Dr.seq2: A quality control and analysis pipeline for parallel single cell transcriptome and epigenome data

A Portrait of Intratumoral Genomic and Transcriptomic Heterogeneity at Single-Cell Level in Colorectal Cancer

In the study of cancer, omics technologies are supporting the transition from traditional clinical approaches to precision medicine. Intra-tumoral heterogeneity (ITH) is detectable within a single tumor in which cancer cell subpopulations with different genome features coexist in a patient in different tumor areas or may evolve/differ over time. Colorectal carcinoma (CRC) is characterized by heterogeneous features involving genomic, epigenomic, and transcriptomic alterations. The study of ITH is a promising new frontier to lay the foundation towards successful CRC diagnosis and treatment. Genome and transcriptome sequencing together with editing technologies are revolutionizing biomedical research, representing the most promising tools for overcoming unmet clinical and research challenges. Rapid advances in both bulk and single-cell next-generation sequencing (NGS) are identifying primary and metastatic intratumoral genomic and transcriptional heterogeneity. They provide critical insight in the origin and spatiotemporal evolution of genomic clones responsible for early and late therapeutic resistance and relapse. Single-cell technologies can be used to define subpopulations within a known cell type by searching for differential gene expression within the cell population of interest and/or effectively isolating signal from rare cell populations that would not be detectable by other methods. Each single-cell sequencing analysis is driven by clustering of cells based on their differentially expressed genes. Genes that drive clustering can be used as unique markers for a specific cell population. In this review we analyzed, starting from published data, the possible achievement of a transition from clinical CRC research to precision medicine with an emphasis on new single-cell based techniques; at the same time, we focused on all approaches and issues related to this promising technology. This transition might enable noninvasive screening for early diagnosis, individualized prediction of therapeutic response, and discovery of additional novel drug targets.

Single-cell chromatin state analysis with Signac

The recent development of experimental methods for measuring chromatin state at single-cell resolution has created a need for computational tools capable of analyzing these datasets. Here we developed Signac, a comprehensive toolkit for the analysis of single-cell chromatin data. Signac enables an end-to-end analysis of single-cell chromatin data, including peak calling, quantification, quality control, dimension reduction, clustering, integration with single-cell gene expression datasets, DNA motif analysis and interactive visualization. Through its seamless compatibility with the Seurat package, Signac facilitates the analysis of diverse multimodal single-cell chromatin data, including datasets that co-assay DNA accessibility with gene expression, protein abundance and mitochondrial genotype. We demonstrate scaling of the Signac framework to analyze datasets containing over 700,000 cells.

Molecular markers of systemic therapy response in urothelial carcinoma

Identification of reliable molecular biomarkers that can complement clinical practice represents a fascinating challenge in any cancer field. Urothelial carcinoma is a very heterogeneous disease and responses to systemic therapies, and outcomes after radical cystectomy are difficult to predict. Advances in molecular biology such as next generation sequencing and whole genome or transcriptomic analysis provide promising platforms to achieve a full understanding of the biology behind the disease and can identify emerging predictive biomarkers. Moreover, the ability to categorize patients' risk of recurrence after curative treatment, or even predict benefit from a conventional or targeted therapies, represents a compelling challenge that may reshape both selection for tailored treatment and disease monitoring. Progress has been made but currently no molecular biomarkers are used in the clinical setting to predict response to systemic agents in either neoadjuvant or adjuvant settings highlighting a relevant unmet need. Here, we aim to present the emerging role of molecular biomarkers in predicting response to systemic agents in urothelial carcinoma.

Single Cell Omics for Musculoskeletal Research

PURPOSE OF REVIEW

The ability to analyze the molecular events occurring within individual cells as opposed to populations of cells is revolutionizing our understanding of musculoskeletal tissue development and disease. Single cell studies have the great potential of identifying cellular subpopulations that work in a synchronized fashion to regenerate and repair damaged tissues during normal homeostasis. In addition, such studies can elucidate how these processes break down in disease as well as identify cellular subpopulations that drive the disease. This review highlights three emerging technologies: single cell RNA sequencing (scRNA-seq), Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq), and Cytometry by Time-Of-Flight (CyTOF) mass cytometry.

RECENT FINDINGS

Technological and bioinformatic tools to analyze the transcriptome, epigenome, and proteome at the individual cell level have advanced rapidly making data collection relatively easy; however, understanding how to access and interpret the data remains a challenge for many scientists. It is, therefore, of paramount significance to educate the musculoskeletal community on how single cell technologies can be used to answer research questions and advance translation. This article summarizes talks given during a workshop on "Single Cell Omics" at the 2020 annual meeting of the Orthopedic Research Society. Studies that applied scRNA-seq, ATAC-seq, and CyTOF mass cytometry to cartilage development and osteoarthritis are reviewed. This body of work shows how these cutting-edge tools can advance our understanding of the cellular heterogeneity and trajectories of lineage specification during development and disease.

simATAC: a single-cell ATAC-seq simulation framework

Single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) identifies regulated chromatin accessibility modules at the single-cell resolution. Robust evaluation is critical to the development of scATAC-seq pipelines, which calls for reproducible datasets for benchmarking. We hereby present the simATAC framework, an R package that generates scATAC-seq count matrices that highly resemble real scATAC-seq datasets in library size, sparsity, and chromatin accessibility signals. simATAC deploys statistical models derived from analyzing 90 real scATAC-seq cell groups. simATAC provides a robust and systematic approach to generate in silico scATAC-seq samples with known cell labels for assessing analytical pipelines.

Profiling Chromatin Accessibility at Single-cell Resolution

How distinct transcriptional programs are enacted to generate cellular heterogeneity and plasticity, and enable complex fate decisions are important open questions. One key regulator is the cell’s epigenome state that drives distinct transcriptional programs by regulating chromatin accessibility. Genome-wide chromatin accessibility measurements can impart insights into regulatory sequences (in)accessible to DNA-binding proteins at a single-cell resolution. This review outlines molecular methods and bioinformatic tools for capturing cell-to-cell chromatin variation using single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) in a scalable fashion. It also covers joint profiling of chromatin with transcriptome/proteome measurements, computational strategies to integrate multi-omic measurements, and predictive bioinformatic tools to infer chromatin accessibility from single-cell transcriptomic datasets. Methodological refinements that increase power for cell discovery through robust chromatin coverage and integrate measurements from multiple modalities will further expand our understanding of gene regulation during homeostasis and disease.

Statistical and Bioinformatics Analysis of Data from Bulk and Single-Cell RNA Sequencing Experiments

High-throughput sequencing (HTS) has revolutionized researchers' ability to study the human transcriptome, particularly as it relates to cancer. Recently, HTS technology has advanced to the point where now one is able to sequence individual cells (i.e., "single-cell sequencing"). Prior to single-cell sequencing technology, HTS would be completed on RNA extracted from a tissue sample consisting of multiple cell types (i.e., "bulk sequencing"). In this chapter, we review the various bioinformatics and statistical methods used in the processing, quality control, and analysis of bulk and single-cell RNA sequencing methods. Additionally, we discuss how these methods are also being used to study tumor heterogeneity.

Integrative analyses of single-cell transcriptome and regulome using MAESTRO

We present Model-based AnalysEs of Transcriptome and RegulOme (MAESTRO), a comprehensive open-source computational workflow ( http://github.com/liulab-dfci/MAESTRO ) for the integrative analyses of single-cell RNA-seq (scRNA-seq) and ATAC-seq (scATAC-seq) data from multiple platforms. MAESTRO provides functions for pre-processing, alignment, quality control, expression and chromatin accessibility quantification, clustering, differential analysis, and annotation. By modeling gene regulatory potential from chromatin accessibilities at the single-cell level, MAESTRO outperforms the existing methods for integrating the cell clusters between scRNA-seq and scATAC-seq. Furthermore, MAESTRO supports automatic cell-type annotation using predefined cell type marker genes and identifies driver regulators from differential scRNA-seq genes and scATAC-seq peaks.

Single-cell ATAC-seq signal extraction and enhancement with SCATE

Single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) is the state-of-the-art technology for analyzing genome-wide regulatory landscapes in single cells. Single-cell ATAC-seq data are sparse and noisy, and analyzing such data is challenging. Existing computational methods cannot accurately reconstruct activities of individual cis-regulatory elements (CREs) in individual cells or rare cell subpopulations. We present a new statistical framework, SCATE, that adaptively integrates information from co-activated CREs, similar cells, and publicly available regulome data to substantially increase the accuracy for estimating activities of individual CREs. We demonstrate that SCATE can be used to better reconstruct the regulatory landscape of a heterogeneous sample.

Single-cell ATAC sequencing analysis: From data preprocessing to hypothesis generation

Most genetic variations associated with human complex traits are located in non-coding genomic regions. Therefore, understanding the genotype-to-phenotype axis requires a comprehensive catalog of functional non-coding genomic elements, most of which are involved in epigenetic regulation of gene expression. Genome-wide maps of open chromatin regions can facilitate functional analysis of cis- and trans-regulatory elements via their connections with trait-associated sequence variants. Currently, Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) is considered the most accessible and cost-effective strategy for genome-wide profiling of chromatin accessibility. Single-cell ATAC-seq (scATAC-seq) technology has also been developed to study cell type-specific chromatin accessibility in tissue samples containing a heterogeneous cellular population. However, due to the intrinsic nature of scATAC-seq data, which are highly noisy and sparse, accurate extraction of biological signals and devising effective biological hypothesis are difficult. To overcome such limitations in scATAC-seq data analysis, new methods and software tools have been developed over the past few years. Nevertheless, there is no consensus for the best practice of scATAC-seq data analysis yet. In this review, we discuss scATAC-seq technology and data analysis methods, ranging from preprocessing to downstream analysis, along with an up-to-date list of published studies that involved the application of this method. We expect this review will provide a guideline for successful data generation and analysis methods using appropriate software tools and databases for the study of chromatin accessibility at single-cell resolution.

The new technologies of high-throughput single-cell RNA sequencing

A wealth of genome and transcriptome data obtained using new generation sequencing (NGS) technologies for whole organisms could not answer many questions in oncology, immunology, physiology, neurobiology, zoology and other fields of science and medicine. Since the cell is the basis for the living of all unicellular and multicellular organisms, it is necessary to study the biological processes at its level. This understanding gave impetus to the development of a new direction – the creation of technologies that allow working with individual cells (single-cell technology). The rapid development of not only instruments, but also various advanced protocols for working with single cells is due to the relevance of these studies in many fields of science and medicine. Studying the features of various stages of ontogenesis, identifying patterns of cell differentiation and subsequent tissue development, conducting genomic and transcriptome analyses in various areas of medicine (especially in demand in immunology and oncology), identifying cell types and states, patterns of biochemical and physiological processes using single cell technologies, allows the comprehensive research to be conducted at a new level. The first RNA-sequencing technologies of individual cell transcriptomes (scRNA-seq) captured no more than one hundred cells at a time, which was insufficient due to the detection of high cell heterogeneity, existence of the minor cell types (which were not detected by morphology) and complex regulatory pathways. The unique techniques for isolating, capturing and sequencing transcripts of tens of thousands of cells at a time are evolving now. However, new technologies have certain differences both at the sample preparation stage and during the bioinformatics analysis. In the paper we consider the most effective methods of multiple parallel scRNA-seq using the example of 10XGenomics, as well as the specifics of such an experiment, further bioinformatics analysis of the data, future outlook and applications of new high-performance technologies.

Single-cell chromatin immunocleavage sequencing (scChIC-seq) to profile histone modification

Our method for analyzing histone modifications, scChIC-seq (single-cell chromatin immunocleavage sequencing), involves targeting of the micrococcal nuclease (MNase) to a histone mark of choice by tethering to a specific antibody. Cleaved target sites are then selectively PCR amplified. We show that scChIC-seq reliably detects H3K4me3 and H3K27me3 target sites in single human white blood cells. The resulting data are used for clustering of blood cell types.

How low can you go? Pushing the limits of low-input ChIP-seq

In the past decade, chromatin immunoprecipitation sequencing (ChIP-seq) has emerged as the dominant technique for those wishing to perform genome-wide protein:DNA profiling. Owing to the tissue- and cell-type-specific nature of epigenetic marks, the field has been driven towards obtaining data from ever-lower cell numbers. In this review, we focus on the methodological developments that have lowered input requirements and the biological findings they have enabled, as we strive towards the ultimate goal of robust single-cell ChIP-seq.

An Introduction to the Analysis of Single-Cell RNA-Sequencing Data

The recent development of single-cell RNA sequencing has deepened our understanding of the cell as a functional unit, providing new insights based on gene expression profiles of hundreds to hundreds of thousands of individual cells, and revealing new populations of cells with distinct gene expression profiles previously hidden within analyses of gene expression performed on bulk cell populations. However, appropriate analysis and utilization of the massive amounts of data generated from single-cell RNA sequencing experiments are challenging and require an understanding of the experimental and computational pathways taken between preparation of input cells and output of interpretable data. In this review, we will discuss the basic principles of these new technologies, focusing on concepts important in the analysis of single-cell RNA-sequencing data. Specifically, we summarize approaches to quality-control measures for determination of which single cells to include for further examination, methods of data normalization and scaling to overcome the relatively inefficient capture rate of mRNA from each cell, and clustering and visualization algorithms used for dimensional reduction of the data to a two-dimensional plot.

Circulating Tumor Cells for the Management of Renal Cell Carcinoma

Renal cell carcinoma is a highly malignant cancer that would benefit from non-invasive innovative markers providing early diagnosis and recurrence detection. Circulating tumor cells are a particularly promising marker of tumor invasion that could be used to improve the management of patients with RCC. However, the extensive genetic and immunophenotypic heterogeneity of cells from RCC and their trend to transition to the mesenchymal phenotype when they circulate in blood constitute a challenge for their sensitive and specific detection. This review analyzes published studies targeting CTC in patients with RCC, in the context of the biological, pathological, and molecular complexity of this particular cancer. Although further analytical and clinical studies are needed to pinpoint the most suitable approach for highly sensitive CTC detection in RCC patients, it is clear that this field can bring a relevant guide to clinicians and help to RCC patients. Furthermore, as described, a particular subtype of RCC-the ccRCC-can be used as a model to study the relationship between cytomorphological and genetic cellular markers of malignancy, an important issue for the study of CTC from any type of solid cancer.

Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database

As single-cell RNA-sequencing (scRNA-seq) datasets have become more widespread the number of tools designed to analyse these data has dramatically increased. Navigating the vast sea of tools now available is becoming increasingly challenging for researchers. In order to better facilitate selection of appropriate analysis tools we have created the scRNA-tools database (www.scRNA-tools.org) to catalogue and curate analysis tools as they become available. Our database collects a range of information on each scRNA-seq analysis tool and categorises them according to the analysis tasks they perform. Exploration of this database gives insights into the areas of rapid development of analysis methods for scRNA-seq data. We see that many tools perform tasks specific to scRNA-seq analysis, particularly clustering and ordering of cells. We also find that the scRNA-seq community embraces an open-source and open-science approach, with most tools available under open-source licenses and preprints being extensively used as a means to describe methods. The scRNA-tools database provides a valuable resource for researchers embarking on scRNA-seq analysis and records the growth of the field over time.