offers a comprehensive overview of multiplex isotope labeling techniques, including precursor-based, mass defect-based, reporter ion-based, and hybrid labeling methods. It details their fundamental principles, advantages, and inherent limitations along with strategies to mitigate the limitation of ratio-distortion. This review will also cover the applications and latest progress in these labeling techniques across various domains, including cancer biomarker discovery, neuroproteomics, post-translational modification analysis, cross-linking MS, and single-cell proteomics. This Review aims to provide guidance for researchers on selecting appropriate methods for their specific goals while also highlighting the potential future directions in this rapidly evolving field.

Immune profiling of age and adjuvant-specific activation of human blood mononuclear cells in vitro

Vaccination reduces morbidity and mortality due to infections, but efficacy may be limited due to distinct immunogenicity at the extremes of age. This raises the possibility of employing adjuvants to enhance immunogenicity and protection. Early IFNγ production is a hallmark of effective vaccine immunogenicity in adults serving as a biomarker that may predict effective adjuvanticity. We utilized mass cytometry (CyTOF) to dissect the source of adjuvant-induced cytokine production in human blood mononuclear cells (BMCs) from newborns (~39-week-gestation), adults (~18-63 years old) and elders (>65 years of age) after stimulation with pattern recognition receptors agonist (PRRa) adjuvants. Dimensionality reduction analysis of CyTOF data mapped the BMC compartment, elucidated age-specific immune responses and profiled PRR-mediated activation of monocytes and DCs upon adjuvant stimulation. Furthermore, we demonstrated PRRa adjuvants mediated innate IFNγ induction and mapped NK cells as the key source of TLR7/8 agonist (TLR7/8a) specific innate IFNγ responses. Hierarchical clustering analysis revealed age and TLR7/8a-specific accumulation of innate IFNγ producing γδ T cells. Our study demonstrates the application of mass cytometry and cutting-edge computational approaches to characterize immune responses across immunologically distinct age groups and may inform identification of the bespoke adjuvantation systems tailored to enhance immunity in distinct vulnerable populations.

A critical evaluation of ultrasensitive single-cell proteomics strategies

Success of mass spectrometry characterization of the proteome of single cells allows us to gain a greater understanding than afforded by transcriptomics alone but requires clear understanding of the tradeoffs between analytical throughput and precision. Recent advances in mass spectrometry acquisition techniques, including updated instrumentation and sample preparation, have improved the quality of peptide signals obtained from single cell data. However, much of the proteome remains uncharacterized, and higher throughput techniques often come at the expense of reduced sensitivity and coverage, which diminish the ability to measure proteoform heterogeneity, including splice variants and post-translational modifications, in single cell data analysis. Here, we assess the growing body of ultrasensitive single-cell approaches and their tradeoffs as researchers try to balance throughput and precision in their experiments.

High-Throughput Quantitative Analysis of Amino Acids in Freeze-Dried Drops Using Time-of-Flight Secondary Ion Mass Spectrometry

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) has become a promising analytical tool for molecular profiling in biological applications. However, its ultrahigh vacuum environment and matrix effects hamper the absolute quantitation of solution samples. Herein, we present a rapid high-throughput platform for quantitative ToF-SIMS analysis of amino acids in matrix deposits formed from freeze-dried solution drops through ice sublimation on a parylene film microarray substrate. Droplets of the amino acid solutions, which were mixed with stable isotope-labeled phenylalanine (F*) of high concentration (10 mM), were loaded on wells of the microarray, then frozen and evaporated slowly below the freezing point, forming continuous solid-phase F* matrix deposits. The amino acids (≤500 μM), adequately well dispersed throughout the F* matrix deposits on each well, were quantitatively analyzed by ToF-SIMS in a rapid and high-throughput fashion. The lower limit of quantitation reached below 10 μM.

A review of innovative electrochemical strategies for bioactive molecule detection and cell imaging: Current advances and challenges

Cellular heterogeneity poses a major challenge for tumor theranostics, requiring high-resolution intercellular bioanalysis strategies. Over the past decades, the advantages of electrochemical analysis, such as high sensitivity, good spatio-temporal resolution, and ease of use, have made it the preferred method to uncover cellular differences. To inspire more creative research, herein, we highlight seminal works in electrochemical techniques for biomolecule analysis and bioimaging. Specifically, micro/nano-electrode-based electrochemical techniques enable real-time quantitative analysis of electroactive substances relevant to life processes in the micro-nanostructure of cells and tissues. Nanopore-based technique plays a vital role in biosensing by utilizing nanoscale pores to achieve high-precision detection and analysis of biomolecules with exceptional sensitivity and single-molecule resolution. Electrochemiluminescence (ECL) technology is utilized for real-time monitoring of the behavior and features of individual cancer cells, enabling observation of their dynamic processes due to its capability of providing high-resolution and highly sensitive bioimaging of cells. Particularly, scanning electrochemical microscopy (SECM) and scanning ion conductance microscopy (SICM) which are widely used in real-time observation of cell surface biological processes and three-dimensional imaging of micro-nano structures, such as metabolic activity, ion channel activity, and cell morphology are introduced in this review. Furthermore, the expansion of the scope of cellular electrochemistry research by innovative functionalized electrodes and electrochemical imaging models and strategies to address future challenges and potential applications is also discussed in this review.