Advances In Single-cell Technologies Research Articles

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.

The Chemical Fluctuation Theorem governing gene expression

Gene expression is a complex stochastic process composed of numerous enzymatic reactions with rates coupled to hidden cell-state variables. Despite advances in single-cell technologies, the lack of a theory accurately describing the gene expression process has restricted a robust, quantitative understanding of gene expression variability among cells. Here we present the Chemical Fluctuation Theorem (CFT), providing an accurate relationship between the environment-coupled chemical dynamics of gene expression and gene expression variability. Combined with a general, accurate model of environment-coupled transcription processes, the CFT provides a unified explanation of mRNA variability for various experimental systems. From this analysis, we construct a quantitative model of transcription dynamics enabling analytic predictions for the dependence of mRNA noise on the mRNA lifetime distribution, confirmed against stochastic simulation. This work suggests promising new directions for quantitative investigation into cellular control over biological functions by making complex dynamics of intracellular reactions accessible to rigorous mathematical deductions.

Beyond the bulk: disclosing the life of single microbial cells.

Microbial single cell analysis has led to discoveries that are beyond what can be resolved with population-based studies. It provides a pristine view of the mechanisms that organize cellular physiology, unbiased by population heterogeneity or uncontrollable environmental impacts. A holistic description of cellular functions at the single cell level requires analytical concepts beyond the miniaturization of existing technologies, defined but uncontrolled by the biological system itself. This review provides an overview of the latest advances in single cell technologies and demonstrates their potential. Opportunities and limitations of single cell microbiology are discussed using selected application-related examples.

Sampling strategies to capture single-cell heterogeneity.

Advances in single-cell technologies have highlighted the prevalence and biological significance of cellular heterogeneity. A critical question is how to design experiments that faithfully capture the true range of heterogeneity from samples of cellular populations. Here, we develop a data-driven approach, illustrated in the context of image data, that estimates sampling depth required for prospective investigations of single-cell heterogeneity from an existing collection of samples.

New insights into human primordial germ cells and early embryonic development from single-cell analysis.

Human preimplantation developmental studies are difficult to accomplish due to associated ethical and moral issues. Preimplantation cells are rare and exist only in transient cell states. From a single cell, it is very challenging to analyse the origination of the heterogeneity and complexity inherent to the human body. However, recent advances in single-cell technology and data analysis have provided new insights into the process of early human development and germ cell specification. In this Review, we examine the latest single-cell datasets of human preimplantation embryos and germ cell development, compare them to bulk cell analyses, and interpret their biological implications.

Linking the T cell receptor to the single cell transcriptome in antigen-specific human T cells.

Heterogeneity of T cells is a hallmark of a successful adaptive immune response, harnessing the vast diversity of antigen-specific T cells into a coordinated evolution of effector and memory outcomes. The T cell receptor (TCR) repertoire is highly diverse to account for the highly heterogeneous antigenic world. During the response to a virus multiple individual clones of antigen specific CD8+ (Ag-specific) T cells can be identified against a single epitope and multiple epitopes are recognised. Advances in single-cell technologies have provided the potential to study Ag-specific T cell heterogeneity at both surface phenotype and transcriptome levels, thereby allowing investigation of the diversity within the same apparent sub-population. We propose a new method (VDJPuzzle) to reconstruct the native TCRαβ from single cell RNA-seq data of Ag-specific T cells and then to link these with the gene expression profile of individual cells. We applied this method using rare Ag-specific T cells isolated from peripheral blood of a subject who cleared hepatitis C virus infection. We successfully reconstructed productive TCRαβ in 56 of a total of 63 cells (89%), with double α and double β in 18, and 7% respectively, and double TCRαβ in 2 cells. The method was validated via standard single cell PCR sequencing of the TCR. We demonstrate that single-cell transcriptome analysis can successfully distinguish Ag-specific T cell populations sorted directly from resting memory cells in peripheral blood and sorted after ex vivo stimulation. This approach allows a detailed analysis of the TCR diversity and its relationship with the transcriptional profile of different clones.

Abstract 349: Gene Regulatory Modules Regulate Cardiac Lineage Specification Revealed by Single-cell Rna Sequencing

Background: The molecular definition of the differentiation process is of intense interest for cardiovascular developmental biologists. With the advent of single cell technologies, a number of a transcriptomic approaches have been developed to obtain a complete description of gene expression. While single cell RNA sequencing is a significant advance for describing gene expression at the cellular resolution, novel obstacles have emerged such as computational management of dropout events, discovery of the gene regulatory modules responsible for lineage specification and identification of novel lineage markers. Methods: Using Etv2- and Nkx2-5 EYFP transgenic embryos, from which progenitors of hematopoietic, endothelial and cardiac lineages can be isolated with high purity, we performed single cell transcriptome analysis. We developed a Bayesian statistical framework to (1) model the dropout events, (2) reconstruct the lineage specification pathways from mesodermal progenitors to hematopoietic, endothelial and cardiac lineages, (3) discover the core gene regulatory modules that govern lineage specification and (4) predict novel cell surface markers for cardiac components. We developed a structural stochastic variational inference method to efficiently estimate the model parameters for sequencing data with thousands of cells. Results: The analysis of 421 Etv2- and Nkx2-5 EYFP positive cells predicted the core regulatory networks involving transcription factors, signaling molecules and cell surface proteins, where the signaling cascades are significantly correlated the specification of the endothelial, hematopoietic and various cardiac lineages from the mesodermal progenitors. The method also predicted novel cell surface markers for the first and second heart fields. Conclusions: We report the analysis of complete gene expression profiles of mesodermal lineage specification at single cell resolution. We provide the first unified statistical framework to systematically analyze single cell RNA-seq data and quantitatively couple the network analysis and lineage specification. This strategy provides a platform for the identification of regulatory nodes and cascades that govern mesodermal and cardiovascular development.

Automated Cell Counting in a High Density, Polymer-Coated, Live Single Cell Sandwich Microarray

Single cell analysis is essential for elucidating the contribution of rare cellular events or sub-populations in the onset and progression of disease pathology. Despite the importance of identifying cellular heterogeneity, most of the current understanding of disease is based on bulk population measurements. To address this need, recent advances in single cell technology and associated methods are gaining popularity and are resulting in the identification of critical features of disease.