Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues

Single-molecule analysis of nucleic acid biomarkers - A review

Nucleic acids are important biomarkers for disease detection, monitoring, and treatment. Advances in technologies for nucleic acid analysis have enabled discovery and clinical implementation of nucleic acid biomarkers. However, challenges remain with technologies for nucleic acid analysis, thereby limiting the use of nucleic acid biomarkers in certain contexts. Here, we review single-molecule technologies for nucleic acid analysis that can be used to overcome these challenges. We first discuss the various types of nucleic acid biomarkers important for clinical applications and conventional technologies for nucleic acid analysis. We then discuss technologies for single-molecule in vitro and in situ analysis of nucleic acid biomarkers. Finally, we discuss other ultra-sensitive techniques for nucleic acid biomarker detection.

Systems analysis of benign bladder disorders: insights from omics analysis

The signaling pathways and effectors that drive the response of the bladder to nonmalignant insults or injury are incompletely defined. Interrogation of biological systems has been revolutionized by the ability to generate high-content data sets that capture information on a variety of biomolecules in cells and tissues, from DNA to RNA to proteins. In oncology, such an approach has led to the identification of cancer subtypes, improved prognostic capability, and has provided a basis for precision treatment of patients. In contrast, systematic molecular characterization of benign bladder disorders has lagged behind, such that our ability to uncover novel therapeutic interventions or increase our mechanistic understanding of such conditions is limited. Here, we discuss existing literature on the application of omics approaches, including transcriptomics and proteomics, to urinary tract conditions characterized by pathological tissue remodeling. We discuss molecular pathways implicated in remodeling, challenges in the field, and aspirations for omics-based research in the future.

Eleven grand challenges in single-cell data science

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

Studying immune to non-immune cell cross-talk using single-cell technologies

Single-cell RNA-sequencing has uncovered immune heterogeneity, including novel cell types, states and lineages that have expanded our understanding of the immune system as a whole. More recently, studies involving both immune and non-immune cells have demonstrated the importance of immune microenvironment in development, homeostasis and disease. This review focuses on the single-cell studies mapping cell-cell interactions for variety of tissues in development, health and disease. In addition, we address the need to generate a comprehensive interaction map to answer fundamental questions in immunology as well as experimental and computational strategies required for this purpose.

Multimodal single-cell approaches shed light on T cell heterogeneity

Single-cell methods have revolutionized the study of T cell biology by enabling the identification and characterization of individual cells. This has led to a deeper understanding of T cell heterogeneity by generating functionally relevant measurements - like gene expression, surface markers, chromatin accessibility, T cell receptor sequences - in individual cells. While these methods are independently valuable, they can be augmented when applied jointly, either on separate cells from the same sample or on the same cells. Multimodal approaches are already being deployed to characterize T cells in diverse disease contexts and demonstrate the value of having multiple insights into a cell's function. But, these data sets pose new statistical challenges for integration and joint analysis.

Decoding the Body Language of Immunity: Tackling the Immune System at the Organism Level

The immune system is a dynamic mesh of molecules, cells and tissues spanning the entire organism. Despite a wealth of knowledge about the components of the immune system, little is known about the general rules governing the organismal circuitry of immunity. Deciphering the immune system at the scale of the whole organism is crucial to understanding fundamental problems in immunobiology and physiology, and to manipulate immunity for maintaining health and preventing disease. Here I discuss the emerging principles of inter-organ communications during immune responses by focusing on three common themes that are the regulation of the (i) composition, (ii) condition and (iii) coordination of communicating organs by molecular and cellular factors. Based on these common principles, I emphasize fundamental gaps in our knowledge of organismal immune processes and the outlook to tackle immunity at the scale of the whole organism.

Unravelling tumour heterogeneity by single-cell profiling of circulating tumour cells

Single-cell technologies have contributed to unravelling tumour heterogeneity, now considered a hallmark of cancer and one of the main causes of tumour resistance to cancer therapies. Liquid biopsy (LB), defined as the detection and analysis of cells or cell products released by tumours into the blood, offers an appealing minimally invasive approach that allows the characterization and monitoring of tumour heterogeneity in individual patients. Here, we will review and discuss how circulating tumour cell (CTC) analysis at single-cell resolution provides unique insights into tumour heterogeneity that are not revealed by analysis of circulating tumour DNA (ctDNA) derived from LBs. The molecular analysis of CTCs provides complementary information to that of genomic aberrations determined using ctDNA to fully describe many different cellular components (for example, DNA, RNA, proteins and metabolites) that can influence tumour heterogeneity.

Invited Review: Long non-coding RNAs: important regulators in the development, function and disorders of the central nervous system

Genome-wide transcriptional studies have demonstrated that tens of thousands of long non-coding RNAs (lncRNA) genes are expressed in the central nervous system (CNS) and that they exhibit tissue- and cell-type specificity. Their regulated and dynamic expression and their co-expression with protein-coding gene neighbours have led to the study of the functions of lncRNAs in CNS development and disorders. In this review, we describe the general characteristics, localization and classification of lncRNAs. We also elucidate the examples of the molecular mechanisms of nuclear and cytoplasmic lncRNA actions in the CNS and discuss common experimental approaches used to identify and unveil the functions of lncRNAs. Additionally, we provide examples of lncRNA studies of cell differentiation and CNS disorders including CNS injuries and neurodegenerative diseases. Finally, we review novel lncRNA-based therapies. Overall, this review highlights the important biological roles of lncRNAs in CNS functions and disorders.

A computational framework for DNA sequencing microscopy

We describe a method whereby microscale spatial information such as the relative positions of biomolecules on a surface can be transferred to a sequence-based format and reconstructed into images without conventional optics. Barcoded DNA "polymerase colony" (polony) amplification techniques enable one to distinguish specific locations of a surface by their sequence. Image formation is based on pairwise fusion of uniquely tagged and spatially adjacent polonies. The network of polonies connected by shared borders forms a graph whose topology can be reconstructed from pairs of barcodes fused during a polony cross-linking phase, the sequences of which are determined by recovery from the surface and next-generation (next-gen) sequencing. We developed a mathematical and computational framework for this principle called polony adjacency reconstruction for spatial inference and topology and show that Euclidean spatial data may be stored and transmitted in the form of graph topology. Images are formed by transferring molecular information from a surface of interest, which we demonstrated in silico by reconstructing images formed from stochastic transfer of hypothetical molecular markers. The theory developed here could serve as a basis for an automated, multiplexable, and potentially superresolution imaging method based purely on molecular information.

Qualifying antibodies for image-based immune profiling and multiplexed tissue imaging

Multiplexed tissue imaging enables precise, spatially resolved enumeration and characterization of cell types and states in human resection specimens. A growing number of methods applicable to formalin-fixed, paraffin-embedded (FFPE) tissue sections have been described, the majority of which rely on antibodies for antigen detection and mapping. This protocol provides step-by-step procedures for confirming the selectivity and specificity of antibodies used in fluorescence-based tissue imaging and for the construction and validation of antibody panels. Although the protocol is implemented using tissue-based cyclic immunofluorescence (t-CyCIF) as an imaging platform, these antibody-testing methods are broadly applicable. We demonstrate assembly of a 16-antibody panel for enumerating and localizing T cells and B cells, macrophages, and cells expressing immune checkpoint regulators. The protocol is accessible to individuals with experience in microscopy and immunofluorescence; some experience in computation is required for data analysis. A typical 30-antibody dataset for 20 FFPE slides can be generated within 2 weeks.

Next-generation computational tools for interrogating cancer immunity

The remarkable success of cancer therapies with immune checkpoint blockers is revolutionizing oncology and has sparked intensive basic and translational research into the mechanisms of cancer-immune cell interactions. In parallel, numerous novel cutting-edge technologies for comprehensive molecular and cellular characterization of cancer immunity have been developed, including single-cell sequencing, mass cytometry and multiplexed spatial cellular phenotyping. In order to process, analyse and visualize multidimensional data sets generated by these technologies, computational methods and software tools are required. Here, we review computational tools for interrogating cancer immunity, discuss advantages and limitations of the various methods and provide guidelines to assist in method selection.

Mouse Models for Diseases in the Cholangiocyte Lineage

Cholangiopathies are an important group of liver diseases affecting the biliary system, and the purpose of this review is to describe how diseases in the biliary system can be studied in mouse models. A particular focus is placed on mouse models for Alagille syndrome, a cholangiopathy with a strong genetic link to dysfunctional Notch signaling. Recently, a number of different genetic mouse models based on various manipulations of the Notch signaling pathway have been generated to study Alagille syndrome, and we discuss the resulting phenotypes, and possible causes for the phenotypic heterogeneity among the various models. In the final section, we provide a more general discussion on how well mouse models can be expected to mimic human liver disease, as well as an outlook toward the need for new technologies that can help us to gain new insights from mouse models for liver disease.

BART-Seq: cost-effective massively parallelized targeted sequencing for genomics, transcriptomics, and single-cell analysis

We describe a highly sensitive, quantitative, and inexpensive technique for targeted sequencing of transcript cohorts or genomic regions from thousands of bulk samples or single cells in parallel. Multiplexing is based on a simple method that produces extensive matrices of diverse DNA barcodes attached to invariant primer sets, which are all pre-selected and optimized in silico. By applying the matrices in a novel workflow named Barcode Assembly foR Targeted Sequencing (BART-Seq), we analyze developmental states of thousands of single human pluripotent stem cells, either in different maintenance media or upon Wnt/β-catenin pathway activation, which identifies the mechanisms of differentiation induction. Moreover, we apply BART-Seq to the genetic screening of breast cancer patients and identify BRCA mutations with very high precision. The processing of thousands of samples and dynamic range measurements that outperform global transcriptomics techniques makes BART-Seq first targeted sequencing technique suitable for numerous research applications.

Activity-Regulated Transcription: Bridging the Gap between Neural Activity and Behavior

Gene transcription is the process by which the genetic codes of organisms are read and interpreted as a set of instructions for cells to divide, differentiate, migrate, and mature. As cells function in their respective niches, transcription further allows mature cells to interact dynamically with their external environment while reliably retaining fundamental information about past experiences. In this Review, we provide an overview of the field of activity-dependent transcription in the vertebrate brain and highlight contemporary work that ranges from studies of activity-dependent chromatin modifications to plasticity mechanisms underlying adaptive behaviors. We identify key gaps in knowledge and propose integrated approaches toward a deeper understanding of how activity-dependent transcription promotes the refinement and plasticity of neural circuits for cognitive function.

Network modeling of single-cell omics data: challenges, opportunities, and progresses

Single-cell multi-omics technologies are rapidly evolving, prompting both methodological advances and biological discoveries at an unprecedented speed. Gene regulatory network modeling has been used as a powerful approach to elucidate the complex molecular interactions underlying biological processes and systems, yet its application in single-cell omics data modeling has been met with unique challenges and opportunities. In this review, we discuss these challenges and opportunities, and offer an overview of the recent development of network modeling approaches designed to capture dynamic networks, within-cell networks, and cell-cell interaction or communication networks. Finally, we outline the remaining gaps in single-cell gene network modeling and the outlooks of the field moving forward.

The Human Lung Cell Atlas: A High-Resolution Reference Map of the Human Lung in Health and Disease

Lung disease accounts for every sixth death globally. Profiling the molecular state of all lung cell types in health and disease is currently revolutionizing the identification of disease mechanisms and will aid the design of novel diagnostic and personalized therapeutic regimens. Recent progress in high-throughput techniques for single-cell genomic and transcriptomic analyses has opened up new possibilities to study individual cells within a tissue, classify these into cell types, and characterize variations in their molecular profiles as a function of genetics, environment, cell-cell interactions, developmental processes, aging, or disease. Integration of these cell state definitions with spatial information allows the in-depth molecular description of cellular neighborhoods and tissue microenvironments, including the tissue resident structural and immune cells, the tissue matrix, and the microbiome. The Human Cell Atlas consortium aims to characterize all cells in the healthy human body and has prioritized lung tissue as one of the flagship projects. Here, we present the rationale, the approach, and the expected impact of a Human Lung Cell Atlas.

Exploring single cells in space and time during tissue development, homeostasis and regeneration

Complex 3D tissues arise during development following tightly organized events in space and time. In particular, gene regulatory networks and local interactions between single cells lead to emergent properties at the tissue and organism levels. To understand the design principles of tissue organization, we need to characterize individual cells at given times, but we also need to consider the collective behavior of multiple cells across different spatial and temporal scales. In recent years, powerful single cell methods have been developed to characterize cells in tissues and to address the challenging questions of how different tissues are formed throughout development, maintained in homeostasis, and repaired after injury and disease. These approaches have led to a massive increase in data pertaining to both mRNA and protein abundances in single cells. As we review here, these new technologies, in combination with in toto live imaging, now allow us to bridge spatial and temporal information quantitatively at the single cell level and generate a mechanistic understanding of tissue development.

X-Chromosome Inactivation and Escape from X Inactivation in Mouse

X chromosome inactivation silences one X chromosome in female mammals. However, this silencing is incomplete, and some genes escape X inactivation. We describe methods to determine the chromosome-wide X inactivation status of genes in tissues or cell lines derived from mice using a combination of skewing of X inactivation and allele-specific analyses of gene expression based on RNA-seq.

DNA sequencing in high-throughput neuroanatomy

Mapping brain connectivity at single cell resolution is critical for understanding brain structure. For decades, such mapping has been principally approached with microscopy techniques, aiming to visualize neurons and their connections. However, these techniques are often very labor intensive and do not scale well to the complexity of mammalian brains. We recently leveraged the speed and parallelization of DNA sequencing to map the projections of thousands of single neurons in single experiments, and to map cortical mesoscale connectivity in single mice. Here, I review the state of sequencing-based neuroanatomy, and discuss future directions in synaptic connectivity mapping and comparative connectomics.

Single cell RNA-sequencing: replicability of cell types

Recent technical advances have enabled transcriptomics experiments at an unprecedented scale, and single-cell profiles from neural tissue are accumulating rapidly. There has been considerable effort to use these profiles to understand cell diversity, primarily through unsupervised clustering and differential expression analysis. However, current practices to validate these findings vary. In this review, we describe recent efforts to evaluate clusters from single-cell RNA-sequencing data, and provide a framework for considering current evidence and practices in terms of their capacity to establish principles of cell biology. Single-cell RNA-sequencing has already transformed neuroscience. By facilitating detailed comparative and genetic perturbation analyses, it may provide the tools to uncover fundamental mechanisms of neural diversity throughout the tree of life.

Studying interactions between dendritic cells and T cells in vivo

Antigen presentation is the key first step in the establishment of an antigen-specific T cell response. Among professional antigen presenting cells (APCs), dendritic cells (DCs) are the major population responsible for the priming of both CD4+ and CD8+ naïve T cells. This priming requires physical interaction between the DC and the T cell; during which signals are exchanged that determine both the magnitude and the quality of the ensuing response. The nature of these signals varies widely depending on the nature of the antigen, the anatomical site in which they take place, and the phenotype of the antigen-presenting DC, making the study of the dynamics, microanatomical distribution and phenotypic variation of DCs a key part of our understanding of adaptive immunity. Here, we provide a brief survey of how our view of T cell activation by DCs has evolved over recent years as intravital multiphoton microscopy and other emerging technologies have expanded our ability to study these events in vivo.

Multiplexed detection of RNA using MERFISH and branched DNA amplification

Multiplexed error-robust fluorescence in situ hybridization (MERFISH) allows simultaneous imaging of numerous RNA species in their native cellular environment and hence spatially resolved single-cell transcriptomic measurements. However, the relatively modest brightness of signals from single RNA molecules can become limiting in a number of applications, such as increasing the imaging throughput, imaging shorter RNAs, and imaging samples with high degrees of background, such as some tissue samples. Here, we report a branched DNA (bDNA) amplification approach for MERFISH measurements. This approach produces a drastic signal increase in RNA FISH samples without increasing the fluorescent spot size for individual RNAs or increasing the variation in brightness from spot to spot, properties that are important for MERFISH imaging. Using this amplification approach in combination with MERFISH, we demonstrated RNA imaging and profiling with a near 100% detection efficiency. We further demonstrated that signal amplification improves MERFISH performance when fewer FISH probes are used for each RNA species, which should allow shorter RNAs to be imaged. We anticipate that the combination of bDNA amplification with MERFISH should facilitate many other applications and extend the range of biological questions that can be addressed by this technique in both cell culture and tissues.

Sequence-Specific Probe-Mediated Isothermal Amplification for the Single-Copy Sensitive Detection of Nucleic Acid

There is currently the lack of a method for precisely monitoring the progress of isothermal amplification reactions by means of sequence-specific fluorescent probes like the TaqMan probe used in the PCR system. Here, we created a circular fluorescent probe-mediated isothermal amplification (CFPA) method. This novel method uses two circular fluorescent probes and Bst DNA polymerase to construct an overlapping structure that can be cut off by flap structure-specific endonuclease 1, separating the fluorescence and quenching groups on the probes. The results showed single-copy sensitivity, ultrahigh specificity, stability (C.V. < 0.1), and anti-interference ability in detecting nucleic acid samples. A clinical trial demonstrated the perfect effectiveness of this method in the diagnosis of rotavirus infection and consistency with the gold standard method used in the clinic ( p > 0.05). In summary, we present a new, reliable, and precise isothermal amplification approach for applications in biomedical research and the clinical accurate diagnosis of pathogen infections.

Personalized medicine-concepts, technologies, and applications in inflammatory skin diseases

The current state, tools, and applications of personalized medicine with special emphasis on inflammatory skin diseases like psoriasis and atopic dermatitis are discussed. Inflammatory pathways are outlined as well as potential targets for monoclonal antibodies and small-molecule inhibitors.

Tissue Engineering: Understanding the Role of Biomaterials and Biophysical Forces on Cell Functionality Through Computational and Structural Biotechnology Analytical Methods

Within the past 25 years, tissue engineering (TE) has grown enormously as a science and as an industry. Although classically concerned with the recapitulation of tissue and organ formation in our body for regenerative medicine, the evolution of TE research is intertwined with progress in other fields through the examination of cell function and behaviour in isolated biomimetic microenvironments. As such, TE applications now extend beyond the field of tissue regeneration research, operating as a platform for modifiable, physiologically-representative in vitro models with the potential to improve the translation of novel therapeutics into the clinic through a more informed understanding of the relevant molecular biology, structural biology, anatomy, and physiology. By virtue of their biomimicry, TE constructs incorporate features of extracellular macrostructure, molecular adhesive moieties, and biomechanical properties, converging with computational and structural biotechnology advances. Accordingly, this mini-review serves to contextualise TE for the computational and structural biotechnology reader and provides an outlook on how the disciplines overlap with respect to relevant advanced analytical applications.

Barcoded solid-phase RNA capture for Spatial Transcriptomics profiling in mammalian tissue sections

Spatial resolution of gene expression enables gene expression events to be pinpointed to a specific location in biological tissue. Spatially resolved gene expression in tissue sections is traditionally analyzed using immunohistochemistry (IHC) or in situ hybridization (ISH). These technologies are invaluable tools for pathologists and molecular biologists; however, their throughput is limited to the analysis of only a few genes at a time. Recent advances in RNA sequencing (RNA-seq) have made it possible to obtain unbiased high-throughput gene expression data in bulk. Spatial Transcriptomics combines the benefits of traditional spatially resolved technologies with the massive throughput of RNA-seq. Here, we present a protocol describing how to apply the Spatial Transcriptomics technology to mammalian tissue. This protocol combines histological staining and spatially resolved RNA-seq data from intact tissue sections. Once suitable tissue-specific conditions have been established, library construction and sequencing can be completed in ~5-6 d. Data processing takes a few hours, with the exact timing dependent on the sequencing depth. Our method requires no special instruments and can be performed in any laboratory with access to a cryostat, microscope and next-generation sequencing.

Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution

Spatial positions of cells in tissues strongly influence function, yet a high-throughput, genome-wide readout of gene expression with cellular resolution is lacking. We developed Slide-seq, a method for transferring RNA from tissue sections onto a surface covered in DNA-barcoded beads with known positions, allowing the locations of the RNA to be inferred by sequencing. Using Slide-seq, we localized cell types identified by single-cell RNA sequencing datasets within the cerebellum and hippocampus, characterized spatial gene expression patterns in the Purkinje layer of mouse cerebellum, and defined the temporal evolution of cell type-specific responses in a mouse model of traumatic brain injury. These studies highlight how Slide-seq provides a scalable method for obtaining spatially resolved gene expression data at resolutions comparable to the sizes of individual cells.

Single-Cell Genomics

BACKGROUND

Single-cell genomics is an approach to investigate cell heterogeneity and to identify new molecular features correlated with clinical outcomes. This approach allows identification of the complexity of cell diversity in a sample without the loss of information that occurs when multicellular or bulk tissue samples are analyzed.

CONTENT

The first single-cell RNA-sequencing study was published in 2009, and since then many more studies and single-cell sequencing methods have been published. These studies have had a major impact on several fields, including microbiology, neurobiology, cancer, and developmental biology. Recently, improvements in reliability and the development of commercial single-cell isolation platforms are opening the potential of this technology to the clinical laboratory.

SUMMARY

In this review we provide an overview of the current state of single-cell genomics. We describe opportunities in clinical research and medical applications.

Highly Multiplexed Fluorescence in Situ Hybridization for in Situ Genomics

The quantification of changes in gene copy number is critical to our understanding of tumor biology and for the clinical management of cancer patients. DNA fluorescence in situ hybridization is the gold standard method to detect copy number alterations, but it is limited by the number of genes one can quantify simultaneously. To increase the throughput of this informative technique, a fluorescent bar-code system for the unique labeling of dozens of genes and an automated image analysis algorithm that enabled their simultaneous hybridization for the quantification of gene copy numbers were devised. We demonstrate the reliability of this multiplex approach on normal human lymphocytes, metaphase spreads of transformed cell lines, and cultured circulating tumor cells. It also opens the door to the development of gene panels for more comprehensive analysis of copy number changes in tissue, including the study of heterogeneity and of high-throughput clinical assays that could provide rapid quantification of gene copy numbers in samples with limited cellularity, such as circulating tumor cells.

A brief review of single-cell transcriptomic technologies

In recent years, there has been an effort to develop new technologies for measuring gene expression and sequence information from thousands of individual cells. Large data sets that were obtained using these 'single cell' technologies have allowed scientists to address fundamental questions in biomedicine ranging from stems cells and development to cancer and immunology. Here, we provide a brief review of recent developments in single-cell technology. Our intention is to provide a quick background for newcomers to the field as well as a deeper description of some of the leading technologies to date.

A microfluidic platform towards automated multiplexed in situ sequencing

Advancements in multiplexed in situ RNA profiling techniques have given unprecedented insight into spatial organization of tissues by enabling single-molecule quantification and sub-micron localization of dozens to thousands of RNA species simultaneously in cells and entire tissue sections. However, the lack of automation of the associated complex experimental procedures represents a potential hurdle towards their routine use in laboratories. Here, we demonstrate an approach towards automated generation and sequencing of barcoded mRNA amplicons in situ, directly in fixed cells. This is achieved through adaptation of a microfluidic tool compatible with standard microscope slides and cover glasses. The adapted tool combines a programmable reagent delivery system with temperature controller and flow cell to perform established in situ sequencing protocols, comprising hybridization and ligation of gene-specific padlock probes, rolling circle amplification of the probes yielding barcoded amplicons and identification of amplicons through barcode sequencing. By adapting assay parameters (e.g. enzyme concentration and temperature), we achieve a near-identical performance in identifying mouse beta-actin transcripts, in comparison with the conventional manual protocol. The technically adapted assay features i) higher detection efficiency, ii) shorter protocol time, iii) lower consumption of oligonucleotide reagents but slightly more enzyme. Such an automated microfluidic tissue processor for in situ sequencing studies would greatly enhance its research potentials especially for cancer diagnostics, thus paving way to rapid and effective therapies.

Design of anapole mode electromagnetic field enhancement structures for biosensing applications

The design of an all-dielectric nanoantenna based on nonradiating "anapole" modes is studied for biosensing applications in an aqueous environment, using FDTD electromagnetic simulation. The strictly confined electromagnetic field within a circular or rectangular opening at the center of a cylindrical silicon disk produces a single point electromagnetic hotspot with up to 6.5x enhancement of |E|, for the 630-650 nm wavelength range, and we can increase the value up to 25x by coupling additional electromagnetic energy from an underlying PEC-backed substrate. We characterize the effects of the substrate design and slot dimensions on the field enhancement magnitude, for devices operating in a water medium.

Is it possible to reconstruct an accurate cell lineage using CRISPR recorders?

Cell lineages provide the framework for understanding how cell fates are decided during development. Describing cell lineages in most organisms is challenging; even a fruit fly larva has ~50,000 cells and a small mammal has >1 billion cells. Recently, the idea of applying CRISPR to induce mutations during development, to be used as heritable markers for lineage reconstruction, has been proposed by several groups. While an attractive idea, its practical value depends on the accuracy of the cell lineages that can be generated. Here, we use computer simulations to estimate the performance of these approaches under different conditions. We incorporate empirical data on CRISPR-induced mutation frequencies in Drosophila. We show significant impacts from multiple biological and technical parameters - variable cell division rates, skewed mutational outcomes, target dropouts and different sequencing strategies. Our approach reveals the limitations of published CRISPR recorders, and indicates how future implementations can be optimised.

Editorial note

This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

Quantitative imaging of lipid droplets in single cells

The combination of next generation sequencing (NGS) and automated liquid handling platforms has led to a revolution in single-cell genomic studies. However, many molecules that are critical to understanding the functional roles of cells in a complex tissue or organs, are not directly encoded in the genome, and therefore cannot be profiled with NGS. Lipids, for example, play a critical role in many metabolic processes but cannot be detected by sequencing. Recent developments in quantitative imaging, particularly coherent Raman scattering (CRS) techniques, have produced a suite of tools for studying lipid content in single cells. This article reviews CRS imaging and computational image processing techniques for non-destructive profiling of dynamic changes in lipid composition and spatial distribution at the single-cell level. As quantitative CRS imaging progresses synergistically with microfluidic and microscopic platforms for single-cell genomic analysis, we anticipate that these techniques will bring researchers closer towards combined lipidomic and genomic analysis.

Detection of mRNA Transfer Between Mammalian Cells in Coculture by Single-Molecule Fluorescent In Situ Hybridization (smFISH)

In eukaryotic cells, a small percentage of mRNA molecules can undergo transfer from one cell to another. mRNA transfer occurs primarily via membrane nanotubes, which are long thin protrusions that are produced by numerous cell types and can connect cells that can be up to hundreds of microns apart. Potentially, mRNAs might also transfer via extracellular vesicles (EVs). Here we describe a method to detect transferred mRNA in cocultures of two different cell types and to distinguish between nanotube- and EVs-mediated transfer. This method uses single molecule fluorescent in situ hybridization (smFISH) to provide an accurate and quantitative detection of transferred mRNA molecules and their subcellular localization. Following the guidelines presented here will allow the user to investigate mRNA transfer of most transcripts in any co-culture system. In addition, we present modifications that improve nanotube preservation during the smFISH procedure.

An Overview of Methodologies in Studying lncRNAs in the High-Throughput Era: When Acronyms ATTACK!

The discovery of pervasive transcription in eukaryotic genomes provided one of many surprising (and perhaps most surprising) findings of the genomic era and led to the uncovering of a large number of previously unstudied transcriptional events. This pervasive transcription leads to the production of large numbers of noncoding RNAs (ncRNAs) and thus opened the window to study these diverse, abundant transcripts of unclear relevance and unknown function. Since that discovery, recent advances in high-throughput sequencing technologies have identified a large collection of ncRNAs, from microRNAs to long noncoding RNAs (lncRNAs). Subsequent discoveries have shown that many lncRNAs play important roles in various eukaryotic processes; these discoveries have profoundly altered our understanding of the regulation of eukaryotic gene expression. Although the identification of ncRNAs has become a standard experimental approach, the functional characterization of these diverse ncRNAs remains a major challenge. In this chapter, we highlight recent progress in the methods to identify lncRNAs and the techniques to study the molecular function of these lncRNAs and the application of these techniques to the study of plant lncRNAs.

Emergence of Bias During the Synthesis and Amplification of cDNA for scRNA-seq

The advent of single-cell omics technology has promoted our understanding of the genomic, epigenomic, and transcriptomic heterogeneity in individual cells. Compared to traditional sequencing studies using bulk cells, single-cell transcriptome technology is naturally more dynamic for in depth analysis of genomic variation resulting from cell division and is useful in unraveling the regulatory mechanisms of gene networks in many diseases. However, there are still some limitations of current single-cell RNA sequencing (scRNA-seq) protocols. Biases that arise during the RNA reverse transcription and cDNA pre-amplification steps are the most common problems and play pivotal roles in limiting the quantitative accuracy of scRNA-seq. In this review, we will describe how these biases emerge and impact scRNA-seq protocols. Moreover, we will introduce several current and convenient modified scRNA-seq methods that allow for bias to be decreased and estimated.

Understanding tumor ecosystems by single-cell sequencing: promises and limitations

Cellular heterogeneity within and across tumors has been a major obstacle in understanding and treating cancer, and the complex heterogeneity is masked if bulk tumor tissues are used for analysis. The advent of rapidly developing single-cell sequencing technologies, which include methods related to single-cell genome, epigenome, transcriptome, and multi-omics sequencing, have been applied to cancer research and led to exciting new findings in the fields of cancer evolution, metastasis, resistance to therapy, and tumor microenvironment. In this review, we discuss recent advances and limitations of these new technologies and their potential applications in cancer studies.

Beyond the H&E: Advanced Technologies for in situ Tissue Biomarker Imaging

For decades, histopathology with routine hematoxylin and eosin staining has been and remains the gold standard for reaching a morphologic diagnosis in tissue samples from humans and veterinary species. However, within the past decade, there has been exponential growth in advanced techniques for in situ tissue biomarker imaging that bridge the divide between anatomic and molecular pathology. It is now possible to simultaneously observe localization and expression magnitude of multiple protein, nucleic acid, and molecular targets in tissue sections and apply machine learning to synthesize vast, image-derived datasets. As these technologies become more sophisticated and widely available, a team-science approach involving subspecialists with medical, engineering, and physics backgrounds is critical to upholding quality and validity in studies generating these data. The purpose of this manuscript is to detail the scientific premise, tools and training, quality control, and data collection and analysis considerations needed for the most prominent advanced imaging technologies currently applied in tissue sections: immunofluorescence, in situ hybridization, laser capture microdissection, matrix-assisted laser desorption ionization imaging mass spectrometry, and spectroscopic/optical methods. We conclude with a brief overview of future directions for ex vivo and in vivo imaging techniques.

Tumour heterogeneity and metastasis at single-cell resolution

Tumours comprise a heterogeneous collection of cells with distinct genetic and phenotypic properties that can differentially promote progression, metastasis and drug resistance. Emerging single-cell technologies provide a new opportunity to profile individual cells within tumours and investigate what roles they play in these processes. This Review discusses key technological considerations for single-cell studies in cancer, new findings using single-cell technologies and critical open questions for future applications.

Single-Cell Transcriptomics in Cancer Immunobiology: The Future of Precision Oncology

Cancer is a heterogeneous and complex disease. Tumors are formed by cancer cells and a myriad of non-cancerous cell types that together with the extracellular matrix form the tumor microenvironment. These cancer-associated cells and components contribute to shape the progression of cancer and are deeply involved in patient outcome. The immune system is an essential part of the tumor microenvironment, and induction of cancer immunotolerance is a necessary step involved in tumor formation and growth. Immune mechanisms are intimately associated with cancer progression, invasion, and metastasis; as well as to tumor dormancy and modulation of sensitivity to drug therapy. Transcriptome analyses have been extensively used to understand the heterogeneity of tumors, classifying tumors into molecular subtypes and establishing signatures that predict response to therapy and patient outcomes. However, the classification of the tumor cell diversity and specially the identification of rare populations has been limited in these transcriptomic analyses of bulk tumor cell populations. Massively-parallel single-cell RNAseq analysis has emerged as a powerful method to unravel heterogeneity and to study rare cell populations in cancer, through unsupervised sampling and modeling of transcriptional states in single cells. In this context, the study of the role of the immune system in cancer would benefit from single cell approaches, as it will enable the characterization and/or discovery of the cell types and pathways involved in cancer immunotolerance otherwise missed in bulk transcriptomic information. Thus, the analysis of gene expression patterns at single cell resolution holds the potential to provide key information to develop precise and personalized cancer treatment including immunotherapy. This review is focused on the latest single-cell RNAseq methodologies able to agnostically study thousands of tumor cells as well as targeted single-cell RNAseq to study rare populations within tumors. In particular, we will discuss methods to study the immune system in cancer. We will also discuss the current challenges to the study of cancer at the single cell level and the potential solutions to the current approaches.

SD-chip enabled quantitative detection of HIV RNA using digital nucleic acid sequence-based amplification (dNASBA)

Quantitative detection of RNA is important in molecular biology and clinical diagnostics. Nucleic acid sequence-based amplification (NASBA), a single-step method to amplify single-stranded RNA, is attractive for use in point-of-care (POC) diagnostics because it is an isothermal technique that is as sensitive as RT-PCR with a shorter reaction time. However, NASBA is limited in its ability to provide accurate quantitative information, such as viral load or RNA copy number. Here we test a digital format of NASBA (dNASBA) using a self-digitization (SD) chip platform, and apply it to quantifying HIV-1 RNA. We demonstrate that dNASBA is more sensitive and accurate than the real-time quantitative NASBA, and can be used to quantify HIV-1 RNA in plasma samples. Digital NASBA is thus a promising POC diagnostics tool for use in resource-limited settings.

Following hearts, one cell at a time: recent applications of single-cell RNA sequencing to the understanding of heart disease

The mammalian heart contains heterogeneous cell types contributing to pathological changes in cardiac disease. In this Comment, we explore how single-cell transcriptomic approaches are unveiling intricate cellular mechanisms and gene co-expression networks that regulate the workings, and failings, of the heart.

Intracellular RNA-tracking methods

RNA tracking allows researchers to visualize RNA molecules in cells and tissues, providing important spatio-temporal information regarding RNA dynamics and function. Methods such as fluorescent in situ hybridization (FISH) and molecular beacons rely on complementary oligonucleotides to label and view endogenous transcripts. Other methods create artificial chimeric transcripts coupled with bacteriophage-derived coat proteins (e.g. MS2, λN) to tag molecules in live cells. In other approaches, endogenous RNAs are recognized by complementary RNAs complexed with noncatalytic Cas proteins. Each technique has its own set of strengths and limitations that must be considered when planning an experiment. Here, we discuss the mechanisms, advantages, and weaknesses of in situ hybridization, molecular beacons, MS2 tagging and Cas-derived systems, as well as how RNA tracking can be employed to study various aspects of molecular biology.

Enhancers active in dopamine neurons are a primary link between genetic variation and neuropsychiatric disease

Enhancers function as DNA logic gates and may control specialized functions of billions of neurons. Here we show a tailored program of noncoding genome elements active in situ in physiologically distinct dopamine neurons of the human brain. We found 71,022 transcribed noncoding elements, many of which were consistent with active enhancers and with regulatory mechanisms in zebrafish and mouse brains. Genetic variants associated with schizophrenia, addiction, and Parkinson's disease were enriched in these elements. Expression quantitative trait locus analysis revealed that Parkinson's disease-associated variants on chromosome 17q21 cis-regulate the expression of an enhancer RNA in dopamine neurons. This study shows that enhancers in dopamine neurons link genetic variation to neuropsychiatric traits.

Post-transcriptional Processing of mRNA in Neurons: The Vestiges of the RNA World Drive Transcriptome Diversity

Neurons are morphologically complex cells that rely on the compartmentalization of protein expression to develop and maintain their extraordinary cytoarchitecture. This formidable task is achieved, at least in part, by targeting mRNA to subcellular compartments where they are rapidly translated. mRNA transcripts are the conveyor of genetic information from DNA to the translational machinery, however, they are also endowed with additional functions linked to both the coding sequence (open reading frame, or ORF) and the flanking 5′ and 3′ untranslated regions (UTRs), that may harbor coding-independent functions. In this review, we will highlight recent evidences supporting new coding-dependent and -independent functions of mRNA and discuss how nuclear and cytoplasmic post-transcriptional modifications of mRNA contribute to localization and translation in mammalian cells with specific emphasis on neurons. We also describe recently developed techniques that can be employed to study RNA dynamics at subcellular level in eukaryotic cells in developing and regenerating neurons.

Centrosome aberrations and chromosome instability contribute to tumorigenesis and intra-tumor heterogeneity

Centrosomes serve as the major microtubule organizing centers in cells and thereby contribute to cell shape, polarity, and motility. Also, centrosomes ensure equal chromosome segregation during mitosis. Centrosome aberrations arise when the centrosome cycle is deregulated, or as a result of cytokinesis failure. A long-standing postulate is that centrosome aberrations are involved in the initiation and progression of cancer. However, this notion has been a subject of controversy because until recently the relationship has been correlative. Recently, it was shown that numerical or structural centrosome aberrations can initiate tumors in certain tissues in mice, as well as invasion. Particularly, we will focus on centrosome amplification and chromosome instability as drivers of intra-tumor heterogeneity and their consequences in cancer. We will also discuss briefly the controversies surrounding this theory to highlight the fact that the role of both centrosome amplification and chromosome instability in cancer is highly context-dependent. Further, we will discuss single-cell sequencing as a novel technique to understand intra-tumor heterogeneity and some therapeutic approaches to target chromosome instability.