Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments

Diffusion maps for high-dimensional single-cell analysis of differentiation data

MOTIVATION

Single-cell technologies have recently gained popularity in cellular differentiation studies regarding their ability to resolve potential heterogeneities in cell populations. Analyzing such high-dimensional single-cell data has its own statistical and computational challenges. Popular multivariate approaches are based on data normalization, followed by dimension reduction and clustering to identify subgroups. However, in the case of cellular differentiation, we would not expect clear clusters to be present but instead expect the cells to follow continuous branching lineages.

RESULTS

Here, we propose the use of diffusion maps to deal with the problem of defining differentiation trajectories. We adapt this method to single-cell data by adequate choice of kernel width and inclusion of uncertainties or missing measurement values, which enables the establishment of a pseudotemporal ordering of single cells in a high-dimensional gene expression space. We expect this output to reflect cell differentiation trajectories, where the data originates from intrinsic diffusion-like dynamics. Starting from a pluripotent stage, cells move smoothly within the transcriptional landscape towards more differentiated states with some stochasticity along their path. We demonstrate the robustness of our method with respect to extrinsic noise (e.g. measurement noise) and sampling density heterogeneities on simulated toy data as well as two single-cell quantitative polymerase chain reaction datasets (i.e. mouse haematopoietic stem cells and mouse embryonic stem cells) and an RNA-Seq data of human pre-implantation embryos. We show that diffusion maps perform considerably better than Principal Component Analysis and are advantageous over other techniques for non-linear dimension reduction such as t-distributed Stochastic Neighbour Embedding for preserving the global structures and pseudotemporal ordering of cells.

AVAILABILITY AND IMPLEMENTATION

The Matlab implementation of diffusion maps for single-cell data is available at https://www.helmholtz-muenchen.de/icb/single-cell-diffusion-map.

CONTACT

fbuettner.phys@gmail.com, fabian.theis@helmholtz-muenchen.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets

Cells, the basic units of biological structure and function, vary broadly in type and state. Single-cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here we describe Drop-seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together. Drop-seq analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin. We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes. Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution. VIDEO ABSTRACT.

Cooperativity of HIV-Specific Cytolytic CD4 T Cells and CD8 T Cells in Control of HIV Viremia

CD4+ T cells play a pivotal role in the control of chronic viral infections. Recently, nontraditional CD4+ T cell functions beyond helper effects have been described, and a role for cytolytic CD4+ T cells in the control of HIV infection has been suggested. We define here the transcriptional, phenotypic, and functional profiles of HIV-specific cytolytic CD4+ T cells. Fluidigm BioMark and multiparameter flow cytometric analysis of HIV-specific cytolytic CD4+ T cells revealed a distinct transcriptional signature compared to Th1 CD4+ cells but shared similar features with HIV-specific cytolytic CD8+ T cells. Furthermore, HIV-specific cytolytic CD4+ T cells showed comparable killing activity relative to HIV-specific CD8+ T cells and worked cooperatively in the elimination of virally infected cells. Interestingly, we found that cytolytic CD4+ T cells emerge early during acute HIV infection and tightly follow acute viral load trajectory. This emergence was associated to the early viral set point, suggesting an involvement in early control, in spite of CD4 T cell susceptibility to HIV infection. Our data suggest cytolytic CD4+ T cells as an independent subset distinct from Th1 cells that show combined activity with CD8+ T cells in the long-term control of HIV infection.

IMPORTANCE

The ability of the immune system to control chronic HIV infection is of critical interest to both vaccine design and therapeutic approaches. Much research has focused on the effect of the ability of CD8+ T cells to control the virus, while CD4+ T cells have been overlooked as effectors in HIV control due to the fact that they are preferentially infected. We show here that a subset of HIV-specific CD4+ T cells cooperate in the cytolytic control of HIV replication. Moreover, these cells represent a distinct subset of CD4+ T cells showing significant transcriptional and phenotypic differences compared to HIV-specific Th1 cells but with similarities to CD8+ T cells. These findings are important for our understanding of HIV immunopathology.

A mine is a terrible thing to waste: high content, single cell technologies for comprehensive immune analysis

In recent years, an incredible variety of single cell technologies have become available to analyze immune responses. These technologies include polychromatic flow cytometry, mass cytometry, highly multiplexed single cell qPCR, RNA sequencing, microtools, and high-resolution imaging. In this article, we review these platforms, describing their power and limitations for comprehensive analysis of the immune system. We relate the properties of these technologies to the various cellular states relevant to an immune response, in order to address which technologies are most appropriate for which settings.

Spatial reconstruction of single-cell gene expression data

Spatial localization is a key determinant of cellular fate and behavior, but spatial RNA assays traditionally rely on staining for a limited number of RNA species. In contrast, single-cell RNA-seq allows for deep profiling of cellular gene expression, but established methods separate cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos, inferring a transcriptome-wide map of spatial patterning. We confirmed Seurat’s accuracy using several experimental approaches, and used it to identify a set of archetypal expression patterns and spatial markers. Additionally, Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.

Generation of multipotent early lymphoid progenitors from human embryonic stem cells

During human embryonic stem cell (ESC) hematopoietic differentiation, the description of the initial steps of lymphopoiesis remains elusive. Using a two-step culture procedure, we identified two original populations of ESC-derived hematopoietic progenitor cells (HPCs) with CD34(+)CD45RA(+)CD7(-) and CD34(+)CD45RA(+)CD7(+) phenotypes. Bulk cultures and limiting dilution assays, culture with MS5 cells in the presence of Notch ligand Delta-like-1 (DL-1), and ex vivo colonization tests using fetal thymic organ cultures showed that although CD34(+)CD45RA(+)CD7(-) HPCs could generate cells of the three lymphoid lineages, their potential was skewed toward the B cell lineages. In contrast, CD34(+)CD45RA(+)CD7(+) HPCs predominantly exhibited a T/natural killer (NK) cell differentiation potential. Furthermore these cells could differentiate equivalently into cells of the granulo-macrophagic lineage and dendritic cells and lacked erythroid potential. Expression profiling of 18 markers by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) revealed that CD34(+)CD45RA(+)CD7(-) and CD34(+)CD45RA(+)CD7(+) HPCs express genes of the lymphoid specification and that CD34(+)CD45RA(+)CD7(-) cells express B-cell-associated genes, while CD34(+)CD45RA(+)CD7(+) HPCs display a T-cell molecular profile. Altogether, these findings indicate that CD34(+)CD45RA(+)CD7(-) and CD34(+)CD45RA(+)CD7(+) HPCs correspond to candidate multipotent early lymphoid progenitors polarized toward either the B or T/NK lineage, respectively. This work should improve our understanding of the early steps of lymphopoiesis from pluripotent stem cells and pave the way for the production of lymphocytes for cell-based immunotherapy and lymphoid development studies.

Microfluidics for single-cell genetic analysis

The ability to correlate single-cell genetic information to cellular phenotypes will provide the kind of detailed insight into human physiology and disease pathways that is not possible to infer from bulk cell analysis. Microfluidic technologies are attractive for single-cell manipulation due to precise handling and low risk of contamination. Additionally, microfluidic single-cell techniques can allow for high-throughput and detailed genetic analyses that increase accuracy and decrease reagent cost compared to bulk techniques. Incorporating these microfluidic platforms into research and clinical laboratory workflows can fill an unmet need in biology, delivering the highly accurate, highly informative data necessary to develop new therapies and monitor patient outcomes. In this perspective, we describe the current and potential future uses of microfluidics at all stages of single-cell genetic analysis, including cell enrichment and capture, single-cell compartmentalization and manipulation, and detection and analyses.

Single-cell gene expression signatures reveal melanoma cell heterogeneity

It is well established that tumours are not homogenous, but comprise cells with differing invasive, proliferative and tumour-initiating potential. A major challenge in cancer research is therefore to develop methods to characterize cell heterogeneity. In melanoma, proliferative and invasive cells are characterized by distinct gene expression profiles and accumulating evidence suggests that cells can alternate between these states through a process called phenotype switching. We have used microfluidic technology to isolate single melanoma cells grown in vitro as monolayers or melanospheres or in vivo as xenografted tumours and analyse the expression profiles of 114 genes that discriminate the proliferative and invasive states by quantitative PCR. Single-cell analysis accurately recapitulates the specific gene expression programmes of melanoma cell lines and defines subpopulations with distinct expression profiles. Cell heterogeneity is augmented when cells are grown as spheres and as xenografted tumours. Correlative analysis identifies gene-regulatory networks and changes in gene expression under different growth conditions. In tumours, subpopulations of cells that express specific invasion and drug resistance markers can be identified amongst which is the pluripotency factor POUF51 (OCT4) whose expression correlates with the tumorigenic potential. We therefore show that single-cell analysis can be used to define and quantify tumour heterogeneity based on detection of cells with specific gene expression profiles.

Lytic gene expression is frequent in HSV-1 latent infection and correlates with the engagement of a cell-intrinsic transcriptional response

Herpes simplex viruses (HSV) are significant human pathogens that provide one of the best-described examples of viral latency and reactivation. HSV latency occurs in sensory neurons, being characterized by the absence of virus replication and only fragmentary evidence of protein production. In mouse models, HSV latency is especially stable but the detection of some lytic gene transcription and the ongoing presence of activated immune cells in latent ganglia have been used to suggest that this state is not entirely quiescent. Alternatively, these findings can be interpreted as signs of a low, but constant level of abortive reactivation punctuating otherwise silent latency. Using single cell analysis of transcription in mouse dorsal root ganglia, we reveal that HSV-1 latency is highly dynamic in the majority of neurons. Specifically, transcription from areas of the HSV genome associated with at least one viral lytic gene occurs in nearly two thirds of latently-infected neurons and more than half of these have RNA from more than one lytic gene locus. Further, bioinformatics analyses of host transcription showed that progressive appearance of these lytic transcripts correlated with alterations in expression of cellular genes. These data show for the first time that transcription consistent with lytic gene expression is a frequent event, taking place in the majority of HSV latently-infected neurons. Furthermore, this transcription is of biological significance in that it influences host gene expression. We suggest that the maintenance of HSV latency involves an active host response to frequent viral activity.

Primary herpes simplex virus (HSV) infections are characterized by acute disease that resolves rapidly, but the virus persists in a latent form in sensory neurons that can be a source of renewed disease. Analyzing gene expression in single mouse neurons harboring latent HSV, we show directly that HSV latency is dynamic and heterogeneous. HSV lytic gene transcripts were frequently detected in latently infected neurons and often in combinations. Expression of selected cellular anti-viral and survival genes showed that transcriptional profiles differed between latently infected and uninfected neurons from the same ganglia. The pattern of host gene expression also differed between latently infected neurons that were and were not experiencing HSV lytic gene expression. Our study suggests that HSV latency is characterized by very frequent switching on of lytic genes and a rapid response by the host, presumably to halt progression to reactivation.

Modeling bi-modality improves characterization of cell cycle on gene expression in single cells

Advances in high-throughput, single cell gene expression are allowing interrogation of cell heterogeneity. However, there is concern that the cell cycle phase of a cell might bias characterizations of gene expression at the single-cell level. We assess the effect of cell cycle phase on gene expression in single cells by measuring 333 genes in 930 cells across three phases and three cell lines. We determine each cell's phase non-invasively without chemical arrest and use it as a covariate in tests of differential expression. We observe bi-modal gene expression, a previously-described phenomenon, wherein the expression of otherwise abundant genes is either strongly positive, or undetectable within individual cells. This bi-modality is likely both biologically and technically driven. Irrespective of its source, we show that it should be modeled to draw accurate inferences from single cell expression experiments. To this end, we propose a semi-continuous modeling framework based on the generalized linear model, and use it to characterize genes with consistent cell cycle effects across three cell lines. Our new computational framework improves the detection of previously characterized cell-cycle genes compared to approaches that do not account for the bi-modality of single-cell data. We use our semi-continuous modelling framework to estimate single cell gene co-expression networks. These networks suggest that in addition to having phase-dependent shifts in expression (when averaged over many cells), some, but not all, canonical cell cycle genes tend to be co-expressed in groups in single cells. We estimate the amount of single cell expression variability attributable to the cell cycle. We find that the cell cycle explains only 5%–17% of expression variability, suggesting that the cell cycle will not tend to be a large nuisance factor in analysis of the single cell transcriptome.

Recent technological advances have enabled the measurement of gene expression in individual cells, revealing that there is substantial variability in expression, even within a homogeneous cell population. In this paper, we develop new analytical methods that account for the intrinsic, stochastic nature of single cell expression in order to characterize the effect of cell cycle on gene expression at the single-cell level. Applying these methods to populations of asynchronously cycling cells, we are able to identify large numbers of genes with cell cycle-associated expression patterns. By measuring and adjusting for cellular-level factors, we are able to derive estimates of co-expressing gene networks that more closely reflect cellular-level processes as opposed to sample-level processes. We find that cell cycle phase only accounts for a modest amount of the overall variability of gene expression within an individual cell. The analytical methods demonstrated in this paper are universally applicable to single cell expression data and represent a promising tool to the scientific community.

Bayesian approach to single-cell differential expression analysis

Single-cell data provide a means to dissect the composition of complex tissues and specialized cellular environments. However, the analysis of such measurements is complicated by high levels of technical noise and intrinsic biological variability. We describe a probabilistic model of expression-magnitude distortions typical of single-cell RNA-sequencing measurements, which enables detection of differential expression signatures and identification of subpopulations of cells in a way that is more tolerant of noise.

Single-cell RNA-seq reveals dynamic paracrine control of cellular variation

High-throughput single-cell transcriptomics offers an unbiased approach for understanding the extent, basis and function of gene expression variation between seemingly identical cells. Here we sequence single-cell RNA-seq libraries prepared from over 1,700 primary mouse bone-marrow-derived dendritic cells spanning several experimental conditions. We find substantial variation between identically stimulated dendritic cells, in both the fraction of cells detectably expressing a given messenger RNA and the transcript's level within expressing cells. Distinct gene modules are characterized by different temporal heterogeneity profiles. In particular, a 'core' module of antiviral genes is expressed very early by a few 'precocious' cells in response to uniform stimulation with a pathogenic component, but is later activated in all cells. By stimulating cells individually in sealed microfluidic chambers, analysing dendritic cells from knockout mice, and modulating secretion and extracellular signalling, we show that this response is coordinated by interferon-mediated paracrine signalling from these precocious cells. Notably, preventing cell-to-cell communication also substantially reduces variability between cells in the expression of an early-induced 'peaked' inflammatory module, suggesting that paracrine signalling additionally represses part of the inflammatory program. Our study highlights the importance of cell-to-cell communication in controlling cellular heterogeneity and reveals general strategies that multicellular populations can use to establish complex dynamic responses.

Bayesian approach to single-cell differential expression analysis

Single-cell data provide a means to dissect the composition of complex tissues and specialized cellular environments. However, the analysis of such measurements is complicated by high levels of technical noise and intrinsic biological variability. We describe a probabilistic model of expression-magnitude distortions typical of single-cell RNA-sequencing measurements, which enables detection of differential expression signatures and identification of subpopulations of cells in a way that is more tolerant of noise.

Avidity-dependent programming of autoreactive T cells in T1D

Fate determination for autoreactive T cells relies on a series of avidity-dependent interactions during T cell selection, represented by two general types of signals, one based on antigen expression and density during T cell development, and one based on genes that interpret the avidity of TCR interaction to guide developmental outcome. We used proinsulin-specific HLA class II tetramers to purify and determine transcriptional signatures for autoreactive T cells under differential selection in type 1 diabetes (T1D), in which insulin (INS) genotypes consist of protective and susceptible alleles that regulate the level of proinsulin expression in the thymus. Upregulation of steroid nuclear receptor family 4A (NR4A) and early growth response family genes in proinsulin-specific T cells was observed in individuals with susceptible INS-VNTR genotypes, suggesting a mechanism for avidity-dependent fate determination of the T cell repertoire in T1D. The NR4A genes act as translators of TCR signal strength that guide central and peripheral T cell fate decisions through transcriptional modification. We propose that maintenance of an NR4A-guided program in low avidity autoreactive T cells in T1D reflects their prior developmental experience influenced by proinsulin expression, identifying a pathway permissive for autoimmunity.

Old cell, new trick? Cnidocytes as a model for the evolution of novelty

Understanding how new cell types arise is critical for understanding the evolution of organismal complexity. Questions of this nature, however, can be difficult to answer due to the challenge associated with defining the identity of a truly novel cell. Cnidarians (anemones, jellies, and their allies) provide a unique opportunity to investigate the molecular regulation and development of cell-novelty because they possess a cell that is unique to the cnidarian lineage and that also has a very well-characterized phenotype: the cnidocyte (stinging cell). Because cnidocytes are thought to differentiate from the cell lineage that also gives rise to neurons, cnidocytes can be expected to express many of the same genes expressed in their neural "sister" cells. Conversely, only cnidocytes posses a cnidocyst (the explosive organelle that gives cnidocytes their sting); therefore, those genes or gene-regulatory relationships required for the development of the cnidocyst can be expected to be expressed uniquely (or in unique combination) in cnidocytes. This system provides an important opportunity to: (1) construct the gene-regulatory network (GRN) underlying the differentiation of cnidocytes, (2) assess the relative contributions of both conserved and derived genes in the cnidocyte GRN, and (3) test hypotheses about the role of novel regulatory relationships in the generation of novel cell types. In this review, we summarize common challenges to studying the evolution of novelty, introduce the utility of cnidocyte differentiation in the model cnidarian, Nematostella vectensis, as a means of overcoming these challenges, and describe an experimental approach that leverages comparative tissue-specific transcriptomics to generate hypotheses about the GRNs underlying the acquisition of the cnidocyte identity.

Validation of high-throughput single cell analysis methodology

High-throughput quantitative polymerase chain reaction (qPCR) approaches enable profiling of multiple genes in single cells, bringing new insights to complex biological processes and offering opportunities for single cell-based monitoring of cancer cells and stem cell-based therapies. However, workflows with well-defined sources of variation are required for clinical diagnostics and testing of tissue-engineered products. In a study of neural stem cell lines, we investigated the performance of lysis, reverse transcription (RT), preamplification (PA), and nanofluidic qPCR steps at the single cell level in terms of efficiency, precision, and limit of detection. We compared protocols using a separate lysis buffer with cell capture directly in RT-PA reagent. The two methods were found to have similar lysis efficiencies, whereas the direct RT-PA approach showed improved precision. Digital PCR was used to relate preamplified template copy numbers to Cq values and reveal where low-quality signals may affect the analysis. We investigated the impact of calibration and data normalization strategies as a means of minimizing the impact of inter-experimental variation on gene expression values and found that both approaches can improve data comparability. This study provides validation and guidance for the application of high-throughput qPCR workflows for gene expression profiling of single cells.

Probabilistic PCA of censored data: accounting for uncertainties in the visualization of high-throughput single-cell qPCR data

Motivation: High-throughput single-cell quantitative real-time polymerase chain reaction (qPCR) is a promising technique allowing for new insights in complex cellular processes. However, the PCR reaction can be detected only up to a certain detection limit, whereas failed reactions could be due to low or absent expression, and the true expression level is unknown. Because this censoring can occur for high proportions of the data, it is one of the main challenges when dealing with single-cell qPCR data. Principal component analysis (PCA) is an important tool for visualizing the structure of high-dimensional data as well as for identifying subpopulations of cells. However, to date it is not clear how to perform a PCA of censored data. We present a probabilistic approach that accounts for the censoring and evaluate it for two typical datasets containing single-cell qPCR data.

Results: We use the Gaussian process latent variable model framework to account for censoring by introducing an appropriate noise model and allowing a different kernel for each dimension. We evaluate this new approach for two typical qPCR datasets (of mouse embryonic stem cells and blood stem/progenitor cells, respectively) by performing linear and non-linear probabilistic PCA. Taking the censoring into account results in a 2D representation of the data, which better reflects its known structure: in both datasets, our new approach results in a better separation of known cell types and is able to reveal subpopulations in one dataset that could not be resolved using standard PCA.

Availability and implementation: The implementation was based on the existing Gaussian process latent variable model toolbox (https://github.com/SheffieldML/GPmat); extensions for noise models and kernels accounting for censoring are available at http://icb.helmholtz-muenchen.de/censgplvm.

Contact:fbuettner.phys@gmail.com

Supplementary information: Supplementary data are available at Bioinformatics online.

Single-cell technologies for monitoring immune systems

The complex heterogeneity of cells, and their interconnectedness with each other, are major challenges to identifying clinically relevant measurements that reflect the state and capability of the immune system. Highly multiplexed, single-cell technologies may be critical for identifying correlates of disease or immunological interventions as well as for elucidating the underlying mechanisms of immunity. Here we review limitations of bulk measurements and explore advances in single-cell technologies that overcome these problems by expanding the depth and breadth of functional and phenotypic analysis in space and time. The geometric increases in complexity of data make formidable hurdles for exploring, analyzing and presenting results. We summarize recent approaches to making such computations tractable and discuss challenges for integrating heterogeneous data obtained using these single-cell technologies.

Mixture models for single-cell assays with applications to vaccine studies

Blood and tissue are composed of many functionally distinct cell subsets. In immunological studies, these can be measured accurately only using single-cell assays. The characterization of these small cell subsets is crucial to decipher system-level biological changes. For this reason, an increasing number of studies rely on assays that provide single-cell measurements of multiple genes and proteins from bulk cell samples. A common problem in the analysis of such data is to identify biomarkers (or combinations of biomarkers) that are differentially expressed between two biological conditions (e.g. before/after stimulation), where expression is defined as the proportion of cells expressing that biomarker (or biomarker combination) in the cell subset(s) of interest. Here, we present a Bayesian hierarchical framework based on a beta-binomial mixture model for testing for differential biomarker expression using single-cell assays. Our model allows the inference to be subject specific, as is typically required when assessing vaccine responses, while borrowing strength across subjects through common prior distributions. We propose two approaches for parameter estimation: an empirical-Bayes approach using an Expectation-Maximization algorithm and a fully Bayesian one based on a Markov chain Monte Carlo algorithm. We compare our method against classical approaches for single-cell assays including Fisher's exact test, a likelihood ratio test, and basic log-fold changes. Using several experimental assays measuring proteins or genes at single-cell level and simulations, we show that our method has higher sensitivity and specificity than alternative methods. Additional simulations show that our framework is also robust to model misspecification. Finally, we demonstrate how our approach can be extended to testing multivariate differential expression across multiple biomarker combinations using a Dirichlet-multinomial model and illustrate this approach using single-cell gene expression data and simulations.

Highly multiplexed quantitation of gene expression on single cells

Highly multiplexed, single-cell technologies reveal important heterogeneity within cell populations. Recently, technologies to simultaneously measure expression of 96 (or more) genes from a single cell have been developed for immunologic monitoring. Here, we report a rigorous, optimized, quantitative methodology for using this technology. Specifically: we describe a unique primer/probe qualification method necessary for quantitative results; we show that primers do not compete in highly multiplexed amplifications; we define the limit of detection for this assay as a single mRNA transcript; and, we show that the technical reproducibility of the system is very high. We illustrate two disparate applications of the platform: a "bulk" approach that measures expression patterns from 100 cells at a time in high throughput to define gene signatures, and a single-cell approach to define the coordinate expression patterns of multiple genes and reveal unique subsets of cells.