Deep Profiling of Cellular Heterogeneity by Emerging Single-Cell Proteomic Technologies

Distinct Proteomic Brain States Underlying Long-Term Memory Formation in Aversive Operant Conditioning

Long-term memory (LTM) formation relies on de novo protein synthesis; however, the full complement of proteins crucial to LTM formation remains unknown in any system. Using an aversive operant conditioning model of aerial respiratory behavior in the pond snail mollusk, Lymnaea stagnalis (L. stagnalis), we conducted a transcriptome-guided proteomic analysis on the central nervous system (CNS) of LTM, no LTM, and control animals. We identified 366 differentially expressed proteins linked to LTM formation, with 88 upregulated and 36 downregulated in LTM compared to both no LTM and controls. Functional annotation highlighted the importance of balancing protein synthesis and degradation for LTM, as indicated by the upregulation of proteins involved in proteasome activity and translation initiation, including EIF2D, mRNA levels of which were confirmed to be upregulated by conditioning and implicated nuclear factor Y as a potential regulator of LTM-related transcription in this model. This study represents the first transcriptome-guided proteomic analysis of LTM formation ability in this model and lays the groundwork for discovering orthologous proteins critical to LTM in mammals.

Detection of >400 Cluster of Differentiation Biomarkers and Pathway Proteins in Single Immune Cells by Cyclic Multiplex In Situ Tagging for Single-Cell Proteomic Studies

The identification and characterization of immune cell subpopulations are critical to reveal cell development throughout life and immune responses to environmental factors. Next-generation sequencing technologies have dramatically advanced single-cell genomics and transcriptomics for immune cell classification. However, gene expression is often not correlated with protein expression, and immunotyping is mostly accepted in protein format. Current single-cell proteomic technologies are either limited in multiplex capacity or not sensitive enough to detect the critical functional proteins. Herein, we present a single-cell cyclic multiplex in situ tagging (CycMIST) technology to simultaneously measure >400 proteins, a scale of >10 times than similar technologies. Such an ultrahigh multiplexity is achieved by reiterative staining of the single cells coupled with a MIST array for detection. This technology has been thoroughly validated through comparison with flow cytometry and fluorescence immunostaining techniques. Both peripheral blood mononuclear cells (PBMCs) and T cells are analyzed by the CycMIST technology, and almost the entire spectrum of cluster of differentiation (CD) surface markers has been measured. The landscape of fluctuation of CD protein expression in single cells has been uncovered by our technology. Further study found T cell activation signatures and protein-protein networks. This study represents the highest multiplexity of single immune cell marker measurement targeting functional proteins. With additional information from intracellular proteins of the same single cells, our technology can potentially facilitate mechanistic studies of immune responses under various disease conditions.

Proteomics mining of cancer hallmarks on a single-cell resolution

Dysregulated proteome is an essential contributor in carcinogenesis. Protein fluctuations fuel the progression of malignant transformation, such as uncontrolled proliferation, metastasis, and chemo/radiotherapy resistance, which severely impair therapeutic effectiveness and cause disease recurrence and eventually mortality among cancer patients. Cellular heterogeneity is widely observed in cancer and numerous cell subtypes have been characterized that greatly influence cancer progression. Population-averaged research may not fully reveal the heterogeneity, leading to inaccurate conclusions. Thus, deep mining of the multiplex proteome at the single-cell resolution will provide new insights into cancer biology, to develop prognostic biomarkers and treatments. Considering the recent advances in single-cell proteomics, herein we review several novel technologies with particular focus on single-cell mass spectrometry analysis, and summarize their advantages and practical applications in the diagnosis and treatment for cancer. Technological development in single-cell proteomics will bring a paradigm shift in cancer detection, intervention, and therapy.

Coupling Microdroplet-Based Sample Preparation, Multiplexed Isobaric Labeling, and Nanoflow Peptide Fractionation for Deep Proteome Profiling of the Tissue Microenvironment

There is increasing interest in developing in-depth proteomic approaches for mapping tissue heterogeneity in a cell-type-specific manner to better understand and predict the function of complex biological systems such as human organs. Existing spatially resolved proteomics technologies cannot provide deep proteome coverage due to limited sensitivity and poor sample recovery. Herein, we seamlessly combined laser capture microdissection with a low-volume sample processing technology that includes a microfluidic device named microPOTS (microdroplet processing in one pot for trace samples), multiplexed isobaric labeling, and a nanoflow peptide fractionation approach. The integrated workflow allowed us to maximize proteome coverage of laser-isolated tissue samples containing nanogram levels of proteins. We demonstrated that the deep spatial proteomics platform can quantify more than 5000 unique proteins from a small-sized human pancreatic tissue pixel (∼60,000 μm2) and differentiate unique protein abundance patterns in pancreas. Furthermore, the use of the microPOTS chip eliminated the requirement for advanced microfabrication capabilities and specialized nanoliter liquid handling equipment, making it more accessible to proteomic laboratories.

Biological mass spectrometry enables spatiotemporal 'omics: From tissues to cells to organelles

Biological processes unfold across broad spatial and temporal dimensions, and measurement of the underlying molecular world is essential to their understanding. Interdisciplinary efforts advanced mass spectrometry (MS) into a tour de force for assessing virtually all levels of the molecular architecture, some in exquisite detection sensitivity and scalability in space-time. In this review, we offer vignettes of milestones in technology innovations that ushered sample collection and processing, chemical separation, ionization, and 'omics analyses to progressively finer resolutions in the realms of tissue biopsies and limited cell populations, single cells, and subcellular organelles. Also highlighted are methodologies that empowered the acquisition and analysis of multidimensional MS data sets to reveal proteomes, peptidomes, and metabolomes in ever-deepening coverage in these limited and dynamic specimens. In pursuit of richer knowledge of biological processes, we discuss efforts pioneering the integration of orthogonal approaches from molecular and functional studies, both within and beyond MS. With established and emerging community-wide efforts ensuring scientific rigor and reproducibility, spatiotemporal MS emerged as an exciting and powerful resource to study biological systems in space-time.

An extraction-free method for rapid detection of CYP2C19 * 2/3/17 polymorphisms in one tube using melting curve analysis

Drug-metabolizing enzymes play an important role in the metabolism of drugs in vivo. Their activity is an important factor affecting the rate of drug metabolism, which directly determines the intensity and persistence of drug action. Patients taking medication can be divided into different metabolic types through detection of CYP2C19 drug-metabolizing enzyme gene polymorphisms, which can then be used for medication guidance for clopidogrel. Here, we describe a detection method based on real-time polymerase chain reaction (PCR). This method uses multicolor melting curve analysis to accurately identify different mutation sites and genotypes of CYP2C19 * 2, CYP2C19 * 3, and CYP2C19 * 17. The detection limit of plasmid samples was 1 copies μL-1 ; that of genomic samples was 0.1 ng μL-1 . The system can detect nine types of CYP2C19 * 2/3/17 at three sites in one tube, quickly achieving detection within 1 h. Combined with the sample release agent, sample extraction was completed in 5 s, achieving rapid diagnosis without extraction for timely diagnosis and treatment. Furthermore, the system is not limited to blood samples and can also be applied to oropharyngeal and saliva samples, increasing sampling diversity and convenience. When using clinical blood samples (n = 93), the detection system we established was able to quickly and accurately identify different genotypes, and the accuracy and effectiveness of the detection were confirmed by Sanger sequencing. Due to its accuracy, rapidity, simple operation, and low cost, detection technology based on real-time polymerase amplification combined with melting curve analysis is expected to become a powerful tool for detecting and guiding clopidogrel use in countries with limited resources.

Development of a Microfluidic Flow Cytometer with a Uniform Optical Field (Uni-μFCM) Enabling Quantitative Analysis of Single-Cell Proteins and Its Applications in Leukemia Gating, Tumor Classification, and Hierarchy of Cancer Stem Cells

Fast and quantitative estimation of single-cell proteins with various distribution patterns remains a technical challenge. Here, a microfluidic flow cytometer with a uniform optical field (Uni-μFCM) was developed, which enabled the translation of multicolor fluorescence signals of bound antibodies into targeted protein numbers with arbitrary distributions of biological cells. As the core of Uni-μFCM, a uniform optical field for optical excitation and fluorescence detection was realized by adopting a microfabricated metal window to shape the optical beam for excitation, which was modeled and validated by both numerical simulation and experimental characterization. After the validation of Uni-μFCM in single-cell protein quantification by measuring single-cell expressions of three transcriptional factors from four cell lines of variable sizes and origins, Uni-μFCM was applied to (1) quantify membrane and cytoplasmic markers of myeloid and lymphocytic leukocytes to classify cell lines and normal and patient blood samples; (2) measure single-cell expressions of key cytokines affiliated with gene stabilities, differentiating paired oral and colon tumor cell lines with varied malignancies, and (3) quantify single-cell stemming markers of liver tumor cell lines, cell subtypes, and liver patient samples to determine a variety of lineage hierarchy. By quantitatively assessing complex cellular phenotypes, Uni-μFCM substantially expanded the phenotypic space accessible to single-cell applications in leukemia gating, tumor classification, and hierarchy determination of cancer stem cells.

Single-Cell Spatial MIST for Versatile, Scalable Detection of Protein Markers

High-multiplex detection of protein biomarkers across tissue regions has been an attractive spatial biology approach due to significant advantages over traditional immunohistochemistry (IHC) methods. Different from most methods, spatial multiplex in situ tagging (MIST) transfers the spatial protein expression information to an ultrahigh-density, large-scale MIST array. This technique has been optimized to reach single-cell resolution by adoption of smaller array units and 30% 8-arm PEG polymer as transfer medium. Tissue cell nuclei stained with lamin B have been clearly visualized on the MIST arrays and are colocalized with detection of nine mouse brain markers. Pseudocells defined at 10 μm in size have been used to fully profile tissue regions including cells and the intercellular space. We showcased the versatility of our technology by successfully detecting 20 marker proteins in kidney samples with the addition of five minutes atop the duration of standard immunohistochemistry protocols. Spatial MIST is amenable to iterative staining and detection on the same tissue samples. When 25 proteins were co-detected on 1 mouse brain section for each round and 5 rounds were executed, an ultrahigh multiplexity of 125 proteins was obtained for each pseudocell. With its unique abilities, this single-cell spatial MIST technology has the potential to become an important method in advanced diagnosis of complex diseases.

Development of an in situ cell-type specific proteome analysis method using antibody-mediated biotinylation

Since proteins are essential molecules exerting cellular functions, decoding proteome changes is the key to understanding the normal physiology and pathogenesis mechanism of various diseases. However, conventional proteomic studies are often conducted on tissue lumps, in which multiple cell types are entangled, presenting challenges in interpreting the biological dynamics among diverse cell types. While recent cell-specific proteome analysis techniques, like BONCAT, TurboID, and APEX, have emerged, their necessity for genetic modifications limits their usage. The alternative, laser capture microdissection (LCM), although it does not require genetic alterations, is labor-intensive, time-consuming, and requires specialized expertise, making it less suitable for large-scale studies. In this study, we develop the method for in situ cell-type specific proteome analysis using antibody-mediated biotinylation (iCAB), in which we combined immunohistochemistry (IHC) with the biotin-tyramide signal amplification approach. Poly-horseradish peroxidase (HRP) conjugated to the secondary antibody will be localized at a target cell type via a primary antibody specific to the target cell type and biotin-tyramide activated by HRP will biotinylate the nearby proteins. Therefore, the iCAB method can be applied to any tissues that can be used for IHC. As a proof-of-concept, we employed iCAB for mouse brain tissue enriching proteins for neuronal cell bodies, astrocytes, and microglia, followed by identifying the enriched proteins using 16-plex TMT-based proteomics. In total, we identified ~8,400 and ~6,200 proteins from enriched and non-enriched samples. Most proteins from the enriched samples showed differential expressions when we compared different cell type data, while there were no differentially expressed proteins from non-enriched samples. The cell type enrichment analysis with the increased proteins in respective cell types using Azimuth showed that neuronal cell bodies, astrocytes, and microglia data exhibited Glutamatergic Neuron, Astrocyte and Microglia/Perivascular Macrophage as the representative cell types, respectively. The proteome data of the enriched proteins showed similar subcellular distribution as non-enriched proteins, indicating that the iCAB-proteome is not biased toward any subcellular compartment. To our best knowledge, this study represents the first implementation of a cell-type-specific proteome analysis method using an antibody-mediated biotinylation approach. This development paves the way for the routine and widespread use of cell-type-specific proteome analysis. Ultimately, this could accelerate our understanding of biological and pathological phenomena.

Coupling microdroplet-based sample preparation, multiplexed isobaric labeling, and nanoflow peptide fractionation for deep proteome profiling of tissue microenvironment

There is increasing interest in developing in-depth proteomic approaches for mapping tissue heterogeneity at a cell-type-specific level to better understand and predict the function of complex biological systems, such as human organs. Existing spatially resolved proteomics technologies cannot provide deep proteome coverages due to limited sensitivity and poor sample recovery. Herein, we seamlessly combined laser capture microdissection with a low-volume sample processing technology that includes a microfluidic device named microPOTS (Microdroplet Processing in One pot for Trace Samples), the multiplexed isobaric labelling, and a nanoflow peptide fractionation approach. The integrated workflow allowed to maximize proteome coverage of laser-isolated tissue samples containing nanogram proteins. We demonstrated the deep spatial proteomics can quantify more than 5,000 unique proteins from a small-sized human pancreatic tissue pixel (∼60,000 µm2) and reveal unique islet microenvironments.

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

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

Full spectrum flow cytometry and mass cytometry: A 32-marker panel comparison

High-dimensional single-cell data has become an important tool in unraveling the complexity of the immune system and its involvement in homeostasis and a large array of pathologies. As technological tools are developed, researchers are adopting them to answer increasingly complex biological questions. Up until recently, mass cytometry (MC) has been the main technology employed in cytometric assays requiring more than 29 markers. Recently, however, with the introduction of full spectrum flow cytometry (FSFC), it has become possible to break the fluorescence barrier and go beyond 29 fluorescent parameters. In this study, in collaboration with the Stanford Human Immune Monitoring Center (HIMC), we compared five patient samples using an established immune panel developed by the HIMC using their MC platform. Using split samples and the same antibody panel, we were able to demonstrate highly comparable results between the two technologies using multiple data analysis approaches. We report here a direct comparison of two technology platforms (MC and FSFC) using a 32-marker flow cytometric immune monitoring panel that can identify all the previously described and anticipated immune subpopulations defined by this panel.

Cyclic microchip assay for measurement of hundreds of functional proteins in single neurons

Despite the fact that proteins carry out nearly all cellular functions and mark the differences of cells, the existing single-cell tools can only analyze dozens of proteins, a scale far from full characterization of cells and tissue yet. Herein, we present a single-cell cyclic multiplex in situ tagging (CycMIST) technology that affords the comprehensive functional proteome profiling of single cells. We demonstrate the technology by detecting 182 proteins that include surface markers, neuron function proteins, neurodegeneration markers, signaling pathway proteins, and transcription factors. Further studies on cells derived from the 5XFAD mice, an Alzheimer's Disease (AD) model, validate the utility of our technology and reveal the deep heterogeneity of brain cells. Through comparison with control mouse cells, we have identified differentially expressed proteins in AD pathology. Our technology could offer new insights into cell machinery and thus may advance many fields including drug discovery, molecular diagnostics, and clinical studies.

Infrared Laser Ablation Microsampling for Small Volume Proteomics

Infrared (IR) laser ablation was used to remove localized tissue regions from which proteins were extracted and processed with a low volume sample preparation workflow for bottom-up proteomics by liquid chromatography tandem mass spectrometry (LC-MS/MS). A polytetrafluoroethylene (PTFE) coated glass slide with 2 mm diameter microwells was used to capture ablated rat brain tissue for in situ protein digestion with submicroliter solution volumes. The resulting peptides were analyzed with LC-MS/MS for protein identification and label-free quantification. The method was used to identify an average of 600, 1350, and 1900 proteins from ablation areas of 0.01, 0.04, and 0.1 mm², respectively, from a 50 μm thick rat brain tissue section. Differential proteomics of 0.01 mm² regions captured from cerebral cortex and corpus callosum was accomplished to demonstrate the capabilities of the approach.

Spatial Multiplex In Situ Tagging (MIST) Technology for Rapid, Highly Multiplexed Detection of Protein Distribution on Brain Tissue

Highly multiplexed analysis of biospecimens significantly advances the understanding of biological basics of diseases, but these techniques are limited by the number of multiplexity and the speed of processing. Here, we present a rapid multiplex method for quantitative detection of protein markers on brain sections with the cellular resolution. This spatial multiplex in situ tagging (MIST) technology is built upon a MIST microarray that contains millions of small microbeads carrying barcoded oligonucleotides. Using antibodies tagged with UV cleavable oligonucleotides, the distribution of protein markers on a tissue slice could be "printed" on the MIST microarray with high fidelity. The performance of this technology in detection sensitivity, resolution, and signal-to-noise level has been fully characterized by detecting brain cell markers. We showcase the codetection of 31 proteins simultaneously within 2 h, which is about 10 times faster than the other immunofluorescence-based approaches of similar multiplexity. A full set of computational toolkits was developed to segment the small regions and identify the regional differences across the entire mouse brain. This technique enables us to rapidly and conveniently detect dozens of biomarkers on a tissue specimen, and it can find broad applications in clinical pathology and disease mechanistic studies.

A deeper look at carrier proteome effects for single-cell proteomics

Multiplexing approaches using tandem mass tags with a carrier proteome to boost sensitivity have advanced single cell proteomics by mass spectrometry (SCoPE-MS). Here, we probe the carrier proteome effects in single cell proteomics with mixed species TMTpro-labeled samples. We demonstrate that carrier proteomes, while increasing overall identifications, dictate which proteins are identified. We show that quantitative precision and signal intensity are limited at high carrier levels, hindering the recognition of regulated proteins. Guidelines for optimized mass spectrometry acquisition parameters and best practices for fold-change or protein copy number-based comparisons are provided.

Single-Cell RNA-Seq Analysis of Olfactory Mucosal Cells of Alzheimer’s Disease Patients

Olfaction is orchestrated by olfactory mucosal cells located in the upper nasal cavity. Olfactory dysfunction manifests early in several neurodegenerative disorders including Alzheimer’s disease, however, disease-related alterations to the olfactory mucosal cells remain poorly described. The aim of this study was to evaluate the olfactory mucosa differences between cognitively healthy individuals and Alzheimer’s disease patients. We report increased amyloid-beta secretion in Alzheimer’s disease olfactory mucosal cells and detail cell-type-specific gene expression patterns, unveiling 240 differentially expressed disease-associated genes compared to the cognitively healthy controls, and five distinct cell populations. Overall, alterations of RNA and protein metabolism, inflammatory processes, and signal transduction were observed in multiple cell populations, suggesting their role in Alzheimer’s disease-related olfactory mucosa pathophysiology. Furthermore, the single-cell RNA-sequencing proposed alterations in gene expression of mitochondrially located genes in AD OM cells, which were verified by functional assays, demonstrating altered mitochondrial respiration and a reduction of ATP production. Our results reveal disease-related changes of olfactory mucosal cells in Alzheimer’s disease and demonstrate the utility of single-cell RNA sequencing data for investigating molecular and cellular mechanisms associated with the disease.

To the proteome and beyond: advances in single-cell omics profiling for plant systems

Recent advances in single-cell proteomics for animal systems could be adapted for plants to increase our understanding of plant development, response to stimuli, and cell-to-cell signaling.

Analytical approaches to assess metabolic changes in psoriasis

Psoriasis is one of the most common human skin diseases, although its development is not limited to one tissue, but is associated with autoimmune reactions throughout the body. Overproduction of pro-inflammatory cytokines and growth factors systemically stimulates the proliferation of skin cells, which manifests as excessive exfoliation of the epidermis, and/or arthritis, as well as other comorbidities such as insulin resistance, metabolic syndrome, hypertension, and depression. Thus, there is a great need for a thorough analysis of the pathophysiology of psoriatic patients, including classical methods, such as spectrophotometry, chromatography, or Western blot, and also novel omics approaches such as lipidomics and proteomics. Moreover, the extensive pathophysiology forces increased research examining biological changes in both skin cells, and systemically. A wide range of techniques involved in lipidomic research based on a combination of mass spectrometry and different types of chromatography (RP-LC-QTOF-MS/MS, HILIC-QTOF-MS/MS or RP-LC-QTRAP-MS/MS), have allowed comprehensive assessment of lipid modification in psoriatic skin and provided new insight into the role of lipids and their mechanism of action in psoriasis. Moreover, proteomic analysis using gel-nanoLC-OrbiTrap-MS/MS, as well as MALDI-TOF/TOF techniques facilitates the description of panels of enzymes involved in lipidome modifications, and the response of the endocannabinoid system to metabolic changes. Psoriasis is known to alter the expression of proteins that are involved in the inflammatory and antioxidant response, as well as protein biosynthesis, degradation, as well as cell proliferation and apoptosis. Knowledge of changes in the lipidomic and proteomic profile will not only allow the understanding of psoriasis pathophysiology, but also facilitate proper and early diagnosis and effective pharmacotherapy.

Targeted liquid chromatography-tandem mass spectrometry analysis of proteins: Basic principles, applications, and perspectives

Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) is now the main analytical method for the identification and quantification of peptides and proteins in biological samples. In modern research, identification of biomarkers and their quantitative comparison between samples are becoming increasingly important for discovery, validation, and monitoring. Such data can be obtained following specific signals after fragmentation of peptides using multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM) methods, with high specificity, accuracy, and reproducibility. In addition, these methods allow measurement of the amount of post-translationally modified forms and isoforms of proteins. This review article describes the basic principles of MRM assays, guidelines for sample preparation, recent advanced MRM-based strategies, applications and illustrative perspectives of MRM/PRM methods in clinical research and molecular biology.

Proteomics for Low Cell Numbers: How to Optimize the Sample Preparation Workflow for Mass Spectrometry Analysis

Nowadays, massive genomics and transcriptomics data can be generated at the single-cell level. However, proteomics in this setting is still a big challenge. Despite the great improvements in sensitivity and performance of mass spectrometry instruments and the better knowledge on sample preparation processing, it is widely acknowledged that multistep proteomics workflows may lead to substantial sample loss, especially when working with paucicellular samples. Still, in clinical fields, frequently limited sample amounts are available for downstream analysis, thereby hampering comprehensive characterization at protein level. To aim at better protein and peptide recoveries, we compare existing and novel approaches in the multistep sample preparation protocols for mass spectrometry studies, from sample collection, cell lysis, protein quantification, and electrophoresis/staining to protein digestion, peptide recovery, and LC-MS/MS instruments. From this critical evaluation, we conclude that the recent innovations and technologies, together with high quality management of samples, make proteomics on paucicellular samples possible, which will have immediate impact for the proteomics community.

Multiplexed Ion Beam Imaging Readout of Single-Cell Immunoblotting

Improvements in single-cell protein analysis are required to study the cell-to-cell variation inherent to diseases, including cancer. Single-cell immunoblotting (scIB) offers proteoform detection specificity, but often relies on fluorescence-based readout and is therefore limited in multiplexing capability. Among rising multiplexed imaging methods is multiplexed ion beam imaging by time-of-flight (MIBI-TOF), a mass spectrometry imaging technology. MIBI-TOF employs metal-tagged antibodies that do not suffer from spectral overlap to the same degree as fluorophore-tagged antibodies. We report for the first-time MIBI-TOF of single-cell immunoblotting (scIB-MIBI-TOF). The scIB assay subjects single-cell lysate to protein immunoblotting on a microscale device consisting of a 50- to 75-μm thick hydrated polyacrylamide (PA) gel matrix for protein immobilization prior to in-gel immunoprobing. We confirm antibody-protein binding in the PA gel with indirect fluorescence readout of metal-tagged antibodies. Since MIBI-TOF is a layer-by-layer imaging technique, and our protein target is immobilized within a 3D PA gel layer, we characterize the protein distribution throughout the PA gel depth by fluorescence confocal microscopy and confirm that the highest signal-to-noise ratio is achieved by imaging the entirety of the PA gel depth. Accordingly, we report the required MIBI-TOF ion dose strength needed to image varying PA gel depths. Lastly, by imaging ∼42% of PA gel depth with MIBI-TOF, we detect two isoelectrically separated TurboGFP (tGFP) proteoforms from individual glioblastoma cells, demonstrating that highly multiplexed mass spectrometry-based readout is compatible with scIB.

Spatial proteomics for understanding the tissue microenvironment

The human body comprises rich populations of cells, which are arranged into tissues and organs with diverse functionalities. These cells exhibit a broad spectrum of phenotypes and are often organized as a heterogeneous but sophisticatedly regulated ecosystem - tissue microenvironment, inside which every cell interacts with and is reciprocally influenced by its surroundings through its life span. Therefore, it is critical to comprehensively explore the cellular machinery and biological processes in the tissue microenvironment, which is best exemplified by the tumor microenvironment (TME). The past decade has seen increasing advances in the field of spatial proteomics, the main purpose of which is to characterize the abundance and spatial distribution of proteins and their post-translational modifications in the microenvironment of diseased tissues. Herein, we outline the achievements and remaining challenges of mass spectrometry-based tissue spatial proteomics. Exciting technology developments along with important biomedical applications of spatial proteomics are highlighted. In detail, we focus on high-quality resources built by scalpel macrodissection-based region-resolved proteomics, method development of sensitive sample preparation for laser microdissection-based spatial proteomics, and antibody recognition-based multiplexed tissue imaging. In the end, critical issues and potential future directions for spatial proteomics are also discussed.

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

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

[Methods and applications of single-cell proteomics analysis based on mass spectrometry]

The cell is the smallest unit of living organisms. Although cells often assemble to serve a common function, intercellular heterogeneity often exists due to different genetic and environmental effects. Therefore, single-cell analysis has been regarded as an indispensable means to investigate cell heterogeneity, especially when researching cell differentiation, disease diagnosis, and therapy. As the chief factors influencing cell and biological activities, proteins have long been a major concern in biochemistry. However, due to their intrinsic lack of amplification characteristics, wide species variety, low abundance, and wide dynamic range, proteins are scarcely studied in single-cell research when compared with other biological macromolecules. Therefore, ultra-sensitive single-cell proteomics analysis methods are urgently required. Among all general measurement techniques, fluorescence methods possess high sensitivity and a capability of dynamic tracing, but low target numbers impose restrictions on their broad application in real "proteomic" studies. Similarly, electrochemical methods adapt to electrochemically active molecules, which miss the majority of proteins. Mass spectrometry (MS), as the core approach of proteomic studies, provides high-sensitivity and high-throughput analysis of proteins together with abundant structural information, which is unique in all the analytical instruments and has made great progress in single-cell proteomic research. Herein, the representative research methods for single-cell proteomics based on MS are reviewed. According to the different protein separation methods used prior to MS analysis, they are divided into three categories, including capillary electrophoresis (CE), liquid chromatography (LC), and direct infusion without the need for separation. First, CE has been widely used in the separation and analysis of complex biological samples owing to its low cost, high analysis speed, and high separation efficiency. Its unique feature is the extraction and transfer of contents from cellular or subcellular regions using capillaries smaller than a single cell size. This sampling method also offers less substrate interference and negligible oxidative damage to the cells. Nonetheless, single-cell analysis based on CE-MS mainly focuses on proteomic studies of large cells because of the considerable sample loss, interface instability, and reproducibility issues. Compared with CE, LC, especially nanoLC, is more widely used in single-cell proteomic research, which mainly depends on its good reproducibility, nanoliter injection volume, low flow rate, low sample loss, and good compatibility with mass spectrometry. In recent years, it has been increasingly applied in the study of large-volume embryos, germ cells, and even somatic cells. More than 1000 proteins have been identified in single HeLa cells using this state-of-the-art single-cell proteomics method. It is worth noting that the single-cell sampling volume based on LC gradually reduces to the nanoliter level, and that the sample loss can be reduced by integrating a series of proteomic sampling processes into small volumes, setting sealing conditions, and reducing washing steps. However, the adequacy of cell lysis, the completeness and efficiency of protein pretreatment, and the labeling of peptide segments are important factors affecting the number and types of protein identification. Compared with protein separation using CE or LC prior to MS analysis, the direct MS analysis, assisted by labelling and signal transformation, eliminates complicated sample pretreatment and simplifies the operation by reducing enzymatic hydrolysis and separation. It also renders higher resolution as well as multi-omics compatibility. So far, the number of proteins detected using this method is limited due to the complexity of the samples. In conclusion, the aspects of throughput, sensitivity, identified protein species, and applications are summarized for each method mentioned above, and the prospect of single-cell proteomic research based on MS in the future is also discussed.

Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2

BACKGROUND

Macrophages are innate immune cells with diverse functional and molecular phenotypes. This diversity is largely unexplored at the level of single-cell proteomes because of the limitations of quantitative single-cell protein analysis.

RESULTS

To overcome this limitation, we develop SCoPE2, which substantially increases quantitative accuracy and throughput while lowering cost and hands-on time by introducing automated and miniaturized sample preparation. These advances enable us to analyze the emergence of cellular heterogeneity as homogeneous monocytes differentiate into macrophage-like cells in the absence of polarizing cytokines. SCoPE2 quantifies over 3042 proteins in 1490 single monocytes and macrophages in 10 days of instrument time, and the quantified proteins allow us to discern single cells by cell type. Furthermore, the data uncover a continuous gradient of proteome states for the macrophages, suggesting that macrophage heterogeneity may emerge in the absence of polarizing cytokines. Parallel measurements of transcripts by 10× Genomics suggest that our measurements sample 20-fold more protein copies than RNA copies per gene, and thus, SCoPE2 supports quantification with improved count statistics. This allowed exploring regulatory interactions, such as interactions between the tumor suppressor p53, its transcript, and the transcripts of genes regulated by p53.

CONCLUSIONS

Even in a homogeneous environment, macrophage proteomes are heterogeneous. This heterogeneity correlates to the inflammatory axis of classically and alternatively activated macrophages. Our methodology lays the foundation for automated and quantitative single-cell analysis of proteins by mass spectrometry and demonstrates the potential for inferring transcriptional and post-transcriptional regulation from variability across single cells.

Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2

BACKGROUND

Macrophages are innate immune cells with diverse functional and molecular phenotypes. This diversity is largely unexplored at the level of single-cell proteomes because of the limitations of quantitative single-cell protein analysis.

RESULTS

To overcome this limitation, we develop SCoPE2, which substantially increases quantitative accuracy and throughput while lowering cost and hands-on time by introducing automated and miniaturized sample preparation. These advances enable us to analyze the emergence of cellular heterogeneity as homogeneous monocytes differentiate into macrophage-like cells in the absence of polarizing cytokines. SCoPE2 quantifies over 3042 proteins in 1490 single monocytes and macrophages in 10 days of instrument time, and the quantified proteins allow us to discern single cells by cell type. Furthermore, the data uncover a continuous gradient of proteome states for the macrophages, suggesting that macrophage heterogeneity may emerge in the absence of polarizing cytokines. Parallel measurements of transcripts by 10× Genomics suggest that our measurements sample 20-fold more protein copies than RNA copies per gene, and thus, SCoPE2 supports quantification with improved count statistics. This allowed exploring regulatory interactions, such as interactions between the tumor suppressor p53, its transcript, and the transcripts of genes regulated by p53.

CONCLUSIONS

Even in a homogeneous environment, macrophage proteomes are heterogeneous. This heterogeneity correlates to the inflammatory axis of classically and alternatively activated macrophages. Our methodology lays the foundation for automated and quantitative single-cell analysis of proteins by mass spectrometry and demonstrates the potential for inferring transcriptional and post-transcriptional regulation from variability across single cells.

Single Extracellular Vesicle Protein Analysis Using Immuno-Droplet Digital Polymerase Chain Reaction Amplification

There is a need for novel analytical techniques to study the composition of single extracellular vesicles (EV). Such techniques are required to improve the understanding of heterogeneous EV populations, to allow identification of unique subpopulations, and to enable earlier and more sensitive disease detection. Because of the small size of EV and their low protein content, ultrahigh sensitivity technologies are required. Here, an immuno-droplet digital polymerase chain reaction (iddPCR) amplification method is described that allows multiplexed single EV protein profiling. Antibody-DNA conjugates are used to label EV, followed by stochastic microfluidic incorporation of single EV into droplets. In situ PCR with fluorescent reporter probes converts and amplifies the barcode signal for subsequent read-out by droplet imaging. In these proof-of-principle studies, it is shown that multiplex protein analysis is possible in single EV, opening the door for future analyses.

Addressing Cellular Heterogeneity in Cancer through Precision Proteomics

Cells exhibit a broad spectrum of functions driven by differences in molecular phenotype. Understanding the heterogeneity between and within cell types has led to advances in our ability to diagnose and manipulate biological systems. Heterogeneity within and between tumors still poses a challenge to the development and efficacy of therapeutics. In this Perspective we review the toolkit of protein-level experimental approaches for investigating cellular heterogeneity. We describe how innovative approaches and technical developments have supported the advent of bottom-up single-cell proteomic analysis and present opportunities and challenges within cancer research. Finally, we introduce the concept of "precision proteomics" and discuss how the advantages and limitations of various experimental approaches render them suitable for different biological systems and questions.

Midbrain Dopaminergic Neuron Development at the Single Cell Level: In vivo and in Stem Cells

Parkinson's disease (PD) is a progressive neurodegenerative disorder that predominantly affects dopaminergic (DA) neurons of the substantia nigra. Current treatment options for PD are symptomatic and typically involve the replacement of DA neurotransmission by DA drugs, which relieve the patients of some of their motor symptoms. However, by the time of diagnosis, patients have already lost about 70% of their substantia nigra DA neurons and these drugs offer only temporary relief. Therefore, cell replacement therapy has garnered much interest as a potential treatment option for PD. Early studies using human fetal tissue for transplantation in PD patients provided proof of principle for cell replacement therapy, but they also highlighted the ethical and practical difficulties associated with using human fetal tissue as a cell source. In recent years, advancements in stem cell research have made human pluripotent stem cells (hPSCs) an attractive source of material for cell replacement therapy. Studies on how DA neurons are specified and differentiated in the developing mouse midbrain have allowed us to recapitulate many of the positional and temporal cues needed to generate DA neurons in vitro. However, little is known about the developmental programs that govern human DA neuron development. With the advent of single-cell RNA sequencing (scRNA-seq) and bioinformatics, it has become possible to analyze precious human samples with unprecedented detail and extract valuable high-quality information from large data sets. This technology has allowed the systematic classification of cell types present in the human developing midbrain along with their gene expression patterns. By studying human development in such an unbiased manner, we can begin to elucidate human DA neuron development and determine how much it differs from our knowledge of the rodent brain. Importantly, this molecular description of the function of human cells has become and will increasingly be a reference to define, evaluate, and engineer cell types for PD cell replacement therapy and disease modeling.

Resolving Metabolic Heterogeneity in Experimental Models of the Tumor Microenvironment from a Stable Isotope Resolved Metabolomics Perspective

The tumor microenvironment (TME) comprises complex interactions of multiple cell types that determines cell behavior and metabolism such as nutrient competition and immune suppression. We discuss the various types of heterogeneity that exist in solid tumors, and the complications this invokes for studies of TME. As human subjects and in vivo model systems are complex and difficult to manipulate, simpler 3D model systems that are compatible with flexible experimental control are necessary for studying metabolic regulation in TME. Stable Isotope Resolved Metabolomics (SIRM) is a valuable tool for tracing metabolic networks in complex systems, but at present does not directly address heterogeneous metabolism at the individual cell level. We compare the advantages and disadvantages of different model systems for SIRM experiments, with a focus on lung cancer cells, their interactions with macrophages and T cells, and their response to modulators in the immune microenvironment. We describe the experimental set up, illustrate results from 3D cultures and co-cultures of lung cancer cells with human macrophages, and outline strategies to address the heterogeneous TME.

Translating Cancer Molecular Variability into Personalized Information Using Bulk and Single Cell Approaches

Cancer research is striving toward new frontiers of assigning the correct personalized drug(s) to a given patient. However, extensive tumor heterogeneity poses a major obstacle. Tumors of the same type often respond differently to therapy, due to patient-specific molecular aberrations and/or untargeted tumor subpopulations. It is frequently not possible to determine a priori which patients will respond to a certain therapy or how an efficient patient-specific combined therapy should be designed. Large-scale datasets have been growing at an accelerated pace and various technologies and analytical tools for single cell and bulk level analyses are being developed to extract significant individualized signals from such heterogeneous data. However, personalized therapies that dramatically alter the course of the disease remain scarce, and most tumors still respond poorly to medical care. In this review, the basic concepts of bulk and single cell approaches are discussed, as well as their emerging role in individualized designs of drug therapies, including the advantages and limitations of their applications in personalized medicine.

Prospects and challenges of multi-omics data integration in toxicology

Exposure of cells or organisms to chemicals can trigger a series of effects at the regulatory pathway level, which involve changes of levels, interactions, and feedback loops of biomolecules of different types. A single-omics technique, e.g., transcriptomics, will detect biomolecules of one type and thus can only capture changes in a small subset of the biological cascade. Therefore, although applying single-omics analyses can lead to the identification of biomarkers for certain exposures, they cannot provide a systemic understanding of toxicity pathways or adverse outcome pathways. Integration of multiple omics data sets promises a substantial improvement in detecting this pathway response to a toxicant, by an increase of information as such and especially by a systemic understanding. Here, we report the findings of a thorough evaluation of the prospects and challenges of multi-omics data integration in toxicological research. We review the availability of such data, discuss options for experimental design, evaluate methods for integration and analysis of multi-omics data, discuss best practices, and identify knowledge gaps. Re-analyzing published data, we demonstrate that multi-omics data integration can considerably improve the confidence in detecting a pathway response. Finally, we argue that more data need to be generated from studies with a multi-omics-focused design, to define which omics layers contribute most to the identification of a pathway response to a toxicant.