Advances and applications of single-cell sequencing technologies

Single cell transcriptome sequencing: A new approach for the study of mammalian sex determination

Mammalian sex determination is a highly complex developmental process that is particularly difficult to study due to the limited number of gonadal cells present at the bipotential stage, the large cellular heterogeneity in both testis and ovaries and the rapid sex-dependent differentiation processes. Single-cell RNA-sequencing (scRNA-seq) circumvents the averaging artifacts associated with methods traditionally used to profile bulk populations of cells. It is a powerful tool that allows the identification and classification of cell populations in a comprehensive and unbiased manner. In particular, scRNA-seq enables the tracing of cells along developmental trajectories and characterization of the transcriptional dynamics controlling their differentiation. In this review, we describe the current state-of-the-art experimental methods used for scRNA-seq and discuss their strengths and limitations. Additionally, we summarize the multiple key insights that scRNA-seq has provided to the understanding of mammalian sex determination. Finally, we briefly discuss the future of this technology, as well as complementary applications in single cell -omics in the context of mammalian sex determination.

Neural lineage tracing in the mammalian brain

Delineating the lineage of neural cells that captures the progressive steps in their specification is fundamental to understanding brain development, organization, and function. Since the earliest days of embryology, lineage questions have been addressed with methods of increasing specificity, capacity, and resolution. Yet, a full realization of individual cell lineages remains challenging for complex systems. A recent explosion of technical advances in genome-editing and single-cell sequencing has enabled lineage analysis in an unprecedented scale, speed, and depth across different species. In this review, we discuss the application of available as well as future genetic labeling techniques for tracking neural lineages in vivo in the mammalian nervous system.

UMI-count modeling and differential expression analysis for single-cell RNA sequencing

Read counting and unique molecular identifier (UMI) counting are the principal gene expression quantification schemes used in single-cell RNA-sequencing (scRNA-seq) analysis. By using multiple scRNA-seq datasets, we reveal distinct distribution differences between these schemes and conclude that the negative binomial model is a good approximation for UMI counts, even in heterogeneous populations. We further propose a novel differential expression analysis algorithm based on a negative binomial model with independent dispersions in each group (NBID). Our results show that this properly controls the FDR and achieves better power for UMI counts when compared to other recently developed packages for scRNA-seq analysis.

Cancer Diagnosis Using a Liquid Biopsy: Challenges and Expectations

The field of cancer diagnostics has recently been impacted by new and exciting developments in the area of liquid biopsy. A liquid biopsy is a minimally invasive alternative to surgical biopsies of solid tissues, typically achieved through the withdrawal of a blood sample or other body fluids, allowing the interrogation of tumor-derived material including circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) fragments that are present at a given time point. In this short review, we discuss a few studies that summarize the state-of-the-art in the liquid biopsy field from a diagnostic perspective, and speculate on current challenges and expectations of implementing liquid biopsy testing for cancer diagnosis and monitoring in the clinical setting.

Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing

Triple-negative breast cancer (TNBC) is an aggressive subtype that frequently develops resistance to chemotherapy. An unresolved question is whether resistance is caused by the selection of rare pre-existing clones or alternatively through the acquisition of new genomic aberrations. To investigate this question, we applied single-cell DNA and RNA sequencing in addition to bulk exome sequencing to profile longitudinal samples from 20 TNBC patients during neoadjuvant chemotherapy (NAC). Deep-exome sequencing identified 10 patients in which NAC led to clonal extinction and 10 patients in which clones persisted after treatment. In 8 patients, we performed a more detailed study using single-cell DNA sequencing to analyze 900 cells and single-cell RNA sequencing to analyze 6,862 cells. Our data showed that resistant genotypes were pre-existing and adaptively selected by NAC, while transcriptional profiles were acquired by reprogramming in response to chemotherapy in TNBC patients.

Self-digitization chip for single-cell genotyping of cancer-related mutations

Cancer is a heterogeneous disease, and patient-level genetic assessments can guide therapy choice and impact prognosis. However, little is known about the impact of genetic variability within a tumor, intratumoral heterogeneity (ITH), on disease progression or outcome. Current approaches using bulk tumor specimens can suggest the presence of ITH, but only single-cell genetic methods have the resolution to describe the underlying clonal structures themselves. Current techniques tend to be labor and resource intensive and challenging to characterize with respect to sources of biological and technical variability. We have developed a platform using a microfluidic self-digitization chip to partition cells in stationary volumes for cell imaging and allele-specific PCR. Genotyping data from only confirmed single-cell volumes is obtained and subject to a variety of relevant quality control assessments such as allele dropout, false positive, and false negative rates. We demonstrate single-cell genotyping of the NPM1 type A mutation, an important prognostic indicator in acute myeloid leukemia, on single cells of the cell line OCI-AML3, describing a more complex zygosity distribution than would be predicted via bulk analysis.

MHC class I presented antigens from malignancies: A perspective on analytical characterization & immunogenicity

The field of cancer immunotherapy has expanded rapidly in the past few years, with many new approaches entering the clinic for T cell mediated killing of tumors. Several of these clinical approaches involve the exploitation of a CD8 + T cell response against MHC I presented tumor antigens. Here, we describe the types of tumor antigens which are considered as targets in the design of T cell based therapeutic approaches, the rationale for targeting MHC I antigens and the analytical tools commonly employed for the discovery of MHC I presented peptides. The advantages and disadvantages of each approach are discussed and a perspective on the future directions of the MHC I peptide exploration field and biotherapeutic strategies is given. SIGNIFICANCE: This work is the first time a review article has been written to summarize all the various types of tumor antigens, and the analytical tools employed to discover and characterize them.

Single Cell Multi-Omics Technology: Methodology and Application

In the era of precision medicine, multi-omics approaches enable the integration of data from diverse omics platforms, providing multi-faceted insight into the interrelation of these omics layers on disease processes. Single cell sequencing technology can dissect the genotypic and phenotypic heterogeneity of bulk tissue and promises to deepen our understanding of the underlying mechanisms governing both health and disease. Through modification and combination of single cell assays available for transcriptome, genome, epigenome, and proteome profiling, single cell multi-omics approaches have been developed to simultaneously and comprehensively study not only the unique genotypic and phenotypic characteristics of single cells, but also the combined regulatory mechanisms evident only at single cell resolution. In this review, we summarize the state-of-the-art single cell multi-omics methods and discuss their applications, challenges, and future directions.

Sensitivity to sequencing depth in single-cell cancer genomics

BACKGROUND

Querying cancer genomes at single-cell resolution is expected to provide a powerful framework to understand in detail the dynamics of cancer evolution. However, given the high costs currently associated with single-cell sequencing, together with the inevitable technical noise arising from single-cell genome amplification, cost-effective strategies that maximize the quality of single-cell data are critically needed. Taking advantage of previously published single-cell whole-genome and whole-exome cancer datasets, we studied the impact of sequencing depth and sampling effort towards single-cell variant detection.

METHODS

Five single-cell whole-genome and whole-exome cancer datasets were independently downscaled to 25, 10, 5, and 1× sequencing depth. For each depth level, ten technical replicates were generated, resulting in a total of 6280 single-cell BAM files. The sensitivity of variant detection, including structural and driver mutations, genotyping, clonal inference, and phylogenetic reconstruction to sequencing depth was evaluated using recent tools specifically designed for single-cell data.

RESULTS

Altogether, our results suggest that for relatively large sample sizes (25 or more cells) sequencing single tumor cells at depths > 5× does not drastically improve somatic variant discovery, characterization of clonal genotypes, or estimation of single-cell phylogenies.

CONCLUSIONS

We suggest that sequencing multiple individual tumor cells at a modest depth represents an effective alternative to explore the mutational landscape and clonal evolutionary patterns of cancer genomes.

Genome-wide mutation detection by interclonal genetic variation

Genetic toxicology assays estimate mutation frequencies by phenotypically screening for the activation or inactivation of endogenous or exogenous reporter genes. These reporters can only detect mutations in narrow areas of the genome and their use is often restricted to certain in vitro and in vivo models. Here, we show that Interclonal Genetic Variation (ICGV) can directly identify mutations genome-wide by comparing sequencing data of single-cell clones derived from the same source or organism. Upon ethyl methanesulfonate (EMS) exposure, ICGV detected greater levels of mutation in a dose- and time-dependent manner in E. coli. In addition, ICGV was also able to identify a ∼20-fold increase in somatic mutations in T-cell clones derived from an N-ethyl-N-nitrosourea (ENU)-treated rat vs. a vehicle-treated rat. These results demonstrate that the genetic differences of single-cell clones can be used for genome-wide mutation detection.

Host-microbe interactions in octocoral holobionts - recent advances and perspectives

Octocorals are one of the most ubiquitous benthic organisms in marine ecosystems from the shallow tropics to the Antarctic deep sea, providing habitat for numerous organisms as well as ecosystem services for humans. In contrast to the holobionts of reef-building scleractinian corals, the holobionts of octocorals have received relatively little attention, despite the devastating effects of disease outbreaks on many populations. Recent advances have shown that octocorals possess remarkably stable bacterial communities on geographical and temporal scales as well as under environmental stress. This may be the result of their high capacity to regulate their microbiome through the production of antimicrobial and quorum-sensing interfering compounds. Despite decades of research relating to octocoral-microbe interactions, a synthesis of this expanding field has not been conducted to date. We therefore provide an urgently needed review on our current knowledge about octocoral holobionts. Specifically, we briefly introduce the ecological role of octocorals and the concept of holobiont before providing detailed overviews of (I) the symbiosis between octocorals and the algal symbiont Symbiodinium; (II) the main fungal, viral, and bacterial taxa associated with octocorals; (III) the dominance of the microbial assemblages by a few microbial species, the stability of these associations, and their evolutionary history with the host organism; (IV) octocoral diseases; (V) how octocorals use their immune system to fight pathogens; (VI) microbiome regulation by the octocoral and its associated microbes; and (VII) the discovery of natural products with microbiome regulatory activities. Finally, we present our perspectives on how the field of octocoral research should move forward, and the recognition that these organisms may be suitable model organisms to study coral-microbe symbioses.

The use of single-cell RNA-Seq to understand virus-host interactions

Single-cell analyses allow uncovering cellular heterogeneity, not only per se, but also in response to viral infection. Similarly, single cell transcriptome analyses (scRNA-Seq) can highlight specific signatures, identifying cell subsets with particular phenotypes, which are relevant in the understanding of virus-host interactions.

Defining the Transcriptional Landscape during Cytomegalovirus Latency with Single-Cell RNA Sequencing

Primary infection with human cytomegalovirus (HCMV) results in a lifelong infection due to its ability to establish latent infection, with one characterized viral reservoir being hematopoietic cells. Although reactivation from latency causes serious disease in immunocompromised individuals, our molecular understanding of latency is limited. Here, we delineate viral gene expression during natural HCMV persistent infection by analyzing the massive transcriptome RNA sequencing (RNA-seq) atlas generated by the Genotype-Tissue Expression (GTEx) project. This systematic analysis reveals that HCMV persistence in vivo is prevalent in diverse tissues. Notably, we find only viral transcripts that resemble gene expression during various stages of lytic infection with no evidence of any highly restricted latency-associated viral gene expression program. To further define the transcriptional landscape during HCMV latent infection, we also used single-cell RNA-seq and a tractable experimental latency model. In contrast to some current views on latency, we also find no evidence for any highly restricted latency-associated viral gene expression program. Instead, we reveal that latency-associated gene expression largely mirrors a late lytic viral program, albeit at much lower levels of expression. Overall, our work has the potential to revolutionize our understanding of HCMV persistence and suggests that latency is governed mainly by quantitative changes, with a limited number of qualitative changes, in viral gene expression.IMPORTANCE Human cytomegalovirus is a prevalent pathogen, infecting most of the population worldwide and establishing lifelong latency in its hosts. Although reactivation from latency causes significant morbidity and mortality in immunocompromised hosts, our molecular understanding of the latent state remains limited. Here, we examine the viral gene expression during natural and experimental latent HCMV infection on a transcriptome-wide level. In contrast to the classical views on herpesvirus latency, we find no evidence for a restricted latency-associated viral gene expression program. Instead, we reveal that latency gene expression largely resembles a late lytic viral profile, albeit at much lower levels of expression. Taken together, our data transform the current view of HCMV persistence and suggest that latency is mainly governed by quantitative rather than qualitative changes in viral gene expression.

Diverse modes of clonal evolution in HBV-related hepatocellular carcinoma revealed by single-cell genome sequencing

Hepatocellular carcinoma (HCC) is a cancer of substantial morphologic, genetic and phenotypic diversity. Yet we do not understand the relationship between intratumor heterogeneity and the associated morphologic/histological characteristics of the tumor. Using single-cell whole-genome sequencing to profile 96 tumor cells (30-36 each) and 15 normal liver cells (5 each), collected from three male patients with HBV-associated HCC, we confirmed that copy number variations occur early in hepatocarcinogenesis but thereafter remain relatively stable throughout tumor progression. Importantly, we showed that specific HCCs can be of monoclonal or polyclonal origins. Tumors with confluent multinodular morphology are the typical polyclonal tumors and display the highest intratumor heterogeneity. In addition to mutational and copy number profiles, we dissected the clonal origins of HCC using HBV-derived foreign genomic markers. In monoclonal HCC, all the tumor single cells exhibit the same HBV integrations, indicating that HBV integration is an early driver event and remains extremely stable during tumor progression. In addition, our results indicated that both models of metastasis, late dissemination and early seeding, have a role in HCC progression. Notably, early intrahepatic spreading of the initiating clone leads to the formation of synchronous multifocal tumors. Meanwhile, we identified a potential driver gene ZNF717 in HCC, which exhibits a high frequency of mutation at both single-cell and population levels, as a tumor suppressor acting through regulating the IL-6/STAT3 pathway. These findings highlight multiple distinct tumor evolutionary mechanisms in HCC, which suggests the need for specific treatment strategies.

Systems immunology allows a new view on human dendritic cells

As the most important antigen-presenting cells, dendritic cells connect the innate and adaptive part of our immune system and play a pivotal role in our course of action against invading pathogens as well as during successful vaccination. Immunologists have therefore studied these cells in great detail using flow cytometry-based analyses, in vitro assays and in vivo models, both in murine models and in humans. Albeit, sophisticated, classical immunological, and molecular approaches were often unable to unequivocally determine the subpopulation structure of the dendritic cell lineage and not surprisingly, conflicting results about dendritic cell subsets co-existed throughout the last decades. With the advent of systems approaches and the most recent introduction of -omics approaches on the single cell level combined with multi-colour flow cytometry or mass cytometry, we now enter an era allowing us to define cell population structures with an unprecedented precision. We will report here on the most recent studies applying these technologies to human dendritic cells. Proper delineation of and definition of molecular signatures for the different human dendritic cell subsets will greatly facilitate studying these cells in the future: understanding their function under physiological as well as pathological conditions.

Combinatory use of distinct single-cell RNA-seq analytical platforms reveals the heterogeneous transcriptome response

Single-cell RNA-seq is a powerful tool for revealing heterogeneity in cancer cells. However, each of the current single-cell RNA-seq platforms has inherent advantages and disadvantages. Here, we show that combining the different single-cell RNA-seq platforms can be an effective approach to obtaining complete information about expression differences and a sufficient cellular population to understand transcriptional heterogeneity in cancers. We demonstrate that it is possible to estimate missing expression information. We further demonstrate that even in the cases where precise information for an individual gene cannot be inferred, the activity of given transcriptional modules can be analyzed. Interestingly, we found that two distinct transcriptional modules, one associated with the Aurora kinase gene and the other with the DUSP gene, are aberrantly regulated in a minor population of cells and may thus contribute to the possible emergence of dormancy or eventual drug resistance within the population.

Quantitation of nonspecific protein adsorption at solid–liquid interfaces for single-cell proteomics

Protein nonspecific adsorption that occurred at the solid–liquid interface has been subjected to intense physical and chemical characterizations due to its crucial role in a wide range of applications, including food and pharmaceutical industries, medical implants, biosensing, and so on. Protein-adsorption caused sample loss has largely hindered the studies of single-cell proteomics; the prevention of such loss requires the understanding of protein–surface adsorption at the proteome level, in which the competitive adsorption of thousands and millions of proteins with vast dynamic range occurs. To this end, we feel the necessity to review current methodologies on their potentials to characterize — more specifically to quantify — the proteome-wide adsorption. We hope this effort can help advancing single-cell proteomics and trace proteomics.

Defining murine organogenesis at single-cell resolution reveals a role for the leukotriene pathway in regulating blood progenitor formation

During gastrulation, cell types from all three germ layers are specified and the basic body plan is established 1 . However, molecular analysis of this key developmental stage has been hampered by limited cell numbers and a paucity of markers. Single-cell RNA sequencing circumvents these problems, but has so far been limited to specific organ systems 2 . Here, we report single-cell transcriptomic characterization of >20,000 cells immediately following gastrulation at E8.25 of mouse development. We identify 20 major cell types, which frequently contain substructure, including three distinct signatures in early foregut cells. Pseudo-space ordering of somitic progenitor cells identifies dynamic waves of transcription and candidate regulators, which are validated by molecular characterization of spatially resolved regions of the embryo. Within the endothelial population, cells that transition from haemogenic endothelial to erythro-myeloid progenitors specifically express Alox5 and its co-factor Alox5ap, which control leukotriene production. Functional assays using mouse embryonic stem cells demonstrate that leukotrienes promote haematopoietic progenitor cell generation. Thus, this comprehensive single-cell map can be exploited to reveal previously unrecognized pathways that contribute to tissue development.

Single cell analysis of normal and leukemic hematopoiesis

The hematopoietic system is well established as a paradigm for the study of cellular hierarchies, their disruption in disease and therapeutic use in regenerative medicine. Traditional approaches to study hematopoiesis involve purification of cell populations based on a small number of surface markers. However, such population-based analysis obscures underlying heterogeneity contained within any phenotypically defined cell population. This heterogeneity can only be resolved through single cell analysis. Recent advances in single cell techniques allow analysis of the genome, transcriptome, epigenome and proteome in single cells at an unprecedented scale. The application of these new single cell methods to investigate the hematopoietic system has led to paradigm shifts in our understanding of cellular heterogeneity in hematopoiesis and how this is disrupted in disease. In this review, we summarize how single cell techniques have been applied to the analysis of hematopoietic stem/progenitor cells in normal and malignant hematopoiesis, with a particular focus on recent advances in single-cell genomics, including how these might be utilized for clinical application.

The Heterogeneity of Prostate Cancer: A Practical Approach

Prostate cancer is a paradigm tumor model for heterogeneity in almost every sense. Its clinical, spatial, and morphological heterogeneity divided by the high-level molecular genetic diversity outline the complexity of this disease in the clinical and research settings. In this review, we summarize the main aspects of prostate cancer heterogeneity at different levels, with special attention given to the spatial heterogeneity within the prostate, and to the standard morphological heterogeneity, with respect to tumor grading and modern classifications. We also cover the complex issue of molecular genetic heterogeneity, discussing it in the context of the current evidence of the genetic characterization of prostate carcinoma; the interpatient, intertumoral (multifocal disease), and intratumoral heterogeneity; tumor clonality; and metastatic disease. Clinical and research implications are summarized and serve to address the most pertinent problems stemming from the extreme heterogeneity of prostate cancer.

Expanding primary cells from mucoepidermoid and other salivary gland neoplasms for genetic and chemosensitivity testing

Restricted availability of cell and animal models is a rate-limiting step for investigation of salivary gland neoplasm pathophysiology and therapeutic response. Conditionally reprogrammed cell (CRC) technology enables establishment of primary epithelial cell cultures from patient material. This study tested a translational workflow for acquisition, expansion and testing of CRC-derived primary cultures of salivary gland neoplasms from patients presenting to an academic surgical practice. Results showed that cultured cells were sufficient for epithelial cell-specific transcriptome characterization to detect candidate therapeutic pathways and fusion genes, and for screening for cancer risk-associated single nucleotide polymorphisms (SNPs) and driver gene mutations through exome sequencing. Focused study of primary cultures of a low-grade mucoepidermoid carcinoma demonstrated amphiregulin-mechanistic target of rapamycin-protein kinase B (AKT; AKT1) pathway activation, identified through bioinformatics and subsequently confirmed as present in primary tissue and preserved through different secondary 2D and 3D culture media and xenografts. Candidate therapeutic testing showed that the allosteric AKT inhibitor MK2206 reproducibly inhibited cell survival across different culture formats. By contrast, the cells appeared resistant to the adenosine triphosphate competitive AKT inhibitor GSK690693. Procedures employed here illustrate an approach for reproducibly obtaining material for pathophysiological studies of salivary gland neoplasms, and other less common epithelial cancer types, that can be executed without compromising pathological examination of patient specimens. The approach permits combined genetic and cell-based physiological and therapeutic investigations in addition to more traditional pathologic studies, and can be used to build sustainable bio-banks for future inquiries.This article has an associated First Person interview with the first author of the paper.

Single-Cell Genomic Analysis in Plants

Individual cells in an organism are variable, which strongly impacts cellular processes. Advances in sequencing technologies have enabled single-cell genomic analysis to become widespread, addressing shortcomings of analyses conducted on populations of bulk cells. While the field of single-cell plant genomics is in its infancy, there is great potential to gain insights into cell lineage and functional cell types to help understand complex cellular interactions in plants. In this review, we discuss current approaches for single-cell plant genomic analysis, with a focus on single-cell isolation, DNA amplification, next-generation sequencing, and bioinformatics analysis. We outline the technical challenges of analysing material from a single plant cell, and then examine applications of single-cell genomics and the integration of this approach with genome editing. Finally, we indicate future directions we expect in the rapidly developing field of plant single-cell genomic analysis.

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.

Bringing Renal Biopsy Interpretation Into the Molecular Age With Single-Cell RNA Sequencing

The renal biopsy provides critical diagnostic and prognostic information to clinicians including cases of acute kidney injury, chronic kidney disease, and allograft dysfunction. Today, biopsy specimens are read using a combination of light microscopy, electron microscopy, and indirect immunofluorescence, with a limited number of antibodies. These techniques all were perfected decades ago with only incremental changes since then. By contrast, recent advances in single-cell genomics are transforming scientists' ability to characterize cells. Rather than measure the expression of several genes at a time by immunofluorescence, it now is possible to measure the expression of thousands of genes in thousands of single cells simultaneously. Here, we argue that the development of single-cell RNA sequencing offers an opportunity to describe human kidney disease comprehensively at a cellular level. It is particularly well suited for the analysis of immune cells, which are characterized by multiple subtypes and changing functions depending on their environment. In this review, we summarize the development of single-cell RNA sequencing methodologies. We discuss how these approaches are being applied in other organs, and the potential for this powerful technology to transform our understanding of kidney disease once applied to the renal biopsy.

Beyond Enumeration: Functional and Computational Analysis of Circulating Tumor Cells to Investigate Cancer Metastasis

Circulating tumor cells (CTCs) are defined as those cells that detach from a cancerous lesion and enter the bloodstream. While generally most CTCs are subjected to high shear stress, anoikis signals, and immune attack in the circulatory system, few are able to survive and reach a distant organ in a viable state, possibly leading to metastasis formation. A large number of studies, both prospective and retrospective, have highlighted the association between CTC abundance and bad prognosis in patients with various cancer types. Yet, beyond CTC enumeration, much less is known about the distinction between metastatic and nonmetastatic CTCs, namely those features that enable only some CTCs to survive and seed a cancerous lesion at a distant site. In addition, critical aspects such as CTC heterogeneity, mechanisms that trigger CTC intravasation and extravasation, as well as vulnerabilities of metastatic CTCs subpopulations are poorly understood. In this short review, we highlight recent studies that successfully adopted functional and computational analysis to gain insights into CTC biology. We also discuss approaches to overcome challenges that are associated with CTC isolation, molecular and computational analysis, and speculate regarding few open questions that currently frame the CTC research field.

Using single-cell multiple omics approaches to resolve tumor heterogeneity

It has become increasingly clear that both normal and cancer tissues are composed of heterogeneous populations. Genetic variation can be attributed to the downstream effects of inherited mutations, environmental factors, or inaccurately resolved errors in transcription and replication. When lesions occur in regions that confer a proliferative advantage, it can support clonal expansion, subclonal variation, and neoplastic progression. In this manner, the complex heterogeneous microenvironment of a tumour promotes the likelihood of angiogenesis and metastasis. Recent advances in next-generation sequencing and computational biology have utilized single-cell applications to build deep profiles of individual cells that are otherwise masked in bulk profiling. In addition, the development of new techniques for combining single-cell multi-omic strategies is providing a more precise understanding of factors contributing to cellular identity, function, and growth. Continuing advancements in single-cell technology and computational deconvolution of data will be critical for reconstructing patient specific intra-tumour features and developing more personalized cancer treatments.

Robustness, Accuracy, and Cell State Heterogeneity in Biological Systems

The robustness of biological systems is often depicted as a key system-level emergent property that allows uniform phenotypes in fluctuating environments. Yet, analysis of single-cell signaling responses identified multiple examples of cellular responses with high degrees of heterogeneity. Here we discuss the implications of the observed lack of response accuracy in the context of new observations coming from single-cell approaches. Single-cell approaches provide a new way to measure the abundance of thousands of molecular species in a single-cell. Repeatedly, analysis of cell distributions identifies clusters within these distributions where cells can be grouped into specific cell states. If cells in a population occupy distinct cell states, the observed variable response could in fact be accurate for each cell conditioned on its own internal state. In this view, the observed lack of accuracy, i.e. response heterogeneity, could in fact be beneficial and a potentially regulated feature of cell state variability. Therefore, to truly determine whether the observed response heterogeneity is a desired property or a physical limitation, future analysis of signaling heterogeneity must take into account the internal states of cells in the population.

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.

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.

Neuroblastoma pathogenesis: deregulation of embryonic neural crest development

Neuroblastoma (NB) is an aggressive pediatric cancer that originates from neural crest tissues of the sympathetic nervous system. NB is highly heterogeneous both from a clinical and a molecular perspective. Clinically, this cancer represents a wide range of phenotypes ranging from spontaneous regression of 4S disease to unremitting treatment-refractory progression and death of high-risk metastatic disease. At a cellular level, the heterogeneous behavior of NB likely arises from an arrest and deregulation of normal neural crest development. In the present review, we summarize our current knowledge of neural crest development as it relates to pathways promoting 'stemness' and how deregulation may contribute to the development of tumor-initiating CSCs. There is an emerging consensus that such tumor subpopulations contribute to the evolution of drug resistance, metastasis and relapse in other equally aggressive malignancies. As relapsed, refractory disease remains the primary cause of death for neuroblastoma, the identification and targeting of CSCs or other primary drivers of tumor progression remains a critical, clinically significant goal for neuroblastoma. We will critically review recent and past evidence in the literature supporting the concept of CSCs as drivers of neuroblastoma pathogenesis.

Factors involved in cancer metastasis: a better understanding to "seed and soil" hypothesis

Metastasis has intrigued researchers for more than 100 years. Despite the development of technologies and therapeutic strategies, metastasis is still the major cause of cancer-related death until today. The famous "seed and soil" hypothesis is widely cited and accepted, and it still provides significant instructions in cancer research until today. To our knowledge, there are few reviews that comprehensively and correlatively focus on both the seed and soil factors involved in cancer metastasis; moreover, despite the fact that increasingly underlying mechanisms and concepts have been defined recently, previous perspectives are appealing but may be limited. Hence, we reviewed factors involved in cancer metastasis, including both seed and soil factors. By integrating new concepts with the classic hypothesis, we aim to provide a comprehensive understanding of the "seed and soil" hypothesis and to conceptualize the framework for understanding factors involved in cancer metastasis. Based on a dynamic overview of this field, we also discuss potential implications for future research and clinical therapeutic strategies.

Single-cell sequencing and tumorigenesis: improved understanding of tumor evolution and metastasis

Extensive genomic and transcriptomic heterogeneity in human cancer often negatively impacts treatment efficacy and survival, thus posing a significant ongoing challenge for modern treatment regimens. State-of-the-art DNA- and RNA-sequencing methods now provide high-resolution genomic and gene expression portraits of individual cells, facilitating the study of complex molecular heterogeneity in cancer. Important developments in single-cell sequencing (SCS) technologies over the past 5 years provide numerous advantages over traditional sequencing methods for understanding the complexity of carcinogenesis, but significant hurdles must be overcome before SCS can be clinically useful. In this review, we: (1) highlight current methodologies and recent technological advances for isolating single cells, single-cell whole-genome and whole-transcriptome amplification using minute amounts of nucleic acids, and SCS, (2) summarize research investigating molecular heterogeneity at the genomic and transcriptomic levels and how this heterogeneity affects clonal evolution and metastasis, and (3) discuss the promise for integrating SCS in the clinical care arena for improved patient care.

The promise of single-cell RNA sequencing for kidney disease investigation

Recent techniques for single-cell RNA sequencing (scRNA-seq) at high throughput are leading to profound new discoveries in biology. The ability to generate vast amounts of transcriptomic data at cellular resolution represents a transformative advance, allowing the identification of novel cell types, states, and dynamics. In this review, we summarize the development of scRNA-seq methodologies and highlight their advantages and drawbacks. We discuss available software tools for analyzing scRNA-Seq data and summarize current computational challenges. Finally, we outline ways in which this powerful technology might be applied to discovery research in kidney development and disease.

Regulatory Roles of Mitochondrial Ribosome in Lung Diseases and Single Cell Biology

The mitochondria have the most vital processes in eukaryotic cells to produce ATP composed of polypeptides that are produced via ribosomes, as oxidative phosphorylation. Initially, studies regarding human mitochondrial ribosomes were performed in the model system, bovine mitochondrial ribosome, to investigate how ribosomes are biosynthesized and evolved as well as what their structure and function are. Advances in X-ray crystallography have led to dramatic progresses in structural studies of the ribosome. In recent years, there has been a growing interest in the properties of the mitochondrial ribosome. Although one of its main functions is the production of ATP, it was also linked to multiple diseases. A key area that remains unexplored and requires investigation and exploration is how mitochondrial ribosomal RNA (mt-rRNA) variations can affect the mitochondrial ribosomes in developing disease. This review summarizes the structure, elements, functions, and regulatory roles in associated diseases. With the continuous development of technology, studies on the mechanism of mitochondrial ribosome related diseases are crucial, in order to identify methods of prevention and treatment of these disorders.

Progress and challenges of sequencing and analyzing circulating tumor cells

Circulating tumor cells (CTCs) slough off primary tumor tissues and are swept away by the circulatory system. These CTCs can remain in circulation or colonize new sites, forming metastatic clones in distant organs. Recently, CTC analyses have been successfully used as effective clinical tools to monitor tumor progression and prognosis. With advances in next-generation sequencing (NGS) and single-cell sequencing (SCS) technologies, scientists can obtain the complete genome of a CTC and compare it with corresponding primary and metastatic tumors. CTC sequencing has been successfully applied to monitor genomic variations in metastatic and recurrent tumors, infer tumor evolution during treatment, and examine gene expression as well as the mechanism of the epithelial-mesenchymal transition. However, compared with cancer biopsy sequencing and circulating tumor DNA sequencing, the sequencing of CTC genomes and transcriptomes is more complex and technically difficult. Challenges include enriching pure tumor cells from a background of white blood cells, isolating and collecting cells without damaging or losing DNA and RNA, obtaining unbiased and even whole-genome and transcriptome amplification material, and accurately analyzing CTC sequencing data. Here, we review and summarize recent studies using NGS on CTCs. We mainly focus on CTC genome and transcriptome sequencing and the biological and potential clinical applications of these methodologies. Finally, we discuss challenges and future perspectives of CTC sequencing.

The rise and fall of machine learning methods in biomedical research

In the era of explosion in biological data, machine learning techniques are becoming more popular in life sciences, including biology and medicine. This research note examines the rise and fall of the most commonly used machine learning techniques in life sciences over the past three decades.

The rise and fall of machine learning methods in biomedical research

In the era of explosion in biological data, machine learning techniques are becoming more popular in life sciences, including biology and medicine. This research note examines the rise and fall of the most commonly used machine learning techniques in life sciences over the past three decades.

Prevalence and detection of low-allele-fraction variants in clinical cancer samples

Accurate detection of genomic alterations using high-throughput sequencing is an essential component of precision cancer medicine. We characterize the variant allele fractions (VAFs) of somatic single nucleotide variants and indels across 5095 clinical samples profiled using a custom panel, CancerSCAN. Our results demonstrate that a significant fraction of clinically actionable variants have low VAFs, often due to low tumor purity and treatment-induced mutations. The percentages of mutations under 5% VAF across hotspots in EGFR, KRAS, PIK3CA, and BRAF are 16%, 11%, 12%, and 10%, respectively, with 24% for EGFR T790M and 17% for PIK3CA E545. For clinical relevance, we describe two patients for whom targeted therapy achieved remission despite low VAF mutations. We also characterize the read depths necessary to achieve sensitivity and specificity comparable to current laboratory assays. These results show that capturing low VAF mutations at hotspots by sufficient sequencing coverage and carefully tuned algorithms is imperative for a clinical assay.

High-throughput sequencing is used to identify somatic variants in cancer patients. Here, the authors perform panel-based profiling of 5095 clinical samples and demonstrate that many clinically-actionable variants have low variant allele fractions, requiring assays with high detection sensitivity.

Alternative Polyadenylation: Methods, Findings, and Impacts

Alternative polyadenylation (APA), a phenomenon that RNA molecules with different 3' ends originate from distinct polyadenylation sites of a single gene, is emerging as a mechanism widely used to regulate gene expression. In the present review, we first summarized various methods prevalently adopted in APA study, mainly focused on the next-generation sequencing (NGS)-based techniques specially designed for APA identification, the related bioinformatics methods, and the strategies for APA study in single cells. Then we summarized the main findings and advances so far based on these methods, including the preferences of alternative polyA (pA) site, the biological processes involved, and the corresponding consequences. We especially categorized the APA changes discovered so far and discussed their potential functions under given conditions, along with the possible underlying molecular mechanisms. With more in-depth studies on extensive samples, more signatures and functions of APA will be revealed, and its diverse roles will gradually heave in sight.

Single-Cell Sequencing Technologies for Cardiac Stem Cell Studies

Today with the rapid advancements in stem cell studies and the promising potential of using stem cells in clinical therapy, there is an increasing demand for in-depth comprehensive analysis on individual cell transcriptome and epigenome, as they play critical roles in a number of cell functions such as cell differentiation, growth, and reprogramming. The development of single-cell sequencing technologies has helped in revealing some exciting new perspectives in stem cells and regenerative medicine research. Among the various potential applications, single-cell analysis for cardiac stem cells (CSCs) holds tremendous promises in understanding the mechanisms of heart development and regeneration, which might light up the path toward cell therapy for cardiovascular diseases. This review briefly highlights the recent progresses in single-cell sequencing analysis technologies and their applications in CSC research.

Automated deconvolution of structured mixtures from heterogeneous tumor genomic data

With increasing appreciation for the extent and importance of intratumor heterogeneity, much attention in cancer research has focused on profiling heterogeneity on a single patient level. Although true single-cell genomic technologies are rapidly improving, they remain too noisy and costly at present for population-level studies. Bulk sequencing remains the standard for population-scale tumor genomics, creating a need for computational tools to separate contributions of multiple tumor clones and assorted stromal and infiltrating cell populations to pooled genomic data. All such methods are limited to coarse approximations of only a few cell subpopulations, however. In prior work, we demonstrated the feasibility of improving cell type deconvolution by taking advantage of substructure in genomic mixtures via a strategy called simplicial complex unmixing. We improve on past work by introducing enhancements to automate learning of substructured genomic mixtures, with specific emphasis on genome-wide copy number variation (CNV) data, as well as the ability to process quantitative RNA expression data, and heterogeneous combinations of RNA and CNV data. We introduce methods for dimensionality estimation to better decompose mixture model substructure; fuzzy clustering to better identify substructure in sparse, noisy data; and automated model inference methods for other key model parameters. We further demonstrate their effectiveness in identifying mixture substructure in true breast cancer CNV data from the Cancer Genome Atlas (TCGA). Source code is available at https://github.com/tedroman/WSCUnmix.

Understanding development and stem cells using single cell-based analyses of gene expression

In recent years, genome-wide profiling approaches have begun to uncover the molecular programs that drive developmental processes. In particular, technical advances that enable genome-wide profiling of thousands of individual cells have provided the tantalizing prospect of cataloging cell type diversity and developmental dynamics in a quantitative and comprehensive manner. Here, we review how single-cell RNA sequencing has provided key insights into mammalian developmental and stem cell biology, emphasizing the analytical approaches that are specific to studying gene expression in single cells.

SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models

Single-cell sequencing enables the inference of tumor phylogenies that provide insights on intra-tumor heterogeneity and evolutionary trajectories. Recently introduced methods perform this task under the infinite-sites assumption, violations of which, due to chromosomal deletions and loss of heterozygosity, necessitate the development of inference methods that utilize finite-sites models. We propose a statistical inference method for tumor phylogenies from noisy single-cell sequencing data under a finite-sites model. The performance of our method on synthetic and experimental data sets from two colorectal cancer patients to trace evolutionary lineages in primary and metastatic tumors suggests that employing a finite-sites model leads to improved inference of tumor phylogenies.

Enhancing the Mass Spectrometry Sensitivity for Oligonucleotide Detection by Organic Vapor Assisted Electrospray

There are two challenges in oligonucleotide detection by liquid chromatography coupled with mass spectrometry (LC-MS), the serious ion suppression effects caused by ion-pair reagents and the low detection sensitivity in positive mode MS. In this study, highly concentrated alcohol vapors were introduced into an enclosed electrospray ionization chamber, and oligonucleotides could be well detected in negative mode MS even with 100 mM triethylammonium acetate (TEAA) as an ion-pair reagent. The MS signal intensity was improved 600-fold (for standard oligonucleotide dT15) by the isopropanol vapor assisted electrospray, and effective ion-pair LC separation was feasibly coupled with high-sensitive MS detection. Then, oligonucleotides were successfully detected in positive mode MS with few adducts by propanoic acid vapor assisted electrospray. The signal intensity was enhanced more than 10-fold on average compared with adding acids into the electrospray solution. Finally, oligonucleotides and peptides or histones were simultaneously detected in MS with little interference with each other. Our strategy provides a useful alternative for investigating the biological functions of oligonucleotides.