Advances In Single-cell Technologies Research Articles

Recent advances in understanding cancer-associated fibroblasts in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDA) is a devastating disease with a poor survival rate. It is resistant to therapy in part due to its unique tumor microenvironment, characterized by a desmoplastic reaction resulting in a dense stroma that constitutes a large fraction of the tumor volume. A major contributor to the desmoplastic reaction are cancer-associated fibroblasts (CAFs). CAFs actively interact with cancer cells and promote tumor progression by different mechanisms, including extracellular matrix deposition, remodeling, and secretion of tumor promoting factors, making CAFs an attractive target for PDA. However, emerging evidences indicate significant tumor-suppressive functions of CAFs, highlighting the complexity of CAF biology. CAFs were once considered as a uniform cell type within the cancer stroma. Recently, the existence of CAF heterogeneity in PDA has become appreciated. Due to advances in single cell technology, distinct subtypes of CAFs have been identified in PDA. Here we review recent updates in CAF biology in PDA, which may help develop effective CAF-targeted therapies in the future.

Inflammatory Bowel Disease Through the Lens of Single-cell RNA-seq Technologies.

The intestinal mucosa represents a unique environment where the coordinated function of diverse epithelial, mesenchymal, and immune cells maintains a physiologically balanced environment in the presence of gut microbiota. The intestinal mucosa plays a central role in the pathogenesis of inflammatory bowel disease (IBD), yet the molecular and cellular composition of this diverse environment is poorly understood. However, the recent advent of multimodal single-cell technologies, including single-cell RNA sequencing (scRNA-seq), now provides an opportunity to accurately map the tissue architecture, characterize rare cell types that were previously overlooked, and define function at a single-cell level. In this review, we summarize key advances in single-cell technology and provide an overview of important aspects of computational analysis. We describe emerging data in the field of IBD and discuss how the characterization of novel intestinal mucosa cell populations is reshaping our understanding of this complex disease. We conclude by considering the potential clinical applications, including the definition of novel drug targets and the opportunity for personalization of care in this exciting new era of precision medicine.

Single Cell Sequencing: A New Dimension in Cancer Diagnosis and Treatment.

Cancer is one of the leading causes of death worldwide and well known for its complexity. Cancer cells within the same tumor or from different tumors are highly heterogeneous. Furthermore, stromal and immune cells within tumor microenvironment interact with cancer cells to play important roles in how tumors progress and respond to different treatments. Recent advances in single cell technologies, especially massively parallel single cell sequencing, have made it possible to analyze cancer cells and cells in its tumor microenvironment in parallel with unprecedented high resolution. In this chapter, we will review recent developments in single cell sequencing technologies and their applications in cancer research. We will also explain how insights generated from single cell sequencing can be used to develop novel diagnostic and therapeutic approaches to conquer cancer.

Using single-cell technologies to map the human immune system - implications for nephrology.

Advances in single-cell technologies are transforming our understanding of cellular identity. For instance, the application of single-cell RNA sequencing and mass cytometry technologies to the study of immune cell populations in blood, secondary lymphoid organs and the renal tract is helping researchers to map the complex immune landscape within the kidney, define cell ontogeny and understand the relationship of kidney-resident immune cells with their circulating counterparts. These studies also provide insights into the interactions of immune cell populations with neighbouring epithelial and endothelial cells in health, and across a range of kidney diseases and cancer. These data have translational potential and will aid the identification of drug targets and enable better prediction of off-target effects. The application of single-cell technologies to clinical renal biopsy samples, or even cells within urine, will improve diagnostic accuracy and assist with personalized prognostication for patients with various kidney diseases. A comparison of immune cell types in peripheral blood and secondary lymphoid organs in healthyindividuals and in patients with systemic autoimmune diseases that affect the kidney will also help to unravel the mechanisms that underpin the breakdown in self-tolerance and propagation of autoimmune responses. Together, these immune cell atlases have the potential to transform nephrology.

29-Color Flow Cytometry: Unraveling Human Liver NK Cell Repertoire Diversity.

Recent studies have demonstrated extraordinary diversity in peripheral blood human natural killer (NK) cells and have suggested environmental control of receptor expression patterns on distinct subsets of NK cells. However, tissue localization may influence NK cell differentiation to an even higher extent and less is known about the receptor repertoire of human tissue-resident NK cells. Advances in single-cell technologies have allowed higher resolution studies of these cells. Here, the power of high-dimensional flow cytometry was harnessed to unravel the complexity of NK cell repertoire diversity in liver since recent studies had indicated high heterogeneity within liver NK cells. A 29-color flow cytometry panel allowing simultaneous measurement of surface tissue-residency markers, activating and inhibitory receptors, differentiation markers, chemokine receptors, and transcription factors was established. This panel was applied to lymphocytes across three tissues (liver, peripheral blood, and tonsil) with different distribution of distinct NK cell subsets. Dimensionality reduction of this data ordered events according to their lineage, rather than tissue of origin. Notably, narrowing the scope of the analysis to the NK cell lineage in liver and peripheral blood separated subsets according to tissue, enabling phenotypic characterization of NK cell subpopulations in individual tissues. Such dimensionality reduction, coupled with a clustering algorithm, identified CD49e as the preferred marker for future studies of liver-resident NK cell subsets. We present a robust approach for diversity profiling of tissue-resident NK cells that can be applied in various homeostatic and pathological conditions such as reproduction, infection, and cancer.

Using the Immunophenotype to Predict Response to Biologic Drugs in Rheumatoid Arthritis.

Tumour necrosis factor (TNF)-α is a key mediator of inflammation in rheumatoid arthritis, and its discovery led to the development of highly successful anti-TNF therapy. Subsequently, other biologic drugs targeting immune pathways, namely interleukin-6 blockade, B cell depletion, and T cell co-stimulation blockade, have been developed. Not all patients respond to a biologic drug, leading to a knowledge gap between biologic therapies available and the confident prediction of response. So far, genetic studies have failed to uncover clinically informative biomarkers to predict response. Given that the targets of biologics are immune pathways, immunological study has become all the more pertinent. Furthermore, advances in single-cell technology have enabled the characterization of many leucocyte subsets. Studying the blood immunophenotype may therefore, define biomarker profiles relevant to each individual patient’s disease and treatment outcome. This review summarises our current understanding of how immune biomarkers might be able to predict treatment response to biologic drugs.

Identification of somatic mutations in single cell DNA-seq using a spatial model of allelic imbalance

Recent advances in single cell technology have enabled dissection of cellular heterogeneity in great detail. However, analysis of single cell DNA sequencing data remains challenging due to bias and artifacts that arise during DNA extraction and whole-genome amplification, including allelic imbalance and dropout. Here, we present a framework for statistical estimation of allele-specific amplification imbalance at any given position in single cell whole-genome sequencing data by utilizing the allele frequencies of heterozygous single nucleotide polymorphisms in the neighborhood. The resulting allelic imbalance profile is critical for determining whether the variant allele fraction of an observed mutation is consistent with the expected fraction for a true variant. This method, implemented in SCAN-SNV (Single Cell ANalysis of SNVs), substantially improves the identification of somatic variants in single cells. Our allele balance framework is broadly applicable to genotype analysis of any variant type in any data that might exhibit allelic imbalance.

Mammary stem cells, where art thou?

Tremendous progress has been made in the field of stem cell biology. This is in part due to the emergence of various vertebrate organs, including the mammary gland, as an amenable model system for adult stem cell studies and remarkable technical advances in single cell technology and modern genetic lineage tracing. In the current review, we summarize the recent progress in mammary gland stem cell biology at both the adult and embryonic stages. We discuss current challenges and controversies, and potentially new and exciting directions for future research. This article is categorized under: Adult Stem Cells, Tissue Renewal, and Regeneration > Tissue Stem Cells and Niches Adult Stem Cells, Tissue Renewal, and Regeneration > Stem Cell Differentiation and Reversion Adult Stem Cells, Tissue Renewal, and Regeneration > Regeneration.

Statistical test of structured continuous trees based on discordance matrix.

Cell fate determination is a continuous process in which one cell type diversifies to other cell types following a hierarchical path. Advancements in single-cell technologies provide the opportunity to reveal the continuum of cell progression which forms a structured continuous tree (SCTree). Computational algorithms, which are usually based on a priori assumptions on the hidden structures, have previously been proposed as a means of recovering pseudo trajectory along cell differentiation process. However, there still lack of statistical framework on the assessments of intrinsic structure embedded in high-dimensional gene expression profile. Inherit noise and cell-to-cell variation underlie the single-cell data, however, pose grand challenges to testing even basic structures, such as linear versus bifurcation. In this study, we propose an adaptive statistical framework, termed SCTree, to test the intrinsic structure of a high-dimensional single-cell dataset. SCTree test is conducted based on the tools derived from metric geometry and random matrix theory. In brief, by extending the Gromov-Farris transform and utilizing semicircular law, we formulate the continuous tree structure testing problem into a signal matrix detection problem. We show that the SCTree test is most powerful when the signal-to-noise ratio exceeds a moderate value. We also demonstrate that SCTree is able to robustly detect linear, single and multiple branching events with simulated datasets and real scRNA-seq datasets. Overall, the SCTree test provides a unified statistical assessment of the significance of the hidden structure of single-cell data. SCTree software is available at https://github.com/XQBai/SCTree-test. Supplementary data are available at Bioinformatics online.

Challenges in measuring and understanding biological noise.

Biochemical reactions are intrinsically stochastic, leading to variation in the production of mRNAs and proteins within cells. In the scientific literature, this source of variation is typically referred to as 'noise'. The observed variability in molecular phenotypes arises from a combination of processes that amplify and attenuate noise. Our ability to quantify cell-to-cell variability innumerous biological contexts has been revolutionized by recent advances in single-cell technology, from imaging approaches through to 'omics' strategies. However, defining, accurately measuring and disentangling the stochastic and deterministic components of cell-to-cell variability is challenging. In this Review, we discuss the sources, impact and function of molecular phenotypic variability and highlight future directions to understand its role.

Single-cell approaches to understand genome organisation throughout the cell cycle.

Mammalian genomes are ordered at several scales, ranging from nucleosomes (beads on a string), to topologically associated domains (TADs), laminar associated domains (LADs), and chromosome territories. These are described briefly below and we refer the reader to some recent comprehensive reviews on genome architecture summarising the current state of knowledge of the organisational principles of the nucleus [1,2]. Biological observations from populations of millions of individual cells can reveal consensus behaviour. New methods to study and interpret biological data at the single-cell level have recently been instrumental in revealing new understanding of cell-to-cell variation and novel biology. Here we will summarise the recent advances in single-cell technology that have provided insights into the behaviour of the mammalian genome during a cell cycle. We will focus on the interphase domain structure of chromosomes, including TADs and LADs, and how chromosome architecture changes during the cell cycle. The role of genome architecture relating to gene expression has been reviewed elsewhere [3].

The Epigenetics of Stem Cell Aging Comes of Age.

Emerging evidence indicates that epigenetic regulators are critically required for the maintenance of tissue-specific stem cells and that the epigenetic marks are altered in stem cells during physiological aging. Intriguingly, aging-associated stem cell functional decline can be reversed by manipulating epigenetic factors that become dysregulated during aging. These observations lend support to the stem cell theory of aging, which postulates that aging is the result of the inability of tissue-specific stem cells to replenish the tissues with functional differentiated cells that maintain the function of a tissue, and open a new era of research on the epigenetics of stem cell aging that may represent therapeutic potential. Recent advances in single cell technologies are revolutionizing our mechanistic understanding of rare populations of cells, such as stem cells, and offer an unprecedented opportunity to address this challenge.

Immunotherapy for skin cancer.

Among all tumor types, skin cancers are profoundly sensitive to immunotherapy. Indeed, the recently reported response rates for anti-PD-1 (anti-programmed-death 1) therapy for cutaneous malignant melanomas (MM), Merkel cell carcinomas, basal cell carcinomas, cutaneous squamous cell carcinomas and Kaposi sarcomas are all above 40%. This unique immunogenicity renders skin cancers as a paradigm for tumor-immune interactions and is driven by high mutational burdens, over-expressed tumor antigens and/or viral antigens. However, despite the clear demonstration of immunologic cure of skin cancer in some patients, most tumors develop either early (primary) or late (adaptive) resistance to immunotherapy. Resistance mechanisms are complex, and include contributions of tumor cell-intrinsic, T cell and microenvironment factors that have been recently further elucidated with the advent of single-cell technologies. This review will focus on the exciting progress with immunotherapy for skin cancers to date, and also our current understanding of the mechanisms of resistance to immunotherapy.

Dimensionality reduction for visualizing single-cell data using UMAP.

Advances in single-cell technologies have enabled high-resolution dissection of tissue composition. Several tools for dimensionality reduction are available to analyze the large number of parameters generated in single-cell studies. Recently, a nonlinear dimensionality-reduction technique, uniform manifold approximation and projection (UMAP), was developed for the analysis of any type of high-dimensional data. Here we apply it to biological data, using three well-characterized mass cytometry and single-cell RNA sequencing datasets. Comparing the performance of UMAP with five other tools, we find that UMAP provides the fastest run times, highest reproducibility and the most meaningful organization of cell clusters. The work highlights the use of UMAP for improved visualization and interpretation of single-cell data.

The Single-Cell Transcriptional Landscape of Human Hematopoietic Progenitors

In humans, hematopoietic stem/progenitor cells (HSPCs) constitute a heterogeneous population in the bone marrow (BM) endowed with the potential of generating and maintaining an adequate and diverse pool of blood cells lifelong. However, the nature of hematopoietic cell fate choice remains unresolved and is still highly contentious. In fact, despite the recent efforts and the advent of single-cell technologies, conflicting classical, early-split or continuous models for the generation of human blood cells still coexist in the literature as of today.We here used single-cell RNA-Seq (scRNA-Seq) to provide a detailed map of the transcriptional states of hematopoietic progenitors in the human BM. We believe that this work provides unprecedented insights into the structure of early cell-fate choices of the human hematopoietic system.Single-cell analysis of human HSPCs is a well-worn path, but all previous studies aimed at investigating the shape of early cell fate choices human hematopoiesis have been based on the use of immune-selected CD34+ cells. We too started by separating CD34+ cells purified by magnetic beads selection into seven subpopulations, marking cells of differing fate potential and we successfully tagged and sequenced the transcriptome of 6,011 single cells. Our scRNA-Seq map of CD34+ subpopulations shows that HSPCs do not undergo a single-step transition from HSPCs to unilineage states. Instead, they form a clear hierarchy. The earliest fate split separates erythroid-megakaryocyte progenitors from lymphoid-myeloid progenitors, which separate further into lymphoid, dendritic cell and granulocytic progenitors. We noted, however, that branches towards basophils/eosinophils/mast cells and monocytes commitments were missing. Indeed, many cells negative for mature lineage markers in human BM are CD34low/- and could account for additional transitional states at which the CD34 expression is rapidly downregulated, therefore greatly reducing their probability of capture. Thus, differently from previous works, we also extended our analysis to the whole bone marrow fraction lacking the main markers of terminal differentiation (Lin- cells). In doing so we used a novel graded FACS-sorting strategy that corrects for expansion of cells as they differentiated, and thus allows examining early states alongside later ones that comprise the vast majority of Lin- progenitors. In fractionating the cells by maturity, we made use of a cell surface marker, GeneA, which we identified from the initial data set as capturing cells that are multipotent until just beyond the first branch-point.The result of this analysis was a high resolution landscape from the transcriptome of 15,401 single cells, showing multiple branch points, and a topology that remarkably similar to what we reported in mice (Tusi et al. Nature 2018). This map defines the early branching of dendritic cell vs monocyte precursors and supports the novel concept that basophils/eosinophils/mast cells do not derive from common granulocytes/monocytes progenitors but originate instead from what are currently defined as megakaryo/erythroid progenitors.By the in depth analysis of our single-cell maps we generated a number of information of value for the broader hematology community:* We put the “classical” CD34+ FACS-based cell subpopulations into the context of the newly generated BM Lin- population structure (valuable for transplantation research)* We investigated the expression of transcription factors along differentiation trajectories and branching points (valuable for in vitro reprogramming efforts)* We analyzed the expression of several blood cancer genes over the main differentiation hubs and hematopoietic branches (valuable for the study, diagnosis and therapy of hematological tumors)* We provided a detailed comparison of the expression of gene orthologues in human vs mouse hematopoiesis (valuable for the modeling of blood cell disorders in the mouse)* We identified and validated GeneA as a reliable marker for the earliest branches of HSPCs specification in transplantation cell products (valuable for transplantation research and gene therapy)Overall, our study provides a detailed view of the early hematopoietic cell state hierarchy in humans and a valuable resource for HSPC biology. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Current Trends of Microfluidic Single-Cell Technologies.

The investigation of human disease mechanisms is difficult due to the heterogeneity in gene expression and the physiological state of cells in a given population. In comparison to bulk cell measurements, single-cell measurement technologies can provide a better understanding of the interactions among molecules, organelles, cells, and the microenvironment, which can aid in the development of therapeutics and diagnostic tools. In recent years, single-cell technologies have become increasingly robust and accessible, although limitations exist. In this review, we describe the recent advances in single-cell technologies and their applications in single-cell manipulation, diagnosis, and therapeutics development.

Single-cell approaches identify the molecular network driving malignant hematopoietic stem cell self-renewal

AbstractRecent advances in single-cell technologies have permitted the investigation of heterogeneous cell populations at previously unattainable resolution. Here we apply such approaches to resolve the molecular mechanisms driving disease in mouse hematopoietic stem cells (HSCs), using JAK2V617F mutant myeloproliferative neoplasms (MPNs) as a model. Single-cell gene expression and functional assays identified a subset of JAK2V617F mutant HSCs that display defective self-renewal. This defect is rescued at the single HSC level by crossing JAK2V617F mice with mice lacking TET2, the most commonly comutated gene in patients with MPN. Single-cell gene expression profiling of JAK2V617F-mutant HSCs revealed a loss of specific regulator genes, some of which were restored to normal levels in single TET2/JAK2 mutant HSCs. Of these, Bmi1 and, to a lesser extent, Pbx1 and Meis1 overexpression in JAK2-mutant HSCs could drive a disease phenotype and retain durable stem cell self-renewal in functional assays. Together, these single-cell approaches refine the molecules involved in clonal expansion of MPNs and have broad implications for deconstructing the molecular network of normal and malignant stem cells.

Abstract 2275: Individualized drug combination based on single-cell drug perturbations

Abstract Currently, cancer drug combinations primarily focus on mutational heterogeneity of the primary tumor and do not account for single-cell variations that can give rise to drug resistance. Moreover, even with the increasing number of potential FDA-approved targeted drugs including immunotherapies, methods are needed to identify better combination therapy that leverages intratumor heterogeneity, thereby potentially mitigating the need for trials with large numbers of patients. Despite advances in single-cell technologies that capture intratumor heterogeneity, there are no drug combination strategies that utilize single-cell platforms. One idea that has been proposed is to use Mass Cytometry Time-of-Flight (CyTOF) for drug screening by producing drug perturbation effects at the level of the single cell, but the required analytics for the resulting complex data were not addressed. To address this unmet need, we have developed a novel algorithm to optimize combination therapy for an individual patient by analyzing distinct single-cell drug perturbation responses on a tumor sample. This model framework, called “DRUGNEM,” can be applied to CyTOF data, single-cell RNA-seq, or any single-cell imaging data currently available. DRUGNEM is composed of three steps: (1) identify the subpopulations that make up the tumor and may respond differently to treatment; (2) reconstruct a drug-nested-effects model that integrates the drug effects across all subpopulations to capture sub-setting relationships among individual drug effects; and (3) systematically score potential drug combinations to identify or prioritize strategies that will be clinically (and economically) sustainable. Currently, DRUGNEM is optimized to select the minimum number of drugs that produces the maximal desired intracellular effect, predicated on the premise that fewer drugs lower treatment-related toxicities and costs, but the final selection criterion can be easily modified. As proof of concept, we applied the DRUGNEM framework to individualize drug combinations based on CyTOF data generated on de-identified malignant research samples from 30 ALL pediatric patients before and after exposure to 3 targeted FDA -pproved single drugs (dasatinib, tofacitinib and BEZ235). We found that the most common combination treatment strategy (dasatinib and BEZ235) might not be optimal for all 30 ALL patients, with 2 of the 30 likely responding best to tofacitinib alone. Using in vitro survival assays, we validated the DRUGNEM prediction of BEZ235 and dasatinib as a potential synergistic combination on ALL cell lines. In summary, DRUGNEM is a novel framework using single-cell technologies to guide drug-combination strategies and can be adapted to incorporate complementary molecular data and computational methods to ultimately achieve more effective therapy for the individual cancer patient. Citation Format: Benedict Anchang, Kara Davis, Harris Fienberg, Sean Bendall, Loukia Karacosta, Garry Nolan, Sylvia K. Plevritis. Individualized drug combination based on single-cell drug perturbations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2275.

A Guide to Single-Cell Transcriptomics in Adult Rodent Brain: The Medium Spiny Neuron Transcriptome Revisited.

Recent advances in single-cell technologies are paving the way to a comprehensive understanding of the cellular complexity in the brain. Protocols for single-cell transcriptomics combine a variety of sophisticated methods for the purpose of isolating the heavily interconnected and heterogeneous neuronal cell types in a relatively intact and healthy state. The emphasis of single-cell transcriptome studies has thus far been on comparing library generation and sequencing techniques that enable measurement of the minute amounts of starting material from a single cell. However, in order for data to be comparable, standardized cell isolation techniques are essential. Here, we analyzed and simplified methods for the different steps critically involved in single-cell isolation from brain. These include enzymatic digestion, tissue trituration, improved methods for efficient fluorescence-activated cell sorting in samples containing high degree of debris from the neuropil, and finally, highly region-specific cellular labeling compatible with use of stereotaxic coordinates. The methods are exemplified using medium spiny neurons (MSN) from dorsomedial striatum, a cell type that is clinically relevant for disorders of the basal ganglia, including psychiatric and neurodegenerative diseases. We present single-cell RNA sequencing (scRNA-Seq) data from D1 and D2 dopamine receptor expressing MSN subtypes. We illustrate the need for single-cell resolution by comparing to available population-based gene expression data of striatal MSN subtypes. Our findings contribute toward standardizing important steps of single-cell isolation from adult brain tissue to increase comparability of data. Furthermore, our data redefine the transcriptome of MSNs at unprecedented resolution by confirming established marker genes, resolving inconsistencies from previous gene expression studies, and identifying novel subtype-specific marker genes in this important cell type.

Single cell clustering based on cell-pair differentiability correlation and variance analysis.

The rapid advancement of single cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. Identification of intercellular transcriptomic heterogeneity is one of the most critical tasks in single-cell RNA-sequencing studies. We propose a new cell similarity measure based on cell-pair differentiability correlation, which is derived from gene differential pattern among all cell pairs. Through plugging into the framework of hierarchical clustering with this new measure, we further develop a variance analysis based clustering algorithm 'Corr' that can determine cluster number automatically and identify cell types accurately. The robustness and superiority of the proposed algorithm are compared with representative algorithms: shared nearest neighbor (SNN)-Cliq and several other state-of-the-art clustering methods, on many benchmark or real single cell RNA-sequencing datasets in terms of both internal criteria (clustering number and accuracy) and external criteria (purity, adjusted rand index, F1-measure). Moreover, differentiability vector with our new measure provides a new means in identifying potential biomarkers from cancer related single cell datasets even with strong noise. Prognosis analyses from independent datasets of cancers confirmed the effectiveness of our 'Corr' method. The source code (Matlab) is available at http://sysbio.sibcb.ac.cn/cb/chenlab/soft/Corr--SourceCodes.zip. Supplementary data are available at Bioinformatics online.