Advances In Single-cell Technologies Research Articles

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.

Glioblastoma Multiforme: A Look Inside Its Heterogeneous Nature

Heterogeneity is a hallmark of tumors and has a crucial role in the outcome of the malignancy, because it not only confounds diagnosis, but also challenges the design of effective therapies. There are two types of heterogeneity: inter-tumor and intra-tumor heterogeneity. While inter-tumor heterogeneity has been studied widely, intra-tumor heterogeneity has been neglected even though numerous studies support this aspect of tumor pathobiology. The main reason has been the technical difficulties, but with new advances in single-cell technology, intra-tumor heterogeneity is becoming a key area in the study of cancer. Several models try to explain the origin and maintenance of intra-tumor heterogeneity, however, one prominent model compares cancer with a tree where the ubiquitous mutations compose the trunk and mutations present in subpopulations of cells are represented by the branches. In this review we will focus on the intra-tumor heterogeneity of glioblastoma multiforme (GBM), the most common brain tumor in adults that is characterized by a marked heterogeneity at the cellular and molecular levels. Better understanding of this heterogeneity will be essential to design effective therapies against this devastating disease to avoid tumor escape.

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.