Unraveling cell populations in tumors by single-cell mass cytometry

Multiplexed Imaging Mass Cytometry Analysis in Preclinical Models of Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers. PDAC is characterized by a complex tumor microenvironment (TME), that plays a pivotal role in disease progression and resistance to therapy. Investigating the spatial distribution and interaction of TME cells with the tumor is the basis for understanding the mechanisms underlying disease progression and represents a current challenge in PDAC research. Imaging mass cytometry (IMC) is the major multiplex imaging technology for the spatial analysis of tumor heterogeneity. However, there is a dearth of reports of multiplexed IMC panels for different preclinical mouse models, including pancreatic cancer. We addressed this gap by utilizing two preclinical models of PDAC: the genetically engineered, bearing KRAS-TP53 mutations in pancreatic cells, and the orthotopic, and developed a 28-marker panel for single-cell IMC analysis to assess the abundance, distribution and phenotypes of cells involved in PDAC progression and their reciprocal functional interactions. Herein, we provide an unprecedented definition of the distribution of TME cells in PDAC and compare the diversity between transplanted and genetic disease models. The results obtained represent an important and customizable tool for unraveling the complexities of PDAC and deciphering the mechanisms behind therapy resistance.

Mass Cytometry as a Tool for Investigating Senescence in Multiple Model Systems

Cellular senescence is a durable cell cycle arrest as a result of the finite proliferative capacity of cells. Senescence responds to both intrinsic and extrinsic cellular stresses, such as aging, mitochondrial dysfunction, irradiation, and chemotherapy. Here, we report on the use of mass cytometry (MC) to analyze multiple model systems and demonstrate MC as a platform for senescence analysis at the single-cell level. We demonstrate changes to p16 expression, cell cycling fraction, and histone tail modifications in several established senescent model systems and using isolated human T cells. In bone marrow mesenchymal stromal cells (BMSCs), we show increased p16 expression with subsequent passage as well as a reduction in cycling cells and open chromatin marks. In WI-38 cells, we demonstrate increased p16 expression with both culture-induced senescence and oxidative stress-induced senescence (OSIS). We also use Wanderlust, a trajectory analysis tool, to demonstrate how p16 expression changes with histone tail modifications and cell cycle proteins. Finally, we demonstrate that repetitive stimulation of human T cells with CD3/CD28 beads induces an exhausted phenotype with increased p16 expression. This p16-expressing population exhibited higher expression of exhaustion markers such as EOMES and TOX. This work demonstrates that MC is a useful platform for studying senescence at a single-cell protein level, and is capable of measuring multiple markers of senescence at once with high confidence, thereby improving our understanding of senescent pathways.

RespectM revealed metabolic heterogeneity powers deep learning for reshaping the DBTL cycle

Synthetic biology, relying on Design-Build-Test-Learn (DBTL) cycle, aims to solve medicine, manufacturing, and agriculture problems. However, the DBTL cycle's Learn (L) step lacks predictive power for the behavior of biological systems, resulting from the incompatibility between sparse testing data and chaotic metabolic networks. Herein, we develop a method, "RespectM," based on mass spectrometry imaging, which is able to detect metabolites at a rate of 500 cells per hour with high efficiency. In this study, 4,321 single cell level metabolomics data were acquired, representing metabolic heterogeneity. An optimizable deep neural network was applied to learn from metabolic heterogeneity and a "heterogeneity-powered learning (HPL)" based model was trained as well. By testing the HPL based model, we suggest minimal operations to achieve high triglyceride production for engineering. The HPL strategy could revolutionize rational design and reshape the DBTL cycle.

A universal mass tag based on polystyrene nanoparticles for single-cell multiplexing with mass cytometry

Mass cytometry (MC) is an emerging bioanalytical technique for high-dimensional biomarkers interrogation simultaneously on individual cells. However, the sensitivity and multiplexed analysis ability of MC was highly restricted by the current metal chelating polymer (MCP) mass tags. Herein, a new design strategy for MC mass tags by using a commercial available and low cost classical material, polystyrene nanoparticle (PS-NP) to carry metals was reported. Unlike inorganic materials, sub-micron-grade metal-loaded polystyrene can be easily detected by MC, thus it is not essential to pursue extremely small particle size in this mass tag design strategy. An altered cell staining buffer can significantly lower the nonspecific binding (NSB) of non-functionalized PS-NPs, revealing another method to lower NSB beside surface modification. The metal doped PS-NP_Abs mass tags showed high compatibility with MCP mass tags and 5-fold higher sensitivity. By using Hf doped PS-NP_Abs as mass tags, four new MC detection channels (¹⁷⁷Hf, ¹⁷⁸Hf, ¹⁷⁹Hf and ¹⁸⁰Hf) were developed. In general, this work provides a new strategy in designing MC mass tags and lowering NSB, opening up possibility of introducing more potential MC mass tag candidates.

Mass cytometry to characterize the immune lung cancer microenvironment

The human tumor microenvironment requires use of high-dimensional single-cell tools to uncover its cellular complexity and functional variety. For decades, flow cytometry has been the technology of choice to explore immune cell diversity in different pathological contexts. Recently, a new format for flow cytometry - termed mass cytometry - has been developed. It allows for simultaneous interrogation of more than 40 different molecular markers, without the need for spectral compensation, which significantly augments the ability of cytometry to evaluate complex cellular systems and processes. Currently, different multiparametric single-cell analysis approaches are being widely adopted to interrogate the human tumor microenvironment. However, important challenges must be addressed when solid tissues are analyzed by single-cell technologies. This protocol describes the challenge and better use of single-cell mass cytometry to dissect tumor infiltrating leukocytes from surgically resected tumoral lung tissues.

Clinical Assessments of Fracture Healing and Basic Science Correlates: Is There Room for Convergence?

PURPOSE OF REVIEW

The purpose of this review is to summarize the clinical and basic science methods used to assess fracture healing and propose a framework to improve the translational possibilities.

RECENT FINDINGS

Mainstays of fracture healing assessment include clinical examination, various imaging modalities, and assessment of function. Pre-clinical studies have yielded insight into biomechanical progression as well as the genetic, molecular, and cellular processes of fracture healing. Efforts are emerging to identify early markers to predict impaired healing and possibly early intervention to alter these processes. Despite of the differences in clinical and preclinical research, opportunities exist to unify and improve the translational efforts between these arenas to develop and optimize our ability to assess and predict fracture healing, thereby improving the clinical care of these patients.

Characterizing the biology of primary brain tumors and their microenvironment via single-cell profiling methods

Genomic and transcriptional heterogeneity is prevalent among the most common and aggressive primary brain tumors in children and adults. Over the past 20 years, advances in bioengineering, biochemistry and bioinformatics have enabled the development of an array of techniques to study tumor biology at single-cell resolution. The application of these techniques to study primary brain tumors has helped advance our understanding of their intra-tumoral heterogeneity and uncover new insights regarding their co-option of developmental programs and signaling from their microenvironment to promote tumor proliferation and invasion. These insights are currently being harnessed to develop new therapeutic approaches. Here we provide an overview of current single-cell techniques and discuss relevant biology and therapeutic insights uncovered by their application to primary brain tumors in children and adults.

Facets of ICP-MS and their potential in the medical sciences—Part 2: nanomedicine, immunochemistry, mass cytometry, and bioassays

Inductively coupled–plasma mass spectrometry (ICP-MS) has transformed our knowledge on the role of trace and major elements in biology and has emerged as the most versatile technique in elemental mass spectrometry. The scope of ICP-MS has dramatically changed since its inception, and nowadays, it is a mature platform technology that is compatible with chromatographic and laser ablation (LA) systems. Over the last decades, it kept pace with various technological advances and was inspired by interdisciplinary approaches which endorsed new areas of applications. While the first part of this review was dedicated to fundamentals in ICP-MS, its hyphenated techniques and the application in biomonitoring, isotope ratio analysis, elemental speciation analysis, and elemental bioimaging, this second part will introduce relatively current directions in ICP-MS and their potential to provide novel perspectives in the medical sciences. In this context, current directions for the characterisation of novel nanomaterials which are considered for biomedical applications like drug delivery and imaging platforms will be discussed while considering different facets of ICP-MS including single event analysis and dedicated hyphenated techniques. Subsequently, immunochemistry techniques will be reviewed in their capability to expand the scope of ICP-MS enabling analysis of a large range of biomolecules alongside elements. These methods inspired mass cytometry and imaging mass cytometry and have the potential to transform diagnostics and treatment by offering new paradigms for personalised medicine. Finally, the interlacing of immunochemistry methods, single event analysis, and functional nanomaterials has opened new horizons to design novel bioassays which promise potential as assets for clinical applications and larger screening programs and will be discussed in their capabilities to detect low-level proteins and nucleic acids.

Protein Adsorption Loss─The Bottleneck of Single-Cell Proteomics

Single-cell proteomics is a promising field to provide direct yet comprehensive molecular insights into cellular functions without averaging effects. Here, we address a grand technical challenge impeding the maturation of single-cell proteomics─protein adsorption loss (PAL). Even though widely known, there is currently no quantitation on how profoundly and selectively PAL has affected single-cell proteomics. Therefore, the mitigations to this challenge have been generic, and their efficacy was only evaluated by the size of the resolved proteome with no specificity on individual proteins. We use the existing knowledge of PAL, protein expression, and the typical surface area used in single-cell proteomics to discuss the severity of protein loss. We also summarize the current solutions to this challenge and briefly review the available methods to characterize the physical and chemical properties of protein surface adsorption. By citing successful strategies in single-cell genomics for measurement errors in individual transcripts, we pinpoint the urgency to benchmark PAL at the proteome scale with individual protein resolution. Finally, orthogonal single-cell proteomic techniques that have the potential to cross validate PAL are proposed. We hope these efforts can promote the fruition of single-cell proteomics in the near future.

Materiomically Designed Polymeric Vehicles for Nucleic Acids: Quo Vadis?

Despite rapid advances in molecular biology, particularly in site-specific genome editing technologies, such as CRISPR/Cas9 and base editing, financial and logistical challenges hinder a broad population from accessing and benefiting from gene therapy. To improve the affordability and scalability of gene therapy, we need to deploy chemically defined, economical, and scalable materials, such as synthetic polymers. For polymers to deliver nucleic acids efficaciously to targeted cells, they must optimally combine design attributes, such as architecture, length, composition, spatial distribution of monomers, basicity, hydrophilic-hydrophobic phase balance, or protonation degree. Designing polymeric vectors for specific nucleic acid payloads is a multivariate optimization problem wherein even minuscule deviations from the optimum are poorly tolerated. To explore the multivariate polymer design space rapidly, efficiently, and fruitfully, we must integrate parallelized polymer synthesis, high-throughput biological screening, and statistical modeling. Although materiomics approaches promise to streamline polymeric vector development, several methodological ambiguities must be resolved. For instance, establishing a flexible polymer ontology that accommodates recent synthetic advances, enforcing uniform polymer characterization and data reporting standards, and implementing multiplexed in vitro and in vivo screening studies require considerable planning, coordination, and effort. This contribution will acquaint readers with the challenges associated with materiomics approaches to polymeric gene delivery and offers guidelines for overcoming these challenges. Here, we summarize recent developments in combinatorial polymer synthesis, high-throughput screening of polymeric vectors, omics-based approaches to polymer design, barcoding schemes for pooled in vitro and in vivo screening, and identify materiomics-inspired research directions that will realize the long-unfulfilled clinical potential of polymeric carriers in gene therapy.

Novel Techniques and Future Perspective for Investigating Critical-Size Bone Defects

A critical-size bone defect is a challenging clinical problem in which a gap between bone ends will not heal and will become a nonunion. The current treatment is to harvest and transplant an autologous bone graft to facilitate bone bridging. To develop less invasive but equally effective treatment options, one needs to first have a comprehensive understanding of the bone healing process. Therefore, it is imperative to leverage the most advanced technologies to elucidate the fundamental concepts of the bone healing process and develop innovative therapeutic strategies to bridge the nonunion gap. In this review, we first discuss the current animal models to study critical-size bone defects. Then, we focus on four novel analytic techniques and discuss their strengths and limitations. These four technologies are mass cytometry (CyTOF) for enhanced cellular analysis, imaging mass cytometry (IMC) for enhanced tissue special imaging, single-cell RNA sequencing (scRNA-seq) for detailed transcriptome analysis, and Luminex assays for comprehensive protein secretome analysis. With this new understanding of the healing of critical-size bone defects, novel methods of diagnosis and treatment will emerge.

Effect of Excess Ligand on the Reverse Microemulsion Silica Coating of NaLnF4 Nanoparticles

Silica coating of inorganic nanoparticles (NPs) is widely employed as a means of providing colloidal stability in aqueous media and surface functionality for a variety of applications, particularly in biology. When the NPs are synthesized with a surface coating of an organic surfactant like oleic acid, silica coating is performed by using the reverse microemulsion method. There are many reports in the literature of the successful application of this method to NaYF₄ upconversion NPs (doped with Yb and Er), and we have used this method to coat NaHoF₄ NPs designed as a mass cytometry reagent. This method failed when we attempted to apply it to other NaLnF₄ NPs (Ln = Sm, Eu, Tb). In this report we describe an investigation of the problem and show how it can be overcome. To control size in the synthesis of NaLnF₄ NPs and at the same time maintain size uniformity, it is necessary to adjust the Na/F and F/Ln ratios. Problems with silica coating are associated with substoichiometric F/Ln ratios (F/Ln < 4) that leave Ln oleate salts as a byproduct, often as a phase-separated oily layer that could not be purified from the NPs by precipitation with ethanol and redispersion in hexanes. The nature of the oily byproduct was inferred from a combination of TGA, NMR, and FTIR measurements. We explored five different additional purification procedures, and by adopting the appropriate purification method, NaLnF₄ NPs with a variety of compositions and synthesized using different reaction conditions could be coated with a thin shell of silica.

Mass cytometric and transcriptomic profiling of epithelial-mesenchymal transitions in human mammary cell lines

Epithelial-mesenchymal transition (EMT) equips breast cancer cells for metastasis and treatment resistance. However, detection, inhibition, and elimination of EMT-undergoing cells is challenging due to the intrinsic heterogeneity of cancer cells and the phenotypic diversity of EMT programs. We comprehensively profiled EMT transition phenotypes in four non-cancerous human mammary epithelial cell lines using a flow cytometry surface marker screen, RNA sequencing, and mass cytometry. EMT was induced in the HMLE and MCF10A cell lines and in the HMLE-Twist-ER and HMLE-Snail-ER cell lines by prolonged exposure to TGFβ1 or 4-hydroxytamoxifen, respectively. Each cell line exhibited a spectrum of EMT transition phenotypes, which we compared to the steady-state phenotypes of fifteen luminal, HER2-positive, and basal breast cancer cell lines. Our data provide multiparametric insights at single-cell level into the phenotypic diversity of EMT at different time points and in four human cellular models. These insights are valuable to better understand the complexity of EMT, to compare EMT transitions between the cellular models used here, and for the design of EMT time course experiments.

Measurement(s)	RNA-seq gene expression profiling assay • cell surface proteins • protein expression at the single-cell level • Epithelial-to-Mesenchymal Transition • breast cancer cell • mammary gland epithelial cell
Technology Type(s)	mRNA Sequencing • Flow Cytometry • cytometry time of flight assay • Cell Culture
Factor Type(s)	protein level • gene expression • cell morphology
Sample Characteristic - Organism	Homo sapiens
Sample Characteristic - Environment	cell culture

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.16989301

Physical Cytometry: Detecting Mass-Related Properties of Single Cells

The physical properties of a single cell, such as mass, volume, and density, are important indications of the cell's metabolic characteristics and homeostasis. Precise measurement of a single cell's mass has long been a challenge due to its minute size. It is only in the past 10 years that a variety of instruments for measuring living cellular mass have emerged with the development of MEMS, microfluidics, and optics technologies. In this review, we discuss the current developments of physical cytometry for quantifying mass-related physical properties of single cells, highlighting the working principle, applications, and unique merits. The review mainly covers these measurement methods: single-cell mass cytometry, levitation image cytometry, suspended microchannel resonator, phase-shifting interferometry, and opto-electrokinetics cell manipulation. Comparisons are made between these methods in terms of throughput, content, invasiveness, compatibility, and precision. Some typical applications of these methods in pathological diagnosis, drug efficacy evaluation, disease treatment, and other related fields are also discussed in this work.

Quantification of tumor heterogeneity: from data acquisition to metric generation

Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care.

Mass cytometry profiling of human dendritic cells in blood and tissues

The immune system comprises distinct functionally specialized cell populations, which can be characterized in depth by mass cytometry protein profiling. Unfortunately, the low-throughput nature of mass cytometry has made it challenging to analyze minor cell populations. This is the case for dendritic cells, which represent 0.2-2% of all immune cells in tissues and yet perform the critical task of initiating and modulating immune responses. Here, we provide an optimized step-by-step protocol for the characterization of well-known and emerging human dendritic cell populations in blood and tissues using mass cytometry. We provide detailed instructions for the generation of single-cell suspensions, sample enrichment, staining, acquisition and data analysis. We also include a barcoding option that reduces acquisition variability and allows the analysis of low numbers of dendritic cells, i.e., ~20,000. In contrast to other protocols, we emphasize the use of negative selection approaches to enrich for minor populations of immune cells while avoiding their activation. The entire procedure can be completed in 2-3 d and can be conveniently paused at several stages. The procedure described in this robust and reliable protocol allows the analysis of human dendritic cells in health and disease and during vaccination.

New Structure Mass Tag based on Zr-NMOF for Multiparameter and Sensitive Single-Cell Interrogating in Mass Cytometry

Mass cytometry, also called cytometry by time-of-flight (CyTOF), is an emerging powerful proteomic analysis technique that utilizes metal chelated polymer (MCP) as mass tags for interrogating high-dimensional biomarkers simultaneously on millions of individual cells. However, under the typical polymer-based mass tag system, the sensitivity and multiplexing detection ability has been highly restricted. Herein, a new structure mass tag based on a nanometal organic framework (NMOF) is reported for multiparameter and sensitive single-cell biomarker interrogating in CyTOF. A uniform-sized Zr-NMOF (33 nm) carrying 10⁵ metal ions is synthesized under modulator/reaction time coregulation, which is monodispersed and colloidally stable in water for over one-year storage. On functionalization with an antibody, the Zr mass tag exhibits specific molecular recognition properties and minimal cross-reaction toward nontargeted cells. In addition, the Zr-mass tag is compatible with MCP mass tags in a multiparameter assay for mouse spleen cells staining, which exploits four additional channels, m/z = 90, 91, 92, 94, for single-cell immunoassays in CyTOF. Compared to the MCP mass tag, the Zr-mass tag provides an additional fivefold signal amplification. This work provides the fundamental technical capability for exploiting NMOF-based mass tags for CyTOF application, which opens up possibility of high-dimensional single-cell immune profiling, low abundant antigen detection, and development of new barcoding systems.

Censcyt: censored covariates in differential abundance analysis in cytometry

BACKGROUND

Innovations in single cell technologies have lead to a flurry of datasets and computational tools to process and interpret them, including analyses of cell composition changes and transition in cell states. The diffcyt workflow for differential discovery in cytometry data consist of several steps, including preprocessing, cell population identification and differential testing for an association with a binary or continuous covariate. However, the commonly measured quantity of survival time in clinical studies often results in a censored covariate where classical differential testing is inapplicable.

RESULTS

To overcome this limitation, multiple methods to directly include censored covariates in differential abundance analysis were examined with the use of simulation studies and a case study. Results show that multiple imputation based methods offer on-par performance with the Cox proportional hazards model in terms of sensitivity and error control, while offering flexibility to account for covariates. The tested methods are implemented in the R package censcyt as an extension of diffcyt and are available at https://bioconductor.org/packages/censcyt .

CONCLUSION

Methods for the direct inclusion of a censored variable as a predictor in GLMMs are a valid alternative to classical survival analysis methods, such as the Cox proportional hazard model, while allowing for more flexibility in the differential analysis.

Application of Single-Cell Approaches to Study Myeloproliferative Neoplasm Biology

Philadelphia-negative myeloproliferative neoplasms (MPNs) are an excellent tractable disease model of a number of aspects of human cancer biology, including genetic evolution, tissue-associated fibrosis, and cancer stem cells. In this review, we discuss recent insights into MPN biology gained from the application of a number of new single-cell technologies to study human disease, with a specific focus on single-cell genomics, single-cell transcriptomics, and digital pathology.

Understanding cell-cell communication and signaling in the colorectal cancer microenvironment

Carcinomas are complex heterocellular systems containing epithelial cancer cells, stromal fibroblasts, and multiple immune cell-types. Cell-cell communication between these tumor microenvironments (TME) and cells drives cancer progression and influences response to existing therapies. In order to provide better treatments for patients, we must understand how various cell-types collaborate within the TME to drive cancer and consider the multiple signals present between and within different cancer types. To investigate how tissues function, we need a model to measure both how signals are transferred between cells and how that information is processed within cells. The interplay of collaboration between different cell-types requires cell-cell communication. This article aims to review the current in vitro and in vivo mono-cellular and multi-cellular cultures models of colorectal cancer (CRC), and to explore how they can be used for single-cell multi-omics approaches for isolating multiple types of molecules from a single-cell required for cell-cell communication to distinguish cancer cells from normal cells. Integrating the existing single-cell signaling measurements and models, and through understanding the cell identity and how different cell types communicate, will help predict drug sensitivities in tumor cells and between- and within-patients responses.

Carrier-assisted One-pot Sample Preparation for Targeted Proteomics Analysis of Small Numbers of Human Cells

Protein analysis of small numbers of human cells is primarily achieved by targeted proteomics with antibody-based immunoassays, which have inherent limitations (e.g., low multiplex and unavailability of antibodies for new proteins). Mass spectrometry (MS)-based targeted proteomics has emerged as an alternative because it is antibody-free, high multiplex, and has high specificity and quantitation accuracy. Recent advances in MS instrumentation make MS-based targeted proteomics possible for multiplexed quantification of highly abundant proteins in single cells. However, there is a technical challenge for effective processing of single cells with minimal sample loss for MS analysis. To address this issue, we have recently developed a convenient protein carrier-assisted one-pot sample preparation coupled with liquid chromatography (LC) - selected reaction monitoring (SRM) termed cLC-SRM for targeted proteomics analysis of small numbers of human cells. This method capitalizes on using the combined excessive exogenous protein as a carrier and low-volume one-pot processing to greatly reduce surface adsorption losses as well as high-specificity LC-SRM to effectively address the increased dynamic concentration range due to the addition of exogeneous carrier protein. Its utility has been demonstrated by accurate quantification of most moderately abundant proteins in small numbers of cells (e.g., 10-100 cells) and highly abundant proteins in single cells. The easy-to-implement features and no need for specific devices make this method readily accessible to most proteomics laboratories. Herein we have provided a detailed protocol for cLC-SRM analysis of small numbers of human cells including cell sorting, cell lysis and digestion, LC-SRM analysis, and data analysis. Further improvements in detection sensitivity and sample throughput are needed towards targeted single-cell proteomics analysis. We anticipate that cLC-SRM will be broadly applied to biomedical research and systems biology with the potential of facilitating precision medicine.

High-Sensitivity and High-Speed Single-Particle Inductively Coupled Plasma Spectrometry with the Conical Torch

Significant advancement has been achieved in single-particle analysis with the new conical ICP torch in terms of sensitivity, precision, and throughput. Monodisperse desolvated particles of eight elements (Na, Al, Ag, Sr, Ca, Mg, Fe, and Be) were injected into the conical torch, and signal peak characteristics, precision, and kinetics of atomization and ionization were investigated with optical spectrometry. A particle introduction system was designed to ensure a smooth and uninterrupted delivery of desolvated particles to the plasma. The important finding is that, compared with the conventional Fassel torch, the conical torch offers a 1.5-8 times higher peak intensity, a 2-4 times higher peak area, a 2 times shorter peak width, and higher precision (i.e., a 1.5 times lower RSD for peak intensity and a 1.8 times lower RSD for peak width on average). Also, mass detection limits were found to be similar or up to 8 times lower (i.e., 2 times lower diameter detection limit) for the conical torch. The results indicate that these features are due to a much higher electron density, excitation temperature, and robustness which, together with an improved particle trajectory, lead to rapid vaporization/atomization/ionization of particles with minimized atom/ion cloud diffusion. Finally, the torch was demonstrated to be capable of analyzing single particles at a rate of at least 2000 particles per second with high sensitivity and precision. On the basis of these results, the conical torch is expected to bring about new possibilities in ICP-based single-particle analysis.

Advances of Single-Cell Protein Analysis

Proteins play a significant role in the key activities of cells. Single-cell protein analysis provides crucial insights in studying cellular heterogeneities. However, the low abundance and enormous complexity of the proteome posit challenges in analyzing protein expressions at the single-cell level. This review summarizes recent advances of various approaches to single-cell protein analysis. We begin by discussing conventional characterization approaches, including fluorescence flow cytometry, mass cytometry, enzyme-linked immunospot assay, and capillary electrophoresis. We then detail the landmark advances of microfluidic approaches for analyzing single-cell protein expressions, including microfluidic fluorescent flow cytometry, droplet-based microfluidics, microwell-based assay (microengraving), microchamber-based assay (barcoding microchips), and single-cell Western blotting, among which the advantages and limitations are compared. Looking forward, we discuss future research opportunities and challenges for multiplexity, analyte, throughput, and sensitivity of the microfluidic approaches, which we believe will prompt the research of single-cell proteins such as the molecular mechanism of cell biology, as well as the clinical applications for tumor treatment and drug development.

Development of microfluidic platform capable of high-throughput absolute quantification of single-cell multiple intracellular proteins from tumor cell lines and patient tumor samples

Quantification of single-cell proteins plays key roles in cell heterogeneity while due to technical limitations absolute numbers of multiple intracellular proteins from large populations of single cells were still missing, leading to compromised results in cell-type classifications. This paper presents a microfluidic platform capable of high-throughput absolute quantification of single-cell multiple types of intracellular proteins where cells stained with fluorescent labelled antibodies are aspirated into the constriction microchannels with excited fluorescent signals detected and translated into numbers of binding sites of targeted proteins based on calibration curves formed by flushing gradient solutions of fluorescent labelled antibodies directly into constriction microchannels. Based on this approach, single-cell numbers of binding sites of β-actin, α-tubulin and β-tubulin from tens of thousands of five representative tumor cell lines were first quantified, reporting cell-type classification rates of 83.0 ± 7.1%. Then single-cell numbers of binding sites of β-actin, biotin and RhoA from thousands of five tumor cell lines with varieties in malignant levels were quantified, reporting cell-type classification rates of 93.7 ± 2.8%. Furthermore, single-cell numbers of binding sites of Ras, c-Myc and p53 from thousands of cells derived from two oral tumor lines of CAL 27, WSU-HN6 and two oral tumor patient samples were quantified, contributing to high classifications of both tumor cell lines (98.6%) and tumor patient samples (83.4%). In conclusion, the developed microfluidic platform was capable of quantifying multiple intracellular proteins from large populations of single cells, and the collected data of protein expressions enabled effective cell-type classifications.

Proteome analysis of tissues by mass spectrometry

Tissues and biofluids are important sources of information used for the detection of diseases and decisions on patient therapies. There are several accepted methods for preservation of tissues, among which the most popular are fresh-frozen and formalin-fixed paraffin embedded methods. Depending on the preservation method and the amount of sample available, various specific protocols are available for tissue processing for subsequent proteomic analysis. Protocols are tailored to answer various biological questions, and as such vary in lysis and digestion conditions, as well as duration. The existence of diverse tissue-sample protocols has led to confusion in how to choose the best protocol for a given tissue and made it difficult to compare results across sample types. Here, we summarize procedures used for tissue processing for subsequent bottom-up proteomic analysis. Furthermore, we compare protocols for their variations in the composition of lysis buffers, digestion procedures, and purification steps. For example, reports have shown that lysis buffer composition plays an important role in the profile of extracted proteins: the most common are tris(hydroxymethyl)aminomethane, radioimmunoprecipitation assay, and ammonium bicarbonate buffers. Although, trypsin is the most commonly used enzyme for proteolysis, in some protocols it is supplemented with Lys-C and/or chymotrypsin, which will often lead to an increase in proteome coverage. Data show that the selection of the lysis procedure might need to be tissue-specific to produce distinct protocols for individual tissue types. Finally, selection of the procedures is also influenced by the amount of sample available, which range from biopsies or the size of a few dozen of mm² obtained with laser capture microdissection to much larger amounts that weight several milligrams.

Molecular insights into cancer drug resistance from a proteomics perspective

INTRODUCTION

Resistance to chemotherapy and development of specific and effective molecular targeted therapies are major obstacles facing current cancer treatment. Comparative proteomic approaches have been employed for the discovery of putative biomarkers associated with cancer drug resistance and have yielded a number of candidate proteins, showing great promise for both novel drug target identification and personalized medicine for the treatment of drug-resistant cancer. Areas covered: Herein, we review the recent advances and challenges in proteomics studies on cancer drug resistance with an emphasis on biomarker discovery, as well as understanding the interconnectivity of proteins in disease-related signaling pathways. In addition, we highlight the critical role that post-translational modifications (PTMs) play in the mechanisms of cancer drug resistance. Expert opinion: Revealing changes in proteome profiles and the role of PTMs in drug-resistant cancer is key to deciphering the mechanisms of treatment resistance. With the development of sensitive and specific mass spectrometry (MS)-based proteomics and related technologies, it is now possible to investigate in depth potential biomarkers and the molecular mechanisms of cancer drug resistance, assisting the development of individualized therapeutic strategies for cancer patients.

State-of-the-Art of Profiling Immune Contexture in the Era of Multiplexed Staining and Digital Analysis to Study Paraffin Tumor Tissues

Multiplexed platforms for multiple epitope detection have emerged in the last years as very powerful tools to study tumor tissues. These revolutionary technologies provide important visual techniques for tumor examination in formalin-fixed paraffin-embedded specimens to improve the understanding of the tumor microenvironment, promote new treatment discoveries, aid in cancer prevention, as well as allowing translational studies to be carried out. The aim of this review is to highlight the more recent methodologies that use multiplexed staining to study simultaneous protein identification in formalin-fixed paraffin-embedded tumor tissues for immune profiling, clinical research, and potential translational analysis. New multiplexed methodologies, which permit the identification of several proteins at the same time in one single tissue section, have been developed in recent years with the ability to study different cell populations, cells by cells, and their spatial distribution in different tumor specimens including whole sections, core needle biopsies, and tissue microarrays. Multiplexed technologies associated with image analysis software can be performed with a high-quality throughput assay to study cancer specimens and are important tools for new discoveries. The different multiplexed technologies described in this review have shown their utility in the study of cancer tissues and their advantages for translational research studies and application in cancer prevention and treatments.

Emerging approaches to study cell-cell interactions in tumor microenvironment

Cell-cell interactions are of crucial importance for tissue formation, homeostasis, regeneration processes, and immune response. Recent studies underlined contribution of cell-cell interaction in tumor microenvironment (TME) for tumor progression and metastasis. Cancer cells modify the host cells to tumor-supportive traits, and the modified host cells contribute to tumor progression by interacting with cancer cells and further modifying other normal cells. However, the complex interaction networks of cancer cells and host cells remained largely unknown. Recent advances in high throughput microscopy and single cells-based molecular analyses have unlocked a new era for studying cell-cell interactions in the complex tissue microenvironment at the resolution of a single cell. Here, we review various model systems and emerging experimental approaches that are used to study cell-cell interactions focusing on the studies of TME. We discuss strengths and weaknesses of each model system and each experimental approach, and how upcoming approaches can solve current fundamental questions of cell-cell interactions in TME.

Beyond the message: advantages of snapshot proteomics with single-cell mass cytometry in solid tumors

Single-cell technologies that can quantify features of individual cells within a tumor are critical for treatment strategies aiming to target cancer cells while sparing or activating beneficial cells. Given that key players in protein networks are often the primary targets of precision oncology strategies, it is imperative to transcend the nucleic acid message and read cellular actions in human solid tumors. Here, we review the advantages of multiplex, single-cell mass cytometry in tissue and solid tumor investigations. Mass cytometry can quantitatively probe nearly any cellular feature or target. In discussing the ability of mass cytometry to reveal and characterize a broad spectrum of cell types, identify rare cells, and study functional behavior through protein signaling networks in millions of individual cells from a tumor, this review surveys publications of scientific advances in solid tumor biology made with the aid of mass cytometry. Advances discussed include functional identification of rare tumor and tumor-infiltrating immune cells and dissection of cellular mechanisms of immunotherapy in solid tumors and the periphery. The review concludes by highlighting ways to incorporate single-cell mass cytometry in solid tumor precision oncology efforts and rapidly developing cytometry techniques for quantifying cell location and sequenced nucleic acids.

Immunophenotyping by Mass Cytometry

Mass cytometry is a novel technology similar to flow cytometry in which antibodies are tagged with heavy metal molecules rather than fluorophores and then detected with time-of-flight mass spectrometry. This enables measurement of up to 50 simultaneous parameters with no autofluorescent background and little or no spillover or required compensation. Mass cytometry has tremendous potential for the analysis of highly complex research or clinical samples and can measure 40-50 immunophenotypic markers at a time. This chapter describes most of the commonly used methods for performing basic immunophenotyping experiments by mass cytometry, and how this can be combined with measurement of cellular functional properties.

Controlling Dispersion during Single-Cell Polyacrylamide-Gel Electrophoresis in Open Microfluidic Devices

New tools for measuring protein expression in individual cells complement single-cell genomics and transcriptomics. To characterize a population of individual mammalian cells, hundreds to thousands of microwells are arrayed on a polyacrylamide-gel-coated glass microscope slide. In this "open" fluidic device format, we explore the feasibility of mitigating diffusional losses during lysis and polyacrylamide-gel electrophoresis (PAGE) through spatial control of the pore-size of the gel layer. To reduce in-plane diffusion-driven dilution of each single-cell lysate during in-microwell chemical lysis, we photopattern and characterize microwells with small-pore-size sidewalls ringing the microwell except at the injection region. To reduce out-of-plane-diffusion-driven-dilution-caused signal loss during both lysis and single-cell PAGE, we scrutinize a selectively permeable agarose lid layer. To reduce injection dispersion, we photopattern and study a stacking-gel feature at the head of each <1 mm separation axis. Lastly, we explore a semienclosed device design that reduces the cross-sectional area of the chip, thus reducing Joule-heating-induced dispersion during single-cell PAGE. As a result, we observed a 3-fold increase in separation resolution during a 30 s separation and a >2-fold enhancement of the signal-to-noise ratio. We present well-integrated strategies for enhancing overall single-cell-PAGE performance.

Ratiometric Barcoding for Mass Cytometry

Barcoding is of importance for high-throughput cellular and molecular analysis. A ratiometric barcoding strategy using lanthanide-coordinated polymer dots (Ln-Pdots) was developed for mass cytometric analysis. By using 3 metal isotopes and 4 ratio intensity levels, 16 barcodes were generated to code, and later decode, cell samples in mass cytometry. The ratiometric Ln-Pdot barcodes not only provided high-mass-signal intensities but also eliminated the bias caused by different concentrations of the labeling reagents/barcodes and run-to-run differences in cell labeling efficiency. The ability to distinguish clearly the 16 sets of labeled MCF-7 cells with mass cytometry demonstrated the excellent resolving power of the ratiometric Ln-Pdot barcodes. Furthermore, the results from barcoding PBMC samples via CD45-specific cellular targeting indicated that the ratiometric Ln-Pdot barcodes could facilitate mass cytometry in high-throughput and multiplexed analysis, especially with precious human samples.

Defining Cell Identity with Single-Cell Omics

Cells are a fundamental unit of life, and the ability to study the phenotypes and behaviors of individual cells is crucial to understanding the workings of complex biological systems. Cell phenotypes (epigenomic, transcriptomic, proteomic, and metabolomic) exhibit dramatic heterogeneity between and within the different cell types and states underlying cellular functional diversity. Cell genotypes can also display heterogeneity throughout an organism, in the form of somatic genetic variation-most notably in the emergence and evolution of tumors. Recent technical advances in single-cell isolation and the development of omics approaches sensitive enough to reveal these aspects of cell identity have enabled a revolution in the study of multicellular systems. In this review, we discuss the technologies available to resolve the genomes, epigenomes, transcriptomes, proteomes, and metabolomes of single cells from a wide variety of living systems.

Advancing systems immunology through data-driven statistical analysis

Systems biology provides an effective approach to decipher, predict, and ultimately manipulate the complex and inter-connected networks that regulate the immune system. Advances in high-throughput, multiplexed experimental techniques have increased the availability of proteomic and transcriptomic immunological datasets, and as a result, have also accelerated the development of new data-driven computational algorithms to extract biological insight from these data. This review highlights how data-driven statistical models have been used to characterize immune cell subsets and their functions, to map the signaling and intercellular networks that regulate immune responses, and to connect immune cell states to disease outcomes to generate hypotheses for novel therapeutic strategies. We focus on recent advances in evaluating immune cell responses following viral infection and in the tumor microenvironment, which hold promise for improving vaccines, antiviral and cancer immunotherapy.

How can nanotechnology help the fight against breast cancer?

In this review we provide a broad overview on the use of nanotechnology for the fight against breast cancer (BC). Nowadays, detection, diagnosis, treatment, and prevention may be possible thanks to the application of nanotechnology to clinical practice. Taking into consideration the different forms of BC and the disease status, nanomaterials can be designed to meet the most forefront objectives of modern therapy and diagnosis. We have analyzed in detail three main groups of nanomaterial applications for BC treatment and diagnosis. We have identified several types of drugs successfully conjugated with nanomaterials. We have analyzed the main important imaging techniques and all nanomaterials used to help the non-invasive, early detection of the lesions. Moreover, we have examined theranostic nanomaterials as unique tools, combining imaging, detection, and therapy for BC. This state of the art review provides a useful guide depicting how nanotechnology can be used to overcome the current barriers in BC clinical practice, and how it will shape the future scenario of treatments, prevention, and diagnosis, revolutionizing the current approaches, e.g., reducing the suffering related to chemotherapy.

Melanoma Immunotherapy: Next-Generation Biomarkers

The recent emergence of cancer immunotherapies initiated a significant shift in the clinical management of metastatic melanoma. Prior to 2011, melanoma patients only had palliative treatment solutions which offered little to no survival benefit. In 2018, with immunotherapy, melanoma patients can now contemplate durable or even complete remission. Treatment with novel immune checkpoint inhibitors, anti-cytotoxic T-lymphocyte protein 4 and anti-programmed cell death protein 1, clearly result in superior median and long-term survivals compared to standard chemotherapy; however, more than half of the patients do not respond to immune checkpoint blockade. Currently, clinicians do not have any effective way to stratify melanoma patients for immunotherapies. Research is now focusing on identifying biomarkers which could predict a patient’s response prior treatment initiation (or very early during treatment course), in order to maximize therapeutic efficacy, avoid unnecessary costs, and undesirable heavy side effects for the patient. Given the rapid developments in this field and the translational potential for some of the biomarkers, we will summarize the current state of biomarker research for immunotherapy in melanoma, with an emphasis on omics technologies such as next-generation sequencing and mass cytometry (CyTOF).

A Method for the Interpretation of Flow Cytometry Data Using Genetic Algorithms

Background:

Flow cytometry analysis is the method of choice for the differential diagnosis of hematologic disorders. It is typically performed by a trained hematopathologist through visual examination of bidimensional plots, making the analysis time-consuming and sometimes too subjective. Here, a pilot study applying genetic algorithms to flow cytometry data from normal and acute myeloid leukemia subjects is described.

Subjects and Methods:

Initially, Flow Cytometry Standard files from 316 normal and 43 acute myeloid leukemia subjects were transformed into multidimensional FITS image metafiles. Training was performed through introduction of FITS metafiles from 4 normal and 4 acute myeloid leukemia in the artificial intelligence system.

Results:

Two mathematical algorithms termed 018330 and 025886 were generated. When tested against a cohort of 312 normal and 39 acute myeloid leukemia subjects, both algorithms combined showed high discriminatory power with a receiver operating characteristic (ROC) curve of 0.912.

Conclusions:

The present results suggest that machine learning systems hold a great promise in the interpretation of hematological flow cytometry data.

Clonal diversity revealed by morphoproteomic and copy number profiles of single prostate cancer cells at diagnosis

Tumor heterogeneity is prevalent in both treatment-naïve and end-stage metastatic castration-resistant prostate cancer (PCa), and may contribute to the broad range of clinical presentation, treatment response, and disease progression. To characterize molecular heterogeneity associated with de novo metastatic PCa, multiplatform single cell profiling was performed using high definition single cell analysis (HD-SCA). HD-SCA enabled morphoproteomic and morphogenomic profiling of single cells from touch preparations of tissue cores (prostate and bone marrow biopsies) as well as liquid samples (peripheral blood and bone marrow aspirate). Morphology, nuclear features, copy number alterations, and protein expression were analyzed. Tumor cells isolated from prostate tissue touch preparation (PTTP) and bone marrow touch preparation (BMTP) as well as metastatic tumor cells (MTCs) isolated from bone marrow aspirate were characterized by morphology and cytokeratin expression. Although peripheral blood was examined, circulating tumor cells were not definitively observed. Targeted proteomics of PTTP, BMTP, and MTCs revealed cell lineage and luminal prostate epithelial differentiation associated with PCa, including co-expression of EpCAM, PSA, and PSMA. Androgen receptor expression was highest in MTCs. Hallmark PCa copy number alterations, including PTEN and ETV6 deletions and NCOA2 amplification, were observed in cells within the primary tumor and bone marrow biopsy samples. Genomic landscape of MTCs revealed to be a mix of both primary and bone metastatic tissue. This multiplatform analysis of single cells reveals several clonal origins of metastatic PCa in a newly diagnosed, untreated patient with polymetastatic disease. This case demonstrates that real-time molecular profiling of cells collected through prostate and bone marrow biopsies is feasible and has the potential to elucidate the origin and evolution of metastatic tumor cells. Altogether, biological and genomic data obtained through longitudinal biopsies can be used to reveal the properties of PCa and can impact clinical management.

Highly multiplexed and quantitative cell-surface protein profiling using genetically barcoded antibodies

Next-generation sequencing (NGS) has allowed the comprehensive study of the genome and transcriptome. However, a similarly broad, highly multiplexed, and inexpensive method for proteomics using NGS remains elusive. Here, we describe a phage display-based method using preselected antibodies that are genetically encoded and capable of simultaneous profiling of hundreds of cell-surface targets on cells in culture or singly at low cost and without the need for chemical conjugation to purified antibodies. We use the method to identify cell-surface proteins that change in cancer cells, some of which are coordinately regulated and could lead to new biomarkers and cancer targets.

Human cells express thousands of different surface proteins that can be used for cell classification, or to distinguish healthy and disease conditions. A method capable of profiling a substantial fraction of the surface proteome simultaneously and inexpensively would enable more accurate and complete classification of cell states. We present a highly multiplexed and quantitative surface proteomic method using genetically barcoded antibodies called phage-antibody next-generation sequencing (PhaNGS). Using 144 preselected antibodies displayed on filamentous phage (Fab-phage) against 44 receptor targets, we assess changes in B cell surface proteins after the development of drug resistance in a patient with acute lymphoblastic leukemia (ALL) and in adaptation to oncogene expression in a Myc-inducible Burkitt lymphoma model. We further show PhaNGS can be applied at the single-cell level. Our results reveal that a common set of proteins including FLT3, NCR3LG1, and ROR1 dominate the response to similar oncogenic perturbations in B cells. Linking high-affinity, selective, genetically encoded binders to NGS enables direct and highly multiplexed protein detection, comparable to RNA-sequencing for mRNA. PhaNGS has the potential to profile a substantial fraction of the surface proteome simultaneously and inexpensively to enable more accurate and complete classification of cell states.

CellCycleTRACER accounts for cell cycle and volume in mass cytometry data

Recent studies have shown that cell cycle and cell volume are confounding factors when studying biological phenomena in single cells. Here we present a combined experimental and computational method, CellCycleTRACER, to account for these factors in mass cytometry data. CellCycleTRACER is applied to mass cytometry data collected on three different cell types during a TNFα stimulation time-course. CellCycleTRACER reveals signaling relationships and cell heterogeneity that were otherwise masked.

Mass cytometry is a powerful method of single cell analysis, but potential confounding effects of cell cycle and cell volume are not taken into account. Here the authors present a combined experimental and computational method to correct for these effects and reveal features of TNFα stimulation that are otherwise masked.

Quantitation of nonspecific protein adsorption at solid–liquid interfaces for single-cell proteomics

Protein nonspecific adsorption that occurred at the solid–liquid interface has been subjected to intense physical and chemical characterizations due to its crucial role in a wide range of applications, including food and pharmaceutical industries, medical implants, biosensing, and so on. Protein-adsorption caused sample loss has largely hindered the studies of single-cell proteomics; the prevention of such loss requires the understanding of protein–surface adsorption at the proteome level, in which the competitive adsorption of thousands and millions of proteins with vast dynamic range occurs. To this end, we feel the necessity to review current methodologies on their potentials to characterize — more specifically to quantify — the proteome-wide adsorption. We hope this effort can help advancing single-cell proteomics and trace proteomics.

Geometry-induced injection dispersion in single-cell protein electrophoresis

Arrays of microwells are widely used to isolate individual cells, facilitate high throughput cytometry assays, and ensure compatibility of those assays with whole-cell imaging. Microwell geometries have recently been utilized for handling and preparation of single-cell lysate, prior to single-cell protein electrophoresis. It is in the context of single-cell electrophoresis that we investigate the interplay of microwell geometry (circular, rectangular, triangular) and transport (diffusion, electromigration) on the subsequent performance of single-cell polyacrylamide gel electrophoresis (PAGE) for protein targets. We define and measure injector-induced dispersion during PAGE, and develop a numerical model of band broadening sources, experimentally validate the numerical model, and then identify operating conditions (characterized through the Peclet number, Pe) that lead to microwell-geometry induced losses in separation performance. With analysis of mammalian cells as a case study, we sought to understand at what Pe is the PAGE separation performance adversely sensitized to the microwell geometry. In developing design rules, we find that for the microwell geometries that are the most suitable for isolation of mammalian cells and moderate mass protein targets, the Pe is usually small enough (Pe < ∼20) to mitigate the effect of the microwell geometry on protein PAGE of single-cell lysate. In extreme cases where the largest mammalian cells are analyzed (Pe > ∼20), consideration of Pe suggests using a rectangular - and not the widely used circular - microwell geometry to maximize protein PAGE separation performance.

Breast Cancer: Multiple Subtypes within a Tumor?

Breast cancer is a heterogeneous disease, and stratification of tumors is paramount to achieve better clinical outcomes. While it is common to stratify and treat breast tumors as a single entity, insights from studies on intratumoral heterogeneity and cancer stem cells raise the possibility that multiple breast cancer subtypes may coexist within a tumor. A role for plasticity in driving dynamic conversions between breast cancer subtypes is proposed, and the clinical implications include a need for combinatorial therapeutic strategies that account for the discrete disease entities and their plasticity. Accordingly, the advent of single-cell technologies will be crucial in enabling the diagnosis and stratification of distinct disease subtypes down to the cellular level.

Data integration from pathology slides for quantitative imaging of multiple cell types within the tumor immune cell infiltrate

Background

Immune cell infiltrates (ICI) of tumors are scored by pathologists around tumor glands. To obtain a better understanding of the immune infiltrate, individual immune cell types, their activation states and location relative to tumor cells need to be determined. This process requires precise identification of the tumor area and enumeration of immune cell subtypes separately in the stroma and inside tumor nests. Such measurements can be accomplished by a multiplex format using immunohistochemistry (IHC).

Method

We developed a pipeline that combines immunohistochemistry (IHC) and digital image analysis. One slide was stained with pan-cytokeratin and CD45 and the other slide with CD8, CD4 and CD68. The tumor mask generated through pan-cytokeratin staining was transferred from one slide to the other using affine image co-registration. Bland-Altman plots and Pearson correlation were used to investigate differences between densities and counts of immune cell underneath the transferred versus manually annotated tumor masks. One-way ANOVA was used to compare the mask transfer error for tissues with solid and glandular tumor architecture.

Results

The overlap between manual and transferred tumor masks ranged from 20%–90% across all cases. The error of transferring the mask was 2- to 4-fold greater in tumor regions with glandular compared to solid growth pattern (p < 10⁻⁶). Analyzing data from a single slide, the Pearson correlation coefficients of cell type densities outside and inside tumor regions were highest for CD4 + T-cells (r = 0.8), CD8 + T-cells (r = 0.68) or CD68+ macrophages (r = 0.79). The correlation coefficient for CD45+ T- and B-cells was only 0.45. The transfer of the mask generated an error in the measurement of intra- and extra- tumoral CD68+, CD8+ or CD4+ counts (p < 10⁻¹⁰).

Conclusions

In summary, we developed a general method to integrate data from IHC stained slides into a single dataset. Because of the transfer error between slides, we recommend applying the antibody for demarcation of the tumor on the same slide as the ICI antibodies.

Electronic supplementary material

The online version of this article (10.1186/s13000-017-0658-8) contains supplementary material, which is available to authorized users.

CyTOF Mass Cytometry for Click Proliferation Assays

Novel cell analyzers, including polychromatic flow cytometers and isotopical cytometry by time of flight (CyTOF) mass cytometers, enable simultaneous measurement of virtually bondless characteristics at the single-cell level. BrdU assays for quantifying cellular proliferation are common but have several limitations, including the need for a DNA denaturation step and inability to simultaneously resolve multiple parameters and phenotypic complexity. Click chemistry reactions have become popular in the past decade, as they can resolve these issues. This protocol introduces a novel assay able to bridge flow cytometry and CyTOF analysis for active S-phase determination in cell cycle applications, combining well-established click chemistry with a novel iodo-deoxyuridine (IdU) azide derivative and a cross-reactive anti-IdU antibody for detecting incorporated EdU during DNA synthesis. This method is preferred over traditional BrdU-based assays for complex and multiparametric experiments. It provides a feasible cost-effective approach for detecting ethynyl-labeled nucleotides, with the advantage of combining flow and mass cytometry analyses. © 2017 by John Wiley & Sons, Inc.

Overexpression of the human antigen R suppresses the immediate paradoxical proliferation of melanoma cell subpopulations in response to suboptimal BRAF inhibition

Tumor plasticity and the heterogeneous response of melanoma cells to targeted therapies are major limits for the long‐term efficacy of this line of therapy. Targeting tumor plasticity is theoretically possible through the modulation of the expression of RNA‐binding proteins which can affect many different compensatory mechanisms of the adaptive response of malignant cells to targeted therapies. Human antigen R (HuR) is a modulator of gene expression and a transacting factor in the mRNA‐processing machinery used in the cell stress response, and is a potential target for reducing tumor plasticity. In this experiment, we exploit the inherent heterogeneous response of the A375 melanoma line to suboptimal BRAF inhibition as a model of immediate adaptive response. We first observe that HuR overexpression can prevent the heterogeneous response and thus the immediate paradoxical proliferation induced by low‐doses vemurafenib treatment. We then use single‐cell mass cytometry to characterize subpopulations, including those that paradoxically proliferate, based on their proliferation rate and the expression patterns of markers involved in the reversible adaptive resistance to BRAF inhibition and/or recognized as HuR targets involved in cell cycle regulation. Under suboptimal BRAF inhibition, HuR overexpression affects these subpopulations and their expression pattern with contrasting responses depending on their proliferation rate: faster‐proliferating vemurafenib‐sensitive or ‐resistant subpopulations showed higher death tendency and reduced size, and slower‐proliferating subpopulations showed an attenuated resistant expression response and their paradoxical proliferation was inhibited. These observations pave the way to new therapeutic strategies for preventing the heterogeneous response of tumors to targeted therapies.