Single-cell proteomics enabled by next-generation sequencing or mass spectrometry

Single-cell multi-omics in the study of digestive system cancers

Digestive system cancers are prevalent diseases with a high mortality rate, posing a significant threat to public health and economic burden. The diagnosis and treatment of digestive system cancer confront conventional cancer problems, such as tumor heterogeneity and drug resistance. Single-cell sequencing (SCS) emerged at times required and has developed from single-cell RNA-seq (scRNA-seq) to the single-cell multi-omics era represented by single-cell spatial transcriptomics (ST). This article comprehensively reviews the advances of single-cell omics technology in the study of digestive system tumors. While analyzing and summarizing the research cases, vital details on the sequencing platform, sample information, sampling method, and key findings are provided. Meanwhile, we summarize the commonly used SCS platforms and their features, as well as the advantages of multi-omics technologies in combination. Finally, the development trends and prospects of the application of single-cell multi-omics technology in digestive system cancer research are prospected.

Multicolumn Nanoflow Liquid Chromatography with Accelerated Offline Gradient Generation for Robust and Sensitive Single-Cell Proteome Profiling

Peptide separations that combine high sensitivity, robustness, peak capacity, and throughput are essential for extending bottom-up proteomics to smaller samples including single cells. To this end, we have developed a multicolumn nanoLC system with offline gradient generation. One binary pump generates gradients in an accelerated fashion to support multiple analytical columns, and a single trap column interfaces with all analytical columns to reduce required maintenance and simplify troubleshooting. A high degree of parallelization is possible, as one sample undergoes separation while the next sample plus its corresponding mobile phase gradient are transferred into the storage loop and a third sample is loaded into a sample loop. Selective offline elution from the trap column into the sample loop prevents salts and hydrophobic species from entering the analytical column, thus greatly enhancing column lifetime and system robustness. With this design, samples can be analyzed as fast as every 20 min at a flow rate of just 40 nL/min with close to 100% MS utilization time and continuously for as long as several months without column replacement. We utilized the system to analyze the proteomes of single cells from a multiple myeloma cell line upon treatment with the immunomodulatory imide drug lenalidomide.

All the single cells: if you like it then you should put some virus on it

Across a rich 70-year history, single-cell virology has revealed the impact of host and pathogen heterogeneity during virus infections. Recent technological innovations have enabled higher-resolution analyses of cellular and viral heterogeneity. Furthermore, single-cell analysis has revealed extreme phenotypes and provided additional insights into host-pathogen dynamics. Using a single-cell approach to explore fundamental virology questions, contemporary researchers have contributed to a revival of interest in single-cell virology with increased insights and enthusiasm.

scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network

Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it is crucial to identify drug-responsive cell populations in order to accurately elucidate the mechanism of drug action, which is still a great challenge. Here, we address this problem with scRank, which employs a target-perturbed gene regulatory network to rank drug-responsive cell populations via in silico drug perturbations using untreated single-cell transcriptomic data. We benchmark scRank on simulated and real datasets, which shows the superior performance of scRank over existing methods. When applied to medulloblastoma and major depressive disorder datasets, scRank identifies drug-responsive cell types that are consistent with the literature. Moreover, scRank accurately uncovers the macrophage subpopulation responsive to tanshinone IIA and its potential targets in myocardial infarction, with experimental validation. In conclusion, scRank enables the inference of drug-responsive cell types using untreated single-cell data, thus providing insights into the cellular-level impacts of therapeutic interventions.

Deciphering the genetic code of neuronal type connectivity through bilinear modeling

Understanding how different neuronal types connect and communicate is critical to interpreting brain function and behavior. However, it has remained a formidable challenge to decipher the genetic underpinnings that dictate the specific connections formed between neuronal types. To address this, we propose a novel bilinear modeling approach that leverages the architecture similar to that of recommendation systems. Our model transforms the gene expressions of presynaptic and postsynaptic neuronal types, obtained from single-cell transcriptomics, into a covariance matrix. The objective is to construct this covariance matrix that closely mirrors a connectivity matrix, derived from connectomic data, reflecting the known anatomical connections between these neuronal types. When tested on a dataset of Caenorhabditis elegans, our model achieved a performance comparable to, if slightly better than, the previously proposed spatial connectome model (SCM) in reconstructing electrical synaptic connectivity based on gene expressions. Through a comparative analysis, our model not only captured all genetic interactions identified by the SCM but also inferred additional ones. Applied to a mouse retinal neuronal dataset, the bilinear model successfully recapitulated recognized connectivity motifs between bipolar cells and retinal ganglion cells, and provided interpretable insights into genetic interactions shaping the connectivity. Specifically, it identified unique genetic signatures associated with different connectivity motifs, including genes important to cell-cell adhesion and synapse formation, highlighting their role in orchestrating specific synaptic connections between these neurons. Our work establishes an innovative computational strategy for decoding the genetic programming of neuronal type connectivity. It not only sets a new benchmark for single-cell transcriptomic analysis of synaptic connections but also paves the way for mechanistic studies of neural circuit assembly and genetic manipulation of circuit wiring.

Picoliter Single-Cell Reactor for Proteome Profiling by In Situ Cell Lysis, Protein Immobilization, Digestion, and Droplet Transfer

Global profiling of single-cell proteomes can reveal cellular heterogeneity, thus benefiting precision medicine. However, current mass spectrometry (MS)-based single-cell proteomic sample processing still faces technical challenges associated with processing efficiency and protein recovery. Herein, we present an innovative sample processing platform based on a picoliter single-cell reactor (picoSCR) for single-cell proteome profiling, which involves in situ protein immobilization and sample transfer. PicoSCR helped minimize surface adsorptive losses by downscaling the processing volume to 400 pL with a contact area of less than 0.4 mm2. Besides, picoSCR reached highly efficient cell lysis and digestion within 30 min, benefiting from optimal reagent and high reactant concentrations. Using the picoSCR-nanoLC-MS system, over 1400 proteins were identified from an individual HeLa cell using data-dependent acquisition mode. Proteins with copy number below 1000 were identified, demonstrating this system with a detection limit of 1.7 zmol. Furthermore, we profiled the proteome of circulating tumor cells (CTCs). Data are available via ProteomeXchange with the identifier PXD051468. Proteins associated with epithelial-mesenchymal transition and neutrophil extracellular traps formation (which are both related to tumor metastasis) were observed in all CTCs. The cellular heterogeneity was revealed by differences in signaling pathways within individual cells. These results highlighted the potential of the picoSCR platform to help discover new biomarkers and explore differences in biological processes between cells.

Increasing Proteome Coverage Through a Reduction in Analyte Complexity in Single-Cell Equivalent Samples

The advancement of sophisticated instrumentation in mass spectrometry has catalyzed an in-depth exploration of complex proteomes. This exploration necessitates a nuanced balance in experimental design, particularly between quantitative precision and the enumeration of analytes detected. In bottom-up proteomics, a key challenge is that oversampling of abundant proteins can adversely affect the identification of a diverse array of unique proteins. This issue is especially pronounced in samples with limited analytes, such as small tissue biopsies or single-cell samples. Methods such as depletion and fractionation are suboptimal to reduce oversampling in single cell samples, and other improvements on LC and mass spectrometry technologies and methods have been developed to address the trade-off between precision and enumeration. We demonstrate that by using a monosubstrate protease for proteomic analysis of single-cell equivalent digest samples, an improvement in quantitative accuracy can be achieved, while maintaining high proteome coverage established by trypsin. This improvement is particularly vital for the field of single-cell proteomics, where single-cell samples with limited number of protein copies, especially in the context of low-abundance proteins, can benefit from considering analyte complexity. Considerations about analyte complexity, alongside chromatographic complexity, integration with data acquisition methods, and other factors such as those involving enzyme kinetics, will be crucial in the design of future single-cell workflows.

Ketogenic diet modifies ribosomal protein dysregulation in KMT2D Kabuki syndrome

BACKGROUND

Kabuki syndrome (KS) is a genetic disorder caused by DNA mutations in KMT2D, a lysine methyltransferase that methylates histones and other proteins, and therefore modifies chromatin structure and subsequent gene expression. Ketones, derived from the ketogenic diet, are histone deacetylase inhibitors that can 'open' chromatin and encourage gene expression. Preclinical studies have shown that the ketogenic diet rescues hippocampal memory neurogenesis in mice with KS via the epigenetic effects of ketones.

METHODS

Single-cell RNA sequencing and mass spectrometry-based proteomics were used to explore molecular mechanisms of disease in individuals with KS (n = 4) versus controls (n = 4).

FINDINGS

Pathway enrichment analysis indicated that loss of function mutations in KMT2D are associated with ribosomal protein dysregulation at an RNA and protein level in individuals with KS (FDR <0.05). Cellular proteomics also identified immune dysregulation and increased abundance of other lysine modification and histone binding proteins, representing a potential compensatory mechanism. A 12-year-old boy with KS, suffering from recurrent episodes of cognitive decline, exhibited improved cognitive function and neuropsychological assessment performance after 12 months on the ketogenic diet, with concomitant improvement in transcriptomic ribosomal protein dysregulation.

INTERPRETATION

Our data reveals that lysine methyltransferase deficiency is associated with ribosomal protein dysfunction, with secondary immune dysregulation. Diet and the production of bioactive molecules such as ketone bodies serve as a significant environmental factor that can induce epigenetic changes and improve clinical outcomes. Integrating transcriptomic, proteomic, and clinical data can define mechanisms of disease and treatment effects in individuals with neurodevelopmental disorders.

FUNDING

This study was supported by the Dale NHMRC Investigator Grant (APP1193648) (R.D), Petre Foundation (R.D), and The Sydney Children's Hospital Foundation/Kids Research Early and Mid-Career Researcher Grant (E.T).

Cellular control of protein levels: A systems biology perspective

How cells regulate protein levels is a central question of biology. Over the past decades, molecular biology research has provided profound insights into the mechanisms and the molecular machinery governing each step of the gene expression process, from transcription to protein degradation. Recent advances in transcriptomics and proteomics have complemented our understanding of these fundamental cellular processes with a quantitative, systems-level perspective. Multi-omic studies revealed significant quantitative, kinetic and functional differences between the genome, transcriptome and proteome. While protein levels often correlate with mRNA levels, quantitative investigations have demonstrated a substantial impact of translation and protein degradation on protein expression control. In addition, protein-level regulation appears to play a crucial role in buffering protein abundances against undesirable mRNA expression variation. These findings have practical implications for many fields, including gene function prediction and precision medicine.

Full-Length Single Protein Molecules Tracking and Counting in Thin Silicon Channels

Emerging single-molecule protein sensing techniques are ushering in a transformative era in biomedical research. Nevertheless, challenges persist in realizing ultra-fast full-length protein sensing, including loss of molecular integrity due to protein fragmentation, biases introduced by antibodies affinity, identification of proteoforms, and low throughputs. Here, a single-molecule method for parallel protein separation and tracking is introduced, yielding multi-dimensional molecular properties used for their identification. Proteins are tagged by chemo-selective dual amino-acid specific labels and are electrophoretically separated by their mass/charge in custom-designed thin silicon channel with subwavelength height. This approach allows analysis of thousands of individual proteins within a few minutes by tracking their motion during the migration. The power of the method is demonstrated by quantifying a cytokine panel for host-response discrimination between viral and bacterial infections. Moreover, it is shown that two clinically-relevant splice isoforms of Vascular endothelial growth factor (VEGF) can be accurately quantified from human serum samples. Being non-destructive and compatible with full-length intact proteins, this method opens up ways for antibody-free single-protein molecule quantification.

Accessible high-speed image-activated cell sorting

Over the past six decades, fluorescence-activated cell sorting (FACS) has become an essential technology for basic and clinical research by enabling the isolation of cells of interest in high throughput. Recent technological advancements have started a new era of flow cytometry. By combining the spatial resolution of microscopy with high-speed cell sorting, new instruments allow cell sorting based on simple image-derived parameters or sophisticated image analysis algorithms, thereby greatly expanding the scope of applications. In this review, we discuss the systems that are commercially available or have been described in enough methodological and engineering detail to allow their replication. We summarize their strengths and limitations and highlight applications that have the potential to transform various fields in basic life science research and clinical settings.

Contact lenses as novel tear fluid sampling vehicles for total RNA isolation, precipitation, and amplification

The tear fluid is a readily accessible, potential source for biomarkers of disease and could be used to monitor the ocular response to contact lens (CL) wear or ophthalmic pathologies treated by therapeutic CLs. However, the tear fluid remains largely unexplored as a biomarker source for RNA-based molecular analyses. Using a rabbit model, this study sought to determine whether RNA could be collected from commercial CLs and whether the duration of CL wear would impact RNA recovery. The results were referenced to standardized strips of filtered paper (e.g., Shirmer Strips) placed in the inferior fornix. By performing total RNA isolation, precipitation, and amplification with commercial kits and RT-PCR methods, CLs were found to have no significant differences in RNA concentration and purity compared to Schirmer Strips. The study also identified genes that could be used to normalize RNA levels between tear samples. Of the potential control genes or housekeeping genes, GAPDH was the most stable. This study, which to our knowledge has never been done before, provides a methodology for the detection of RNA and gene expression changes from tear fluid that could be used to monitor or study eye diseases.

The potential of proteomics for in-depth bioanalytical investigations of satellite cell function in applied myology

INTRODUCTION

Regenerative myogenesis plays a crucial role in mature myofibers to counteract muscular injury or dysfunction due to neuromuscular disorders. The activation of specialized myogenic stem cells, called satellite cells, is intrinsically involved in proliferation and differentiation, followed by myoblast fusion and the formation of multinucleated myofibers.

AREAS COVERED

This report provides an overview of the role of satellite cells in the neuromuscular system and the potential future impact of proteomic analyses for biomarker discovery, as well as the identification of novel therapeutic targets in muscle disease. The article reviews the ways in which the systematic analysis of satellite cells, myoblasts, and myocytes by single-cell proteomics can help to better understand the process of myofiber regeneration.

EXPERT OPINION

In order to better comprehend satellite cell dysfunction in neuromuscular disorders, mass spectrometry-based proteomics is an excellent large-scale analytical tool for the systematic profiling of pathophysiological processes. The optimized isolation of muscle-derived cells can be routinely performed by mechanical/enzymatic dissociation protocols, followed by fluorescence-activated cell sorting in specialized flow cytometers. Ultrasensitive single-cell proteomics using label-free quantitation methods or approaches that utilize tandem mass tags are ideal bioanalytical approaches to study the pathophysiological role of stem cells in neuromuscular disease.

A review of single-cell transcriptomics and epigenomics studies in maternal and child health

Single-cell sequencing technologies enhance our understanding of cellular dynamics throughout pregnancy. We outlined the workflow of single-cell sequencing techniques and reviewed single-cell studies in maternal and child health. We conducted a literature review of single cell studies on maternal and child health using PubMed. We summarized the findings from 16 single-cell atlases of the human and mammalian placenta across gestational stages and 31 single-cell studies on maternal exposures and complications including infection, obesity, diet, gestational diabetes, pre-eclampsia, environmental exposure and preterm birth. Single-cell studies provides insights on novel cell types in placenta and cell type-specific marks associated with maternal exposures and complications.

Hallmarks of cancer stemness

Cancer stemness is recognized as a key component of tumor development. Previously coined "cancer stem cells" (CSCs) and believed to be a rare population with rigid hierarchical organization, there is good evidence to suggest that these cells exhibit a plastic cellular state influenced by dynamic CSC-niche interplay. This revelation underscores the need to reevaluate the hallmarks of cancer stemness. Herein, we summarize the techniques used to identify and characterize the state of these cells and discuss their defining and emerging hallmarks, along with their enabling and associated features. We also highlight potential future directions in this field of research.

scmFormer Integrates Large-Scale Single-Cell Proteomics and Transcriptomics Data by Multi-Task Transformer

Transformer-based models have revolutionized single cell RNA-seq (scRNA-seq) data analysis. However, their applicability is challenged by the complexity and scale of single-cell multi-omics data. Here a novel single-cell multi-modal/multi-task transformer (scmFormer) is proposed to fill up the existing blank of integrating single-cell proteomics with other omics data. Through systematic benchmarking, it is demonstrated that scmFormer excels in integrating large-scale single-cell multimodal data and heterogeneous multi-batch paired multi-omics data, while preserving shared information across batchs and distinct biological information. scmFormer achieves 54.5% higher average F1 score compared to the second method in transferring cell-type labels from single-cell transcriptomics to proteomics data. Using COVID-19 datasets, it is presented that scmFormer successfully integrates over 1.48 million cells on a personal computer. Moreover, it is also proved that scmFormer performs better than existing methods on generating the unmeasured modality and is well-suited for spatial multi-omic data. Thus, scmFormer is a powerful and comprehensive tool for analyzing single-cell multi-omics data.

scLENS: data-driven signal detection for unbiased scRNA-seq data analysis

High dimensionality and noise have limited the new biological insights that can be discovered in scRNA-seq data. While dimensionality reduction tools have been developed to extract biological signals from the data, they often require manual determination of signal dimension, introducing user bias. Furthermore, a common data preprocessing method, log normalization, can unintentionally distort signals in the data. Here, we develop scLENS, a dimensionality reduction tool that circumvents the long-standing issues of signal distortion and manual input. Specifically, we identify the primary cause of signal distortion during log normalization and effectively address it by uniformizing cell vector lengths with L2 normalization. Furthermore, we utilize random matrix theory-based noise filtering and a signal robustness test to enable data-driven determination of the threshold for signal dimensions. Our method outperforms 11 widely used dimensionality reduction tools and performs particularly well for challenging scRNA-seq datasets with high sparsity and variability. To facilitate the use of scLENS, we provide a user-friendly package that automates accurate signal detection of scRNA-seq data without manual time-consuming tuning.

Multimodal joint deconvolution and integrative signature selection in proteomics

Deconvolution is an efficient approach for detecting cell-type-specific (cs) transcriptomic signals without cellular segmentation. However, this type of methods may require a reference profile from the same molecular source and tissue type. Here, we present a method to dissect bulk proteome by leveraging tissue-matched transcriptome and proteome without using a proteomics reference panel. Our method also selects the proteins contributing to the cellular heterogeneity shared between bulk transcriptome and proteome. The deconvoluted result enables downstream analyses such as cs-protein Quantitative Trait Loci (cspQTL) mapping. We benchmarked the performance of this multimodal deconvolution approach through CITE-seq pseudo bulk data, a simulation study, and the bulk multi-omics data from human brain normal tissues and breast cancer tumors, individually, showing robust and accurate cell abundance quantification across different datasets. This algorithm is implemented in a tool MICSQTL that also provides cspQTL and multi-omics integrative visualization, available at https://bioconductor.org/packages/MICSQTL .

Single-Cell Informatics for Tumor Microenvironment and Immunotherapy

Cancer comprises malignant cells surrounded by the tumor microenvironment (TME), a dynamic ecosystem composed of heterogeneous cell populations that exert unique influences on tumor development. The immune community within the TME plays a substantial role in tumorigenesis and tumor evolution. The innate and adaptive immune cells "talk" to the tumor through ligand-receptor interactions and signaling molecules, forming a complex communication network to influence the cellular and molecular basis of cancer. Such intricate intratumoral immune composition and interactions foster the application of immunotherapies, which empower the immune system against cancer to elicit durable long-term responses in cancer patients. Single-cell technologies have allowed for the dissection and characterization of the TME to an unprecedented level, while recent advancements in bioinformatics tools have expanded the horizon and depth of high-dimensional single-cell data analysis. This review will unravel the intertwined networks between malignancy and immunity, explore the utilization of computational tools for a deeper understanding of tumor-immune communications, and discuss the application of these approaches to aid in diagnosis or treatment decision making in the clinical setting, as well as the current challenges faced by the researchers with their potential future improvements.

Current views of drought research: experimental methods, adaptation mechanisms and regulatory strategies

Drought stress is one of the most important abiotic stresses which causes many yield losses every year. This paper presents a comprehensive review of recent advances in international drought research. First, the main types of drought stress and the commonly used drought stress methods in the current experiment were introduced, and the advantages and disadvantages of each method were evaluated. Second, the response of plants to drought stress was reviewed from the aspects of morphology, physiology, biochemistry and molecular progression. Then, the potential methods to improve drought resistance and recent emerging technologies were introduced. Finally, the current research dilemma and future development direction were summarized. In summary, this review provides insights into drought stress research from different perspectives and provides a theoretical reference for scholars engaged in and about to engage in drought research.

scPROTEIN: a versatile deep graph contrastive learning framework for single-cell proteomics embedding

Single-cell proteomics sequencing technology sheds light on protein-protein interactions, posttranslational modifications and proteoform dynamics in the cell. However, the uncertainty estimation for peptide quantification, data missingness, batch effects and high noise hinder the analysis of single-cell proteomic data. It is important to solve this set of tangled problems together, but the existing methods tailored for single-cell transcriptomes cannot fully address this task. Here we propose a versatile framework designed for single-cell proteomics data analysis called scPROTEIN, which consists of peptide uncertainty estimation based on a multitask heteroscedastic regression model and cell embedding generation based on graph contrastive learning. scPROTEIN can estimate the uncertainty of peptide quantification, denoise protein data, remove batch effects and encode single-cell proteomic-specific embeddings in a unified framework. We demonstrate that scPROTEIN is efficient for cell clustering, batch correction, cell type annotation, clinical analysis and spatially resolved proteomic data exploration.

Proteomics applications in next generation induced pluripotent stem cell models

INTRODUCTION

Induced pluripotent stem (iPS) cell technology has transformed biomedical research. New opportunities now exist to create new organoids, microtissues, and body-on-a-chip systems for basic biology investigations and clinical translations.

AREAS COVERED

We discuss the utility of proteomics for attaining an unbiased view into protein expression changes during iPS cell differentiation, cell maturation, and tissue generation. The ability to discover cell-type specific protein markers during the differentiation and maturation of iPS-derived cells has led to new strategies to improve cell production yield and fidelity. In parallel, proteomic characterization of iPS-derived organoids is helping to realize the goal of bridging in vitro and in vivo systems.

EXPERT OPINIONS

We discuss some current challenges of proteomics in iPS cell research and future directions, including the integration of proteomic and transcriptomic data for systems-level analysis.

Integration of single-cell proteomic datasets through distinctive proteins in cell clusters

The use of mass spectrometry and antibody-based sequencing technologies at the single-cell level has led to an increase in single-cell proteomic datasets. Integrating these datasets is crucial to eliminate the batch effect that often arises due to their limited sequencing molecules. Although methods for horizontally integrating high-dimensional single-cell transcriptomic datasets can also be applied to single-cell proteomic datasets, a specialized approach explicitly tailored for low-dimensional proteomic datasets may enhance the integration process. Here, we introduce SCPRO-HI, an algorithm for the horizontal integration of antibody-based single-cell proteomic datasets. It utilizes a hierarchical cell anchoring technique to match cells based on the similarity of distinctive proteins for constituting cell clusters. A novel variational auto-encoder model is employed for correcting batch effects on the protein abundances, eliminating the need for mapping them into a new domain. Moreover, we propose a technique for extending the algorithm to high-dimensional datasets. The performance of the SCPRO-HI algorithm is evaluated using simulated and real-world single-cell proteomic datasets. The findings demonstrate our algorithm outperforms state-of-the-art methods, achieving a 75% higher silhouette score while preserving HVPs 13% better. Furthermore, the algorithm shows competitive performance in transcriptomic datasets, suggesting potential for integrating high-dimensional mass-spectrometry-based proteomic datasets.

Tumor-infiltrating lymphocytes as a prognostic and predictive factor for Melanoma

INTRODUCTION

Tumor-infiltrating lymphocytes (TILs) have been investigated as prognostic factors in melanoma. Recent advancements in assessing the tumor microenvironment in the setting of more widespread use of immune checkpoint blockade have reignited interest in identifying predictive biomarkers. This review examines the function and significance of TILs in cutaneous melanoma, evaluating their potential as prognostic and predictive markers.

AREAS COVERED

A literature search was conducted on papers covering tumor infiltrating lymphocytes in cutaneous melanoma available online in PubMed and Web of Science from inception to 1 December 2023, supplemented by citation searching. This article encompasses the assessment of TILs, the role of TILs in the immune microenvironment, TILs as a prognostic factor, TILs as a predictive factor for immunotherapy response, and clinical applications of TILs in the treatment of cutaneous melanoma.

EXPERT OPINION

Tumor-infiltrating lymphocytes play a heterogeneous role in cutaneous melanoma. While they have historically been associated with improved survival, their status as independent prognostic or predictive factors remains uncertain. Novel methods of TIL assessment, such as determination of TIL subtypes and molecular signaling, demonstrate potential for predicting therapeutic response. Further, while their clinical utility in risk-stratification in melanoma treatment shows promise, a lack of consensus data hinders standardized application.

Beyond single cells: microfluidics empowering multiomics analysis

Single-cell multiomics technologies empower simultaneous measurement of multiple types of molecules within individual cells, providing a more profound comprehension compared with the analysis of discrete molecular layers from different cells. Microfluidic technology, on the other hand, has emerged as a pivotal facilitator for high-throughput single-cell analysis, offering precise control and manipulation of individual cells. The primary focus of this review encompasses an appraisal of cutting-edge microfluidic platforms employed in the realm of single-cell multiomics analysis. Furthermore, it discusses technological advancements in various single-cell omics such as genomics, transcriptomics, epigenomics, and proteomics, with their perspective applications. Finally, it provides future prospects of these integrated single-cell multiomics methodologies, shedding light on the possibilities for future biological research.

Spatiotemporal analysis of mRNA-protein relationships enhances transcriptome-based developmental inference

Elucidating the complex relationships between mRNA and protein expression at high spatiotemporal resolution is critical for unraveling multilevel gene regulation and enhancing mRNA-based developmental analyses. In this study, we conduct a single-cell analysis of mRNA and protein expression of transcription factors throughout C. elegans embryogenesis. Initially, cellular co-presence of mRNA and protein is low, increasing to a medium-high level (73%) upon factoring in delayed protein synthesis and long-term protein persistence. These factors substantially affect mRNA-protein concordance, leading to potential inaccuracies in mRNA-reliant gene detection and specificity characterization. Building on the learned relationship, we infer protein presence from mRNA expression and demonstrate its utility in identifying tissue-specific genes and elucidating relationships between genes and cells. This approach facilitates identifying the role of sptf-1/SP7 in neuronal lineage development. Collectively, this study provides insights into gene expression dynamics during rapid embryogenesis and approaches for improving the efficacy of transcriptome-based developmental analyses.

Protein glycosylation in cardiovascular health and disease

Protein glycosylation, which involves the attachment of carbohydrates to proteins, is one of the most abundant protein co-translational and post-translational modifications. Advances in technology have substantially increased our knowledge of the biosynthetic pathways involved in protein glycosylation, as well as how changes in glycosylation can affect cell function. In addition, our understanding of the role of protein glycosylation in disease processes is growing, particularly in the context of immune system function, infectious diseases, neurodegeneration and cancer. Several decades ago, cell surface glycoproteins were found to have an important role in regulating ion transport across the cardiac sarcolemma. However, with very few exceptions, our understanding of how changes in protein glycosylation influence cardiovascular (patho)physiology remains remarkably limited. Therefore, in this Review, we aim to provide an overview of N-linked and O-linked protein glycosylation, including intracellular O-linked N-acetylglucosamine protein modification. We discuss our current understanding of how all forms of protein glycosylation contribute to normal cardiovascular function and their roles in cardiovascular disease. Finally, we highlight potential gaps in our knowledge about the effects of protein glycosylation on the heart and vascular system, highlighting areas for future research.

Research progress on the multi-omics and survival status of circulating tumor cells

In the dynamic process of metastasis, circulating tumor cells (CTCs) emanate from the primary solid tumor and subsequently acquire the capacity to disengage from the basement membrane, facilitating their infiltration into the vascular system via the interstitial tissue. Given the pivotal role of CTCs in the intricate hematogenous metastasis, they have emerged as an essential resource for a deeper comprehension of cancer metastasis while also serving as a cornerstone for the development of new indicators for early cancer screening and new therapeutic targets. In the epoch of precision medicine, as CTC enrichment and separation technologies continually advance and reach full fruition, the domain of CTC research has transcended the mere straightforward detection and quantification. The rapid advancement of CTC analysis platforms has presented a compelling opportunity for in-depth exploration of CTCs within the bloodstream. Here, we provide an overview of the current status and research significance of multi-omics studies on CTCs, including genomics, transcriptomics, proteomics, and metabolomics. These studies have contributed to uncovering the unique heterogeneity of CTCs and identifying potential metastatic targets as well as specific recognition sites. We also review the impact of various states of CTCs in the bloodstream on their metastatic potential, such as clustered CTCs, interactions with other blood components, and the phenotypic states of CTCs after undergoing epithelial-mesenchymal transition (EMT). Within this context, we also discuss the therapeutic implications and potential of CTCs.

Nanopore DNA sequencing technologies and their applications towards single-molecule proteomics

Sequencing of nucleic acids with nanopores has emerged as a powerful tool offering rapid readout, high accuracy, low cost and portability. This label-free method for sequencing at the single-molecule level is an achievement on its own. However, nanopores also show promise for the technologically even more challenging sequencing of polypeptides, something that could considerably benefit biological discovery, clinical diagnostics and homeland security, as current techniques lack portability and speed. Here we survey the biochemical innovations underpinning commercial and academic nanopore DNA/RNA sequencing techniques, and explore how these advances can fuel developments in future protein sequencing with nanopores.

Opticool: Cutting-edge transgenic optical tools

Only a few short decades have passed since the sequencing of GFP, yet the modern repertoire of transgenically encoded optical tools implies an exponential proliferation of ever improving constructions to interrogate the subcellular environment. A myriad of tags for labeling proteins, RNA, or DNA have arisen in the last few decades, facilitating unprecedented visualization of subcellular components and processes. Development of a broad array of modern genetically encoded sensors allows real-time, in vivo detection of molecule levels, pH, forces, enzyme activity, and other subcellular and extracellular phenomena in ever expanding contexts. Optogenetic, genetically encoded optically controlled manipulation systems have gained traction in the biological research community and facilitate single-cell, real-time modulation of protein function in vivo in ever broadening, novel applications. While this field continues to explosively expand, references are needed to assist scientists seeking to use and improve these transgenic devices in new and exciting ways to interrogate development and disease. In this review, we endeavor to highlight the state and trajectory of the field of in vivo transgenic optical tools.

The 15-min (Sub)Cellular Proteome

Single-cell mass spectrometry (MS) opens a proteomic window onto the inner workings of cells. Here, we report the discovery characterization of the subcellular proteome of single, identified embryonic cells in record speed and molecular coverage. We integrated subcellular capillary microsampling, fast capillary electrophoresis (CE), high-efficiency nano-flow electrospray ionization, and orbitrap tandem MS. In proof-of-principle tests, we found shorter separation times to hinder proteome detection using DDA, but not DIA. Within a 15-min effective separation window, CE data-independent acquisition (DIA) was able to identify 1,161 proteins from single HeLa-cell-equivalent (∼200 pg) proteome digests vs. 401 proteins by the reference data-dependent acquisition (DDA) on the same platform. The approach measured 1,242 proteins from subcellular niches in an identified cell in the live Xenopus laevis (frog) embryo, including many canonical components of organelles. CE-MS with DIA enables fast, sensitive, and deep profiling of the (sub)cellular proteome, expanding the bioanalytical toolbox of cell biology.

Authorship Contributions

P.N. and B.S. designed the study. L.R.P. collected the X. laevis cell aspirates. B.S. prepared and measured the samples. B.S. and P.N. analyzed the data and interpreted the results. P.N. and B.S. wrote the manuscript. All the authors commented on the manuscript.

Deciphering single-cell protein secretion and gene expressions by constructing cell-antibody conjugates

Secreted proteins play critical roles in regulating immune responses, exerting cytotoxic effects on tumor cells, promoting inflammatory processes, and influencing cellular metabolism. Deciphering the intricate relationship between the heterogeneity of secreted proteins and their transcriptional states is pivotal in the study of cellular heterogeneity. Here we proposed a cell-antibody conjugate-based sequencing methodology (Cellab-seq) for joint characterization of secreted proteins and transcriptome. Cellab-seq utilizes a chemoenzymatic strategy to construct cell-antibody conjugates, which enables the capture of secreted proteins and their signal transduction with the incorporation of barcode detection antibodies. We applied Cellab-seq to investigate how gene expression influences the activity of secreted proteins in NK cells. Altogether, this strategy facilitates a nuanced understanding of cellular dynamics under diverse physiological conditions, ultimately contributing to the prevention, diagnosis and treatment of diseases.

Advances and opportunities in methods to study protein translation - A review

It is generally believed that the regulation of gene expression involves protein translation occurring before RNA transcription. Therefore, it is crucial to investigate protein translation and its regulation. Recent advancements in biological sciences, particularly in the field of omics, have revolutionized protein translation research. These studies not only help characterize changes in protein translation during specific biological or pathological processes but also have significant implications in disease prevention and treatment. In this review, we summarize the latest methods in ribosome-based translation omics. We specifically focus on the application of fluorescence imaging technology and omics technology in studying overall protein translation. Additionally, we analyze the advantages, disadvantages, and application of these experimental methods, aiming to provide valuable insights and references to researchers studying translation.

Novel Insights into the Initiation, Evolution, and Progression of Multiple Myeloma by Multi-Omics Investigation

The evolutionary history of multiple myeloma (MM) includes malignant transformation, followed by progression to pre-malignant stages and overt malignancy, ultimately leading to more aggressive and resistant forms. Over the past decade, large effort has been made to identify the potential therapeutic targets in MM. However, MM remains largely incurable. Most patients experience multiple relapses and inevitably become refractory to treatment. Tumor-initiating cell populations are the postulated population, leading to the recurrent relapses in many hematological malignancies. Clonal evolution of tumor cells in MM has been identified along with the disease progression. As a consequence of different responses to the treatment of heterogeneous MM cell clones, the more aggressive populations survive and evolve. In addition, the tumor microenvironment is a complex ecosystem which plays multifaceted roles in supporting tumor cell evolution. Emerging multi-omics research at single-cell resolution permits an integrative and comprehensive profiling of the tumor cells and microenvironment, deepening the understanding of biological features of MM. In this review, we intend to discuss the novel insights into tumor cell initiation, clonal evolution, drug resistance, and tumor microenvironment in MM, as revealed by emerging multi-omics investigations. These data suggest a promising strategy to unravel the pivotal mechanisms of MM progression and enable the improvement in treatment, both holistically and precisely.

Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases

Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches. We discuss the advancements in low-resolution and high-resolution spatial multi-omics methods which can resolve up to 10,000 of individual molecules at subcellular level. By applying and integrating these techniques, researchers have recently gained valuable insights into the molecular circuits and mechanisms which govern cell biology along the cardiovascular disease spectrum. We provide an overview of current data analysis approaches, with a focus on data integration of multi-omic datasets, highlighting strengths and weaknesses of various computational pipelines. These tools play a crucial role in analyzing and interpreting spatial multi-omics datasets, facilitating the discovery of new findings, and enhancing translational cardiovascular research. Despite nontrivial challenges, such as the need for standardization of experimental setups, data analysis, and improved computational tools, the application of spatial multi-omics holds tremendous potential in revolutionizing our understanding of human disease processes and the identification of novel biomarkers and therapeutic targets. Exciting opportunities lie ahead for the spatial multi-omics field and will likely contribute to the advancement of personalized medicine for cardiovascular diseases.

How Can Proteomics Help to Elucidate the Pathophysiological Crosstalk in Muscular Dystrophy and Associated Multi-System Dysfunction?

This perspective article is concerned with the question of how proteomics, which is a core technique of systems biology that is deeply embedded in the multi-omics field of modern bioresearch, can help us better understand the molecular pathogenesis of complex diseases. As an illustrative example of a monogenetic disorder that primarily affects the neuromuscular system but is characterized by a plethora of multi-system pathophysiological alterations, the muscle-wasting disease Duchenne muscular dystrophy was examined. Recent achievements in the field of dystrophinopathy research are described with special reference to the proteome-wide complexity of neuromuscular changes and body-wide alterations/adaptations. Based on a description of the current applications of top-down versus bottom-up proteomic approaches and their technical challenges, future systems biological approaches are outlined. The envisaged holistic and integromic bioanalysis would encompass the integration of diverse omics-type studies including inter- and intra-proteomics as the core disciplines for systematic protein evaluations, with sophisticated biomolecular analyses, including physiology, molecular biology, biochemistry and histochemistry. Integrated proteomic findings promise to be instrumental in improving our detailed knowledge of pathogenic mechanisms and multi-system dysfunction, widening the available biomarker signature of dystrophinopathy for improved diagnostic/prognostic procedures, and advancing the identification of novel therapeutic targets to treat Duchenne muscular dystrophy.

The critical role of neutrophil-endothelial cell interactions in sepsis: new synergistic approaches employing organ-on-chip, omics, immune cell phenotyping and in silico modeling to identify new therapeutics

Sepsis is a global health concern accounting for more than 1 in 5 deaths worldwide. Sepsis is now defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Sepsis can develop from bacterial (gram negative or gram positive), fungal or viral (such as COVID) infections. However, therapeutics developed in animal models and traditional in vitro sepsis models have had little success in clinical trials, as these models have failed to fully replicate the underlying pathophysiology and heterogeneity of the disease. The current understanding is that the host response to sepsis is highly diverse among patients, and this heterogeneity impacts immune function and response to infection. Phenotyping immune function and classifying sepsis patients into specific endotypes is needed to develop a personalized treatment approach. Neutrophil-endothelium interactions play a critical role in sepsis progression, and increased neutrophil influx and endothelial barrier disruption have important roles in the early course of organ damage. Understanding the mechanism of neutrophil-endothelium interactions and how immune function impacts this interaction can help us better manage the disease and lead to the discovery of new diagnostic and prognosis tools for effective treatments. In this review, we will discuss the latest research exploring how in silico modeling of a synergistic combination of new organ-on-chip models incorporating human cells/tissue, omics analysis and clinical data from sepsis patients will allow us to identify relevant signaling pathways and characterize specific immune phenotypes in patients. Emerging technologies such as machine learning can then be leveraged to identify druggable therapeutic targets and relate them to immune phenotypes and underlying infectious agents. This synergistic approach can lead to the development of new therapeutics and the identification of FDA approved drugs that can be repurposed for the treatment of sepsis.

Expression Atlas update: insights from sequencing data at both bulk and single cell level

Expression Atlas (www.ebi.ac.uk/gxa) and its newest counterpart the Single Cell Expression Atlas (www.ebi.ac.uk/gxa/sc) are EMBL-EBI's knowledgebases for gene and protein expression and localisation in bulk and at single cell level. These resources aim to allow users to investigate their expression in normal tissue (baseline) or in response to perturbations such as disease or changes to genotype (differential) across multiple species. Users are invited to search for genes or metadata terms across species or biological conditions in a standardised consistent interface. Alongside these data, new features in Single Cell Expression Atlas allow users to query metadata through our new cell type wheel search. At the experiment level data can be explored through two types of dimensionality reduction plots, t-distributed Stochastic Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP), overlaid with either clustering or metadata information to assist users' understanding. Data are also visualised as marker gene heatmaps identifying genes that help confer cluster identity. For some data, additional visualisations are available as interactive cell level anatomograms and cell type gene expression heatmaps.

SPDB: a comprehensive resource and knowledgebase for proteomic data at the single-cell resolution

The single-cell proteomics enables the direct quantification of protein abundance at the single-cell resolution, providing valuable insights into cellular phenotypes beyond what can be inferred from transcriptome analysis alone. However, insufficient large-scale integrated databases hinder researchers from accessing and exploring single-cell proteomics, impeding the advancement of this field. To fill this deficiency, we present a comprehensive database, namely Single-cell Proteomic DataBase (SPDB, https://scproteomicsdb.com/), for general single-cell proteomic data, including antibody-based or mass spectrometry-based single-cell proteomics. Equipped with standardized data process and a user-friendly web interface, SPDB provides unified data formats for convenient interaction with downstream analysis, and offers not only dataset-level but also protein-level data search and exploration capabilities. To enable detailed exhibition of single-cell proteomic data, SPDB also provides a module for visualizing data from the perspectives of cell metadata or protein features. The current version of SPDB encompasses 133 antibody-based single-cell proteomic datasets involving more than 300 million cells and over 800 marker/surface proteins, and 10 mass spectrometry-based single-cell proteomic datasets involving more than 4000 cells and over 7000 proteins. Overall, SPDB is envisioned to be explored as a useful resource that will facilitate the wider research communities by providing detailed insights into proteomics from the single-cell perspective.

Functional genomics and small molecules in mitochondrial neurodevelopmental disorders

Mitochondria are critical for brain development and homeostasis. Therefore, pathogenic variation in the mitochondrial or nuclear genome which disrupts mitochondrial function frequently results in developmental disorders and neurodegeneration at the organismal level. Large-scale application of genome-wide technologies to individuals with mitochondrial diseases has dramatically accelerated identification of mitochondrial disease-gene associations in humans. Multi-omic and high-throughput studies involving transcriptomics, proteomics, metabolomics, and saturation genome editing are providing deeper insights into the functional consequence of mitochondrial genomic variation. Integration of deep phenotypic and genomic data through allelic series continues to uncover novel mitochondrial functions and permit mitochondrial gene function dissection on an unprecedented scale. Finally, mitochondrial disease-gene associations illuminate disease mechanisms and thereby direct therapeutic strategies involving small molecules and RNA-DNA therapeutics. This review summarizes progress in functional genomics and small molecule therapeutics in mitochondrial neurodevelopmental disorders.

Efficient and Sensitive Sample Preparation, Separations, and Data Acquisition for Label-Free Single-Cell Proteomics

We describe a sensitive and efficient workflow for label-free single-cell proteomics that spans sample preparation, liquid chromatography separations, and mass spectrometry data acquisition. The Tecan Uno Single Cell Dispenser provides rapid cell isolation and nanoliter-volume reagent dispensing within 384-well PCR plates. A newly developed sample processing workflow achieves cell lysis, protein denaturation, and digestion in 1 h with a single reagent dispensing step. Low-flow liquid chromatography coupled with wide-window data-dependent acquisition results in the quantification of nearly 3000 proteins per cell using an Orbitrap Exploris 480 mass spectrometer. This approach greatly broadens accessibility to sensitive single-cell proteome profiling for nonspecialist laboratories.

Advances in cellular and tissue-based imaging techniques for sarcoid granulomas

Sarcoidosis embodies a complex inflammatory disorder spanning multiple systems, with its origin remaining elusive. It manifests as the infiltration of inflammatory cells that coalesce into distinctive noncaseous granulomas within afflicted organs. Unraveling this disease necessitates the utilization of cellular or tissue-based imaging methods to both visualize and characterize the biochemistry of these sarcoid granulomas. Although hematoxylin and eosin stain, standard in routine use alongside cytological stains have found utility in diagnosis within clinical contexts, special stains such as Masson's trichrome, reticulin, methenamine silver, and Ziehl-Neelsen provide additional varied perspectives of sarcoid granuloma imaging. Immunohistochemistry aids in pinpointing specific proteins and gene expressions further characterizing these granulomas. Finally, recent advances in spatial transcriptomics promise to divulge profound insights into their spatial orientation and three-dimensional (3-D) molecular mapping. This review focuses on a range of preexisting imaging methods employed for visualizing sarcoid granulomas at the cellular level while also exploring the potential of the latest cutting-edge approaches like spatial transcriptomics and matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI), with the overarching goal of shedding light on the trajectory of sarcoidosis research.

Identification of RP11-770J1.4 as immune-related lncRNA regulating the CTXN1-cGAS-STING axis in histologically lower-grade glioma

Human gliomas are lethal brain cancers. Emerging evidence revealed the regulatory role of long noncoding RNAs (lncRNAs) in tumors. Here, we performed a comprehensive analysis of the expression profiles of RNAs in histologically lower-grade glioma (LGG). Enrichment analysis revealed that glioma is influenced by immune-related signatures. Survival analysis further established the close correlation between network features and glioma prognosis. Subsequent experiments showed lncRNA RP11-770J1.4 regulates CTXN1 expression through hsa-miR-124-3p. Correlation analysis identified lncRNA RP11-770J1.4 was immune related, specifically involved in the cytosolic DNA sensing pathway. Downregulated lncRNA RP11-770J1.4 resulted in increased spontaneous gene expression of the cGAS-STING pathway. Single-cell RNA sequencing analysis, along with investigations in a glioblastoma stem cell model and patient sample analysis, demonstrated the predominant localization of CTXN1 within tumor cores rather than peripheral regions. Immunohistochemistry staining established a negative correlation between CTXN1 expression and infiltration of CD8+ T cells. In vivo, Ctxn1 knockdown in GL261 cells led to decreased tumor burden and improved survival while increasing infiltration of CD8+ T cells. These findings unveil novel insights into the lncRNA RP11-770J1.4-CTXN1 as a potential immune regulatory axis, highlighting its therapeutic implications for histologically LGGs.

Exploring the dynamic behavior of leukocytes with zebrafish

Cell migration is a complex and intricate network of physical, chemical, and molecular events that ultimately leads to cell motility. This phenomenon is involved in both physiological and pathological processes such as proper immune and inflammatory responses. Dysregulation of cell migration machinery in immune cells can have a tremendous impact on the trajectory of inflammation, infection, and resolution. The small vertebrate, the zebrafish, has a remarkable capacity for genetic and pharmacological manipulation aligned to transparency that enables modulation and visualization of cell migration in vivo noninvasively. Such characteristics revolutionized the field of leukocyte biology, particularly neutrophils. In this review, we will focus on leukocyte migration and highlight findings made in the zebrafish that demonstrate how this small vertebrate system is a unique model to perform in vivo imaging and study mechanisms that regulate the dynamic behavior of immune cells in their native environment under homeostasis or upon challenge.

Proteomics provides insights into the theranostic potential of extracellular vesicles

Extracellular vesicles (EVs) encompass a diverse range of membranous structures derived from cells, including exosomes and microvesicles. These vesicles are present in biological fluids and play vital roles in various physiological and pathological processes. They facilitate intercellular communication by enabling the exchange of proteins, lipids, and genetic material between cells. Understanding the cellular processes that govern EV biology is essential for unraveling their physiological and pathological functions and their potential clinical applications. Despite significant advancements in EV research in recent years, there is still much to learn about these vesicles. The advent of improved mass spectrometry (MS)-based techniques has allowed for a deeper characterization of EV protein composition, providing valuable insights into their roles in different physiological and pathological conditions. In this chapter, we provide an overview of proteomics studies conducted to identify the protein contents of EVs, which contribute to their therapeutic and pathological features. We also provided evidence on the potential of EV proteome contents as biomarkers for early disease diagnosis, progression, and treatment response, as well as factors that influence their composition. Additionally, we discuss the available databases containing information on EV proteome contents, and finally, we highlight the need for further research to pave the way toward their utilization in clinical settings.

Metabolic predictors of response to immune checkpoint blockade therapy

Metabolism of immune cells in the tumor microenvironment (TME) plays a critical role in cancer patient response to immune checkpoint inhibitors (ICI). Yet, a metabolic characterization of immune cells in the TME of patients treated with ICI is lacking. To bridge this gap we performed a semi-supervised analysis of ∼1700 metabolic genes using single-cell RNA-seq data of > 1 million immune cells from ∼230 samples of cancer patients treated with ICI. When clustering cells based on their metabolic gene expression, we found that similar immunological cellular states are found in different metabolic states. Most importantly, we found metabolic states that are significantly associated with patient response. We then built a metabolic predictor based on a dozen gene signature, which significantly differentiates between responding and non-responding patients across different cancer types (AUC = 0.8-0.92). Taken together, our results demonstrate the power of metabolism in predicting patient response to ICI.

Liquid biopsy: creating opportunities in brain space

In recent years, liquid biopsy has emerged as an alternative method to diagnose and monitor tumors. Compared to classical tissue biopsy procedures, liquid biopsy facilitates the repetitive collection of diverse cellular and acellular analytes from various biofluids in a non/minimally invasive manner. This strategy is of greater significance for high-grade brain malignancies such as glioblastoma as the quantity and accessibility of tumors are limited, and there are collateral risks of compromised life quality coupled with surgical interventions. Currently, blood and cerebrospinal fluid (CSF) are the most common biofluids used to collect circulating cells and biomolecules of tumor origin. These liquid biopsy analytes have created opportunities for real-time investigations of distinct genetic, epigenetic, transcriptomics, proteomics, and metabolomics alterations associated with brain tumors. This review describes different classes of liquid biopsy biomarkers present in the biofluids of brain tumor patients. Moreover, an overview of the liquid biopsy applications, challenges, recent technological advances, and clinical trials in the brain have also been provided.

Chemical tagging mass spectrometry: an approach for single-cell omics

Single-cell (SC) analysis offers new insights into the study of fundamental biological phenomena and cellular heterogeneity. The superior sensitivity, high throughput, and rich chemical information provided by mass spectrometry (MS) allow MS to emerge as a leading technology for molecular profiling of SC omics, including the SC metabolome, lipidome, and proteome. However, issues such as ionization suppression, low concentration, and huge span of dynamic concentrations of SC components lead to poor MS response for certain types of molecules. It is noted that chemical tagging/derivatization has been adopted in SCMS analysis, and this strategy has been proven an effective solution to circumvent these issues in SCMS analysis. Herein, we review the basic principle and general strategies of chemical tagging/derivatization in SCMS analysis, along with recent applications of chemical derivatization to single-cell metabolomics and multiplexed proteomics, as well as SCMS imaging. Furthermore, the challenges and opportunities for the improvement of chemical derivatization strategies in SCMS analysis are discussed.