Advances and applications of single-cell sequencing technologies

Transcribing the heart: faithfully interpreting cardiac transcriptional insights

Transcriptional profiling continues to produce phenotypical data essential for understanding of basic cardiac biology and required to improve efficiency of cardiac regenerative and therapeutic approaches after injury. Accurate interpretation of cardiac transcriptional data comes with the unique challenges of heart biology including cardiomyocyte morphology, cryopreservation of limited samples and adequate selection of transcriptional platform at a single-cell resolution. Consequently, development and implementation of novel transcriptional platforms and creative bioinformatic analysis are essential to resolve standing questions in the field of cardiac regenerative medicine. Targeted bioinformatic approaches, advancing technological access, increase technical availability and fostering communication between interdisciplinary groups is critical to improve therapeutic approaches and to overcome challenges inherent to transcriptomic cardiac research.

Single-cell RNA sequencing for the study of development, physiology and disease

An ongoing technological revolution is continually improving our ability to carry out very high-resolution studies of gene expression patterns. Current technology enables the global gene expression profiles of single cells to be defined, facilitating dissection of heterogeneity in cell populations that was previously hidden. In contrast to gene expression studies that use bulk RNA samples and provide only a virtual average of the diverse constituent cells, single-cell studies enable the molecular distinction of all cell types within a complex population mix, such as a tumour or developing organ. For instance, single-cell gene expression profiling has contributed to improved understanding of how histologically identical, adjacent cells make different differentiation decisions during development. Beyond development, single-cell gene expression studies have enabled the characteristics of previously known cell types to be more fully defined and facilitated the identification of novel categories of cells, contributing to improvements in our understanding of both normal and disease-related physiological processes and leading to the identification of new treatment approaches. Although limitations remain to be overcome, technology for the analysis of single-cell gene expression patterns is improving rapidly and beginning to provide a detailed atlas of the gene expression patterns of all cell types in the human body.

Isoform-level gene expression patterns in single-cell RNA-sequencing data

Motivation

RNA sequencing of single cells enables characterization of transcriptional heterogeneity in seemingly homogeneous cell populations. Single-cell sequencing has been applied in a wide range of researches fields. However, few studies have focus on characterization of isoform-level expression patterns at the single-cell level. In this study, we propose and apply a novel method, ISOform-Patterns (ISOP), based on mixture modeling, to characterize the expression patterns of isoform pairs from the same gene in single-cell isoform-level expression data.

Results

We define six principal patterns of isoform expression relationships and describe a method for differential-pattern analysis. We demonstrate ISOP through analysis of single-cell RNA-sequencing data from a breast cancer cell line, with replication in three independent datasets. We assigned the pattern types to each of 16 562 isoform-pairs from 4929 genes. Among those, 26% of the discovered patterns were significant (P<0.05), while remaining patterns are possibly effects of transcriptional bursting, drop-out and stochastic biological heterogeneity. Furthermore, 32% of genes discovered through differential-pattern analysis were not detected by differential-expression analysis. Finally, the effects of drop-out events and expression levels of isoforms on ISOP's performances were investigated through simulated datasets. To conclude, ISOP provides a novel approach for characterization of isoform-level preference, commitment and heterogeneity in single-cell RNA-sequencing data.

Availability and implementation

The ISOP method has been implemented as a R package and is available at https://github.com/nghiavtr/ISOP under a GPL-3 license.

Supplementary information

Supplementary data are available at Bioinformatics online.

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.

Systems Immunology: Learning the Rules of the Immune System

Given the many cell types and molecular components of the human immune system, along with vast variations across individuals, how should we go about developing causal and predictive explanations of immunity? A central strategy in human studies is to leverage natural variation to find relationships among variables, including DNA variants, epigenetic states, immune phenotypes, clinical descriptors, and others. Here, we focus on how natural variation is used to find patterns, infer principles, and develop predictive models for two areas: (a) immune cell activation-how single-cell profiling boosts our ability to discover immune cell types and states-and (b) antigen presentation and recognition-how models can be generated to predict presentation of antigens on MHC molecules and their detection by T cell receptors. These are two examples of a shift in how we find the drivers and targets of immunity, especially in the human system in the context of health and disease.

Ultrasonically-guided flow focusing generates precise emulsion droplets for high-throughput single cell analyses

Emulsion-based techniques have dramatically advanced our understanding of single-cell biology and complex single-cell features over the past two decades. Most approaches for precise single cell isolation rely on microfluidics, which has proven highly effective but requires substantial investment in equipment and expertise that can be difficult to access for researchers that specialize in other areas of bioengineering and molecular biotechnology. Inspired by the robust droplet generation technologies in modern flow cytometry instrumentation, here we established a new platform for high-throughput isolation of single cells within droplets of tunable sizes by combining flow focusing with ultrasonic vibration for rapid and effective droplet formation. Application of ultrasonic pressure waves to the flowing jet provided enhanced control of emulsion droplet size, permitting capture of 25,000 to 50,000 single cells per minute. As an example application, we applied this new droplet generation platform to sequence the antibody variable region heavy and light chain pairings (VH:VL) from large repertoires of single B cells. We demonstrated the recovery of > 40,000 paired CDRH3:CDRL3 antibody clusters from a single individual, validating that these droplet systems can enable the genetic analysis of very large single-cell populations. These accessible new technologies will allow rapid, large-scale, and precise single-cell analyses for a broad range of bioengineering and molecular biotechnology applications.

PaSD-qc: quality control for single cell whole-genome sequencing data using power spectral density estimation

Single cell whole-genome sequencing (scWGS) is providing novel insights into the nature of genetic heterogeneity in normal and diseased cells. However, the whole-genome amplification process required for scWGS introduces biases into the resulting sequencing that can confound downstream analysis. Here, we present a statistical method, with an accompanying package PaSD-qc (Power Spectral Density-qc), that evaluates the properties and quality of single cell libraries. It uses a modified power spectral density to assess amplification uniformity, amplicon size distribution, autocovariance and inter-sample consistency as well as to identify chromosomes with aberrant read-density profiles due either to copy alterations or poor amplification. These metrics provide a standard way to compare the quality of single cell samples as well as yield information necessary to improve variant calling strategies. We demonstrate the usefulness of this tool in comparing the properties of scWGS protocols, identifying potential chromosomal copy number variation, determining chromosomal and subchromosomal regions of poor amplification, and selecting high-quality libraries from low-coverage data for deep sequencing. The software is available free and open-source at https://github.com/parklab/PaSDqc.

Accurate estimation of cell-type composition from gene expression data

The rapid development of single-cell transcriptomic technologies has helped uncover the cellular heterogeneity within cell populations. However, bulk RNA-seq continues to be the main workhorse for quantifying gene expression levels due to technical simplicity and low cost. To most effectively extract information from bulk data given the new knowledge gained from single-cell methods, we have developed a novel algorithm to estimate the cell-type composition of bulk data from a single-cell RNA-seq-derived cell-type signature. Comparison with existing methods using various real RNA-seq data sets indicates that our new approach is more accurate and comprehensive than previous methods, especially for the estimation of rare cell types. More importantly, our method can detect cell-type composition changes in response to external perturbations, thereby providing a valuable, cost-effective method for dissecting the cell-type-specific effects of drug treatments or condition changes. As such, our method is applicable to a wide range of biological and clinical investigations.

Bulk RNA-seq data harbors valuable information about gene expression levels from different cell types in tissue samples. Here, the authors develop DWLS, a computational method for estimating cell-type composition of bulk data by leveraging single-cell RNA-seq-derived cell-type signatures.

Circulating Tumor Cells in Diagnosis and Treatment of Lung Cancer

Circulating tumor cells (CTCs), detached from the primary tumor or metastases and shed in the patient's bloodstream, represent a relatively easily obtainable sample of the cancer tissue that can indicate the actual state of cancer, and their evaluation can be repeated many times during the course of treatment. As part of liquid biopsy, evaluation of CTCs provides a lot of clinically relevant information, which reflects the actual, real-time conditions of the disease. CTCs can be used in cancer diagnosis or screening, real-time long-term disease monitoring and even therapy guidance. Their analysis can include their number, morphology, and biological features by using immunocytochemistry and all "-omic" technologies. This review describes methods of CTC isolation and potential clinical utilization in lung cancer.

Preparation of Cells from Embryonic Organs for Single-Cell RNA Sequencing

Although single-cell RNA sequencing (scRNA-seq) has become one of the most powerful methods available for transcriptome analysis, the quality of scRNA-seq data largely depends on cell preparation. Cell preparation from cultured cells and tissues requires different methods because of the inherent differences between these two categories of cells. Compared to cultured cells, tissues have more extracellular matrix, and the cells are generally more adherent and thus difficult to dissociate. The challenge is to achieve sufficient dissociation, cell counts, and viability all at the same time. This protocol describes approaches that help achieve these goals. These include a cold dissociation technique using cryophilic proteases active at cold temperature, timing of trituration during protease digestion, as well as filtration and washing methods that optimize cell viability and retention. Materials and equipment that optimize the process also discussed. © 2019 by John Wiley & Sons, Inc.

Truncated Robust Principal Component Analysis and Noise Reduction for Single Cell RNA Sequencing Data

The development of single cell RNA sequencing (scRNA-seq) has enabled innovative approaches to investigating mRNA abundances. In our study, we are interested in extracting the systematic patterns of scRNA-seq data in an unsupervised manner; thus, we have developed two extensions of robust principal component analysis (RPCA). First, we present a truncated version of RPCA (tRPCA), which is much faster and memory efficient. Second, we introduce a noise reduction in tRPCA with L₂ regularization. Unlike RPCA that only considers a low-rank L and sparse S matrices, the proposed method can also extract a noise E matrix inherent in modern genomic data. We demonstrate its usefulness by applying our methods on the peripheral blood mononuclear cell scRNA-seq data. Particularly, the clustering of a low-rank L matrix showcases better classification of unlabeled single cells. Overall, the proposed variants are well suited for high-dimensional and noisy data that are routinely generated in genomics.

ChimeraMiner: An Improved Chimeric Read Detection Pipeline and Its Application in Single Cell Sequencing

As the most widely-used single cell whole genome amplification (WGA) approach, multiple displacement amplification (MDA) has a superior performance, due to the high-fidelity and processivity of phi29 DNA polymerase. However, chimeric reads, generated in MDA, cause severe disruption in many single-cell studies. Herein, we constructed ChimeraMiner, an improved chimeric read detection pipeline for analyzing the sequencing data of MDA and classified the chimeric sequences. Two datasets (MDA1 and MDA2) were used for evaluating and comparing the efficiency of ChimeraMiner and previous pipeline. Under the same hardware condition, ChimeraMiner spent only 43.4% (43.8% for MDA1 and 43.0% for MDA2) processing time. Respectively, 24.4 million (6.31%) read pairs out of 773 million reads, and 17.5 million (6.62%) read pairs out of 528 million reads were accurately classified as chimeras by ChimeraMiner. In addition to finding 83.60% (17,639,371) chimeras, which were detected by previous pipelines, ChimeraMiner screened 6,736,168 novel chimeras, most of which were missed by the previous pipeline. Applying in single-cell datasets, all three types of chimera were discovered in each dataset, which introduced plenty of false positives in structural variation (SV) detection. The identification and filtration of chimeras by ChimeraMiner removed most of the false positive SVs (83.8%). ChimeraMiner revealed improved efficiency in discovering chimeric reads, and is promising to be widely used in single-cell sequencing.

Recent advances in neuroepigenetic editing

A wealth of studies in the mammalian nervous system indicate the role of epigenetic gene regulation in both basic neurobiological function and disease. However, the relationship between epigenetic regulation and neuropathology is largely correlational due to the presence of mixed cell populations within brain regions and the genome-wide effects of classical approaches to manipulate the epigenome. Locus-specific epigenetic editing allows direct epigenetic regulation of specific genes to elucidate the direct causal relationship between epigenetic modifications and transcription. This review discusses some of the latest innovations in the efficacy and flexibility in this approach that hold promise for neurobiological application.

Cell-free DNA testing: future applications in gastroenterology and hepatology

The application of next-generation sequencing in clinical practice is increasing as accuracy and interpretation have improved and the cost continues to decline rapidly. Cell-free DNA is a unique source for next-generation sequencing that could change routine clinical practice in gastroenterology and hepatology. Testing of cell-free DNA in blood and fecal samples is an easy, rapid, and noninvasive method to assess for premalignant, malignant, metabolic, infectious, inflammatory, and autoimmune gastrointestinal and liver diseases. In this review, we describe cell-free DNA technologies, current applications of cell-free DNA testing, and proposed cell-free DNA targets for gastrointestinal and hepatic diseases, with a specific focus on malignancy. In addition, we provide commentary on how cell-free DNA can be integrated into clinical practice and help guide diagnosis, prognosis, disease management, and therapeutic response.

100 Years of evolving gene-disease complexities and scientific debutants

It's been over 100 years since the word `gene' is around and progressively evolving in several scientific directions. Time-to-time technological advancements have heavily revolutionized the field of genomics, especially when it's about, e.g. triple code development, gene number proposition, genetic mapping, data banks, gene-disease maps, catalogs of human genes and genetic disorders, CRISPR/Cas9, big data and next generation sequencing, etc. In this manuscript, we present the progress of genomics from pea plant genetics to the human genome project and highlight the molecular, technical and computational developments. Studying genome and epigenome led to the fundamentals of development and progression of human diseases, which includes chromosomal, monogenic, multifactorial and mitochondrial diseases. World Health Organization has classified, standardized and maintained all human diseases, when many academic and commercial online systems are sharing information about genes and linking to associated diseases. To efficiently fathom the wealth of this biological data, there is a crucial need to generate appropriate gene annotation repositories and resources. Our focus has been how many gene-disease databases are available worldwide and which sources are authentic, timely updated and recommended for research and clinical purposes. In this manuscript, we have discussed and compared 43 such databases and bioinformatics applications, which enable users to connect, explore and, if possible, download gene-disease data.

Analysis of Single-Cell RNA-seq Data by Clustering Approaches

Background:

The recently developed single-cell RNA sequencing (scRNA-seq) has attracted a great amount of attention due to its capability to interrogate expression of individual cells, which is superior to traditional bulk cell sequencing that can only measure mean gene expression of a population of cells. scRNA-seq has been successfully applied in finding new cell subtypes. New computational challenges exist in the analysis of scRNA-seq data.

Objective:

We provide an overview of the features of different similarity calculation and clustering methods, in order to facilitate users to select methods that are suitable for their scRNA-seq. We would also like to show that feature selection methods are important to improve clustering performance.

Results:

We first described similarity measurement methods, followed by reviewing some new clustering methods, as well as their algorithmic details. This analysis revealed several new questions, including how to automatically estimate the number of clustering categories, how to discover novel subpopulation, and how to search for new marker genes by using feature selection methods.

Conclusion:

Without prior knowledge about the number of cell types, clustering or semisupervised learning methods are important tools for exploratory analysis of scRNA-seq data.</P>

Translating insights into tumor evolution to clinical practice: promises and challenges

Accelerating technological advances have allowed the widespread genomic profiling of tumors. As yet, however, the vast catalogues of mutations that have been identified have made only a modest impact on clinical medicine. Massively parallel sequencing has informed our understanding of the genetic evolution and heterogeneity of cancers, allowing us to place these mutational catalogues into a meaningful context. Here, we review the methods used to measure tumor evolution and heterogeneity, and the potential and challenges for translating the insights gained to achieve clinical impact for cancer therapy, monitoring, early detection, risk stratification, and prevention. We discuss how tumor evolution can guide cancer therapy by targeting clonal and subclonal mutations both individually and in combination. Circulating tumor DNA and circulating tumor cells can be leveraged for monitoring the efficacy of therapy and for tracking the emergence of resistant subclones. The evolutionary history of tumors can be deduced for late-stage cancers, either directly by sampling precursor lesions or by leveraging computational approaches to infer the timing of driver events. This approach can identify recurrent early driver mutations that represent promising avenues for future early detection strategies. Emerging evidence suggests that mutational processes and complex clonal dynamics are active even in normal development and aging. This will make discriminating developing malignant neoplasms from normal aging cell lineages a challenge. Furthermore, insight into signatures of mutational processes that are active early in tumor evolution may allow the development of cancer-prevention approaches. Research and clinical studies that incorporate an appreciation of the complex evolutionary patterns in tumors will not only produce more meaningful genomic data, but also better exploit the vulnerabilities of cancer, resulting in improved treatment outcomes.

Dissecting CLL through high-dimensional single-cell technologies

We now have the potential to undertake detailed analysis of the inner workings of thousands of cancer cells, one cell at a time, through the emergence of a range of techniques that probe the genome, transcriptome, and proteome combined with the development of bioinformatics pipelines that enable their interpretation. This provides an unprecedented opportunity to better understand the heterogeneity of chronic lymphocytic leukemia and how mutations, activation states, and protein expression at the single-cell level have an impact on disease course, response to treatment, and outcomes. Herein, we review the emerging application of these new techniques to chronic lymphocytic leukemia and examine the insights already attained through this transformative technology.

Single-Cell Omics Analyses Enabled by Microchip Technologies

Single-cell omics studies provide unique information regarding cellular heterogeneity at various levels of the molecular biology central dogma. This knowledge facilitates a deeper understanding of how underlying molecular and architectural changes alter cell behavior, development, and disease processes. The emerging microchip-based tools for single-cell omics analysis are enabling the evaluation of cellular omics with high throughput, improved sensitivity, and reduced cost. We review state-of-the-art microchip platforms for profiling genomics, epigenomics, transcriptomics, proteomics, metabolomics, and multi-omics at single-cell resolution. We also discuss the background of and challenges in the analysis of each molecular layer and integration of multiple levels of omics data, as well as how microchip-based methodologies benefit these fields. Additionally, we examine the advantages and limitations of these approaches. Looking forward, we describe additional challenges and future opportunities that will facilitate the improvement and broad adoption of single-cell omics in life science and medicine.

Epigenetic regulation of gene expression in cancer: techniques, resources and analysis

Cancer is a complex disease, driven by aberrant activity in numerous signaling pathways in even individual malignant cells. Epigenetic changes are critical mediators of these functional changes that drive and maintain the malignant phenotype. Changes in DNA methylation, histone acetylation and methylation, noncoding RNAs, posttranslational modifications are all epigenetic drivers in cancer, independent of changes in the DNA sequence. These epigenetic alterations were once thought to be crucial only for the malignant phenotype maintenance. Now, epigenetic alterations are also recognized as critical for disrupting essential pathways that protect the cells from uncontrolled growth, longer survival and establishment in distant sites from the original tissue. In this review, we focus on DNA methylation and chromatin structure in cancer. The precise functional role of these alterations is an area of active research using emerging high-throughput approaches and bioinformatics analysis tools. Therefore, this review also describes these high-throughput measurement technologies, public domain databases for high-throughput epigenetic data in tumors and model systems and bioinformatics algorithms for their analysis. Advances in bioinformatics data that combine these epigenetic data with genomics data are essential to infer the function of specific epigenetic alterations in cancer. These integrative algorithms are also a focus of this review. Future studies using these emerging technologies will elucidate how alterations in the cancer epigenome cooperate with genetic aberrations during tumor initiation and progression. This deeper understanding is essential to future studies with epigenetics biomarkers and precision medicine using emerging epigenetic therapies.

Single-Cell RNA Profiling of Glomerular Cells Shows Dynamic Changes in Experimental Diabetic Kidney Disease

BACKGROUND

Recent single-cell RNA sequencing (scRNA-seq) analyses have offered much insight into cell-specific gene expression profiles in normal kidneys. However, in diseased kidneys, understanding of changes in specific cells, particularly glomerular cells, remains limited.

METHODS

To elucidate the glomerular cell-specific gene expression changes in diabetic kidney disease, we performed scRNA-seq analysis of isolated glomerular cells from streptozotocin-induced diabetic endothelial nitric oxide synthase (eNOS)-deficient (eNOS^-/-) mice and control eNOS^-/- mice.

RESULTS

We identified five distinct cell populations, including glomerular endothelial cells, mesangial cells, podocytes, immune cells, and tubular cells. Using scRNA-seq analysis, we confirmed the expression of glomerular cell-specific markers and also identified several new potential markers of glomerular cells. The number of immune cells was significantly higher in diabetic glomeruli compared with control glomeruli, and further cluster analysis showed that these immune cells were predominantly macrophages. Analysis of differential gene expression in endothelial and mesangial cells of diabetic and control mice showed dynamic changes in the pattern of expressed genes, many of which are known to be involved in diabetic kidney disease. Moreover, gene expression analysis showed variable responses of individual cells to diabetic injury.

CONCLUSIONS

Our findings demonstrate the ability of scRNA-seq analysis in isolated glomerular cells from diabetic and control mice to reveal dynamic changes in gene expression in diabetic kidneys, with variable responses of individual cells. Such changes, which might not be apparent in bulk transcriptomic analysis of glomerular cells, may help identify important pathophysiologic factors contributing to the progression of diabetic kidney disease.

The Impact of Heterogeneity on Single-Cell Sequencing

The importance of diversity and cellular specialization is clear for many reasons, from population-level diversification, to improved resiliency to unforeseen stresses, to unique functions within metazoan organisms during development and differentiation. However, the level of cellular heterogeneity is just now becoming clear through the integration of genome-wide analyses and more cost effective Next Generation Sequencing (NGS). With easy access to single-cell NGS (scNGS), new opportunities exist to examine different levels of gene expression and somatic mutational heterogeneity, but these assays can generate yottabyte scale data. Here, we model the importance of heterogeneity for large-scale analysis of scNGS data, with a focus on the utilization in oncology and other diseases, providing a guide to aid in sample size and experimental design.

Genome and pan-genome analysis to classify emerging bacteria

Background

In the recent years, genomic and pan-genomic studies have become increasingly important. Culturomics allows to study human microbiota through the use of different culture conditions, coupled with a method of rapid identification by MALDI-TOF, or 16S rRNA. Bacterial taxonomy is undergoing many changes as a consequence. With the help of pan-genomic analyses, species can be redefined, and new species definitions generated.

Results

Genomics, coupled with culturomics, has led to the discovery of many novel bacterial species or genera, including Akkermansia muciniphila and Microvirga massiliensis. Using the genome to define species has been applied within the genus Klebsiella. A discontinuity or an abrupt break in the core/pan-genome ratio can uncover novel species.

Conclusions

Applying genomic and pan-genomic analyses to the reclassification of other bacterial species or genera will be important in the future of medical microbiology. The pan-genome is one of many new innovative tools in bacterial taxonomy.

Reviewers

This article was reviewed by William Martin, Eric Bapteste and James Mcinerney.

Open peer review

Reviewed by William Martin, Eric Bapteste and James Mcinerney.

Detecting Chromosome Instability in Cancer: Approaches to Resolve Cell-to-Cell Heterogeneity

Chromosome instability (CIN) is defined as an increased rate of chromosome gains and losses that manifests as cell-to-cell karyotypic heterogeneity and drives cancer initiation and evolution. Current research efforts are aimed at identifying the etiological origins of CIN, establishing its roles in cancer pathogenesis, understanding its implications for patient prognosis, and developing novel therapeutics that are capable of exploiting CIN. Thus, the ability to accurately identify and evaluate CIN is critical within both research and clinical settings. Here, we provide an overview of quantitative single cell approaches that evaluate and resolve cell-to-cell heterogeneity and CIN, and discuss considerations when selecting the most appropriate approach to suit both research and clinical contexts.

Emerging approaches and technologies in transplantation: the potential game changers

Newly emerging technologies are rapidly changing conventional approaches to organ transplantation. In the modern era, the key challenges to transplantation include (1) how to best individualize and possibly eliminate the need for life-long immunosuppression and (2) how to expand the donor pool suitable for human transplantation. This article aims to provide readers with an updated review of three new technologies that address these challenges. First, single-cell RNA sequencing technology is rapidly evolving and has recently been employed in settings related to transplantation. The new sequencing data indicate an unprecedented cellular heterogeneity within organ transplants, as well as exciting new molecular signatures involved in alloimmune responses. Second, sophisticated nanotechnology platforms provide a means of therapeutically delivering immune modulating reagents to promote transplant tolerance. Tolerogenic nanoparticles with regulatory molecules and donor antigens are capable of targeting host immune responses with tremendous precision, which, in some cases, results in donor-specific tolerance. Third, CRISPR/Cas9 gene editing technology has the potential to precisely remove immunogenic molecules while inserting desirable regulatory molecules. This technology is particularly useful in generating genetically modified pigs for xenotransplantation to solve the issue of the shortage of human organs. Collectively, these new technologies are positioning the transplant community for major breakthroughs that will significantly advance transplant medicine.

Microfluidic reactors for advancing the MS analysis of fast biological responses

The response of cells to physical or chemical stimuli is complex, unfolding on time-scales from seconds to days, with or without de novo protein synthesis, and involving signaling processes that are transient or sustained. By combining the technology of microfluidics that supports fast and precise execution of a variety of cell handling operations, with that of mass spectrometry detection that facilitates an accurate and complex characterization of the protein complement of cells, in this work, we developed a platform that supports (near) real-time sampling and proteome-level capturing of cellular responses to a perturbation such as treatment with mitogens. The geometric design of the chip supports three critical features: (a) capture of a sufficient number of cells to meet the detection limit requirements of mass spectrometry instrumentation, (b) fluid delivery for uniform stimulation of the resident cells, and (c) fast cell recovery, lysis and processing for accurate sampling of time-sensitive cellular responses to a stimulus. COMSOL simulations and microscopy were used to predict and evaluate the flow behavior inside the microfluidic device. Proteomic analysis of the cellular extracts generated by the chip experiments revealed that the identified proteins were representative of all cellular locations, exosomes, and major biological processes related to proliferation and signaling, demonstrating that the device holds promising potential for integration into complex lab-on-chip work-flows that address systems biology questions. The applicability of the chips to study time-sensitive cellular responses is discussed in terms of technological challenges and biological relevance.

A component overlapping attribute clustering (COAC) algorithm for single-cell RNA sequencing data analysis and potential pathobiological implications

Recent advances in next-generation sequencing and computational technologies have enabled routine analysis of large-scale single-cell ribonucleic acid sequencing (scRNA-seq) data. However, scRNA-seq technologies have suffered from several technical challenges, including low mean expression levels in most genes and higher frequencies of missing data than bulk population sequencing technologies. Identifying functional gene sets and their regulatory networks that link specific cell types to human diseases and therapeutics from scRNA-seq profiles are daunting tasks. In this study, we developed a Component Overlapping Attribute Clustering (COAC) algorithm to perform the localized (cell subpopulation) gene co-expression network analysis from large-scale scRNA-seq profiles. Gene subnetworks that represent specific gene co-expression patterns are inferred from the components of a decomposed matrix of scRNA-seq profiles. We showed that single-cell gene subnetworks identified by COAC from multiple time points within cell phases can be used for cell type identification with high accuracy (83%). In addition, COAC-inferred subnetworks from melanoma patients' scRNA-seq profiles are highly correlated with survival rate from The Cancer Genome Atlas (TCGA). Moreover, the localized gene subnetworks identified by COAC from individual patients' scRNA-seq data can be used as pharmacogenomics biomarkers to predict drug responses (The area under the receiver operating characteristic curves ranges from 0.728 to 0.783) in cancer cell lines from the Genomics of Drug Sensitivity in Cancer (GDSC) database. In summary, COAC offers a powerful tool to identify potential network-based diagnostic and pharmacogenomics biomarkers from large-scale scRNA-seq profiles. COAC is freely available at https://github.com/ChengF-Lab/COAC.

Carrier-Assisted Single-Tube Processing Approach for Targeted Proteomics Analysis of Low Numbers of Mammalian Cells

Heterogeneity in composition is inherent in all cell populations, even those containing a single cell type. Single-cell proteomics characterization of cell heterogeneity is currently achieved by antibody-based technologies, which are limited by the availability of high-quality antibodies. Herein we report a simple, easily implemented, mass spectrometry (MS)-based targeted proteomics approach, termed cLC-SRM (carrier-assisted liquid chromatography coupled to selected reaction monitoring), for reliable multiplexed quantification of proteins in low numbers of mammalian cells. We combine a new single-tube digestion protocol to process low numbers of cells with minimal loss together with sensitive LC-SRM for protein quantification. This single-tube protocol builds upon trifluoroethanol digestion and further minimizes sample losses by tube pretreatment and the addition of carrier proteins. We also optimized the denaturing temperature and trypsin concentration to significantly improve digestion efficiency. cLC-SRM was demonstrated to have sufficient sensitivity for reproducible detection of most epidermal growth factor receptor (EGFR) pathway proteins expressed at levels ≥30 000 and ≥3000 copies per cell for 10 and 100 mammalian cells, respectively. Thus, cLC-SRM enables reliable quantification of low to moderately abundant proteins in less than 100 cells and could be broadly useful for multiplexed quantification of important proteins in small subpopulations of cells or in size-limited clinical samples. Further improvements of this method could eventually enable targeted single-cell proteomics when combined with either SRM or other emerging ultrasensitive MS detection.

Exploiting single-cell RNA sequencing data to link alternative splicing and cancer heterogeneity: A computational approach

Cell heterogeneity studies using single-cell sequencing are gaining great significance in the era of personalized medicine. In particular, characterization of tumor heterogeneity is an emergent issue to improve clinical oncology, since both inter- and intra-tumor level heterogeneity influence the utility and application of molecular classifications through specific biomarkers. Majority of studies have exploited gene expression to discriminate cell types. However, to provide a more nuanced view of the underlying differences, isoform expression and alternative splicing events have to be analyzed in detail. In this study, we utilize publicly available single cell and bulk RNA sequencing datasets of breast cancer cells from primary tumors and immortalized cell lines. Breast cancer is very heterogeneous with well defined molecular subtypes and was therefore chosen for this study. RNA-seq data were explored in terms of genes, isoforms abundance and splicing events. The study was conducted from an average based approach (gene level expression) to detailed and deeper ones (isoforms abundance/splicing events) to perform a comparative analysis, and, thus, highlight the importance of the splicing machinery in defining the tumor heterogeneity. Moreover, here we demonstrate how the investigation of gene isoforms expression can help to identify the appropriate in vitro models. We furthermore extracted marker isoforms, and alternatively spliced genes between and within the different single cell populations to improve the classification of the breast cancer subtypes.

Selection and optimization of enzyme reporters for chemical cytometry

Chemical cytometry, sensitive analytical measurements of single cells, reveals inherent heterogeneity of cells within a population which is masked or averaged out when using bulk analysis techniques. A particular challenge of chemical cytometry is the development of a suitable reporter or probe for the desired measurement. These reporters must be sufficiently specific for measuring the desired process; possess a lifetime long enough to accomplish the measurement; and have the ability to be loaded into single cells. This chapter details our approach to rationally design and improve peptide substrates as reporters of enzyme activity utilizing chemical cytometry. This method details the iterative approach used to design, characterize, and identify a peptidase-resistant peptide reporter which acts as a kinase substrate within intact cells. Small-scale, rationally designed peptide libraries are generated to rapidly and economically screen candidate reporter peptides for substrate suitability and peptidase resistance. Also detailed are strategies to characterize and validate the designed reporters by determining kinetic parameters, intracellular substrate specificity, resistance to degradation by intracellular peptidases, and behavior within lysates and intact cells.

An Informative Approach to Single-Cell Sequencing Analysis

Recent advances in sequencing technologies enable us to obtain genome, epigenome and transcriptome data in individual cells. In this review, we describe various platforms for single-cell sequencing analysis across multiple layers. We mainly introduce an automated single-cell RNA-seq platform, the Chromium Single Cell 3' RNA-seq system, and its technical features and compare it with other single-cell RNA-seq systems. We also describe computational methods for analyzing large, complex single-cell datasets. Due to the insufficient depth of single-cell RNA-seq data, resulting in a critical lack of transcriptome information for low-expressed genes, it is occasionally difficult to interpret the data as is. To overcome the analytical problems for such sparse datasets, there are many bioinformatics reports that provide informative approaches, including imputation, correction of batch effects, dimensional reduction and clustering.

MISC: missing imputation for single-cell RNA sequencing data

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) technology provides an effective way to study cell heterogeneity. However, due to the low capture efficiency and stochastic gene expression, scRNA-seq data often contains a high percentage of missing values. It has been showed that the missing rate can reach approximately 30% even after noise reduction. To accurately recover missing values in scRNA-seq data, we need to know where the missing data is; how much data is missing; and what are the values of these data.

METHODS

To solve these three problems, we propose a novel model with a hybrid machine learning method, namely, missing imputation for single-cell RNA-seq (MISC). To solve the first problem, we transformed it to a binary classification problem on the RNA-seq expression matrix. Then, for the second problem, we searched for the intersection of the classification results, zero-inflated model and false negative model results. Finally, we used the regression model to recover the data in the missing elements.

RESULTS

We compared the raw data without imputation, the mean-smooth neighbor cell trajectory, MISC on chronic myeloid leukemia data (CML), the primary somatosensory cortex and the hippocampal CA1 region of mouse brain cells. On the CML data, MISC discovered a trajectory branch from the CP-CML to the BC-CML, which provides direct evidence of evolution from CP to BC stem cells. On the mouse brain data, MISC clearly divides the pyramidal CA1 into different branches, and it is direct evidence of pyramidal CA1 in the subpopulations. In the meantime, with MISC, the oligodendrocyte cells became an independent group with an apparent boundary.

CONCLUSIONS

Our results showed that the MISC model improved the cell type classification and could be instrumental to study cellular heterogeneity. Overall, MISC is a robust missing data imputation model for single-cell RNA-seq data.

Machine learning based classification of cells into chronological stages using single-cell transcriptomics

Age-associated deterioration of cellular physiology leads to pathological conditions. The ability to detect premature aging could provide a window for preventive therapies against age-related diseases. However, the techniques for determining cellular age are limited, as they rely on a limited set of histological markers and lack predictive power. Here, we implement GERAS (GEnetic Reference for Age of Single-cell), a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes. GERAS displays greater than 90% accuracy in classifying the chronological stage of zebrafish and human pancreatic cells. The framework demonstrates robustness against biological and technical noise, as evaluated by its performance on independent samplings of single-cells. Additionally, GERAS determines the impact of differences in calorie intake and BMI on the aging of zebrafish and human pancreatic cells, respectively. We further harness the classification ability of GERAS to identify molecular factors that are potentially associated with the aging of beta-cells. We show that one of these factors, junba, is necessary to maintain the proliferative state of juvenile beta-cells. Our results showcase the applicability of a machine learning framework to classify the chronological stage of heterogeneous cell populations, while enabling detection of candidate genes associated with aging.

Tumour growth and immune evasion as targets for a new strategy in advanced cancer

It has become clearer that advanced cancer, especially advanced breast cancer, is an entirely displayed pathological system that is much more complex than previously considered. However, the direct relationship between tumour growth and immune evasion can represent a general rule governing the pathological cancer system from the initial cancer cells to when the system is entirely displayed. Accordingly, a refined pathobiological model and a novel therapeutic strategy are proposed. The novel therapeutic strategy is based on therapeutically induced conditions (undetectable tumour burden and/or a prolonged tumour ‘resting state’), which enable an efficacious immune response in advanced breast and other types of solid cancers.

SwissMTB: establishing comprehensive molecular cancer diagnostics in Swiss clinics

Background

Molecular precision oncology is an emerging practice to improve cancer therapy by decreasing the risk of choosing treatments that lack efficacy or cause adverse events. However, the challenges of integrating molecular profiling into routine clinical care are manifold. From a computational perspective these include the importance of a short analysis turnaround time, the interpretation of complex drug-gene and gene-gene interactions, and the necessity of standardized high-quality workflows. In addition, difficulties faced when integrating molecular diagnostics into clinical practice are ethical concerns, legal requirements, and limited availability of treatment options beyond standard of care as well as the overall lack of awareness of their existence.

Methods

To the best of our knowledge, we are the first group in Switzerland that established a workflow for personalized diagnostics based on comprehensive high-throughput sequencing of tumors at the clinic. Our workflow, named SwissMTB (Swiss Molecular Tumor Board), links genetic tumor alterations and gene expression to therapeutic options and clinical trial opportunities. The resulting treatment recommendations are summarized in a clinical report and discussed in a molecular tumor board at the clinic to support therapy decisions.

Results

Here we present results from an observational pilot study including 22 late-stage cancer patients. In this study we were able to identify actionable variants and corresponding therapies for 19 patients. Half of the patients were analyzed retrospectively. In two patients we identified resistance-associated variants explaining lack of therapy response. For five out of eleven patients analyzed before treatment the SwissMTB diagnostic influenced treatment decision.

Conclusions

SwissMTB enables the analysis and clinical interpretation of large numbers of potentially actionable molecular targets. Thus, our workflow paves the way towards a more frequent use of comprehensive molecular diagnostics in Swiss hospitals.

Electronic supplementary material

The online version of this article (10.1186/s12911-018-0680-0) contains supplementary material, which is available to authorized users.

PHLI-seq: constructing and visualizing cancer genomic maps in 3D by phenotype-based high-throughput laser-aided isolation and sequencing

Spatial mapping of genomic data to tissue context in a high-throughput and high-resolution manner has been challenging due to technical limitations. Here, we describe PHLI-seq, a novel approach that enables high-throughput isolation and genome-wide sequence analysis of single cells or small numbers of cells to construct genomic maps within cancer tissue in relation to the images or phenotypes of the cells. By applying PHLI-seq, we reveal the heterogeneity of breast cancer tissues at a high resolution and map the genomic landscape of the cells to their corresponding spatial locations and phenotypes in the 3D tumor mass.

Accurate and sensitive single-cell-level detection of copy number variations by micro-channel multiple displacement amplification (μcMDA)

Whole genome amplification (WGA) has laid the foundation for investigating complex genomic alteration with single-cell or even single-molecule resolution. Coupled with sequencing-based copy number variation (CNV) analysis, it promotes understanding of the nature of commonly existing genetic heterogeneity by constructing the sequencing profiles for every single cell. However, prevailing methods only provide insights into limited aspects due to their intrinsic technical challenges. Their output data, as a result, fails to render comprehensive information (which is) concerned. Here, we describe the CNV detection analysis based on micro-channel multiple displacement amplification (μcMDA), a protocol able to provide optimized amplification uniformity while inheriting the advantages of MDA chemistry. We demonstrate the analysis of both the normal diploid YH-1 cell line and the aneuploid K562 cancer cell line. In the detection of simulated CNVs ranging from 300 kb to 2 Mb, μcMDA can respectively increase the detection rates of copy number loss and gain by 28.8% and 40.2% on average, using only 0.2× sequencing data. When detecting the inherent CNVs in tumor cells, the resolution of CNV recognition can be improved to 250 kb. Starting from either superabundant template copies or minute single-cell-level input, this easily accessible approach is capable of providing quantitatively reliable coverage as well as more robust GC-content regression for CNV detection.

Revealing cellular and molecular complexity of the central nervous system using single cell sequencing

The mammalian central nervous system (CNS) is one of the most complex systems, with thousands of cell types and subtypes with distinct and unique morphology and gene expression profiles. Based on classic histological methods and conventional cellular and molecular approaches, single cell sequencing is becoming a powerful tool to uncover the complexity of the CNS. In this review, we summarize the principle of single cell sequencing and highlight its use for studying the development of neural stem cells, neural progenitors, and distinct neurons. By revealing transcriptomes in each individual cell using single cell sequencing, we are now able to dissect the cellular heterogeneity of a hundred billion cells in the CNS and comprehensively investigate mechanisms of brain development and function at the cellular and molecular levels.

Genetics and biology of prostate cancer

Despite the high long-term survival in localized prostate cancer, metastatic prostate cancer remains largely incurable even after intensive multimodal therapy. The lethality of advanced disease is driven by the lack of therapeutic regimens capable of generating durable responses in the setting of extreme tumor heterogeneity on the genetic and cell biological levels. Here, we review available prostate cancer model systems, the prostate cancer genome atlas, cellular and functional heterogeneity in the tumor microenvironment, tumor-intrinsic and tumor-extrinsic mechanisms underlying therapeutic resistance, and technological advances focused on disease detection and management. These advances, along with an improved understanding of the adaptive responses to conventional cancer therapies, anti-androgen therapy, and immunotherapy, are catalyzing development of more effective therapeutic strategies for advanced disease. In particular, knowledge of the heterotypic interactions between and coevolution of cancer and host cells in the tumor microenvironment has illuminated novel therapeutic combinations with a strong potential for more durable therapeutic responses and eventual cures for advanced disease. Improved disease management will also benefit from artificial intelligence-based expert decision support systems for proper standard of care, prognostic determinant biomarkers to minimize overtreatment of localized disease, and new standards of care accelerated by next-generation adaptive clinical trials.

Facile carrier-assisted targeted mass spectrometric approach for proteomic analysis of low numbers of mammalian cells

There is an unmet technical challenge for mass spectrometry (MS)-based proteomic analysis of single mammalian cells. Quantitative proteomic analysis of single cells has been previously achieved by antibody-based immunoassays but is limited by the availability of high-quality antibodies. Herein we report a facile targeted MS-based proteomics method, termed cPRISM-SRM (carrier-assisted high-pressure, high-resolution separations with intelligent selection and multiplexing coupled to selected reaction monitoring), for reliable analysis of low numbers of mammalian cells. The method capitalizes on using “carrier protein” to assist processing of low numbers of cells with minimal loss, high-resolution PRISM separation for target peptide enrichment, and sensitive SRM for protein quantification. We have demonstrated that cPRISM-SRM has sufficient sensitivity to quantify proteins expressed at ≥200,000 copies per cell at the single-cell level and ≥3000 copies per cell in 100 mammalian cells. We envision that with further improvement cPRISM-SRM has the potential to move toward targeted MS-based single-cell proteomics.

Tujin Shi et al. report a mass spectrometry-based proteomics approach, cPRISM-SRM, that allows for accurate quantification of proteins in small numbers of mammalian cells through the use of a carrier protein to prevent sample loss. The sensitivity of cPRISM-SRM allows for measurement of the 2500 most abundant proteins in a human cell.

Pipette Petri Dish Single-Cell Trapping (PP-SCT) in Microfluidic Platforms: A Passive Hydrodynamic Technique

Microfluidics-based biochips play a vital role in single-cell research applications. Handling and positioning of single cells at the microscale level are an essential need for various applications, including genomics, proteomics, secretomics, and lysis-analysis. In this article, the pipette Petri dish single-cell trapping (PP-SCT) technique is demonstrated. PP-SCT is a simple and cost-effective technique with ease of implementation for single cell analysis applications. In this paper a wide operation at different fluid flow rates of the novel PP-SCT technique is demonstrated. The effects of the microfluidic channel shape (straight, branched, and serpent) on the efficiency of single-cell trapping are studied. This article exhibited passive microfluidic-based biochips capable of vertical cell trapping with the hexagonally-positioned array of microwells. Microwells were 35 μm in diameter, a size sufficient to allow the attachment of captured cells for short-term study. Single-cell capture (SCC) capabilities of the microfluidic-biochips were found to be improving from the straight channel, branched channel, and serpent channel, accordingly. Multiple cell capture (MCC) was on the order of decreasing from the straight channel, branch channel, and serpent channel. Among the three designs investigated, the serpent channel biochip offers high SCC percentage with reduced MCC and NC (no capture) percentage. SCC was around 52%, 42%, and 35% for the serpent, branched, and straight channel biochips, respectively, for the tilt angle, θ values were between 10–15°. Human lung cancer cells (A549) were used for characterization. Using the PP-SCT technique, flow rate variations can be precisely achieved with a flow velocity range of 0.25–4 m/s (fluid channel of 2 mm width and 100 µm height). The upper dish (UD) can be used for low flow rate applications and the lower dish (LD) for high flow rate applications. Passive single-cell analysis applications will be facilitated using this method.

Single-cell transcriptomics reveal the dynamic of haematopoietic stem cell production in the aorta

Haematopoietic stem cells (HSCs) are generated from haemogenic endothelial (HE) cells via the formation of intra-aortic haematopoietic clusters (IAHCs) in vertebrate embryos. The molecular events controlling endothelial specification, endothelial-to-haematopoietic transition (EHT) and IAHC formation, as it occurs in vivo inside the aorta, are still poorly understood. To gain insight in these processes, we performed single-cell RNA-sequencing of non-HE cells, HE cells, cells undergoing EHT, IAHC cells, and whole IAHCs isolated from mouse embryo aortas. Our analysis identified the genes and transcription factor networks activated during the endothelial-to-haematopoietic switch and IAHC cell maturation toward an HSC fate. Our study provides an unprecedented complete resource to study in depth HSC generation in vivo. It will pave the way for improving HSC production in vitro to address the growing need for tailor-made HSCs to treat patients with blood-related disorders.

Single cell transcriptome analysis of MCF-7 reveals consistently and inconsistently expressed gene groups each associated with distinct cellular localization and functions

Single cell transcriptome (SCT) analysis provides superior resolution to illustrate tumor cell heterogeneity for clinical implications. We characterized four SCTs of MCF-7 using 143 housekeeping genes (HKGs) as control, of which lactate dehydrogenase B (LDHB) expression is silenced. These SCT libraries mapped to 11,423, 11,486, 10,380, and 11,306 RefSeq genes (UCSC), respectively. High consistency in HKG expression levels across all four SCTs, along with transcriptional silencing of LDHB, was observed, suggesting a high sensitivity and reproducibility of the SCT analysis. Cross-library comparison on expression levels by scatter plotting revealed a linear correlation and an 83–94% overlap in transcript isoforms and expressed genes were also observed. To gain insight of transcriptional diversity among the SCTs, expressed genes were split into consistently expressed (CE) (expressed in all SCTs) and inconsistently expressed (IE) (expressed in some but not all SCTs) genes for further characterization, along with the 142 expressed HKGs as a reference. Distinct transcriptional strengths were found among these groups, with averages of 1,612.0, 88.0 and 1.2 FPKM for HKGs, CE and IE, respectively. Comparison between CE and IE groups further indicated that expressions of CE genes vary more significantly than that of IE genes. Gene Ontology analysis indicated that proteins encoded by CE genes are mainly involved in fundamental intracellular activities, while proteins encoded by IE genes are mainly for extracellular activities, especially acting as receptors or ion channels. The diversified gene expressions, especially for those encoded by IE genes, may contribute to cancer drug resistance.