Highly multiplexed quantitation of gene expression on single cells

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.

Beyond model antigens: high-dimensional methods for the analysis of antigen-specific T cells

Adaptive immune responses often begin with the formation of a molecular complex between a T-cell receptor (TCR) and a peptide antigen bound to a major histocompatibility complex (MHC) molecule. These complexes are highly variable, however, due to the polymorphism of MHC genes, the random, inexact recombination of TCR gene segments, and the vast array of possible self and pathogen peptide antigens. As a result, it has been very difficult to comprehensively study the TCR repertoire or identify and track more than a few antigen-specific T cells in mice or humans. For mouse studies, this had led to a reliance on model antigens and TCR transgenes. The study of limited human clinical samples, in contrast, requires techniques that can simultaneously survey TCR phenotype and function, and TCR reactivity to many T-cell epitopes. Thanks to recent advances in single-cell and cytometry methodologies, as well as high-throughput sequencing of the TCR repertoire, we now have or will soon have the tools needed to comprehensively analyze T-cell responses in health and disease.

The who's who of T-cell differentiation: human memory T-cell subsets

Following antigen encounter and subsequent resolution of the immune response, a single naïve T cell is able to generate multiple subsets of memory T cells with different phenotypic and functional properties and gene expression profiles. Single-cell technologies, first and foremost flow cytometry, have revealed the complex heterogeneity of the memory T-cell compartment and its organization into subsets. However, a consensus has still to be reached, both at the semantic (nomenclature) and phenotypic level, regarding the identification of these subsets. Here, we review recent developments in the characterization of the heterogeneity of the memory T-cell compartment, and propose a unified classification of both human and nonhuman primate T cells on the basis of phenotypic traits and in vivo properties. Given that vaccine studies and adoptive cell transfer immunotherapy protocols are influenced by these recent findings, it is important to use uniform methods for identifying and discussing functionally distinct subsets of T cells.

High-dimensional analysis of human CD8(+) T cell phenotype, function, and antigen specificity

Antigen-specific T cells are critical initiators and orchestrators of the adaptive immune response. Categorizing antigen-specific T cell subsets is not a simple task given the diversity of these cells and the large number of parameters that can be considered. Here, we focus on human CD8(+) T cells and discuss the utility of high-dimensional mass cytometric analysis techniques for the concurrent identification and characterization of antigen-specific T cells involved in immunological homeostasis and disease. We first provide an overview of previously identified T cell subsets. We then discuss the segregation of antigen-specific T cells based on protein expression through surface and/or intracellular staining, on functional capacity through measurement expression of cytokines or other inducible markers, and on the antigen-specificity of the cell assessed using peptide-major histocompatibility complex multimers. High-dimensional mass cytometry enables a deeper and more integrated view of all three aspects of antigen-specific T cell diversity than do traditional techniques. Use of mass cytometry for precise measurement of the status of antigen-specific immune responses should result in better prediction of vaccine efficacy and disease outcomes.