Screening: CyTOF-the next generation of cell detection

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 Atlas: a single-cell multi-omics human cell encyclopedia

Single-cell sequencing datasets are key in biology and medicine for unraveling insights into heterogeneous cell populations with unprecedented resolution. Here, we construct a single-cell multi-omics map of human tissues through in-depth characterizations of datasets from five single-cell omics, spatial transcriptomics, and two bulk omics across 125 healthy adult and fetal tissues. We construct its complement web-based platform, the Single Cell Atlas (SCA, www.singlecellatlas.org ), to enable vast interactive data exploration of deep multi-omics signatures across human fetal and adult tissues. The atlas resources and database queries aspire to serve as a one-stop, comprehensive, and time-effective resource for various omics studies.

Enhancing Single-Cell Western Blotting Sensitivity Using Diffusive Analyte Blotting and Antibody Conjugate Amplification

While there are many techniques to achieve highly sensitive, multiplex detection of RNA and DNA from single cells, detecting protein content often suffers from low limits of detection and throughput. Miniaturized, high-sensitivity Western blots on single cells (scWesterns) are attractive because they do not require advanced instrumentation. By physically separating analytes, scWesterns also uniquely mitigate limitations to target protein multiplexing posed by the affinity reagent performance. However, a fundamental limitation of scWesterns is their limited sensitivity for detecting low-abundance proteins, which arises from transport barriers posed by the separation gel against detection species. Here we address the sensitivity by decoupling the electrophoretic separation medium from the detection medium. We transfer scWestern separations to a nitrocellulose blotting medium with distinct mass transfer advantages over traditional in-gel probing, yielding a 5.9-fold improvement in the limit of detection. We next amplify probing of blotted proteins with enzyme-antibody conjugates, which are incompatible with traditional in-gel probing to achieve further improvement in the limit of detection to 1000 molecules, a 120-fold improvement. This enables us to detect 100% of cells in an EGFP-expressing population using fluorescently tagged and enzyme-conjugated antibodies compared to 84.5% of cells using in-gel detection. These results suggest the compatibility of nitrocellulose-immobilized scWesterns with a variety of affinity reagents─not previously accessible for in-gel use─for further signal amplification and detection of low-abundance targets.

High-dimensional mass cytometry reveals systemic and local immune signatures in necrotizing enterocolitis

Objective

Patients with necrotizing enterocolitis display severe gastrointestinal complications of prematurity, but the mechanism driving this clinical profile remains unknown. We used mass cytometry time-of-flight to characterize and compare immune cell populations in the blood and intestine tissue from patients with and without (controls) necrotizing enterocolitis at single-cell resolution.

Methods

We completed a deep mapping of the immune system of the peripheral blood mononuclear cells and intestinal mucosa tissue using mass cytometry to evaluate immune cell types, which revealed global immune dysregulation characteristics underlying necrotizing enterocolitis.

Results

Compared with controls, natural killer cells display signs of heightened activation and increased cytotoxic potential in the peripheral blood and mucosa of patients with necrotizing enterocolitis. Furthermore, CD4+ T effector memory cells, non-classical monocytes, active dendritic cells, and neutrophils were specifically enriched in the mucosa, suggesting trafficking from the periphery to areas of inflammation. Moreover, we mapped the systemic and local distinct immune signatures suggesting patterns of cell localization in necrotizing enterocolitis.

Conclusion

We used mass cytometry time-of-flight technology to identify immune cell populations specific to the peripheral blood and intestinal mucosa tissue from patients with necrotizing enterocolitis and controls. This information might be used to develop precise diagnosis and therapies that target specific cell populations in patients with necrotizing enterocolitis.

sccomp: Robust differential composition and variability analysis for single-cell data

Cellular omics such as single-cell genomics, proteomics, and microbiomics allow the characterization of tissue and microbial community composition, which can be compared between conditions to identify biological drivers. This strategy has been critical to revealing markers of disease progression, such as cancer and pathogen infection. A dedicated statistical method for differential variability analysis is lacking for cellular omics data, and existing methods for differential composition analysis do not model some compositional data properties, suggesting there is room to improve model performance. Here, we introduce sccomp, a method for differential composition and variability analyses that jointly models data count distribution, compositionality, group-specific variability, and proportion mean-variability association, being aware of outliers. sccomp provides a comprehensive analysis framework that offers realistic data simulation and cross-study knowledge transfer. Here, we demonstrate that mean-variability association is ubiquitous across technologies, highlighting the inadequacy of the very popular Dirichlet-multinomial distribution. We show that sccomp accurately fits experimental data, significantly improving performance over state-of-the-art algorithms. Using sccomp, we identified differential constraints and composition in the microenvironment of primary breast cancer.

Enhancing single-cell western blotting sensitivity using diffusive analyte blotting and antibody conjugate amplification

While there are many techniques to achieve highly sensitive, multiplex detection of RNA and DNA from single cells, detecting protein contents often suffers from low limits of detection and throughput. Miniaturized, high-sensitivity western blots on single cells (scWesterns) are attractive since they do not require advanced instrumentation. By physically separating analytes, scWesterns also uniquely mitigate limitations to target protein multiplexing posed by affinity reagent performance. However, a fundamental limitation of scWesterns is their limited sensitivity for detecting low-abundance proteins, which arises from transport barriers posed by the separation gel against detection species. Here we address sensitivity by decoupling the electrophoretic separation medium from the detection medium. We transfer scWestern separations to a nitrocellulose blotting medium with distinct mass transfer advantages over traditional in-gel probing, yielding a 5.9-fold improvement in limit of detection. We next amplify probing of blotted proteins with enzyme-antibody conjugates which are incompatible with traditional in-gel probing to achieve further improvement in the limit of detection to 103 molecules, a 520-fold improvement. This enables us to detect 85% and 100% of cells in an EGFP-expressing population using fluorescently tagged and enzyme-conjugated antibodies respectively, compared to 47% of cells using in-gel detection. These results suggest compatibility of nitrocellulose-immobilized scWesterns with a variety of affinity reagents - not previously accessible for in-gel use - for further signal amplification and detection of low abundance targets.

A Bayesian feature allocation model for identifying cell subpopulations using CyTOF data

A Bayesian feature allocation model (FAM) is presented for identifying cell subpopulations based on multiple samples of cell surface or intracellular marker expression level data obtained by cytometry by time of flight (CyTOF). Cell subpopulations are characterized by differences in marker expression patterns, and cells are clustered into subpopulations based on their observed expression levels. A model-based method is used to construct cell clusters within each sample by modeling subpopulations as latent features, using a finite Indian buffet process. Non-ignorable missing data due to technical artifacts in mass cytometry instruments are accounted for by defining a static missingship mechanism. In contrast with conventional cell clustering methods, which cluster observed marker expression levels separately for each sample, the FAM-based method can be applied simultaneously to multiple samples, and also identify important cell subpopulations likely to be otherwise missed. The proposed FAM-based method is applied to jointly analyse three CyTOF datasets to study natural killer (NK) cells. Because the subpopulations identified by the FAM may define novel NK cell subsets, this statistical analysis may provide useful information about the biology of NK cells and their potential role in cancer immunotherapy which may lead, in turn, to development of improved NK cell therapies.

Applications of single-cell RNA sequencing in drug discovery and development

Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action. In clinical development, scRNA-seq can inform decision-making via improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression. Here, we illustrate how scRNA-seq methods are being applied in key steps in drug discovery and development, and discuss ongoing challenges for their implementation in the pharmaceutical industry.

Comparative analysis of dimension reduction methods for cytometry by time-of-flight data

While experimental and informatic techniques around single cell sequencing (scRNA-seq) are advanced, research around mass cytometry (CyTOF) data analysis has severely lagged behind. CyTOF data are notably different from scRNA-seq data in many aspects. This calls for the evaluation and development of computational methods specific for CyTOF data. Dimension reduction (DR) is one of the critical steps of single cell data analysis. Here, we benchmark the performances of 21 DR methods on 110 real and 425 synthetic CyTOF samples. We find that less well-known methods like SAUCIE, SQuaD-MDS, and scvis are the overall best performers. In particular, SAUCIE and scvis are well balanced, SQuaD-MDS excels at structure preservation, whereas UMAP has great downstream analysis performance. We also find that t-SNE (along with SQuad-MDS/t-SNE Hybrid) possesses the best local structure preservation. Nevertheless, there is a high level of complementarity between these tools, so the choice of method should depend on the underlying data structure and the analytical needs.

Advances in Mass Spectrometry-Based Single Cell Analysis

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

Utilizing CyTOF to Examine Hematopoietic Stem and Progenitor Phenotype

Regulation of hematopoiesis is dependent upon interactions between hematopoietic stem/progenitor cells and niche components, requiring a highly diverse array of different cell-cell interactions and cell signaling events. The overwhelming diversity of the components that can regulate hematopoiesis, especially when factoring in how the cell surface and intracellular protein expression profiles of hematopoietic stem/progenitor cells and niche components differ between homeostatic conditions and stressed conditions such as aging and irradiation, can make utilizing techniques like flow cytometry daunting, particularly while examining small cell populations such as hematopoietic stem cells (HSCs). Due to the complexity of the hematopoietic system, high-dimensional single-cell genomics and proteomics are constantly performed to understand the heterogeneity and expression profiles within this system. This chapter describes one such single-cell assay, which utilizes mass cytometry Time of Flight (CyTOF) technology to determine differences in expression profile within HSC, using changes in HSC populations due to gender and aging.

A SLC35C2 Transporter-Targeting Fluorescent Probe for the Selective Detection of B Lymphocytes Identified by SLC-CRISPRi and Unbiased Fluorescence Library Screening

T and B lymphocytes are two major adaptive immune cells in the human defense system. To real-time monitor their diverse functions, a live-cell-selective probe for only one cell type is need to investigate the complex interaction of the immune cells. Herein, a small-molecule probe CDyB for live B cells is developed by an unbiased fluorescence library screening. The cell selectivity was confirmed by multiparametric single-cell analysis using CyTOF. Through a systematic SLC-CRISPRi library screening, the molecular target of CDyB was identified as SLC35C2 transporter based on a gating-oriented live-cell distinction (GOLD) mechanism. The gene expression analysis and knock-out experiments validated that the SLC35C2 transporter was the target for CDyB distinction. Interestingly, when CDyB was applied to study B cell development, the CDyB fluorescence and SLC35C2 expression were positively correlated with the B cell maturation process, and not involved in the T cell development.

Single-cell multi-modal GAN reveals spatial patterns in single-cell data from triple-negative breast cancer

Exciting advances in technologies to measure biological systems are currently at the forefront of research. The ability to gather data along an increasing number of omic dimensions has created a need for tools to analyze all of this information together, rather than siloing each technology into separate analysis pipelines. To advance this goal, we introduce a framework called the single-cell multi-modal generative adversarial network (scMMGAN) that integrates data from multiple modalities into a unified representation in the ambient data space for downstream analysis using a combination of adversarial learning and data geometry techniques. The framework’s key improvement is an additional diffusion geometry loss with a new kernel that constrains the otherwise over-parameterized GAN. We demonstrate scMMGAN’s ability to produce more meaningful alignments than alternative methods on a wide variety of data modalities and that its output can be used to draw conclusions from real-world biological experimental data.

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Integrating data from multiple modalities into one analysis

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Using data geometry to regularize cycle-consistent GANs

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Quantifying uncertainty through noise augmentation

Biological experimental data are increasingly being generated along multiple different axes, with new and more complex technologies specializing in particular measurements being developed every year. Measuring a single subject or system with multiple specialized data-collecting tools creates a natural interest in integrating the results of these individual instruments to form a single unified view. The model introduced here presents a computational technique designed for this purpose. With the single-cell multi-modal GAN (scMMGAN), there is an opportunity to measure along many different omic directions and synthesize the information from each into one larger understanding of the system under study.

Case report: Single-cell mapping of peripheral blood mononuclear cells from a patient with both Crohn’s disease and isolated congenital asplenia

Crohn’s disease (CD), as one of the principal form of inflammatory bowel disease (IBD), is characterized by the chronic and recurring inflammatory conditions in the intestine resulting from the over-activation of intestinal immunity. Hyposplenism is strongly associated with CD, while the effect of human spleen on the differentiation and development of immune cell subsets in CD patients remains unclear. Isolated congenital asplenia (ICA) is an extremely rare condition characterized by the absence of a spleen at birth without any other developmental defects. Here, we describe the first case of a patient with both ICA and CD, and follow the progression of CD from remission to active stage. Using cytometry by time of flight (CyTOF) analysis, we draw the first single-cell mapping of peripheral blood mononuclear cells (PBMC) from this unique patient, tracing back to the innate or adaptive immune cell subsets and cell surface markers affected by the spleen. Based on our analysis, it is speculated that the spleen contributes to maintaining immune homeostasis, alleviating intestinal inflammation and improving prognosis by influencing the differentiation and development of peripheral immune cell subsets and the expression of cell surface markers in patients with CD.

Characterization of adipose depot-specific stromal cell populations by single-cell mass cytometry

The increased prevalence of obesity and metabolic diseases has heightened interest in adipose tissue biology and its potential as a therapeutic target. To better understand cellular heterogeneity and complexity of white adipose tissue (WAT), we employed cytometry by time-of-flight (CyTOF) to characterize immune and stromal cells in visceral and subcutaneous WAT depots under normal and high-fat diet feeding, by quantifying the expression levels of 32 surface marker proteins. We observed comparable proportions of immune cells in two WAT depots under steady state, but depot-distinct subtypes of adipose precursor cells (APC), suggesting differences in their adipogenic and fibrogenic potential. Furthermore, in addition to pro-inflammatory immune cell shifts, significant pro-fibrotic changes were observed in APCs under high-fat diet, suggesting that APCs are early responders to dietary challenges. We propose CyTOF as a complementary and alternative tool to current high-throughput single-cell transcriptomic analyses to better understand the function and plasticity of adipose tissue.

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Application of CyTOF for cellular characterization in two adipose depots

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Adipose depot-distinct APC subpopulations

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APCs are early responders under obesogenic conditions to regulate WAT fibrosis

Cutting-Edge Platforms for Analysis of Immune Cells in the Hepatic Microenvironment-Focus on Tumor-Associated Macrophages in Hepatocellular Carcinoma

The role of tumor-associated macrophages (TAMs) in the pathogenesis of hepatocellular carcinoma (HCC) is poorly understood. Most studies rely on platforms that remove intrahepatic macrophages from the microenvironment prior to evaluation. Cell isolation causes activation and phenotypic changes that may not represent their actual biology and function in situ. State-of-the-art methods provides new strategies to study TAMs without losing the context of tissue architecture and spatial relationship with neighboring cells. These technologies, such as multispectral imaging (e.g., Vectra Polaris), mass cytometry by time-of-flight (e.g., Fluidigm CyTOF), cycling of fluorochromes (e.g., Akoya Biosciences CODEX/PhenoCycler-Fusion, Bruker Canopy, Lunaphore Comet, and CyCIF) and digital spatial profiling or transcriptomics (e.g., GeoMx or Visium, Vizgen Merscope) are being utilized to accurately assess the complex cellular network within the tissue microenvironment. In cancer research, these platforms enable characterization of immune cell phenotypes and expression of potential therapeutic targets, such as PDL-1 and CTLA-4. Newer spatial profiling platforms allow for detection of numerous protein targets, in combination with whole transcriptome analysis, in a single liver biopsy tissue section. Macrophages can also be specifically targeted and analyzed, enabling quantification of both protein and gene expression within specific cell phenotypes, including TAMs. This review describes the workflow of each platform, summarizes recent research using these approaches, and explains the advantages and limitations of each.

Sickle-like Inertial Microfluidic System for Online Rare Cell Separation and Tandem Label-Free Quantitative Proteomics (Orcs-Proteomics)

Label-free proteomics with trace clinical samples provides a wealth of actionable insights for personalized medicine. Clinically acquired primary cells, such as circulating tumor cells (CTCs), are usually with low abundance that is prohibitive for conventional label-free proteomics analysis. Here, we present a sickle-like inertial microfluidic system for online rare cell separation and tandem label-free proteomics (namely, Orcs-proteomics). Orcs-proteomics adopts a buffer system with 0.1% N-dodecyl β-d-maltoside (DDM), 1 mM Tris (2-carboxyethyl) phosphine (TCEP), and 2 mM 2-chloroacetamide (CAA) for cell lysis and reductive alkylation. We demonstrate the application of Orcs-proteomics with 293T cells and manage to identify 913, 1563, 2271, and 2770 protein groups with 4, 13, 68, and 119 cells, respectively. We then spike MCF7 cells with white blood cells (WBCs) to simulate the patient's blood sample. Orcs-proteomics identifies more than 2000 protein groups with an average of 61 MCF7 cells. We further recruit two advanced breast cancer patients and collect 5 and 7 CTCs from each patient through minimally invasive blood drawing. Orcs-proteomics manages to identify 973 and 1135 protein groups for each patient. Therefore, Orcs-proteomics empowers rare cells simultaneously to be separated and counted for proteomics and provides technical support for personalized treatment decision making with rare primary patient samples.

The Application of Single-Cell Technologies in Cardiovascular Research

Cardiovascular diseases (CVDs) are the leading cause of deaths in the world. The intricacies of the cellular composition and tissue microenvironment in heart and vasculature complicate the dissection of molecular mechanisms of CVDs. Over the past decade, the rapid development of single-cell omics technologies generated vast quantities of information at various biological levels, which have shed light on the cellular and molecular dynamics in cardiovascular development, homeostasis and diseases. Here, we summarize the latest single-cell omics techniques, and show how they have facilitated our understanding of cardiovascular biology. We also briefly discuss the clinical value and future outlook of single-cell applications in the field.

Multi-omics reveals clinically relevant proliferative drive associated with mTOR-MYC-OXPHOS activity in chronic lymphocytic leukemia

Chronic Lymphocytic Leukemia (CLL) has a complex pattern of driver mutations and much of its clinical diversity remains unexplained. We devised a method for simultaneous subgroup discovery across multiple data types and applied it to genomic, transcriptomic, DNA methylation and ex-vivo drug response data from 217 Chronic Lymphocytic Leukemia (CLL) cases. We uncovered a biological axis of heterogeneity strongly associated with clinical behavior and orthogonal to the known biomarkers. We validated its presence and clinical relevance in four independent cohorts (n=547 patients). We find that this axis captures the proliferative drive (PD) of CLL cells, as it associates with lymphocyte doubling rate, global hypomethylation, accumulation of driver aberrations and response to pro-proliferative stimuli. CLL-PD was linked to the activation of mTOR-MYC-oxidative phosphorylation (OXPHOS) through transcriptomic, proteomic and single cell resolution analysis. CLL-PD is a key determinant of disease outcome in CLL. Our multi-table integration approach may be applicable to other tumors whose inter-individual differences are currently unexplained.

Multi-Omics Approaches in Immunological Research

The immune system plays a vital role in health and disease, and is regulated through a complex interactive network of many different immune cells and mediators. To understand the complexity of the immune system, we propose to apply a multi-omics approach in immunological research. This review provides a complete overview of available methodological approaches for the different omics data layers relevant for immunological research, including genetics, epigenetics, transcriptomics, proteomics, metabolomics, and cellomics. Thereafter, we describe the various methods for data analysis as well as how to integrate different layers of omics data. Finally, we discuss the possible applications of multi-omics studies and opportunities they provide for understanding the complex regulatory networks as well as immune variation in various immune-related diseases.

Activity-Based Probes to Utilize the Proteolytic Activity of Cathepsin G in Biological Samples

Neutrophils, migrating to the site of infection, are able to release serine proteases after being activated. These serine proteases comprise cathepsin G (CatG), neutrophil elastase protease 3 (PR3), and neutrophil serine protease 4 (NSP4). A disadvantage of the uncontrolled proteolytic activity of proteases is the outcome of various human diseases, including cardiovascular diseases, thrombosis, and autoimmune diseases. Activity-based probes (ABPs) are used to determine the proteolytic activity of proteases, containing a set of three essential elements: Warhead, recognition sequence, and the reporter tag for detection of the covalent enzyme activity–based probe complex. Here, we summarize the latest findings of ABP-mediated detection of proteases in both locations intracellularly and on the cell surface of cells, thereby focusing on CatG. Particularly, application of ABPs in regular flow cytometry, imaging flow cytometry, and mass cytometry by time-of-flight (CyTOF) approaches is advantageous when distinguishing between immune cell subsets. ABPs can be included in a vast panel of markers to detect proteolytic activity and determine whether proteases are properly regulated during medication. The use of ABPs as a detection tool opens the possibility to interfere with uncontrolled proteolytic activity of proteases by employing protease inhibitors.

Hallmarks and detection techniques of cellular senescence and cellular ageing in immune cells

The ageing of the global population brings about unprecedented challenges. Chronic age‐related diseases in an increasing number of people represent an enormous burden for health and social care. The immune system deteriorates during ageing and contributes to many of these age‐associated diseases due to its pivotal role in pathogen clearance, tissue homeostasis and maintenance. Moreover, in order to develop treatments for COVID‐19, we urgently need to acquire more knowledge about the aged immune system, as older adults are disproportionally and more severely affected. Changes with age lead to impaired responses to infections, malignancies and vaccination, and are accompanied by chronic, low‐degree inflammation, which together is termed immunosenescence. However, the molecular and cellular mechanisms that underlie immunosenescence, termed immune cell senescence, are mostly unknown. Cellular senescence, characterised by an irreversible cell cycle arrest, is thought to be the cause of tissue and organismal ageing. Thus, better understanding of cellular senescence in immune populations at single‐cell level may provide us with insight into how immune cell senescence develops over the life time of an individual. In this review, we will briefly introduce the phenotypic characterisation of aged innate and adaptive immune cells, which also contributes to overall immunosenescence, including subsets and function. Next, we will focus on the different hallmarks of cellular senescence and cellular ageing, and the detection techniques most suitable for immune cells. Applying these techniques will deepen our understanding of immune cell senescence and to discover potential druggable pathways, which can be modulated to reverse immune ageing.

Application of an Activity-Based Probe to Determine Proteolytic Activity of Cell Surface Cathepsin G by Mass Cytometry Data Acquisition

During an immune response, cathepsin G (CatG) takes on the role of adaptive and innate immunity and the outcome depends on the localization of CatG. Soluble, cell surface-bound, or intracellular CatG is also responsible for pathophysiology conditions. We applied the activity-based probe MARS116-Bt to mass cytometry by time-of-flight to analyze CatG activity on the cell surface of immune cells. The phosphonate warhead of MARS116-Bt binds covalently to the serine amino acid residue S195 of the catalytic center and thereby CatG activity can be detected. This method contributes to observing the activation or inhibition status of cells during pathogenesis of diseases and enables accurate data acquisition from complex biological samples with a vast panel of cell subset markers in a single-cell resolution.

The prospects of single-cell analysis in autoimmunity

In the last decade, there has been a tremendous development of technologies focused on analysing various molecular attributes in single cells, with an ever-increasing number of parameters becoming available at the DNA, RNA and protein levels. Much of this progress has involved cells in suspension, but also in situ analysis of tissues has taken great leaps. Paralleling the development in the laboratory, and because of increasing complexity, the analysis of single-cell data is also constantly being updated with new algorithms and analysis platforms. Our immune system shares this complexity, and immunologists have therefore been in the forefront of this technological development. These technologies clearly open new avenues for immunology research, maybe particularly within autoimmunity where the interaction between the faulty immune system and the thymus or the target organ is important. However, the technologies currently available can seem overwhelming and daunting. The aim of this review is to remedy this by giving a balanced overview of the prospects of using single-cell analysis in basal and clinical autoimmunity research, with an emphasis on endocrine autoimmunity.

Neuroinflammation at single cell level: What is new?

Multiple sclerosis is a chronic and demyelinating disease of the central nervous system (CNS), most prevalent in women, and with an important social and economic cost worldwide. It is triggered by self-reacting lymphocytes that infiltrate the CNS and initiate neuroinflammation. Further, axonal loss and neuronal death takes place, leading to neurodegeneration and brain atrophy. The murine model for studying MS, experimental autoimmune encephalomyelitis (EAE), consists in immunizing mice with myelin-derived epitopes. APCs activate encephalitogenic T CD4 and CD8 lymphocytes that migrate mainly to the spinal cord resulting in neuroinflammation. Most of the knowledge on the pathophysiology and treatment of MS was obtained from EAE experiments, as Th17 cells, anti-alpha4 blocking Abs and the role of microbiota. Conversely, recent technology breakthroughs, such as CyTOF and single-cell RNA-seq, promise to revolutionize our understanding on the mechanisms involved both in MS and EAE. In fact, the importance of specific cellular populations and key molecules in MS/EAE is a constant matter of debate. It is well accepted that both Th1 and Th17 T CD4 lymphocytes play a relevant role in disease initiation after re-activation in situ. What is still under constant investigation, however, is the plasticity of the lymphocyte population, and the individual contribution of both resident and inflammatory cells for the progression or recovery of the disease. Thus, in this review, new findings obtained after single-cell analysis of blood and central nervous system infiltrating cells from MS/EAE and how they have contributed to a better knowledge on the cellular and molecular mechanisms of neuroinflammation are discussed.

Rare osteosarcoma cell subpopulation protein array and profiling using imaging mass cytometry and bioinformatics analysis

BACKGROUND

Single rare cell characterization represents a new scientific front in personalized therapy. Imaging mass cytometry (IMC) may be able to address all these questions by combining the power of MS-CyTOF and microscopy.

METHODS

We have investigated this IMC method using < 100 to up to 1000 cells from human sarcoma tumor cell lines by incorporating bioinformatics-based t-Distributed Stochastic Neighbor Embedding (t-SNE) analysis of highly multiplexed IMC imaging data. We tested this process on osteosarcoma cell lines TC71, OHS as well as osteosarcoma patient-derived xenograft (PDX) cell lines M31, M36, and M60. We also validated our analysis using sarcoma patient-derived CTCs.

RESULTS

We successfully identified heterogeneity within individual tumor cell lines, the same PDX cells, and the CTCs from the same patient by detecting multiple protein targets and protein localization. Overall, these data reveal that our t-SNE-based approach can not only identify rare cells within the same cell line or cell population, but also discriminate amongst varied groups to detect similarities and differences.

CONCLUSIONS

This method helps us make greater inroads towards generating patient-specific CTC fingerprinting that could provide an accurate tumor status from a minimally-invasive liquid biopsy.

Theory of cell fate

Cell fate decisions are controlled by complex intracellular molecular regulatory networks. Studies increasingly reveal the scale of this complexity: not only do cell fate regulatory networks contain numerous positive and negative feedback loops, they also involve a range of different kinds of nonlinear protein-protein and protein-DNA interactions. This inherent complexity and nonlinearity makes cell fate decisions hard to understand using experiment and intuition alone. In this primer, we will outline how tools from mathematics can be used to understand cell fate dynamics. We will briefly introduce some notions from dynamical systems theory, and discuss how they offer a framework within which to build a rigorous understanding of what we mean by a cell "fate", and how cells change fate. We will also outline how modern experiments, particularly high-throughput single-cell experiments, are enabling us to test and explore the limits of these ideas, and build a better understanding of cellular identities. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Biological Mechanisms > Cell Fates Models of Systems Properties and Processes > Cellular Models.

Dynamics of Early Signalling Events during Fracture Healing and Potential Serum Biomarkers of Fracture Non-Union in Humans

To characterise the dynamic of events during the early phases of fracture repair in humans, we investigated molecular events using gene expression profiling of bone fragments from the fracture site at different time points after trauma and immune/stromal cells recruitment at the fracture site using flow cytometry. Bone and inflammatory markers were expressed at low levels at homeostasis, while transcripts for bone constituent proteins were consistently detected at higher levels. Early after fracture (range 2-4 days), increased expression of CXCL12, suggested recruitment of immune cells associated with a change in the balance of degradation enzymes and their inhibitors. At intermediate time after fracture (4-8 days), we observed high expression of inflammatory cytokines (IL1-beta, IL6), CCL2, the T-cell activation marker CD69. Late after fracture (8-14 days), high expression of factors co-operating towards the regulation of bone turnover was detected. We identified potential soluble factors and explored circulating levels in patients for whom a union/non-union (U/NU) outcome was known. This showed a clear difference for PlGF (p = 0.003) at day 1. These findings can inform future studies further investigating the cascade of molecular events following fractures and for the prediction of fracture non-union.

SCINA: A Semi-Supervised Subtyping Algorithm of Single Cells and Bulk Samples

Advances in single-cell RNA sequencing (scRNA-Seq) have allowed for comprehensive analyses of single cell data. However, current analyses of scRNA-Seq data usually start from unsupervised clustering or visualization. These methods ignore prior knowledge of transcriptomes and the probable structures of the data. Moreover, cell identification heavily relies on subjective and possibly inaccurate human inspection afterwards. To address these analytical challenges, we developed SCINA (Semi-supervised Category Identification and Assignment), a semi-supervised model that exploits previously established gene signatures using an expectation-maximization (EM) algorithm. SCINA is applicable to scRNA-Seq and flow cytometry/CyTOF data, as well as other data of similar format. We applied SCINA to a wide range of datasets, and showed its accuracy, stability and efficiency, which exceeded most popular unsupervised approaches. SCINA discovered an intermediate stage of oligodendrocytes from mouse brain scRNA-Seq data. SCINA also detected immune cell population changes in cytometry data in a genetically-engineered mouse model. Furthermore, SCINA performed well with bulk gene expression data. Specifically, we identified a new kidney tumor clade with similarity to FH-deficient tumors (FHD), which we refer to as FHD-like tumors (FHDL). Overall, SCINA provides both methodological advances and biological insights from perspectives different from traditional analytical methods.

IL-15 regulates susceptibility of CD4+ T cells to HIV infection

HIV integrates into the host genome to create a persistent viral reservoir. Stimulation of CD4⁺ memory T lymphocytes with common γc-chain cytokines renders these cells more susceptible to HIV infection, making them a key component of the reservoir itself. IL-15 is up-regulated during primary HIV infection, a time when the HIV reservoir established. Therefore, we investigated the molecular and cellular impact of IL-15 on CD4⁺ T-cell infection. We found that IL-15 stimulation induces SAM domain and HD domain-containing protein 1 (SAMHD1) phosphorylation due to cell cycle entry, relieving an early block to infection. Perturbation of the pathways downstream of IL-15 receptor (IL-15R) indicated that SAMHD1 phosphorylation after IL-15 stimulation is JAK dependent. Treating CD4⁺ T cells with Ruxolitinib, an inhibitor of JAK1 and JAK2, effectively blocked IL-15-induced SAMHD1 phosphorylation and protected CD4⁺ T cells from HIV infection. Using high-resolution single-cell immune profiling using mass cytometry by TOF (CyTOF), we found that IL-15 stimulation altered the composition of CD4⁺ T-cell memory populations by increasing proliferation of memory CD4⁺ T cells, including CD4⁺ T memory stem cells (T_SCM). IL-15-stimulated CD4⁺ T_SCM, harboring phosphorylated SAMHD1, were preferentially infected. We propose that IL-15 plays a pivotal role in creating a self-renewing, persistent HIV reservoir by facilitating infection of CD4⁺ T cells with stem cell-like properties. Time-limited interventions with JAK1 inhibitors, such as Ruxolitinib, should prevent the inactivation of the endogenous restriction factor SAMHD1 and protect this long-lived CD4⁺ T-memory cell population from HIV infection.

Static droplet array for culturing single live adherent cells in an isolated chemical microenvironment

We present here a new method to easily and reliably generate an array of hundreds of dispersed nanoliter-volume semi-droplets for single-cells culture and analysis. The liquid segmentation step occurs directly in indexed traps by a tweezer-like mechanism and is stabilized by spatial confinement. Unlike common droplet-based techniques, the semi-droplet wets its surrounding trap walls thus supporting the culturing of both adherent and non-adherent cells. To eliminate cross-droplet cell migration and chemical cross-talk each semi-droplet is separated from a nearby trap by an ∼80 pL air plug. The overall setup and injection procedure takes less than 10 minutes, is insensitive to fabrication defects and supports cell recovery for downstream analysis. The method offers a new approach to easily capture, image and culture single cells in a chemically isolated microenvironment as a preliminary step towards high-throughput single-cell assays.

Prospects in non-invasive assessment of liver fibrosis: Liquid biopsy as the future gold standard?

Liver fibrosis is the result of persistent liver injury, and is characterized by sustained scar formation and disruption of the normal liver architecture. The extent of fibrosis is considered as an important prognostic factor for the patient outcome, as an absence of (early) treatment can lead to the development of liver cirrhosis and hepatocellular carcinoma. Till date, the most sensitive and specific way for the diagnosis and staging of liver fibrosis remains liver biopsy, an invasive diagnostic tool, which is associated with high costs and discomfort for the patient. Over time, non-invasive scoring systems have been developed, of which the measurements of serum markers and liver stiffness are validated for use in the clinic. These tools lack however the sensitivity and specificity to detect small changes in the progression or regression of both early and late stages of fibrosis. Novel non-invasive diagnostic markers with the potential to overcome these limitations have been developed, but often lack validation in large patient cohorts. In this review, we will summarize novel trends in non-invasive markers of liver fibrosis development and will discuss their (dis-)advantages for use in the clinic.

Application of Chemogenetics and Optogenetics to Dissect Brain-Immune Interactions

For many years, the complexity and multifactorial nature of brain-immune interactions limited our ability to dissect their underlying mechanisms. An especially challenging question was how the brain controls immunity, since the repertoire of techniques to control the brain's activity was extremely limited. New tools, such as optogenetics and chemogenetics (e.g., DREADDs), developed over the last decade, opened new frontiers in neuroscience with major implications for neuroimmunology. These tools enable mapping the causal effects of activating/attenuating defined neurons in the brain, on the immune system. Here, we present a detailed experimental protocol for the analysis of brain-immune interactions, based on chemogenetic or optogenetic manipulation of defined neuronal populations in the brain, and the subsequent analysis of immune cells. Such detailed and systematic dissection of brain-immune interactions has the potential to revolutionize our understanding of how mental and neurological states affect health and disease.

Automation of sample preparation for mass cytometry barcoding in support of clinical research: protocol optimization

Analysis of multiplexed assays is highly important for clinical diagnostics and other analytical applications. Mass cytometry enables multi-dimensional, single-cell analysis of cell type and state. In mass cytometry, the rare earth metals used as reporters on antibodies allow determination of marker expression in individual cells. Barcode-based bioassays for CyTOF are able to encode and decode for different experimental conditions or samples within the same experiment, facilitating progress in producing straightforward and consistent results. Herein, an integrated protocol for automated sample preparation for barcoding used in conjunction with mass cytometry for clinical bioanalysis samples is described; we offer results of our work with barcoding protocol optimization. In addition, we present some points to be considered in order to minimize the variability of quantitative mass cytometry measurements. For example, we discuss the importance of having multiple populations during titration of the antibodies and effect of storage and shipping of labelled samples on the stability of staining for purposes of CyTOF analysis. Data quality is not affected when labelled samples are stored either frozen or at 4 °C and used within 10 days; we observed that cell loss is greater if cells are washed with deionized water prior to shipment or are shipped in lower concentration. Once the labelled samples for CyTOF are suspended in deionized water, the analysis should be performed expeditiously, preferably within the first hour. Damage can be minimized if the cells are resuspended in phosphate-buffered saline (PBS) rather than deionized water while waiting for data acquisition.

TRIM28 interacts with EZH2 and SWI/SNF to activate genes that promote mammosphere formation

Histone methyl transferase EZH2 (Enhancer of Zeste Homolog 2) is generally associated with H3K27 methylation and gene silencing, as a member of the polycomb repressor 2 (PRC2) complex. Immunoprecipitation and mass spectrometry of the EZH2-protein interactome in estrogen receptor positive, breast cancer-derived MCF7 cells revealed EZH2 interactions with subunits of chromatin remodeler SWI/SNF complex and TRIM28, which formed a complex with EZH2 distinct from PRC2. Unexpectedly, transcriptome profiling showed that EZH2 primarily activates, rather than represses, transcription in MCF7 cells and with TRIM28 co-regulates a set of genes associated with stem cell maintenance and poor survival of breast cancer patients. TRIM28 depletion repressed EZH2 recruitment to chromatin and expression of this gene set, in parallel with decreased CD44^hi/CD24^lo mammosphere formation. Mammosphere formation, inhibited by EZH2 depletion, was rescued by ectopic expression of EZH2 but not by TRIM28 expression or by EZH2 mutated at the region (pre-SET domain) of TRIM28 interaction. These results support PRC2-independent functions of EZH2 and TRIM28 in activation of gene expression that promotes mammary stem cell enrichment and maintenance.

Immunomics: a 21st century approach to vaccine development for complex pathogens

Immunomics is a relatively new field of research which integrates the disciplines of immunology, genomics, proteomics, transcriptomics and bioinformatics to characterize the host-pathogen interface. Herein, we discuss how rapid advances in molecular immunology, sophisticated tools and molecular databases are facilitating in-depth exploration of the immunome. In our opinion, an immunomics-based approach presides over traditional antigen and vaccine discovery methods that have proved ineffective for highly complex pathogens such as the causative agents of malaria, tuberculosis and schistosomiasis that have evolved genetic and immunological host-parasite adaptations over time. By using an integrative multidisciplinary approach, immunomics offers enormous potential to advance 21st century antigen discovery and rational vaccine design against complex pathogens such as the Plasmodium parasite.

Analyses of 123 Peripheral Human Immune Cell Subsets: Defining Differences with Age and between Healthy Donors and Cancer Patients Not Detected in Analysis of Standard Immune Cell Types

Recent advances in human immunology have led to the identification of novel immune cell subsets and the biological function of many of these subsets has now been identified. The recent US Food and Drug Administration approval of several immunotherapeutics for the treatment of a variety of cancer types and the results of ongoing immunotherapy clinical studies requires a more thorough interrogation of the immune system. We report here the use of flow cytometry-based analyses to identify 123 immune cell subsets of peripheral blood mononuclear cells. The use of these panels defines multiple differences in younger (< 40 years) vs. older (≥ 40 years) individuals and between aged-matched apparently healthy individuals and metastatic cancer patients, aspects not seen in the analysis of the following standard immune cell types: CD8, CD4, natural killer, natural killer-T, regulatory T, myeloid derived suppressor cells, conventional dendritic cells (DCs), plasmacytoid DCs and B cells. The use of these panels identifying 123 immune cell subsets may aid in the identification of patients who may benefit from immunotherapy, either prior to therapy or early in the immunotherapeutic regimen, for the treatment of cancer or other chronic or infectious diseases.

Polystyrene microspheres enable 10-color compensation for immunophenotyping of primary human leukocytes

Compensation is a critical process for the unbiased analysis of flow cytometry data. Numerous compensation strategies exist, including the use of bead‐based products. The purpose of this study was to determine whether beads, specifically polystyrene microspheres (PSMS) compare to the use of primary leukocytes for single color based compensation when conducting polychromatic flow cytometry. To do so, we stained individual tubes of both PSMS and leukocytes with panel specific antibodies conjugated to fluorochromes corresponding to fluorescent channels FL1‐FL10. We compared the matrix generated by PSMS to that generated using peripheral blood mononuclear cells (PBMC). Ideal for compensation is a sample with both a discrete negative population and a bright positive population. We demonstrate that PSMS display autofluorescence properties similar to PBMC. When comparing PSMS to PBMC for compensation PSMS yielded more evenly distributed and discrete negative and positive populations to use for compensation. We analyzed three donors' PBMC stained with our 10‐color T cell subpopulation panel using compensation generated by PSMS vs.PBMC and detected no significant differences in the population distribution. Panel specific antibodies bound to PSMS represent an invaluable valid tool to generate suitable compensation matrices especially when sample material is limited and/or the sample requires analysis of dynamically modulated or rare events. © 2015 The Authors. Cytometry Part A Published by Wiley Periodicals, Inc.

Impact of recent innovations in the use of mass cytometry in support of drug development

Cytometry by time-of-flight (CyTOF) is a novel technology for the real-time analysis of single cells. CyTOF is a significant advance in fields including immunology, hematology, and oncology. It resolves multiple metal-conjugated probes per cell with minimal signal overlap, which maximizes the information obtained from each individual sample. CyTOF provides the ability to phenotypically and functionally profile cells from normal and diseased states. Single cell technologies enable researchers to measure the effects of a drug at the single cell level and better understand its mechanism of action. Here, we discuss novel instruments for the analysis of individual biological cells, the impact of recent innovations in support of drug development, and the important roles of CyTOF in drug profiling.