Trajectory-based differential expression analysis for single-cell sequencing data

Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. By incorporating observation-level weights, the model additionally allows to account for zero inflation. We evaluate the method on simulated datasets and on real datasets from droplet-based and full-length protocols, and show that it yields biological insights through a clear interpretation of the data. Downstream of trajectory inference for cell lineages based on scRNA-seq data, differential expression analysis yields insight into biological processes. Here, Van den Berge et al. develop tradeSeq, a framework for the inference of within and between-lineage differential expression, based on negative binomial generalized additive models

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing.

Sequencing technologies have undergone a paradigm shift from bulk to single-cell resolution in response to an evolving understanding of the role of cellular heterogeneity in biological systems. However, single-cell sequencing of large populations has been hampered by limitations in processing genomes for sequencing. In this paper, we describe a method for single-cell genome sequencing (SiC-seq) which uses droplet microfluidics to isolate, amplify, and barcode the genomes of single cells. Cell encapsulation in microgels allows the compartmentalized purification and tagmentation of DNA, while a microfluidic merger efficiently pairs each genome with a unique single-cell oligonucleotide barcode, allowing >50,000 single cells to be sequenced per run. The sequencing data is demultiplexed by barcode, generating groups of reads originating from single cells. As a high-throughput and low-bias method of single-cell sequencing, SiC-seq will enable a broader range of genomic studies targeted at diverse cell populations

Exploring viral infection using single-cell sequencing.

Single-cell sequencing (SCS) has emerged as a valuable tool to study cellular heterogeneity in diverse fields, including virology. By studying the viral and cellular genome and/or transcriptome, the dynamics of viral infection can be investigated at single cell level. Most studies have explored the impact of cell-to-cell variation on the viral life cycle from the point of view of the virus, by analyzing viral sequences, and from the point of view of the cell, mainly by analyzing the cellular host transcriptome. In this review, we will focus on recent studies that use single-cell sequencing to explore viral diversity and cell variability in response to viral replication

Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage.

Tissue fibrosis is a major cause of mortality that results from the deposition of matrix proteins by an activated mesenchyme. Macrophages accumulate in fibrosis, but the role of specific subgroups in supporting fibrogenesis has not been investigated in vivo. Here, we used single-cell RNA sequencing (scRNA-seq) to characterize the heterogeneity of macrophages in bleomycin-induced lung fibrosis in mice. A novel computational framework for the annotation of scRNA-seq by reference to bulk transcriptomes (SingleR) enabled the subclustering of macrophages and revealed a disease-associated subgroup with a transitional gene expression profile intermediate between monocyte-derived and alveolar macrophages. These CX3CR1+SiglecF+ transitional macrophages localized to the fibrotic niche and had a profibrotic effect in vivo. Human orthologs of genes expressed by the transitional macrophages were upregulated in samples from patients with idiopathic pulmonary fibrosis. Thus, we have identified a pathological subgroup of transitional macrophages that are required for the fibrotic response to injury

BASiCS: Bayesian Analysis of Single-Cell Sequencing Data

Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of unexplained technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model where: (i) cell-specific normalisation constants are estimated as part of the model parameters, (ii) technical variability is quantified based on spike-in genes that are artificially introduced to each analysed cell's lysate and (iii) the total variability of the expression counts is decomposed into technical and biological components. BASiCS also provides an intuitive detection criterion for highly (or lowly) variable genes within the population of cells under study. This is formalised by means of tail posterior probabilities associated to high (or low) biological cell-to-cell variance contributions, quantities that can be easily interpreted by users. We demonstrate our method using gene expression measurements from mouse Embryonic Stem Cells. Cross-validation and meaningful enrichment of gene ontology categories within genes classified as highly (or lowly) variable supports the efficacy of our approach

Identifying cell types with single cell sequencing data

Single-cell RNA sequencing (scRNA-seq) techniques, which examine the genetic information of individual cells, provide an unparalleled resolution to discern deeply into cellular heterogeneity. On the contrary, traditional RNA sequencing technologies (bulk RNA sequencing technologies), measure the average RNA expression level of a large number of input cells, which are insufficient for studying heterogeneous systems. Hence, scRNA-seq technologies make it possible to tackle many inaccessible problems, such as rare cell types identification, cancer evolution and cell lineage relationship inference. Cell population identification is the fundamental of the analysis of scRNA-seq data. Generally, the workflow of scRNA-seq analysis includes data processing, dropout imputation, feature selection, dimensionality reduction, similarity matrix construction and unsupervised clustering. Many single-cell clustering algorithms rely on similarity matrices of cells, but many existing studies have not received the expectant results. There are some unique challenges in analyzing scRNA-seq data sets, including a significant level of biological and technical noise, so similarity matrix construction still deserves further study. In my study, I present a new method, named Learning Sparse Similarity Matrices (LSSM), to construct cell-cell similarity matrices, and then several clustering methods are used to identify cell populations respectively with scRNA-seq data. Firstly, based on sparse subspace theory, the relationship between a cell and the other cells in the same cell type is expressed by a linear combination. Secondly, I construct a convex optimization objective function to find the similarity matrix, which is consist of the corresponding coefficients of the linear combinations mentioned above. Thirdly, I design an algorithm with column-wise learning and greedy algorithm to solve the objective function. As a result, the large optimization problem on the similarity matrix can be decomposed into a series of smaller optimization problems on the single column of the similarity matrix respectively, and the sparsity of the whole matrix can be ensured by the sparsity of each column. Fourthly, in order to pick an optimal clustering method for identifying cell populations based on the similarity matrix developed by LSSM, I use several clustering methods separately based on the similarity matrix calculated by LSSM from eight scRNA-seq data sets. The clustering results show that my method performs the best when combined with spectral clustering (Laplacian eigenmaps + k-means clustering). In addition, compared with five state-of-the-art methods, my method outperforms most competing methods on eight data sets. Finally, I combine LSSM with t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the data points of scRNA-seq data in the two-dimensional space. The results show that for most data points, in the same cell types they are close, while from different cell clusters, they are separated

The role of single-cell genomics in human genetics

Single-cell sequencing is a powerful approach that can detect genetic alterations and their phenotypic consequences in the context of human development, with cellular resolution. Humans start out as single-cell zygotes and undergo fission and differentiation to develop into multicellular organisms. Before fertilisation and during development, the cellular genome acquires hundreds of mutations that propagate down the cell lineage. Whether germline or somatic in nature, some of these mutations may have significant genotypic impact and lead to diseased cellular phenotypes, either systemically or confined to a tissue. Single-cell sequencing enables the detection and monitoring of the genotype and the consequent molecular phenotypes at a cellular resolution. It offers powerful tools to compare the cellular lineage between 'normal' and 'diseased' conditions and to establish genotype-phenotype relationships. By preserving cellular heterogeneity, single-cell sequencing, unlike bulk-sequencing, allows the detection of even small, diseased subpopulations of cells within an otherwise normal tissue. Indeed, the characterisation of biopsies with cellular resolution can provide a mechanistic view of the disease. While single-cell approaches are currently used mainly in basic research, it can be expected that applications of these technologies in the clinic may aid the detection, diagnosis and eventually the treatment of rare genetic diseases as well as cancer. This review article provides an overview of the single-cell sequencing technologies in the context of human genetics, with an aim to empower clinicians to understand and interpret the single-cell sequencing data and analyses. We discuss the state-of-the-art experimental and analytical workflows and highlight current challenges/limitations

Recent advances and current issues in single-cell sequencing of tumors

AbstractIntratumoral heterogeneity is a recently recognized but important feature of cancer that underlies the various biocharacteristics of cancer tissues. The advent of next-generation sequencing technologies has facilitated large scale capture of genomic data, while the recent development of single-cell sequencing has allowed for more in-depth studies into the complex molecular mechanisms of intratumoral heterogeneity. In this review, the recent advances and current challenges in single-cell sequencing methodologies are discussed, highlighting the potential power of these data to provide insights into oncological processes, from tumorigenesis through progression to metastasis and therapy resistance

Combined aptamer and transcriptome sequencing of single cells.

The transcriptome and proteome encode distinct information that is important for characterizing heterogeneous biological systems. We demonstrate a method to simultaneously characterize the transcriptomes and proteomes of single cells at high throughput using aptamer probes and droplet-based single cell sequencing. With our method, we differentiate distinct cell types based on aptamer surface binding and gene expression patterns. Aptamers provide advantages over antibodies for single cell protein characterization, including rapid, in vitro, and high-purity generation via SELEX, and the ability to amplify and detect them with PCR and sequencing