An Integrated Strategy for Mass Spectrometry-Based Multiomics Analysis of Single Cells

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.

Advances in mass spectrometry-enabled multiomics at single-cell resolution

Biological organisms are multifaceted, intricate systems where slight perturbations can result in extensive changes in gene expression, protein abundance and/or activity, and metabolic flux. These changes occur at different timescales, spatially across cells of heterogeneous origins, and within single-cells. Hence, multimodal measurements at the smallest biological scales are necessary to capture dynamic changes in heterogeneous biological systems. Of the analytical techniques used to measure biomolecules, mass spectrometry (MS) has proven to be a powerful option due to its sensitivity, robustness, and flexibility with regard to the breadth of biomolecules that can be analyzed. Recently, many studies have coupled MS to other analytical techniques with the goal of measuring multiple modalities from the same single-cell. It is with these concepts in mind that we focus this review on MS-enabled multiomic measurements at single-cell or near-single- cell resolution.

Microfluidic Biochips for Single-Cell Isolation and Single-Cell Analysis of Multiomics and Exosomes

Single-cell multiomic and exosome analyses are potent tools in various fields, such as cancer research, immunology, neuroscience, microbiology, and drug development. They facilitate the in-depth exploration of biological systems, providing insights into disease mechanisms and aiding in treatment. Single-cell isolation, which is crucial for single-cell analysis, ensures reliable cell isolation and quality control for further downstream analyses. Microfluidic chips are small lightweight systems that facilitate efficient and high-throughput single-cell isolation and real-time single-cell analysis on- or off-chip. Therefore, most current single-cell isolation and analysis technologies are based on the single-cell microfluidic technology. This review offers comprehensive guidance to researchers across different fields on the selection of appropriate microfluidic chip technologies for single-cell isolation and analysis. This review describes the design principles, separation mechanisms, chip characteristics, and cellular effects of various microfluidic chips available for single-cell isolation. Moreover, this review highlights the implications of using this technology for subsequent analyses, including single-cell multiomic and exosome analyses. Finally, the current challenges and future prospects of microfluidic chip technology are outlined for multiplex single-cell isolation and multiomic and exosome analyses.

One-Shot Single-Cell Proteome and Metabolome Analysis Strategy for the Same Single Cell

Characterizing the profiles of proteome and metabolome at the single-cell level is of great significance in single-cell multiomic studies. Herein, we proposed a novel strategy called one-shot single-cell proteome and metabolome analysis (scPMA) to acquire the proteome and metabolome information in a single-cell individual in one injection of LC-MS/MS analysis. Based on the scPMA strategy, a total workflow was developed to achieve the single-cell capture, nanoliter-scale sample pretreatment, one-shot LC injection and separation of the enzyme-digested peptides and metabolites, and dual-zone MS/MS detection for proteome and metabolome profiling. Benefiting from the scPMA strategy, we realized dual-omic analysis of single tumor cells, including A549, HeLa, and HepG2 cells with 816, 578, and 293 protein groups and 72, 91, and 148 metabolites quantified on average. A single-cell perspective experiment for investigating the doxorubicin-induced antitumor effects in both the proteome and metabolome aspects was also performed.

Pick-up single-cell proteomic analysis for quantifying up to 3000 proteins in a Mammalian cell

The shotgun proteomic analysis is currently the most promising single-cell protein sequencing technology, however its identification level of ~1000 proteins per cell is still insufficient for practical applications. Here, we develop a pick-up single-cell proteomic analysis (PiSPA) workflow to achieve a deep identification capable of quantifying up to 3000 protein groups in a mammalian cell using the label-free quantitative method. The PiSPA workflow is specially established for single-cell samples mainly based on a nanoliter-scale microfluidic liquid handling robot, capable of achieving single-cell capture, pretreatment and injection under the pick-up operation strategy. Using this customized workflow with remarkable improvement in protein identification, 2449-3500, 2278-3257 and 1621-2904 protein groups are quantified in single A549 cells (n = 37), HeLa cells (n = 44) and U2OS cells (n = 27) under the DIA (MBR) mode, respectively. Benefiting from the flexible cell picking-up ability, we study HeLa cell migration at the single cell proteome level, demonstrating the potential in practical biological research from single-cell insight.

DeepRTAlign: toward accurate retention time alignment for large cohort mass spectrometry data analysis

Retention time (RT) alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based proteomic and metabolomic experiments, especially for large cohort studies. The most popular alignment tools are based on warping function method and direct matching method. However, existing tools can hardly handle monotonic and non-monotonic RT shifts simultaneously. Here, we develop a deep learning-based RT alignment tool, DeepRTAlign, for large cohort LC-MS data analysis. DeepRTAlign has been demonstrated to have improved performances by benchmarking it against current state-of-the-art approaches on multiple real-world and simulated proteomic and metabolomic datasets. The results also show that DeepRTAlign can improve identification sensitivity without compromising quantitative accuracy. Furthermore, using the MS features aligned by DeepRTAlign, we trained and validated a robust classifier to predict the early recurrence of hepatocellular carcinoma. DeepRTAlign provides an advanced solution to RT alignment in large cohort LC-MS studies, which is currently a major bottleneck in proteomics and metabolomics research.

On-capillary alkylation micro-reactor: a facile strategy for proteo-metabolome profiling in the same single cells

Single-cell multi-omics analysis can provide comprehensive insights to study cell-to-cell heterogeneity in normal and disease physiology. However, due to the lack of amplification technique, the measurement of proteome and metabolome in the same cell is challenging. Herein, a novel on-capillary alkylation micro-reactor (OCAM) was developed to achieve proteo-metabolome profiling in the same single cells, by which proteins were first covalently bound to an iodoacetic acid functionalized open-tubular capillary micro-reactor via sulfhydryl alkylation reaction, and metabolites were rapidly eluted, followed by on-column digestion of captured proteins. Compared with existing methods for low-input proteome sample preparation, OCAM exhibited improved efficiency, anti-interference ability and recovery, enabling the identification of an average of 1509 protein groups in single HeLa cells. This strategy was applied to single-cell proteo-metabolome analysis of mouse oocytes at different stages, 3457 protein groups and 171 metabolites were identified in single oocytes, which is the deepest coverage of proteome and metabolome from single mouse oocytes to date, achieving complementary characterization of metabolic patterns during oocyte maturation.

Data-Driven Optimization of DIA Mass Spectrometry by DO-MS

Mass spectrometry (MS) enables specific and accurate quantification of proteins with ever-increasing throughput and sensitivity. Maximizing this potential of MS requires optimizing data acquisition parameters and performing efficient quality control for large datasets. To facilitate these objectives for data-independent acquisition (DIA), we developed a second version of our framework for data-driven optimization of MS methods (DO-MS). The DO-MS app v2.0 (do-ms.slavovlab.net) allows one to optimize and evaluate results from both label-free and multiplexed DIA (plexDIA) and supports optimizations particularly relevant to single-cell proteomics. We demonstrate multiple use cases, including optimization of duty cycle methods, peptide separation, number of survey scans per duty cycle, and quality control of single-cell plexDIA data. DO-MS allows for interactive data display and generation of extensive reports, including publication of quality figures that can be easily shared. The source code is available at github.com/SlavovLab/DO-MS.

Data-Driven Optimization of DIA Mass Spectrometry by DO-MS

Mass spectrometry (MS) enables specific and accurate quantification of proteins with ever increasing throughput and sensitivity. Maximizing this potential of MS requires optimizing data acquisition parameters and performing efficient quality control for large datasets. To facilitate these objectives for data independent acquisition (DIA), we developed a second version of our framework for data-driven optimization of mass spectrometry methods (DO-MS). The DO-MS app v2.0 ( do-ms.slavovlab.net ) allows to optimize and evaluate results from both label free and multiplexed DIA (plexDIA) and supports optimizations particularly relevant for single-cell proteomics. We demonstrate multiple use cases, including optimization of duty cycle methods, peptide separation, number of survey scans per duty cycle, and quality control of single-cell plexDIA data. DO-MS allows for interactive data display and generation of extensive reports, including publication quality figures, that can be easily shared. The source code is available at: github.com/SlavovLab/DO-MS .

Sensitive N-Glycopeptide Profiling of Single and Rare Cells Using an Isobaric Labeling Strategy without Enrichment

Single-cell omics is critical in revealing population heterogeneity, discovering unique features of individual cells, and identifying minority subpopulations of interest. As one of the major post-translational modifications, protein N-glycosylation plays crucial roles in various important biological processes. Elucidation of the variation in N-glycosylation patterns at single-cell resolution may largely facilitate the understanding of their key roles in the tumor microenvironment and immune therapy. However, comprehensive N-glycoproteome profiling for single cells has not been achieved due to the extremely limited sample amount and incompatibility with the available enrichment strategies. Here, we have developed an isobaric labeling-based carrier strategy for highly sensitive intact N-glycopeptide profiling for single cells or a small number of rare cells without enrichment. Isobaric labeling has unique multiplexing properties, by which the "total" signal from all channels triggers MS/MS fragmentation for N-glycopeptide identification, while the reporter ions provide quantitative information. In our strategy, a carrier channel using N-glycopeptides obtained from bulk-cell samples significantly improved the "total" signal of N-glycopeptides and, therefore, promoted the first quantitative analysis of averagely 260 N-glycopeptides from single HeLa cells. We further applied this strategy to study the regional heterogeneity of N-glycosylation of microglia in mouse brain and discovered region-specific N-glycoproteome patterns and cell subtypes. In conclusion, the glycocarrier strategy provides an attractive solution for sensitive and quantitative N-glycopeptide profiling of single/rare cells that cannot be enriched by traditional workflows.

Recent progress in quantitative phosphoproteomics

INTRODUCTION

Protein phosphorylation is a critical post-translational modification involved in the regulation of numerous cellular processes from signal transduction to modulation of enzyme activities. Knowledge of dynamic changes of phosphorylation levels during biological processes, under various treatments or between healthy and disease models is fundamental for understanding the role of each phosphorylation event. Thereby, LC-MS/MS based technologies in combination with quantitative proteomics strategies evolved as a powerful strategy to investigate the function of individual protein phosphorylation events.

AREAS COVERED

State-of-the-art labeling techniques including stable isotope and isobaric labeling provide precise and accurate quantification of phosphorylation events. Here, we review the strengths and limitations of recent quantification methods and provide examples based on current studies, how quantitative phosphoproteomics can be further optimized for enhanced analytic depth, dynamic range, site localization, and data integrity. Specifically, reducing the input material demands is key to a broader implementation of quantitative phosphoproteomics, not least for clinical samples.

EXPERT OPINION

Despite quantitative phosphoproteomics is one of the most thriving fields in the proteomics world, many challenges still have to be overcome to facilitate even deeper and more comprehensive analyses as required in the current research, especially at single cell levels and in clinical diagnostics.

A rapid and sensitive single-cell proteomic method based on fast liquid-chromatography separation, retention time prediction and MS1-only acquisition

Single-cell analysis has received much attention in recent years for elucidating the widely existing cellular heterogeneity in biological systems. However, the ability to measure the proteome in single cells is still far behind that of transcriptomics due to the lack of sensitive and high-throughput mass spectrometry methods. Herein, we report an integrated strategy termed "SCP-MS1" that combines fast liquid chromatography (LC) separation, deep learning-based retention time (RT) prediction and MS1-only acquisition for rapid and sensitive single-cell proteome analysis. In SCP-MS1, the peptides were identified via four-dimensional MS1 feature (m/z, RT, charge and FAIMS CV) matching, therefore relieving MS acquisition from the time consuming and information losing MS2 step and making this method particularly compatible with fast LC separation. By completely omitting the MS2 step, all the MS analysis time was utilized for MS1 acquisition in SCP-MS1 and therefore led to 65%-138% increased MS1 feature collection. Unlike "match between run" methods that still needed MS2 information for RT alignment, SCP-MS1 used deep learning-based RT prediction to transfer the measured RTs in long gradient bulk analyses to short gradient single cell analyses, which was the key step to enhance both identification scale and matching accuracy. Using this strategy, more than 2000 proteins were obtained from 0.2 ng of peptides with a 14-min active gradient at a false discovery rate (FDR) of 0.8%. Comparing with the DDA method, improved quantitative performance was also observed for SCP-MS1 with approximately 50% decreased median coefficient of variation of quantified proteins. For single-cell analysis, 1715 ± 204 and 1604 ± 224 proteins were quantified in single 293T and HeLa cells, respectively. Finally, SCP-MS1 was applied to single-cell proteome analysis of sorafenib resistant and non-resistant HepG2 cells and revealed clear cellular heterogeneity in the resistant population that may be masked in bulk studies.

Recent advances in microfluidics for single-cell functional proteomics

Single-cell proteomics (SCP) reveals phenotypic heterogeneity by profiling individual cells, their biological states and functional outcomes upon signaling activation that can hardly be probed via other omics characterizations. This has become appealing to researchers as it enables an overall more holistic view of biological details underlying cellular processes, disease onset and progression, as well as facilitates unique biomarker identification from individual cells. Microfluidic-based strategies have become methods of choice for single-cell analysis because they allow facile assay integrations, such as cell sorting, manipulation, and content analysis. Notably, they have been serving as an enabling technology to improve the sensitivity, robustness, and reproducibility of recently developed SCP methods. Critical roles of microfluidics technologies are expected to further expand rapidly in advancing the next phase of SCP analysis to reveal more biological and clinical insights. In this review, we will capture the excitement of the recent achievements of microfluidics methods for both targeted and global SCP, including efforts to enhance the proteomic coverage, minimize sample loss, and increase multiplexity and throughput. Furthermore, we will discuss the advantages, challenges, applications, and future prospects of SCP.

An angled-shape tip-based strategy for highly sensitive proteomic profiling of a low number of cells

Profiling proteins plays an essential role in understanding the functions and dynamic networks in biological systems. Mass spectrometry-based proteomic analysis commonly requires multistep sample processing, which results in severe sample loss. Although the recently developed microproteomic strategies have substantially reduced sample loss via droplet microfluidic technology, specialized equipment and well-trained personnel are needed, which may limit their wide adoption. Here, we report an angled-shape tip-based strategy for rapid sample preparation and sensitive proteomic profiling of small cell populations (<1000 cells). The angled-shape tip provided a 'reactor' for the entire proteomic sample processing workflow, from cell capture and lysis to protein digestion, eliminating the sample transfer-induced protein loss. The angled-shape tip was surface-treated for anti-protein adsorption which further reduced the sample loss. Using this strategy, 1241 ± 38-4110 ± 37 protein groups and 4010 ± 700-34 879 ± 575 peptides were identified from 10-1000 HeLa cells with high quantification reproducibility in only 4.5 h sample processing time, which was superior to the reported methods and commercial kits, especially for <100 cells. This approach was easily accessible, straightforward to operate, and compatible with flow cytometry-based cell sorting. It showed great potential for in-depth proteomic profiling of rare cells (<1000 cells) in both basic biological research and clinical application.

Melanopsin (OPN4) is a novel player in skin homeostasis and attenuates UVA-induced effects

The presence of melanopsin (OPN4) has been shown in cultured murine melanocytes and was associated with ultraviolet A radiation (UVA) reception. Here we demonstrated the protective role of OPN4 in skin physiology and the increased UVA-induced damage in its absence. Histological analysis showed a thicker dermis and thinner hypodermal white adipose tissue layer in Opn4^-/- (KO) mice than in wild-type (WT) animals. Proteomics analyses revealed molecular signatures associated with proteolysis, remodeling chromatin, DNA damage response (DDR), immune response, and oxidative stress coupled with antioxidant responses in the skin of Opn4 KO mice compared to WT. Skin protein variants were found in Opn4 KO mice and Opn2, Opn3, and Opn5 gene expressions were increased in the genotype. We investigated each genotype response to UVA stimulus (100 kJ/m²). We found an increase of Opn4 gene expression following stimulus on the skin of WT mice suggesting melanopsin as a UVA sensor. Proteomics findings suggest that UVA decreases DDR pathways associated with ROS accumulation and lipid peroxidation in the skin of Opn4 KO mice. Relative changes in methylation (H3-K79) and acetylation sites of histone between genotypes and differentially modulated by UVA stimulus were also observed. We also identified alterations of molecular traits of the central hypothalamus-pituitary- adrenal (HPA) and the skin HPA-like axes in the absence of OPN4. Higher skin corticosterone levels were detected in UVA-stimulated Opn4 KO compared to irradiated WT mice. Taken altogether, functional proteomics associated with gene expression experiments allowed a high-throughput evaluation that suggests an important protective role of OPN4 in regulating skin physiology in the presence and absence of UVA radiation.

The Current State of Single-Cell Proteomics Data Analysis

Sound data analysis is essential to retrieve meaningful biological information from single-cell proteomics experiments. This analysis is carried out by computational methods that are assembled into workflows, and their implementations influence the conclusions that can be drawn from the data. In this work, we explore and compare the computational workflows that have been used over the last four years and identify a profound lack of consensus on how to analyze single-cell proteomics data. We highlight the need for benchmarking of computational workflows and standardization of computational tools and data, as well as carefully designed experiments. Finally, we cover the current standardization efforts that aim to fill the gap, list the remaining missing pieces, and conclude with lessons learned from the replication of published single-cell proteomics analyses. © 2023 Wiley Periodicals LLC.

Optimized data-independent acquisition approach for proteomic analysis at single-cell level

BACKGROUND

Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis.

METHODS

We report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow.

RESULTS

We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number.

CONCLUSIONS

Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

Standard-free single magnetic bead evaluation: a stable nanoplatform for prostate disease differentiation

Explicit interpretation of heterogeneity between prostate-specific antigen (PSA) subtypes is essential for prostate cancer differentiation during different disease courses, whereas a universal protocol with uniform criteria is still lacking across the globe. In this work, a standard-free single magnetic bead (SMB) nanoplatform utilizing metal nanoparticles with optimal diameters was proposed for prostate disease differentiation in a 134-donor model. The inaccuracy of detection in absolute quantification was diminished via evaluations of metal intensities on the single magnetic bead. The intrinsic proportion of fPSA in tPSA was successfully evaluated by direct use of the Pt to Au intensity ratio (Pt/Au ratio), exhibiting better differentiation between healthy and unhealthy, benign prostatic hyperplasia (BPH) and cancer individuals compared with solo fPSA or tPSA. We generated thresholds respectively for prostate disease differentiation, envisioning that this standard-free SMB nanoplatform would establish a standardized methodology with uniform criteria worldwide in cancer diagnosis, staging, and postoperative assessments.

Proteomics in Biomarker Discovery for Tuberculosis: Current Status and Future Perspectives

Tuberculosis (TB) continues to threaten many peoples' health worldwide, regardless of their country of residence or age. The current diagnosis of TB still uses mainly traditional, time-consuming, and/or culture-based techniques. Efforts have focused on discovering new biomarkers with higher efficiency and accuracy for TB diagnosis. Proteomics-the systematic study of protein diversity-is being applied to the discovery of novel protein biomarkers for different types of diseases. Mass spectrometry (MS) technology plays a revolutionary role in proteomics, and its applicability benefits from the development of other technologies, such as matrix-based and immune-based methods. MS and derivative strategies continuously contribute to disease-related discoveries, and some promising proteomic biomarkers for efficient TB diagnosis have been identified, but challenges still exist. For example, there are discrepancies in the biomarkers identified among different reports and the diagnostic accuracy of clinically applied proteomic biomarkers. The present review summarizes the current status and future perspectives of proteomics in the field of TB biomarker discovery and aims to elicit more promising findings for rapid and accurate TB diagnosis.

A Self-Made Optical Tweezers Integrated Upconversion Luminescence Confocal Scanning Instrument Enables Stable and Noninvasive Long-Term In Situ Imaging a Single Suspension Cell Under Exceptionally Efficient Luminescent Resonance Energy Transfer Sensing

It is necessary to explore labeling probes with worthy optical properties and a noninvasive fluorescence imaging manner for stable long-term in situ measuring a single suspension cell. In response to these goals, we herein make a breakthrough on two fronts. On one hand, a co-sensitizer-induced efficient 808 nm near-infrared light-excited luminescence-confined upconversion nanoparticle with a low thermal effect is fabricated by employing a layer-by-layer seed growing approach to develop a sandwich structure, under which the luminescence domain is vastly restricted into an extremely thin inner shell (∼ 2.77 nm) to finally bring about a high-efficiency luminescent resonance energy transfer (LRET) sensing behavior. On the other hand, a self-made optical tweezers integrated upconversion luminescence confocal scanning instrument is applied to enhance the imaging accuracy, after which the liquid viscous force is sufficiently overcome by the resulting single beam gradient force and the analyzed suspension cell is always immobilized to the focal plane to ensure a constant luminescence excitation condition. By making use of a metal ion-dependent DNAzyme and a hairpin DNA strand to design a corresponding LRET sensing system, our nanoprobe shows satisfactory assay performance for two model biomolecules (Ca²⁺ and TK1 messenger RNA). Following the optical trapping-assisted imaging, this exceptional measurement method is capable of effectively monitoring the intracellular target changes in different physiological states, endowing a powerful toolbox for single cell analysis.