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Background Intra-sample cellular heterogeneity presents numerous challenges to the identification of biomarkers in large Epigenome-Wide Association Studies (EWAS). While a number of reference-based deconvolution algorithms have emerged, their potential remains underexplored and a comparative evaluation of these algorithms beyond tissues such as blood is still lacking. Results Here we present a novel framework for reference-based inference, which leverages cell-type specific DNAse Hypersensitive Site (DHS) information from the NIH Epigenomics Roadmap to construct an improved reference DNA methylation database. We show that this leads to a marginal but statistically significant improvement of cell-count estimates in whole blood as well as in mixtures involving epithelial cell-types. Using this framework we compare a widely used state-of-the-art reference-based algorithm (called constrained projection) to two non-constrained approaches including CIBERSORT and a method based on robust partial correlations. We conclude that the widely-used constrained projection technique may not always be optimal. Instead, we find that the method based on robust partial correlations is generally more robust across a range of different tissue types and for realistic noise levels. We call the combined algorithm which uses DHS data and robust partial correlations for inference, EpiDISH ( Epi genetic D issection of I ntra- S ample H eterogeneity). Finally, we demonstrate the added value of EpiDISH in an EWAS of smoking. Conclusions Estimating cell-type fractions and subsequent inference in EWAS may benefit from the use of non-constrained reference-based cell-type deconvolution methods.

Owing to recent advances in mass spectrometry (MS), tens to hundreds of proteins, lipids, and small molecules can be measured in single cells. The ability to characterize the molecular heterogeneity of individual cells is necessary to define the full assortment of cell subtypes and identify their function. We review single-cell MS including high-throughput, targeted, mass cytometry-based approaches and antibody-free methods for broad profiling of the proteome and metabolome of single cells. The advantages and disadvantages of different methods are discussed, as well as the challenges and opportunities for further improvements in single-cell MS. These methods is being used in biomedicine in several applications including revealing tumor heterogeneity and high-content drug screening. Single-cell mass spectrometry (MS) can now be used to measure tens to hundreds of proteins, metabolites, and lipids in individual cells.The pace of the development of single-cell MS has been breathtaking. Some key performance metrics in single-cell MS that differentiate between methods include measurement throughput, the extent of analyte coverage, and the type of analytes measured (proteins, small molecules, lipids).Key challenges and opportunities for improvement can be classified into increasing measurement throughput and the extent of analyte coverage, in addition to enhancing the depth of analyte characterization (e.g., distinguishing between lipid isomers and characterizing proteoforms).Emergent and potential applications of single-cell MS include early disease diagnosis, analysis of tumor heterogeneity, and high information content drug screening.

The burden of heart failure (HF), developing after myocardial infarction MI, still represents a major issue in clinical practice. Failure of appropriate resolution of inflammation during post-myocardial injury is associated with unsuccessful left ventricular remodeling and underlies HF pathogenesis. Cells of the immune system have been shown to mediate both protective and damaging effects in heart remodeling. This ambiguity of the role of the immune system and inconsistent results of the recent clinical trials question the benefits of anti-inflammatory therapies during acute MI. The present review will summarize knowledge of the roles that different cells of the immune system play in the process of post-infarct cardiac healing. Data on the phenotype, active molecules and functions of the immune cells, based on the results of both experimental and clinical studies, will be provided. For some cellular subsets, such as macrophages, neutrophils, dendritic cells and lymphocytes, an anti-inflammatory activity has been attributed to the specific subpopulations. Activity of other cells, such as eosinophils, mast cells, natural killer (NK) cells and NKT cells has been shown to be highly dependent of the signals created by micro-environment. Also, new approaches for classification of cellular phenotypes based on the single-cell RNA sequencing allow better understanding of the phenotype of the cells involved in resolution of inflammation. Possible perspectives of immune-mediated therapy for AMI patients are discussed in the conclusion. We also outline unresolved questions that need to be solved in order to implement the current knowledge on the role of the immune cells in post-MI tissue repair into practice.

Extracellular vesicles (EVs) are important intercellular mediators regulating health and diseases. Conventional methods for EV surface marker profiling, which was based on population measurements, masked the cell-to-cell heterogeneity in the quantity and phenotypes of EV secretion. Herein, by using spatially patterned antibody barcodes, we realized multiplexed profiling of single-cell EV secretion from more than 1,000 single cells simultaneously. Applying this platform to profile human oral squamous cell carcinoma (OSCC) cell lines led to a deep understanding of previously undifferentiated single-cell heterogeneity underlying EV secretion. Notably, we observed that the decrement of certain EV phenotypes (e.g., CD63+EV) was associated with the invasive feature of both OSCC cell lines and primary OSCC cells. We also realized multiplexed detection of EV secretion and cytokines secretion simultaneously from the same single cells to investigate the multidimensional spectrum of cellular communications, from which we resolved tiered functional subgroups with distinct secretion profiles by visualized clustering and principal component analysis. In particular, we found that different cell subgroups dominated EV secretion and cytokine secretion. The technology introduced here enables a comprehensive evaluation of EV secretion heterogeneity at single-cell level, which may become an indispensable tool to complement current singlecell analysis and EV research.

The development of more intricate devices for the analysis of small molecules and protein activity in single cells would advance our knowledge of cellular heterogeneity and signaling cascades. Therefore, in this study, a nanokit was produced by filling a nanometer-sized capillary with a ring electrode at the tip with components from traditional kits, which could be egressed outside the capillary by electrochemical pumping. At the tip, femtoliter amounts of the kit components were reacted with the analyte to generate hydrogen peroxide for the electrochemical measurement by the ring electrode. Taking advantage of the nanotip and small volume injection, the nanokit was easily inserted into a single cell to determine the intracellular glucose levels and sphingomyelinase (SMase) activity, which had rarely been achieved. High cellular heterogeneities of these two molecules were observed, showing the significance of the nanokit. Compared with the current methods that use a complicated structural design or surface functionalization for the recognition of the analytes, the nanokit has adapted features of the well-established kits and integrated the kit components and detector in one nanometer-sized capillary, which provides a specific device to characterize the reactivity and concentrations of cellular compounds in single cells.

Molecular analysis on the single-cell level represents a rapidly growing field in the life sciences. While bulk analysis from a pool of cells provides a general molecular profile, it is blind to heterogeneities between individual cells. This heterogeneity, however, is an inherent property of every cell population. Its analysis is fundamental to understanding the development, function, and role of specific cells of the same genotype that display different phenotypical properties. Single-cell mass spectrometry (MS) aims to provide broad molecular information for a significantly large number of cells to help decipher cellular heterogeneity using statistical analysis. Here, we present a sensitive approach to single-cell MS based on high-resolution MALDI-2-MS imaging in combination with MALDI-compatible staining and use of optical microscopy. Our approach allowed analyzing large amounts of unperturbed cells directly from the growth chamber. Confident coregistration of both modalities enabled a reliable compilation of single-cell mass spectra and a straightforward inclusion of optical as well as mass spectrometric features in the interpretation of data. The resulting multimodal datasets permit the use of various statistical methods like machine learning–driven classification and multivariate analysis based on molecular profile and establish a direct connection of MS data with microscopy information of individual cells. Displaying data in the form of histograms for individual signal intensities helps to investigate heterogeneous expression of specific lipids within the cell culture and to identify subpopulations intuitively. Ultimately, t-MALDI-2-MSI measurements at 2-μm pixel sizes deliver a glimpse of intracellular lipid distributions and reveal molecular profiles for subcellular domains.

Induced pluripotent stem cell (iPSC) technologies have provided in vitro models of inaccessible human cell types, yielding new insights into disease mechanisms especially for neurological disorders. However, without due consideration, the thousands of new human iPSC lines generated in the past decade will inevitably affect the reproducibility of iPSC-based experiments. Differences between donor individuals, genetic stability and experimental variability contribute to iPSC model variation by impacting differentiation potency, cellular heterogeneity, morphology, and transcript and protein abundance. Such effects will confound reproducible disease modelling in the absence of appropriate strategies. In this Review, we explore the causes and effects of iPSC heterogeneity, and propose approaches to detect and account for experimental variation between studies, or even exploit it for deeper biological insight.

Significance We demonstrated codetection of 42 immune effector proteins in single cells, representing the highest multiplexing recorded to date for a single-cell secretion assay. Using this platform to profile differentiated macrophages stimulated with lipopolysaccharide reveals previously unobserved deep functional heterogeneity and varying levels of pathogenic activation, which is conserved throughout the cell activation process and prevails as it is extended to other Toll-like receptor (TLR) ligands and to primary human macrophages. The results indicate that the phenotypically similar cell population could still exhibit a large degree of intrinsic heterogeneity at the cell function level. This technology enables full-spectrum dissection of immune functional states in response to pathogenic stimulation and allows for more comprehensive and accurate monitoring of cellular immunity. Despite recent advances in single-cell genomic, transcriptional, and mass-cytometric profiling, it remains a challenge to collect highly multiplexed measurements of secreted proteins from single cells for comprehensive analysis of functional states. Herein, we combine spatial and spectral encoding with polydimethylsiloxane (PDMS) microchambers for codetection of 42 immune effector proteins secreted from single cells, representing the highest multiplexing recorded to date for a single-cell secretion assay. Using this platform to profile differentiated macrophages stimulated with lipopolysaccharide (LPS), the ligand of Toll-like receptor 4 (TLR4), reveals previously unobserved deep functional heterogeneity and varying levels of pathogenic activation. Uniquely protein profiling on the same single cells before and after LPS stimulation identified a role for macrophage inhibitory factor (MIF) to potentiate the activation of LPS-induced cytokine production. Advanced clustering analysis identified functional subsets including quiescent, polyfunctional fully activated, partially activated populations with different cytokine profiles. This population architecture is conserved throughout the cell activation process and prevails as it is extended to other TLR ligands and to primary macrophages derived from a healthy donor. This work demonstrates that the phenotypically similar cell population still exhibits a large degree of intrinsic heterogeneity at the functional and cell behavior level. This technology enables full-spectrum dissection of immune functional states in response to pathogenic or environmental stimulation, and opens opportunities to quantify deep functional heterogeneity for more comprehensive and accurate immune monitoring.

We present a method enabling the noninvasive study of minute cellular changes in response to stimuli, based on the acquisition of multiple parameters through label-free microscopy. The retrieved parameters are related to different attributes of the cell. Morphological variables are extracted from quantitative phase microscopy and autofluorescence images, while molecular indicators are retrieved via Raman spectroscopy. We show that these independent parameters can be used to build a multivariate statistical model based on logistic regression, which we apply to the detection at the single-cell level of macrophage activation induced by lipopolysaccharide (LPS) exposure and compare their respective performance in assessing the individual cellular state. The models generated from either morphology or Raman can reliably and independently detect the activation state of macrophage cells, which is validated by comparison with their cytokine secretion and intracellular expression of molecules related to the immune response. The independent models agree on the degree of activation, showing that the features provide insight into the cellular response heterogeneity. We found that morphological indicators are linked to the phenotype, which is mostly related to downstream effects, making the results obtained with these variables dose-dependent. On the other hand, Raman indicators are representative of upstream intracellular molecular changes related to specific activation pathways. By partially inhibiting the LPS-induced activation using progesterone, we could identify several subpopulations, showing the ability of our approach to identify the effect of LPS activation, specific inhibition of LPS, and also the effect of progesterone alone on macrophage cells.

Background Solid tumors such as pancreatic ductal adenocarcinoma (PDAC) comprise not just tumor cells but also a microenvironment with which the tumor cells constantly interact. Detailed characterization of the cellular composition of the tumor microenvironment is critical to the understanding of the disease and treatment of the patient. Single-cell transcriptomics has been used to study the cellular composition of different solid tumor types including PDAC. However, almost all of those studies used primary tumor tissues. Methods In this study, we employed a single-cell RNA sequencing technology to profile the transcriptomes of individual cells from dissociated primary tumors or metastatic biopsies obtained from patients with PDAC. Unsupervised clustering analysis as well as a new supervised classification algorithm, SuperCT, was used to identify the different cell types within the tumor tissues. The expression signatures of the different cell types were then compared between primary tumors and metastatic biopsies. The expressions of the cell type-specific signature genes were also correlated with patient survival using public datasets. Results Our single-cell RNA sequencing analysis revealed distinct cell types in primary and metastatic PDAC tissues including tumor cells, endothelial cells, cancer-associated fibroblasts (CAFs), and immune cells. The cancer cells showed high inter-patient heterogeneity, whereas the stromal cells were more homogenous across patients. Immune infiltration varies significantly from patient to patient with majority of the immune cells being macrophages and exhausted lymphocytes. We found that the tumor cellular composition was an important factor in defining the PDAC subtypes. Furthermore, the expression levels of cell type-specific markers for EMT + cancer cells, activated CAFs, and endothelial cells significantly associated with patient survival. Conclusions Taken together, our work identifies significant heterogeneity in cellular compositions of PDAC tumors and between primary tumors and metastatic lesions. Furthermore, the cellular composition was an important factor in defining PDAC subtypes and significantly correlated with patient outcome. These findings provide valuable insights on the PDAC microenvironment and could potentially inform the management of PDAC patients.