Advances and applications of single-cell sequencing technologies

A Single-Cell Biochemistry Approach Reveals PAR Complex Dynamics during Cell Polarization

Regulated protein-protein interactions are critical for cell signaling, differentiation, and development. For the study of dynamic regulation of protein interactions in vivo, there is a need for techniques that can yield time-resolved information and probe multiple protein binding partners simultaneously, using small amounts of starting material. Here we describe a single-cell protein interaction assay. Single-cell lysates are generated at defined time points and analyzed using single-molecule pull-down, yielding information about dynamic protein complex regulation in vivo. We established the utility of this approach by studying PAR polarity proteins, which mediate polarization of many animal cell types. We uncovered striking regulation of PAR complex composition and stoichiometry during Caenorhabditis elegans zygote polarization, which takes place in less than 20 min. PAR complex dynamics are linked to the cell cycle by Polo-like kinase 1 and govern the movement of PAR proteins to establish polarity. Our results demonstrate an approach to study dynamic biochemical events in vivo.

Optimization and Modeling of Quadrupole Orbitrap Parameters for Sensitive Analysis toward Single-Cell Proteomics

Single-cell proteomics represents a field of extremely sensitive proteomic analysis, owing to the minute amount of yet complex proteins in a single cell. Without amplification potential as of nucleic acids, single-cell mass spectrometry (MS) analysis demands special instrumentation running with optimized parameters to maximize the sensitivity and throughput for comprehensive proteomic discovery. To facilitate such analysis, we here investigated two factors critical to peptide sequencing and protein detection in shotgun proteomics, i.e. precursor ion isolation window (IW) and maximum precursor ion injection time (ITmax), on an ultrahigh-field quadrupole Orbitrap (Q-Exactive HF). Counterintuitive to the frequently used proteomic parameters for bulk samples (>100 ng), our experimental data and subsequent modeling suggested a universally optimal IW of 4.0 Th for sample quantity ranging from 100 ng to 1 ng, and a sample-quantity dependent ITmax of more than 250 ms for 1-ng samples. Compared with the benchmark condition of IW = 2.0 Th and ITmax = 50 ms, our optimization generated up to 300% increase to the detected protein groups for 1-ng samples. The additionally identified proteins allowed deeper penetration of proteome for better revealing crucial cellular functions such as signaling and cell adhesion. We hope this effort can prompt single-cell and trace proteomic analysis and enable a rational selection of MS parameters.

Challenges and Opportunities in Studying the Epidemiology of Ovarian Cancer Subtypes

PURPOSE OF REVIEW

Only recently has it become clear that epithelial ovarian cancer (EOC) is comprised of such distinct histotypes--with different cells of origin, morphology, molecular features, epidemiologic factors, clinical features, and survival patterns-that they can be thought of as different diseases sharing an anatomical location. Herein, we review opportunities and challenges in studying EOC heterogeneity.

RECENT FINDINGS

The 2014 World Health Organization diagnostic guidelines incorporate accumulated evidence that high- and low-grade serous tumors have different underlying pathogenesis, and that, on the basis of shared molecular features, most high grade tumors, including some previously classified as endometrioid, are now considered to be high-grade serous. At the same time, several studies have reported that high-grade serous EOC, which is the most common histotype, is itself made up of reproducible subtypes discernable by gene expression patterns.

SUMMARY

These major advances in understanding set the stage for a new era of research on EOC risk and clinical outcomes with the potential to reduce morbidity and mortality. We highlight the need for multidisciplinary studies with pathology review using the current guidelines, further molecular characterization of the histotypes and subtypes, inclusion of women of diverse racial/ethnic and socioeconomic backgrounds, and updated epidemiologic and clinical data relevant to current generations of women at risk of EOC.

High-dimension single-cell analysis applied to cancer

High-dimension single-cell technology is transforming our ability to study and understand cancer. Numerous studies and reviews have reported advances in technology development. The biological insights gleaned from single-cell technology about cancer biology are less reviewed. Here we focus on research studies that illustrate novel aspects of cancer biology that bulk analysis could not achieve, and discuss the fresh insights gained from the application of single-cell technology across basic and clinical cancer studies.

Unravelling biology and shifting paradigms in cancer with single-cell sequencing

The fundamental operative unit of a cancer is the genetically and epigenetically innovative single cell. Whether proliferating or quiescent, in the primary tumour mass or disseminated elsewhere, single cells govern the parameters that dictate all facets of the biology of cancer. Thus, single-cell analyses provide the ultimate level of resolution in our quest for a fundamental understanding of this disease. Historically, this quest has been hampered by technological shortcomings. In this Opinion article, we argue that the rapidly evolving field of single-cell sequencing has unshackled the cancer research community of these shortcomings. From furthering an elemental understanding of intra-tumoural genetic heterogeneity and cancer genome evolution to illuminating the governing principles of disease relapse and metastasis, we posit that single-cell sequencing promises to unravel the biology of all facets of this disease.

The Aging Epigenome

During aging, the mechanisms that normally maintain health and stress resistance strikingly decline, resulting in decrepitude, frailty, and ultimately death. Exactly when and how this decline occurs is unknown. Changes in transcriptional networks and chromatin state lie at the heart of age-dependent decline. These epigenomic changes are not only observed during aging but also profoundly affect cellular function and stress resistance, thereby contributing to the progression of aging. We propose that the dysregulation of transcriptional and chromatin networks is a crucial component of aging. Understanding age-dependent epigenomic changes will yield key insights into how aging begins and progresses and should lead to the development of new therapeutics that delay or even reverse aging and age-related diseases.

Nanogrid single-nucleus RNA sequencing reveals phenotypic diversity in breast cancer

Single cell RNA sequencing has emerged as a powerful tool for resolving transcriptional diversity in tumors, but is limited by throughput, cost and the ability to process archival frozen tissue samples. Here we develop a high-throughput 3' single-nucleus RNA sequencing approach that combines nanogrid technology, automated imaging, and cell selection to sequence up to ~1800 single nuclei in parallel. We compare the transcriptomes of 485 single nuclei to 424 single cells in a breast cancer cell line, which shows a high concordance (93.34%) in gene levels and abundance. We also analyze 416 nuclei from a frozen breast tumor sample and 380 nuclei from normal breast tissue. These data reveal heterogeneity in cancer cell phenotypes, including angiogenesis, proliferation, and stemness, and a minor subpopulation (19%) with many overexpressed cancer genes. Our studies demonstrate the utility of nanogrid single-nucleus RNA sequencing for studying the transcriptional programs of tumor nuclei in frozen archival tissue samples.Single cell RNA sequencing is a powerful tool for understanding cellular diversity but is limited by cost, throughput and sample preparation. Here the authors use nanogrid technology with integrated imaging to sequence thousands of cancer nuclei in parallel from fresh or frozen tissue.

Single-cell DNA sequencing reveals a late-dissemination model in metastatic colorectal cancer

Metastasis is a complex biological process that has been difficult to delineate in human colorectal cancer (CRC) patients. A major obstacle in understanding metastatic lineages is the extensive intra-tumor heterogeneity at the primary and metastatic tumor sites. To address this problem, we developed a highly multiplexed single-cell DNA sequencing approach to trace the metastatic lineages of two CRC patients with matched liver metastases. Single-cell copy number or mutational profiling was performed, in addition to bulk exome and targeted deep-sequencing. In the first patient, we observed monoclonal seeding, in which a single clone evolved a large number of mutations prior to migrating to the liver to establish the metastatic tumor. In the second patient, we observed polyclonal seeding, in which two independent clones seeded the metastatic liver tumor after having diverged at different time points from the primary tumor lineage. The single-cell data also revealed an unexpected independent tumor lineage that did not metastasize, and early progenitor clones with the "first hit" mutation in APC that subsequently gave rise to both the primary and metastatic tumors. Collectively, these data reveal a late-dissemination model of metastasis in two CRC patients and provide an unprecedented view of metastasis at single-cell genomic resolution.

Current Progresses of Single Cell DNA Sequencing in Breast Cancer Research

Breast cancers display striking genetic and phenotypic diversities. To date, several hypotheses are raised to explain and understand the heterogeneity, including theories for cancer stem cell (CSC) and clonal evolution. According to the CSC theory, the most tumorigenic cells, while maintaining themselves through symmetric division, divide asymmetrically to generate non-CSCs with less tumorigenic and metastatic potential, although they can also dedifferentiate back to CSCs. Clonal evolution theory recapitulates that a tumor initially arises from a single cell, which then undergoes clonal expansion to a population of cancer cells. During tumorigenesis and evolution process, cancer cells undergo different degrees of genetic instability and consequently obtain varied genetic aberrations. Yet the heterogeneity in breast cancers is very complex, poorly understood and subjected to further investigation. In recent years, single cell sequencing (SCS) technology developed rapidly, providing a powerful new way to better understand the heterogeneity, which may lay foundations to some new strategies for breast cancer therapies. In this review, we will summarize development of SCS technologies and recent advances of SCS in breast cancer.

Massively parallel whole genome amplification for single-cell sequencing using droplet microfluidics

Massively parallel single-cell genome sequencing is required to further understand genetic diversities in complex biological systems. Whole genome amplification (WGA) is the first step for single-cell sequencing, but its throughput and accuracy are insufficient in conventional reaction platforms. Here, we introduce single droplet multiple displacement amplification (sd-MDA), a method that enables massively parallel amplification of single cell genomes while maintaining sequence accuracy and specificity. Tens of thousands of single cells are compartmentalized in millions of picoliter droplets and then subjected to lysis and WGA by passive droplet fusion in microfluidic channels. Because single cells are isolated in compartments, their genomes are amplified to saturation without contamination. This enables the high-throughput acquisition of contamination-free and cell specific sequence reads from single cells (21,000 single-cells/h), resulting in enhancement of the sequence data quality compared to conventional methods. This method allowed WGA of both single bacterial cells and human cancer cells. The obtained sequencing coverage rivals those of conventional techniques with superior sequence quality. In addition, we also demonstrate de novo assembly of uncultured soil bacteria and obtain draft genomes from single cell sequencing. This sd-MDA is promising for flexible and scalable use in single-cell sequencing.

The comparison of the performance of four whole genome amplification kits on ion proton platform in copy number variation detection

With the development and clinical application of genomics, more and more concern is focused on single-cell sequencing. In the process of single-cell sequencing, whole genome amplification is a key step to enrich sample DNA. Previous studies have compared the performance of different whole genome amplification (WGA) strategies on Illumina sequencing platforms, but there is no related research aimed at Ion Proton platform, which is also a popular next-generation sequencing platform. Here by amplifying cells from six cell lines with different karyotypes, we estimated the data features of four common commercial WGA kits (PicoPLEX WGA Kit, GenomePlex Single Cell Whole Genome Amplification Kit, MALBAC Single Cell Whole Genome Amplification Kit, and REPLI-g Single Cell Kit), including median absolute pairwise difference, uniformity, reproducibility, and fidelity, and examined their performance of copy number variation detection. The results showed that both MALBAC and PicoPLEX could yield high-quality data and had high reproducibility and fidelity; and as for uniformity, PicoPLEX was slightly superior to MALBAC.

New insights into human primordial germ cells and early embryonic development from single-cell analysis

Human preimplantation developmental studies are difficult to accomplish due to associated ethical and moral issues. Preimplantation cells are rare and exist only in transient cell states. From a single cell, it is very challenging to analyse the origination of the heterogeneity and complexity inherent to the human body. However, recent advances in single-cell technology and data analysis have provided new insights into the process of early human development and germ cell specification. In this Review, we examine the latest single-cell datasets of human preimplantation embryos and germ cell development, compare them to bulk cell analyses, and interpret their biological implications.

Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells

Single-cell epigenome sequencing techniques have recently been developed. However, the combination of different layers of epigenome sequencing in an individual cell has not yet been achieved. Here, we developed a single-cell multi-omics sequencing technology (single-cell COOL-seq) that can analyze the chromatin state/nucleosome positioning, DNA methylation, copy number variation and ploidy simultaneously from the same individual mammalian cell. We used this method to analyze the reprogramming of the chromatin state and DNA methylation in mouse preimplantation embryos. We found that within < 12 h of fertilization, each individual cell undergoes global genome demethylation together with the rapid and global reprogramming of both maternal and paternal genomes to a highly opened chromatin state. This was followed by decreased openness after the late zygote stage. Furthermore, from the late zygote to the 4-cell stage, the residual DNA methylation is preferentially preserved on intergenic regions of the paternal alleles and intragenic regions of maternal alleles in each individual blastomere. However, chromatin accessibility is similar between paternal and maternal alleles in each individual cell from the late zygote to the blastocyst stage. The binding motifs of several pluripotency regulators are enriched at distal nucleosome depleted regions from as early as the 2-cell stage. This indicates that the cis-regulatory elements of such target genes have been primed to an open state from the 2-cell stage onward, long before pluripotency is eventually established in the ICM of the blastocyst. Genes may be classified into homogeneously open, homogeneously closed and divergent states based on the chromatin accessibility of their promoter regions among individual cells. This can be traced to step-wise transitions during preimplantation development. Our study offers the first single-cell and parental allele-specific analysis of the genome-scale chromatin state and DNA methylation dynamics at single-base resolution in early mouse embryos and provides new insights into the heterogeneous yet highly ordered features of epigenomic reprogramming during this process.

Somatosensory Neuron Typing with High-Coverage Single-Cell RNA Sequencing and Functional Analysis

Different physical and chemical stimuli are detected by the peripheral sensory receptors of dorsal root ganglion (DRG) neurons, and the generated inputs are transmitted via afferent fibers into the central nervous system. The gene expression profiles of DRG neurons contribute to the generation, transmission, and regulation of various somatosensory signals. Recently, the single-cell transcriptomes, cell types, and functional annotations of somatosensory neurons have been studied. In this review, we introduce our classification of DRG neurons based on single-cell RNA-sequencing and functional analyses, and discuss the technical approaches. Moreover, studies on the molecular and cellular mechanisms underlying somatic sensations are discussed.

Single-Cell Transcriptional Analysis

Despite being a relatively recent technological development, single-cell transcriptional analysis through high-throughput sequencing has already been used in hundreds of fruitful studies to make exciting new biological discoveries that would otherwise be challenging or even impossible. Consequently, this has fueled a virtuous cycle of even greater interest in the field and compelled development of further improved technical methodologies and approaches. Thanks to the combined efforts of the research community, including the fields of biochemistry and molecular biology, technology and instrumentation, data science, computational biology, and bioinformatics, the single-cell RNA-sequencing field is advancing at a pace that is both astounding and unprecedented. In this review, we provide a broad introduction to this revolutionary technology by presenting the state-of-the-art in sample preparation methodologies, technology platforms, and computational analysis methods, while highlighting the key considerations for designing, executing, and interpreting a study using single-cell RNA sequencing.

Revealing the vectors of cellular identity with single-cell genomics

Single-cell genomics has now made it possible to create a comprehensive atlas of human cells. At the same time, it has reopened definitions of a cell's identity and of the ways in which identity is regulated by the cell's molecular circuitry. Emerging computational analysis methods, especially in single-cell RNA sequencing (scRNA-seq), have already begun to reveal, in a data-driven way, the diverse simultaneous facets of a cell's identity, from discrete cell types to continuous dynamic transitions and spatial locations. These developments will eventually allow a cell to be represented as a superposition of 'basis vectors', each determining a different (but possibly dependent) aspect of cellular organization and function. However, computational methods must also overcome considerable challenges-from handling technical noise and data scale to forming new abstractions of biology. As the scale of single-cell experiments continues to increase, new computational approaches will be essential for constructing and characterizing a reference map of cell identities.

Single-cell transcriptomics uncovers distinct molecular signatures of stem cells in chronic myeloid leukemia

Recent advances in single-cell transcriptomics are ideally placed to unravel intratumoral heterogeneity and selective resistance of cancer stem cell (SC) subpopulations to molecularly targeted cancer therapies. However, current single-cell RNA-sequencing approaches lack the sensitivity required to reliably detect somatic mutations. We developed a method that combines high-sensitivity mutation detection with whole-transcriptome analysis of the same single cell. We applied this technique to analyze more than 2,000 SCs from patients with chronic myeloid leukemia (CML) throughout the disease course, revealing heterogeneity of CML-SCs, including the identification of a subgroup of CML-SCs with a distinct molecular signature that selectively persisted during prolonged therapy. Analysis of nonleukemic SCs from patients with CML also provided new insights into cell-extrinsic disruption of hematopoiesis in CML associated with clinical outcome. Furthermore, we used this single-cell approach to identify a blast-crisis-specific SC population, which was also present in a subclone of CML-SCs during the chronic phase in a patient who subsequently developed blast crisis. This approach, which might be broadly applied to any malignancy, illustrates how single-cell analysis can identify subpopulations of therapy-resistant SCs that are not apparent through cell-population analysis.

Ancient plant DNA in lake sediments

Contents 924 I. 925 II. 925 III. 927 IV. 929 V. 930 VI. 930 VII. 931 VIII. 933 IX. 935 X. 936 XI. 938 938 References 938 SUMMARY: Recent advances in sequencing technologies now permit the analyses of plant DNA from fossil samples (ancient plant DNA, plant aDNA), and thus enable the molecular reconstruction of palaeofloras. Hitherto, ancient frozen soils have proved excellent in preserving DNA molecules, and have thus been the most commonly used source of plant aDNA. However, DNA from soil mainly represents taxa growing a few metres from the sampling point. Lakes have larger catchment areas and recent studies have suggested that plant aDNA from lake sediments is a more powerful tool for palaeofloristic reconstruction. Furthermore, lakes can be found globally in nearly all environments, and are therefore not limited to perennially frozen areas. Here, we review the latest approaches and methods for the study of plant aDNA from lake sediments and discuss the progress made up to the present. We argue that aDNA analyses add new and additional perspectives for the study of ancient plant populations and, in time, will provide higher taxonomic resolution and more precise estimation of abundance. Despite this, key questions and challenges remain for such plant aDNA studies. Finally, we provide guidelines on technical issues, including lake selection, and we suggest directions for future research on plant aDNA studies in lake sediments.

Single-Cell-Based Platform for Copy Number Variation Profiling through Digital Counting of Amplified Genomic DNA Fragments

We develop a novel single-cell-based platform through digital counting of amplified genomic DNA fragments, named multifraction amplification (mfA), to detect the copy number variations (CNVs) in a single cell. Amplification is required to acquire genomic information from a single cell, while introducing unavoidable bias. Unlike prevalent methods that directly infer CNV profiles from the pattern of sequencing depth, our mfA platform denatures and separates the DNA molecules from a single cell into multiple fractions of a reaction mix before amplification. By examining the sequencing result of each fraction for a specific fragment and applying a segment-merge maximum likelihood algorithm to the calculation of copy number, we digitize the sequencing-depth-based CNV identification and thus provide a method that is less sensitive to the amplification bias. In this paper, we demonstrate a mfA platform through multiple displacement amplification (MDA) chemistry. When performing the mfA platform, the noise of MDA is reduced; therefore, the resolution of single-cell CNV identification can be improved to 100 kb. We can also determine the genomic region free of allelic drop-out with mfA platform, which is impossible for conventional single-cell amplification methods.

Two Components of Aversive Memory in Drosophila, Anesthesia-Sensitive and Anesthesia-Resistant Memory, Require Distinct Domains Within the Rgk1 Small GTPase

Multiple components have been identified that exhibit different stabilities for aversive olfactory memory in Drosophila These components have been defined by behavioral and genetic studies and genes specifically required for a specific component have also been identified. Intermediate-term memory generated after single cycle conditioning is divided into anesthesia-sensitive memory (ASM) and anesthesia-resistant memory (ARM), with the latter being more stable. We determined that the ASM and ARM pathways converged on the Rgk1 small GTPase and that the N-terminal domain-deleted Rgk1 was sufficient for ASM formation, whereas the full-length form was required for ARM formation. Rgk1 is specifically accumulated at the synaptic site of the Kenyon cells (KCs), the intrinsic neurons of the mushroom bodies, which play a pivotal role in olfactory memory formation. A higher than normal Rgk1 level enhanced memory retention, which is consistent with the result that Rgk1 suppressed Rac-dependent memory decay; these findings suggest that rgk1 bolsters ASM via the suppression of forgetting. We propose that Rgk1 plays a pivotal role in the regulation of memory stabilization by serving as a molecular node that resides at KC synapses, where the ASM and ARM pathway may interact.SIGNIFICANCE STATEMENT Memory consists of multiple components. Drosophila olfactory memory serves as a fundamental model with which to investigate the mechanisms that underlie memory formation and has provided genetic and molecular means to identify the components of memory, namely short-term, intermediate-term, and long-term memory, depending on how long the memory lasts. Intermediate memory is further divided into anesthesia-sensitive memory (ASM) and anesthesia-resistant memory (ARM), with the latter being more stable. We have identified a small GTPase in Drosophila, Rgk1, which plays a pivotal role in the regulation of olfactory memory stability. Rgk1 is required for both ASM and ARM. Moreover, N-terminal domain-deleted Rgk1 was sufficient for ASM formation, whereas the full-length form was required for ARM formation.

The alternative life of RNA-sequencing meets single molecule approaches

The central dogma of RNA processing has started to totter. Single genes produce a variety of mRNA isoforms by mRNA modification, alternative polyadenylation (APA), and splicing. Different isoforms, even those that code for the identical protein, may differ in function or spatiotemporal expression. One option of how this can be achieved is by the selective recruitment of trans-acting factors to the 3'-untranslated region of a given isoform. Recent innovations in high-throughput RNA-sequencing methods allow deep insight into global RNA regulation, whereas novel imaging-based technologies enable researchers to explore single RNA molecules during different stages of development, in different tissues and different compartments of the cell. Resolving the dynamic function of ribonucleoprotein particles in splicing, APA, or RNA modification will enable us to understand their contribution to pathological conditions.

Single-Cell Transcriptome Analysis of Neural Stem Cells

Neural stem cells (NSCs) in the adult central nervous system play essential roles in both normal homeostasis and repair of damaged tissue after injury. The study of adult NSCs is hampered by the heterogeneous NSC population. In this review, we describe recent progresses in using single-cell RNA-sequencing (scRNA-seq) technique for the investigation of NSCs. The first part of this review focuses on the scRNA-seq techniques and bioinformatic analysis. The second part emphasizes the applications of scRNA-seq analysis in NSC research. Finally, we discuss the challenges and future directions of scRNA-seq technique for both basic research and regenerative medicine.

Advances in understanding tumour evolution through single-cell sequencing

The mutational heterogeneity observed within tumours poses additional challenges to the development of effective cancer treatments. A thorough understanding of a tumour's subclonal composition and its mutational history is essential to open up the design of treatments tailored to individual patients. Comparative studies on a large number of tumours permit the identification of mutational patterns which may refine forecasts of cancer progression, response to treatment and metastatic potential. The composition of tumours is shaped by evolutionary processes. Recent advances in next-generation sequencing offer the possibility to analyse the evolutionary history and accompanying heterogeneity of tumours at an unprecedented resolution, by sequencing single cells. New computational challenges arise when moving from bulk to single-cell sequencing data, leading to the development of novel modelling frameworks. In this review, we present the state of the art methods for understanding the phylogeny encoded in bulk or single-cell sequencing data, and highlight future directions for developing more comprehensive and informative pictures of tumour evolution. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

Building a lineage from single cells: genetic techniques for cell lineage tracking

Resolving lineage relationships between cells in an organism is a fundamental interest of developmental biology. Furthermore, investigating lineage can drive understanding of pathological states, including cancer, as well as understanding of developmental pathways that are amenable to manipulation by directed differentiation. Although lineage tracking through the injection of retroviral libraries has long been the state of the art, a recent explosion of methodological advances in exogenous labelling and single-cell sequencing have enabled lineage tracking at larger scales, in more detail, and in a wider range of species than was previously considered possible. In this Review, we discuss these techniques for cell lineage tracking, with attention both to those that trace lineage forwards from experimental labelling, and those that trace backwards across the life history of an organism.

Categorizing Cells on the Basis of their Chemical Profiles: Progress in Single-Cell Mass Spectrometry

The chemical differences between individual cells within large cellular populations provide unique information on organisms' homeostasis and the development of diseased states. Even genetically identical cell lineages diverge due to local microenvironments and stochastic processes. The minute sample volumes and low abundance of some constituents in cells hinder our understanding of cellular heterogeneity. Although amplification methods facilitate single-cell genomics and transcriptomics, the characterization of metabolites and proteins remains challenging both because of the lack of effective amplification approaches and the wide diversity in cellular constituents. Mass spectrometry has become an enabling technology for the investigation of individual cellular metabolite profiles with its exquisite sensitivity, large dynamic range, and ability to characterize hundreds to thousands of compounds. While advances in instrumentation have improved figures of merit, acquiring measurements at high throughput and sampling from large populations of cells are still not routine. In this Perspective, we highlight the current trends and progress in mass-spectrometry-based analysis of single cells, with a focus on the technologies that will enable the next generation of single-cell measurements.

A population genetics perspective on the determinants of intra-tumor heterogeneity

Cancer results from the acquisition of somatic alterations in a microevolutionary process that typically occurs over many years, much of which is occult. Understanding the evolutionary dynamics that are operative at different stages of progression in individual tumors might inform the earlier detection, diagnosis, and treatment of cancer. Although these processes cannot be directly observed, the resultant spatiotemporal patterns of genetic variation amongst tumor cells encode their evolutionary histories. Such intra-tumor heterogeneity is pervasive not only at the genomic level, but also at the transcriptomic, phenotypic, and cellular levels. Given the implications for precision medicine, the accurate quantification of heterogeneity within and between tumors has become a major focus of current research. In this review, we provide a population genetics perspective on the determinants of intra-tumor heterogeneity and approaches to quantify genetic diversity. We summarize evidence for different modes of evolution based on recent cancer genome sequencing studies and discuss emerging evolutionary strategies to therapeutically exploit tumor heterogeneity. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

ddClone: joint statistical inference of clonal populations from single cell and bulk tumour sequencing data

Next-generation sequencing (NGS) of bulk tumour tissue can identify constituent cell populations in cancers and measure their abundance. This requires computational deconvolution of allelic counts from somatic mutations, which may be incapable of fully resolving the underlying population structure. Single cell sequencing (SCS) is a more direct method, although its replacement of NGS is impeded by technical noise and sampling limitations. We propose ddClone, which analytically integrates NGS and SCS data, leveraging their complementary attributes through joint statistical inference. We show on real and simulated datasets that ddClone produces more accurate results than can be achieved by either method alone.

Single cell sequencing: a distinct new field

Single cell sequencing (SCS) has become a new approach to study biological heterogeneity. The advancement in technologies for single cell isolation, amplification of genome/transcriptome and next-generation sequencing enables SCS to reveal the inherent properties of a single cell from the large scale of the genome, transcriptome or epigenome at high resolution. Recently, SCS has been widely applied in various clinical and research fields, such as cancer biology and oncology, immunology, microbiology, neurobiology and prenatal diagnosis. In this review, we will discuss the development of SCS methods and focus on the latest clinical and research applications of SCS.

Tumor evolution: Linear, branching, neutral or punctuated?

Intratumor heterogeneity has been widely reported in human cancers, but our knowledge of how this genetic diversity emerges over time remains limited. A central challenge in studying tumor evolution is the difficulty in collecting longitudinal samples from cancer patients. Consequently, most studies have inferred tumor evolution from single time-point samples, providing very indirect information. These data have led to several competing models of tumor evolution: linear, branching, neutral and punctuated. Each model makes different assumptions regarding the timing of mutations and selection of clones, and therefore has different implications for the diagnosis and therapeutic treatment of cancer patients. Furthermore, emerging evidence suggests that models may change during tumor progression or operate concurrently for different classes of mutations. Finally, we discuss data that supports the theory that most human tumors evolve from a single cell in the normal tissue. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

Endozoicomonas genomes reveal functional adaptation and plasticity in bacterial strains symbiotically associated with diverse marine hosts

Endozoicomonas bacteria are globally distributed and often abundantly associated with diverse marine hosts including reef-building corals, yet their function remains unknown. In this study we generated novel Endozoicomonas genomes from single cells and metagenomes obtained directly from the corals Stylophora pistillata, Pocillopora verrucosa, and Acropora humilis. We then compared these culture-independent genomes to existing genomes of bacterial isolates acquired from a sponge, sea slug, and coral to examine the functional landscape of this enigmatic genus. Sequencing and analysis of single cells and metagenomes resulted in four novel genomes with 60–76% and 81–90% genome completeness, respectively. These data also confirmed that Endozoicomonas genomes are large and are not streamlined for an obligate endosymbiotic lifestyle, implying that they have free-living stages. All genomes show an enrichment of genes associated with carbon sugar transport and utilization and protein secretion, potentially indicating that Endozoicomonas contribute to the cycling of carbohydrates and the provision of proteins to their respective hosts. Importantly, besides these commonalities, the genomes showed evidence for differential functional specificity and diversification, including genes for the production of amino acids. Given this metabolic diversity of Endozoicomonas we propose that different genotypes play disparate roles and have diversified in concert with their hosts.

Development of a Protocol for Single-Cell Analysis of Circulating Tumor Cells in Patients with Solid Tumors

Genomic characterization of circulating tumor cells (CTCs) enables the monitoring of tumor progression and of adaption occurring during treatment. CTC molecular characterization represents indeed a precious tool to implement in the clinical practice for better dealing with acquired resistance to systemic treatment and tumor evolution. Unfortunately CTCs are very rare and enrichments from blood samples and subsequent identification of these cells are technically very challenging. We describe here the main steps leading to the development of a technical protocol for visualization, enumeration and recovery of single CTCs exploiting the recently developed DEPArray™platform. Our description of the technical workflow starts with evaluation of pre-analytical aspects related to blood sample collection warning about the possible effects on immunoreactivity profiles which may bias the interpretation. Subsequently, other CTC-enrichment approaches are critically discussed and compared in relation to their performances with the DEPArray™. Identification of CTCs represents another critical point due to their heterogeneity and due to the still-to-be clarified role of different subpopulations, typically epithelial, mesenchymal or mixed. Finally, issues related to single cell analysis are illustrated. The chapter ends with an overview of results obtained on real clinical samples which support the reliability of the protocol and its transferability to the daily clinical routine.

Genome evolution in ductal carcinoma in situ: invasion of the clones

Ductal carcinoma in situ (DCIS) is the most frequently diagnosed early-stage breast cancer. Only a subset of patients progress to invasive ductal carcinoma (IDC), and this presents a formidable clinical challenge for determining which patients to treat aggressively and which patients to monitor without therapeutic intervention. Understanding the molecular and genomic basis of invasion has been difficult to study in DCIS cancers due to several technical obstacles, including low tumour cellularity, lack of fresh-frozen tissues, and intratumour heterogeneity. In this review, we discuss the role of intratumour heterogeneity in the progression of DCIS to IDC in the context of three evolutionary models: independent lineages, evolutionary bottlenecks, and multiclonal invasion. We examine the evidence in support of these models and their relevance to the diagnosis and treatment of patients with DCIS. We also discuss how emerging technologies, such as single-cell sequencing, STAR-FISH, and imaging mass spectrometry, are likely to provide new insights into the evolution of this enigmatic disease. Copyright © 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Measuring intratumor heterogeneity by network entropy using RNA-seq data

Intratumor heterogeneity (ITH) is observed at different stages of tumor progression, metastasis and reouccurence, which can be important for clinical applications. We used RNA-sequencing data from tumor samples, and measured the level of ITH in terms of biological network states. To model complex relationships among genes, we used a protein interaction network to consider gene-gene dependency. ITH was measured by using an entropy-based distance metric between two networks, nJSD, with Jensen-Shannon Divergence (JSD). With nJSD, we defined transcriptome-based ITH (tITH). The effectiveness of tITH was extensively tested for the issues related with ITH using real biological data sets. Human cancer cell line data and single-cell sequencing data were investigated to verify our approach. Then, we analyzed TCGA pan-cancer 6,320 patients. Our result was in agreement with widely used genome-based ITH inference methods, while showed better performance at survival analysis. Analysis of mouse clonal evolution data further confirmed that our transcriptome-based ITH was consistent with genetic heterogeneity at different clonal evolution stages. Additionally, we found that cell cycle related pathways have significant contribution to increasing heterogeneity on the network during clonal evolution. We believe that the proposed transcriptome-based ITH is useful to characterize heterogeneity of a tumor sample at RNA level.

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.

Magnetophoretic transistors in a tri-axial magnetic field

The ability to direct and sort individual biological and non-biological particles into spatially addressable locations is fundamentally important to the emerging field of single cell biology. Towards this goal, we demonstrate a new class of magnetophoretic transistors, which can switch single magnetically labeled cells and magnetic beads between different paths in a microfluidic chamber. Compared with prior work on magnetophoretic transistors driven by a two-dimensional in-plane rotating field, the addition of a vertical magnetic field bias provides significant advantages in preventing the formation of particle clumps and in better replicating the operating principles of circuits in general. However, the three-dimensional driving field requires a complete redesign of the magnetic track geometry and switching electrodes. We have solved this problem by developing several types of transistor geometries which can switch particles between two different tracks by either presenting a local energy barrier or by repelling magnetic objects away from a given track, hereby denoted as "barrier" and "repulsion" transistors, respectively. For both types of transistors, we observe complete switching of magnetic objects with currents of ∼40 mA, which is consistent over a range of particle sizes (8-15 μm). The switching efficiency was also tested at various magnetic field strengths (50-90 Oe) and driving frequencies (0.1-0.6 Hz); however, we again found that the device performance only weakly depended on these parameters. These findings support the use of these novel transistor geometries to form circuit architectures in which cells can be placed in defined locations and retrieved on demand.

A Single-Cell Transcriptome Atlas of the Human Pancreas

To understand organ function, it is important to have an inventory of its cell types and of their corresponding marker genes. This is a particularly challenging task for human tissues like the pancreas, because reliable markers are limited. Hence, transcriptome-wide studies are typically done on pooled islets of Langerhans, obscuring contributions from rare cell types and of potential subpopulations. To overcome this challenge, we developed an automated platform that uses FACS, robotics, and the CEL-Seq2 protocol to obtain the transcriptomes of thousands of single pancreatic cells from deceased organ donors, allowing in silico purification of all main pancreatic cell types. We identify cell type-specific transcription factors and a subpopulation of REG3A-positive acinar cells. We also show that CD24 and TM4SF4 expression can be used to sort live alpha and beta cells with high purity. This resource will be useful for developing a deeper understanding of pancreatic biology and pathophysiology of diabetes mellitus.

•

Single-cell sequencing of human pancreas allows in silico purification of cell types

•

We provide cell-type-specific genes, transcription factors, and cell-surface markers

•

StemID finds outlier populations of acinar and beta cells

•

CD24 and TM4SF4 function as two markers to enrich for alpha and beta cells

Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data

Single-cell RNA-Sequencing (scRNA-Seq) is a revolutionary technique for discovering and describing cell types in heterogeneous tissues, yet its measurement of expression often suffers from large systematic bias. A major source of this bias is the cell cycle, which introduces large within-cell-type heterogeneity that can obscure the differences in expression between cell types. The current method for removing the cell-cycle effect is unable to effectively identify this effect and has a high risk of removing other biological components of interest, compromising downstream analysis. We present ccRemover, a new method that reliably identifies the cell-cycle effect and removes it. ccRemover preserves other biological signals of interest in the data and thus can serve as an important pre-processing step for many scRNA-Seq data analyses. The effectiveness of ccRemover is demonstrated using simulation data and three real scRNA-Seq datasets, where it boosts the performance of existing clustering algorithms in distinguishing between cell types.

Analysis of Body-wide Unfractionated Tissue Data to Identify a Core Human Endothelial Transcriptome

Endothelial cells line blood vessels and regulate hemostasis, inflammation, and blood pressure. Proteins critical for these specialized functions tend to be predominantly expressed in endothelial cells across vascular beds. Here, we present a systems approach to identify a panel of human endothelial-enriched genes using global, body-wide transcriptomics data from 124 tissue samples from 32 organs. We identified known and unknown endothelial-enriched gene transcripts and used antibody-based profiling to confirm expression across vascular beds. The majority of identified transcripts could be detected in cultured endothelial cells from various vascular beds, and we observed maintenance of relative expression in early passage cells. In summary, we describe a widely applicable method to determine cell-type-specific transcriptome profiles in a whole-organism context, based on differential abundance across tissues. We identify potential vascular drug targets or endothelial biomarkers and highlight candidates for functional studies to increase understanding of the endothelium in health and disease.

Diversity and function of prevalent symbiotic marine bacteria in the genus Endozoicomonas

Endozoicomonas bacteria are emerging as extremely diverse and flexible symbionts of numerous marine hosts inhabiting oceans worldwide. Their hosts range from simple invertebrate species, such as sponges and corals, to complex vertebrates, such as fish. Although widely distributed, the functional role of Endozoicomonas within their host microenvironment is not well understood. In this review, we provide a summary of the currently recognized hosts of Endozoicomonas and their global distribution. Next, the potential functional roles of Endozoicomonas, particularly in light of recent microscopic, genomic, and genetic analyses, are discussed. These analyses suggest that Endozoicomonas typically reside in aggregates within host tissues, have a free-living stage due to their large genome sizes, show signs of host and local adaptation, participate in host-associated protein and carbohydrate transport and cycling, and harbour a high degree of genomic plasticity due to the large proportion of transposable elements residing in their genomes. This review will finish with a discussion on the methodological tools currently employed to study Endozoicomonas and host interactions and review future avenues for studying complex host-microbial symbioses.

The first five years of single-cell cancer genomics and beyond

Single-cell sequencing (SCS) is a powerful new tool for investigating evolution and diversity in cancer and understanding the role of rare cells in tumor progression. These methods have begun to unravel key questions in cancer biology that have been difficult to address with bulk tumor measurements. Over the past five years, there has been extraordinary progress in technological developments and research applications, but these efforts represent only the tip of the iceberg. In the coming years, SCS will greatly improve our understanding of invasion, metastasis, and therapy resistance during cancer progression. These tools will also have direct translational applications in the clinic, in areas such as early detection, noninvasive monitoring, and guiding targeted therapy. In this perspective, I discuss the progress that has been made and the myriad of unexplored applications that still lie ahead in cancer research and medicine.

Next generation sequencing technology and genomewide data analysis: Perspectives for retinal research

The advent of high throughput next generation sequencing (NGS) has accelerated the pace of discovery of disease-associated genetic variants and genomewide profiling of expressed sequences and epigenetic marks, thereby permitting systems-based analyses of ocular development and disease. Rapid evolution of NGS and associated methodologies presents significant challenges in acquisition, management, and analysis of large data sets and for extracting biologically or clinically relevant information. Here we illustrate the basic design of commonly used NGS-based methods, specifically whole exome sequencing, transcriptome, and epigenome profiling, and provide recommendations for data analyses. We briefly discuss systems biology approaches for integrating multiple data sets to elucidate gene regulatory or disease networks. While we provide examples from the retina, the NGS guidelines reviewed here are applicable to other tissues/cell types as well.