Laser capture microscopy coupled with Smart-seq2 for precise spatial transcriptomic profiling

Application of single cell genomics to focal epilepsies: A call to action

Focal epilepsies are the largest epilepsy subtype and associated with significant morbidity. Somatic variation is a newly recognized genetic mechanism underlying a subset of focal epilepsies, but little is known about the processes through which somatic mosaicism causes seizures, the cell types carrying the pathogenic variants, or their developmental origin. Meanwhile, the inception of single cell biology has completely revolutionized the study of neurological diseases and has the potential to answer some of these key questions. Focusing on single cell genomics, transcriptomics, and epigenomics in focal epilepsy research, circumvents the averaging artifact associated with studying bulk brain tissue and offers the kind of granularity that is needed for investigating the consequences of somatic mosaicism. Here we have provided a brief overview of some of the most developed single cell techniques and the major considerations around applying them to focal epilepsy research.

Single-cell RNA sequencing in cancer: Applications, advances, and emerging challenges

Cancer has become one of the greatest threats to human health, and new technologies are urgently needed to further clarify the mechanisms of cancer so that better detection and treatment strategies can be developed. At present, extensive genomic analysis and testing of clinical specimens shape the insights into carcinoma. Nevertheless, carcinoma of humans is a complex ecosystem of cells, including carcinoma cells and immunity-related and stroma-related subsets, with accurate characteristics obscured by extensive genome-related approaches. A growing body of research shows that sequencing of single-cell RNA (scRNA-seq) is emerging to be an effective way for dissecting human tumor tissue at single-cell resolution, presenting one prominent way for explaining carcinoma biology. This review summarizes the research progress of scRNA-seq in the field of tumors, focusing on the application of scRNA-seq in tumor circulating cells, tumor stem cells, tumor drug resistance, the tumor microenvironment, and so on, which provides a new perspective for tumor research.

Single-Cell TCR and Transcriptome Analysis: An Indispensable Tool for Studying T-Cell Biology and Cancer Immunotherapy

T cells have been known to be the driving force for immune response and cancer immunotherapy. Recent advances on single-cell sequencing techniques have empowered scientists to discover new biology at the single-cell level. Here, we review the single-cell techniques used for T-cell studies, including T-cell receptor (TCR) and transcriptome analysis. In addition, we summarize the approaches used for the identification of T-cell neoantigens, an important aspect for T-cell mediated cancer immunotherapy. More importantly, we discuss the applications of single-cell techniques for T-cell studies, including T-cell development and differentiation, as well as the role of T cells in autoimmunity, infectious disease and cancer immunotherapy. Taken together, this powerful tool not only can validate previous observation by conventional approaches, but also can pave the way for new discovery, such as previous unidentified T-cell subpopulations that potentially responsible for clinical outcomes in patients with autoimmunity or cancer.

Single-Cell Sequencing Methodologies: From Transcriptome to Multi-Dimensional Measurement

Cells are the basic building blocks of biological systems, with inherent unique molecular features and development trajectories. The study of single cells facilitates in-depth understanding of cellular diversity, disease processes, and organization of multicellular organisms. Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for the interrogation of gene expression patterns and the dynamics of single cells, allowing cellular heterogeneity to be dissected at unprecedented resolution. Nevertheless, measuring at only transcriptome level or 1D is incomplete; the cellular heterogeneity reflects in multiple dimensions, including the genome, epigenome, transcriptome, spatial, and even temporal dimensions. Hence, integrative single cell analysis is highly desired. In addition, the way to interpret sequencing data by virtue of bioinformatic tools also exerts critical roles in revealing differential gene expression. Here, a comprehensive review that summarizes the cutting-edge single-cell transcriptome sequencing methodologies, including scRNA-seq, spatial and temporal transcriptome profiling, multi-omics sequencing and computational methods developed for scRNA-seq data analysis is provided. Finally, the challenges and perspectives of this field are discussed.

Polar Gini Curve: A Technique to Discover Gene Expression Spatial Patterns from Single-cell RNA-seq Data

In this work, we describe the development of Polar Gini Curve, a method for characterizing cluster markers by analyzing single-cell RNA sequencing (scRNA-seq) data. Polar Gini Curve combines the gene expression and the 2D coordinates ("spatial") information to detect patterns of uniformity in any clustered cells from scRNA-seq data. We demonstrate that Polar Gini Curve can help users characterize the shape and density distribution of cells in a particular cluster, which can be generated during routine scRNA-seq data analysis. To quantify the extent to which a gene is uniformly distributed in a cell cluster space, we combine two polar Gini curves (PGCs)-one drawn upon the cell-points expressing the gene (the "foreground curve") and the other drawn upon all cell-points in the cluster (the "background curve"). We show that genes with highly dissimilar foreground and background curves tend not to uniformly distributed in the cell cluster-thus having spatially divergent gene expression patterns within the cluster. Genes with similar foreground and background curves tend to uniformly distributed in the cell cluster-thus having uniform gene expression patterns within the cluster. Such quantitative attributes of PGCs can be applied to sensitively discover biomarkers across clusters from scRNA-seq data. We demonstrate the performance of the Polar Gini Curve framework in several simulation case studies. Using this framework to analyze a real-world neonatal mouse heart cell dataset, the detected biomarkers may characterize novel subtypes of cardiac muscle cells. The source code and data for Polar Gini Curve could be found at http://discovery.informatics.uab.edu/PGC/ or https://figshare.com/projects/Polar_Gini_Curve/76749.

Mapping Spatial Genetic Landscapes in Tissue Sections through Microscale Integration of Sampling Methodology into Genomic Workflows

In cancer research, genomic profiles are often extracted from homogenized macrodissections of tissues, with the histological context lost and a large fraction of material underutilized. Pertinently, the spatial genomic landscape provides critical complementary information in deciphering disease heterogeneity and progression. Microscale sampling methods such as microdissection to obtain such information are often destructive to a sizeable fraction of the biopsy sample, thus showing limited multiplexability and adaptability to different assays. A modular microfluidic technology is here implemented to recover cells at the microscale from tumor tissue sections, with minimal disruption of unsampled areas and tailored to interface with genome profiling workflows, which is directed here toward evaluating intratumoral genomic heterogeneity. The integrated workflow-GeneScape-is used to evaluate heterogeneity in a metastatic mammary carcinoma, showing distinct single nucleotide variants and copy number variations in different tumor tissue regions, suggesting the polyclonal origin of the metastasis as well as development driven by multiple location-specific drivers.

A coordinated progression of progenitor cell states initiates urinary tract development

The kidney and upper urinary tract develop through reciprocal interactions between the ureteric bud and the surrounding mesenchyme. Ureteric bud branching forms the arborized collecting duct system of the kidney, while ureteric tips promote nephron formation from dedicated progenitor cells. While nephron progenitor cells are relatively well characterized, the origin of ureteric bud progenitors has received little attention so far. It is well established that the ureteric bud is induced from the nephric duct, an epithelial duct derived from the intermediate mesoderm of the embryo. However, the cell state transitions underlying the progression from intermediate mesoderm to nephric duct and ureteric bud remain unknown. Here we show that nephric duct morphogenesis results from the coordinated organization of four major progenitor cell populations. Using single cell RNA-seq and Cluster RNA-seq, we show that these progenitors emerge in time and space according to a stereotypical pattern. We identify the transcription factors Tfap2a/b and Gata3 as critical coordinators of this progenitor cell progression. This study provides a better understanding of the cellular origin of the renal collecting duct system and associated urinary tract developmental diseases, which may inform guided differentiation of functional kidney tissue.

Bridging scales: From cell biology to physiology using in situ single-cell technologies

Biological organization crosses multiple spatial scales: from molecular, cellular, to tissues and organs. The proliferation of molecular profiling technologies enables increasingly detailed cataloging of the components at each scale. However, the scarcity of spatial profiling has made it challenging to bridge across these scales. Emerging technologies based on highly multiplexed in situ profiling are paving the way to study the spatial organization of cells and tissues in greater detail. These new technologies provide the data needed to cross the scale from cell biology to physiology and identify the fundamental principles that govern tissue organization. Here, we provide an overview of these key technologies and discuss the current and future insights these powerful techniques enable.

Integration of Transformative Platforms for the Discovery of Causative Genes in Cardiovascular Diseases

Cardiovascular diseases are the leading cause of morbidity and mortality worldwide. Genome-wide association studies (GWAS) are powerful epidemiological tools to find genes and variants associated with cardiovascular diseases while follow-up biological studies allow to better understand the etiology and mechanisms of disease and assign causality. Improved methodologies and reduced costs have allowed wider use of bulk and single-cell RNA sequencing, human-induced pluripotent stem cells, organoids, metabolomics, epigenomics, and novel animal models in conjunction with GWAS. In this review, we feature recent advancements relevant to cardiovascular diseases arising from the integration of genetic findings with multiple enabling technologies within multidisciplinary teams to highlight the solidifying transformative potential of this approach. Well-designed workflows integrating different platforms are greatly improving and accelerating the unraveling and understanding of complex disease processes while promoting an effective way to find better drug targets, improve drug design and repurposing, and provide insight towards a more personalized clinical practice.

Pan-Cancer Drivers Are Recurrent Transcriptional Regulatory Heterogeneities in Early-Stage Luminal Breast Cancer

The heterogeneous composition of solid tumors is known to impact disease progression and response to therapy. Malignant cells coexist in different regulatory states that can be accessed transcriptomically by single-cell RNA sequencing, but these methods have many caveats related to sensitivity, noise, and sample handling. We revised a statistical fluctuation analysis called stochastic profiling to combine with 10-cell RNA sequencing, which was designed for laser-capture microdissection (LCM) and extended here for immuno-LCM. When applied to a cohort of late-onset, early-stage luminal breast cancers, the integrated approach identified thousands of candidate regulatory heterogeneities. Intersecting the candidates from different tumors yielded a relatively stable set of 710 recurrent heterogeneously expressed genes (RHEG), which were significantly variable in >50% of patients. RHEGs were not strongly confounded by dissociation artifacts, cell-cycle oscillations, or driving mutations for breast cancer. Rather, RHEGs were enriched for epithelial-to-mesenchymal transition genes and, unexpectedly, the latest pan-cancer assembly of driver genes across cancer types other than breast. These findings indicate that heterogeneous transcriptional regulation conceivably provides a faster, reversible mechanism for malignant cells to evaluate the effects of potential oncogenes or tumor suppressors on cancer hallmarks. SIGNIFICANCE: Profiling intratumor heterogeneity of luminal breast carcinoma cells identifies a recurrent set of genes, suggesting sporadic activation of pathways known to drive other types of cancer.See related articles by Schaff and colleagues, p. 1853 and Sutcliffe and colleagues, p. 1868.

Defining the signalling determinants of a posterior ventral spinal cord identity in human neuromesodermal progenitor derivatives

The anteroposterior axial identity of motor neurons (MNs) determines their functionality and vulnerability to neurodegeneration. Thus, it is a crucial parameter in the design of strategies aiming to produce MNs from human pluripotent stem cells (hPSCs) for regenerative medicine/disease modelling applications. However, the in vitro generation of posterior MNs corresponding to the thoracic/lumbosacral spinal cord has been challenging. Although the induction of cells resembling neuromesodermal progenitors (NMPs), the bona fide precursors of the spinal cord, offers a promising solution, the progressive specification of posterior MNs from these cells is not well defined. Here, we determine the signals guiding the transition of human NMP-like cells toward thoracic ventral spinal cord neurectoderm. We show that combined WNT-FGF activities drive a posterior dorsal pre-/early neural state, whereas suppression of TGFβ-BMP signalling pathways promotes a ventral identity and neural commitment. Based on these results, we define an optimised protocol for the generation of thoracic MNs that can efficiently integrate within the neural tube of chick embryos. We expect that our findings will facilitate the comparison of hPSC-derived spinal cord cells of distinct axial identities.

Single-Cell Sequencing Applications in the Inner Ear

Genomics studies face specific challenges in the inner ear due to the multiple types and limited amounts of inner ear cells that are arranged in a very delicate structure. However, advances in single-cell sequencing (SCS) technology have made it possible to analyze gene expression variations across different cell types as well as within specific cell groups that were previously considered to be homogeneous. In this review, we summarize recent advances in inner ear research brought about by the use of SCS that have delineated tissue heterogeneity, identified unknown cell subtypes, discovered novel cell markers, and revealed dynamic signaling pathways during development. SCS opens up new avenues for inner ear research, and the potential of the technology is only beginning to be explored.

A spinal organ of proprioception for integrated motor action feedback

Proprioception is essential for behavior and provides a sense of our body movements in physical space. Proprioceptor organs are thought to be only in the periphery. Whether the central nervous system can intrinsically sense its own movement remains unclear. Here we identify a segmental organ of proprioception in the adult zebrafish spinal cord, which is embedded by intraspinal mechanosensory neurons expressing Piezo2 channels. These cells are late-born, inhibitory, commissural neurons with unique molecular and physiological profiles reflecting a dual sensory and motor function. The central proprioceptive organ locally detects lateral body movements during locomotion and provides direct inhibitory feedback onto rhythm-generating interneurons responsible for the central motor program. This dynamically aligns central pattern generation with movement outcome for efficient locomotion. Our results demonstrate that a central proprioceptive organ monitors self-movement using hybrid neurons that merge sensory and motor entities into a unified network.

Engineered Tools to Study Intercellular Communication

All multicellular organisms rely on intercellular communication networks to coordinate physiological functions. As members of a dynamic social network, each cell receives, processes, and redistributes biological information to define and maintain tissue homeostasis. Uncovering the molecular programs underlying these processes is critical for prevention of disease and aging and development of therapeutics. The study of intercellular communication requires techniques that reduce the scale and complexity of in vivo biological networks while resolving the molecular heterogeneity in "omic" layers that contribute to cell state and function. Recent advances in microengineering and high-throughput genomics offer unprecedented spatiotemporal control over cellular interactions and the ability to study intercellular communication in a high-throughput and mechanistic manner. Herein, this review discusses how salient engineered approaches and sequencing techniques can be applied to understand collective cell behavior and tissue functions.

A hitchhiker's guide to single-cell transcriptomics and data analysis pipelines

Single-cell transcriptomics (SCT) is a tour de force in the era of big omics data that has led to the accumulation of massive cellular transcription data at an astounding resolution of single cells. It provides valuable insights into cells previously unachieved by bulk cell analysis and is proving crucial in uncovering cellular heterogeneity, identifying rare cell populations, distinct cell-lineage trajectories, and mechanisms involved in complex cellular processes. SCT data is highly complex and necessitates advanced statistical and computational methods for analysis. This review provides a comprehensive overview of the steps in a typical SCT workflow, starting from experimental protocol to data analysis, deliberating various pipelines used. We discuss recent trends, challenges, machine learning methods for data analysis, and future prospects. We conclude by listing the multitude of scRNA-seq data applications and how it shall revolutionize our understanding of cellular biology and diseases.

A Microfluidic Chip for Efficient Circulating Tumor Cells Enrichment, Screening, and Single-Cell RNA Sequencing

Single-cell RNA sequencing on circulating tumor cells (CTCs) proves useful to study mechanisms of tumor heterogeneity, metastasis, and drug resistance. Currently, single-cell RNA sequencing of CTCs usually takes three prerequisite steps: enrichment of CTCs from whole blood, characterization of captured cells by immunostaining and microscopic imaging, and single-cell isolation through micromanipulation. However, multiple pipetting and transferring steps can easily cause the loss of rare CTCs. To address this issue, a novel integrated microfluidic chip for sequential enrichment, isolation, and characterization of CTCs at single-cell level, is developed. And, single CTC lysis is achieved on the same chip. The microfluidic chip includes functions of blood clot filtration, single-cell isolation, identification, and target single-cell lysate collection. By spiking tumor cells into whole blood, it is validated that this microfluidic chip can effectively conduct single-cell CTCs RNA sequencing. The approach lays a solid foundation for the analysis of RNA expression profiling of single-cell CTCs.

Subcellular sequencing of single neurons reveals the dendritic transcriptome of GABAergic interneurons

Although mRNAs are localized in the processes of excitatory neurons, it is still unclear whether interneurons also localize a large population of mRNAs. In addition, the variability in the localized mRNA population within and between cell types is unknown. Here we describe the unbiased transcriptomic characterization of the subcellular compartments of hundreds of single neurons. We separately profiled the dendritic and somatic transcriptomes of individual rat hippocampal neurons and investigated mRNA abundances in the soma and dendrites of single glutamatergic and GABAergic neurons. We found that, like their excitatory counterparts, interneurons contain a rich repertoire of ~4000 mRNAs. We observed more cell type-specific features among somatic transcriptomes than their associated dendritic transcriptomes. Finally, using celltype-specific metabolic labeling of isolated neurites, we demonstrated that the processes of glutamatergic and, notably, GABAergic neurons were capable of local translation, suggesting mRNA localization and local translation are general properties of neurons.

Insights Into Development and Progression of Idiopathic Pulmonary Fibrosis From Single Cell RNA Studies

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease with limited therapeutic options. The current model suggests that chronic or repetitive "micro-injuries" of the alveolar epithelium lead to activation and proliferation of fibroblasts and excessive extracellular matrix (ECM) deposition. Disruption of alveolar type II (ATII) epithelial cell homeostasis and the characteristics of mesenchymal cell populations in IPF have received particular attention in recent years. Emerging data from single cell RNA sequencing (scRNAseq) analysis shed novel light on alterations in ATII cell progenitor dysfunction and the diversity of mesenchymal cells within the fibrotic lung. Within this minireview, we summarize the data from most recent human scRNAseq studies. We aim to collate the current knowledge on cellular plasticity and heterogeneity in the development and progression of IPF, effects of drug treatment on transcriptional changes. Finally, we provide a brief outlook on future challenges and promises for large scale sequencing studies in the development of novel therapeutics for IPF.

Laser Capture Microdissection of Mouse Growth Plate Cartilage

The ability to identify, isolate, and study pure populations of cells is critical for understanding normal physiology in organs and tissues, which involves spatial regulation of signaling pathways and interactions between cells with different functions, expression profiles, and lineages. Here, we focus on assessing the growth plate cartilage, composed of multiple functionally and histologically distinct zones, to investigate temporally and spatially dependent gene expression differences. In this chapter, we describe the method of laser capture microdissection to isolate chondrocytes from different zones of differentiation in the mouse growth plate cartilage for RNA isolation, and subsequent downstream applications, such as RNA-sequencing and quantitative real-time PCR. We also provide an assessment of different factors contributing to the integrity of the isolated RNA, such as staining methods and procedures in RNA isolation.

High-Resolution Transcriptomic and Proteomic Profiling of Heterogeneity of Brain-Derived Microglia in Multiple Sclerosis

Microglia are important for central nervous system (CNS) homeostasis and first to respond to tissue damage and perturbations. Microglia are heterogeneous cells; in case of pathology, microglia adopt a range of phenotypes with altered functions. However, how these different microglia subtypes are implicated in CNS disease is largely unresolved. Multiple sclerosis (MS) is a chronic demyelinating disease of the CNS, characterized by inflammation and axonal degeneration, ultimately leading to neurological decline. One way microglia are implicated in MS is through stimulation of remyelination. They facilitate efficient remyelination by phagocytosis of myelin debris. In addition, microglia recruit oligodendrocyte precursor cells (OPCs) to demyelinated areas and stimulate remyelination. The development of high-resolution technologies to profile individual cells has greatly contributed to our understanding of microglia heterogeneity and function under normal and pathological conditions. Gene expression profiling technologies have evolved from whole tissue RNA sequencing toward single-cell or nucleus sequencing. Single microglia proteomic profiles are also increasingly generated, offering another layer of high-resolution data. Here, we will review recent studies that have employed these technologies in the context of MS and their respective advantages and disadvantages. Moreover, recent developments that allow for (single) cell profiling while retaining spatial information and tissue context will be discussed.

Stem Cells, Cell Therapies, and Bioengineering in Lung Biology and Disease 2019

A workshop entitled "Stem Cells, Cell Therapies and Bioengineering in Lung Biology and Diseases" was hosted by the University of Vermont Larner College of Medicine in collaboration with the National Heart, Lung and Blood Institute, the Alpha-1 Foundation, the Cystic Fibrosis Foundation, the International Society for Cell and Gene Therapy and the Pulmonary Fibrosis Foundation. The event was held from July 15 to 18, 2019 at the University of Vermont, Burlington, Vermont. The objectives of the conference were to review and discuss the current status of the following active areas of research: 1) technological advancements in the analysis and visualisation of lung stem and progenitor cells; 2) evaluation of lung stem and progenitor cells in the context of their interactions with the niche; 3) progress toward the application and delivery of stem and progenitor cells for the treatment of lung diseases such as cystic fibrosis; 4) progress in induced pluripotent stem cell models and application for disease modelling; and 5) the emerging roles of cell therapy and extracellular vesicles in immunomodulation of the lung. This selection of topics represents some of the most dynamic research areas in which incredible progress continues to be made. The workshop also included active discussion on the regulation and commercialisation of regenerative medicine products and concluded with an open discussion to set priorities and recommendations for future research directions in basic and translation lung biology.

Multi-layered Spatial Transcriptomics Identify Secretory Factors Promoting Human Hematopoietic Stem Cell Development

Hematopoietic stem cells (HSCs) first emerge in the embryonic aorta-gonad-mesonephros (AGM) region. Studies of model organisms defined intersecting signaling pathways that converge to promote HSC emergence predominantly in the ventral domain of the dorsal aorta. Much less is known about mechanisms driving HSC development in humans. Here, to identify secreted signals underlying human HSC development, we combined spatial transcriptomics analysis of dorsoventral polarized signaling in the aorta with gene expression profiling of sorted cell populations and single cells. Our analysis revealed a subset of aortic endothelial cells with a downregulated arterial signature and a predicted lineage relationship with the emerging HSC/progenitor population. Analysis of the ventrally polarized molecular landscape identified endothelin 1 as an important secreted regulator of human HSC development. The obtained gene expression datasets will inform future studies on mechanisms of HSC development in vivo and on generation of clinically relevant HSCs in vitro.

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Spatial transcriptome profiling of the human HSC developmental niche

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Characterization of an HSC precursor population at single-cell resolution

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Cardiac EGF pathway is ventrally enriched next to developing IAHCs/HSCs

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Ventrally secreted endothelin promotes development of HSCs

LCM-seq reveals unique transcriptional adaptation mechanisms of resistant neurons and identifies protective pathways in spinal muscular atrophy

Somatic motor neurons are selectively vulnerable in spinal muscular atrophy (SMA), which is caused by a deficiency of the ubiquitously expressed survival of motor neuron protein. However, some motor neuron groups, including oculomotor and trochlear (ocular), which innervate eye muscles, are for unknown reasons spared. To reveal mechanisms of vulnerability and resistance in SMA, we investigate the transcriptional dynamics in discrete neuronal populations using laser capture microdissection coupled with RNA sequencing (LCM-seq). Using gene correlation network analysis, we reveal a TRP53-mediated stress response that is intrinsic to all somatic motor neurons independent of their vulnerability, but absent in relatively resistant red nucleus and visceral motor neurons. However, the temporal and spatial expression analysis across neuron types shows that the majority of SMA-induced modulations are cell type-specific. Using Gene Ontology and protein network analyses, we show that ocular motor neurons present unique disease-adaptation mechanisms that could explain their resilience. Specifically, ocular motor neurons up-regulate (1) Syt1, Syt5, and Cplx2, which modulate neurotransmitter release; (2) the neuronal survival factors Gdf15, Chl1, and Lif; (3) Aldh4, that protects cells from oxidative stress; and (4) the caspase inhibitor Pak4. Finally, we show that GDF15 can rescue vulnerable human spinal motor neurons from degeneration. This confirms that adaptation mechanisms identified in resilient neurons can be used to reduce susceptibility of vulnerable neurons. In conclusion, this in-depth longitudinal transcriptomics analysis in SMA reveals novel cell type-specific changes that, alone and combined, present compelling targets, including Gdf15, for future gene therapy studies aimed toward preserving vulnerable motor neurons.

Use of Single-Cell -Omic Technologies to Study the Gastrointestinal Tract and Diseases, From Single Cell Identities to Patient Features

Single cells are the building blocks of tissue systems that determine organ phenotypes, behaviors, and functions. Understanding the differences between cell types and their activities might provide us with insights into normal tissue physiology, development of disease, and new therapeutic strategies. Although -omic level single-cell technologies are a relatively recent development that have been used only in research settings, these approaches might eventually be used in the clinic. We review the prospects of applying single-cell genome, transcriptome, epigenome, proteome, and metabolome analyses to gastroenterology and hepatology research. Combining data from multi-omic platforms coupled to rapid technological development could lead to new diagnostic, prognostic, and therapeutic approaches.

Mapping Cellular Coordinates through Advances in Spatial Transcriptomics Technology

Complex cell-to-cell communication underlies the basic processes essential for homeostasis in the given tissue architecture. Obtaining quantitative gene-expression of cells in their native context has significantly advanced through single-cell RNA sequencing technologies along with mechanical and enzymatic tissue manipulation. This approach, however, is largely reliant on the physical dissociation of individual cells from the tissue, thus, resulting in a library with unaccounted positional information. To overcome this, positional information can be obtained by integrating imaging and positional barcoding. Collectively, spatial transcriptomics strategies provide tissue architecture-dependent as well as position-dependent cellular functions. This review discusses the current technologies for spatial transcriptomics ranging from the methods combining mechanical dissociation and single-cell RNA sequencing to computational spatial re-mapping.

Disrupted function of lactate transporter MCT1, but not MCT4, in Schwann cells affects the maintenance of motor end-plate innervation

Recent studies in neuron-glial metabolic coupling have shown that, in the CNS, astrocytes and oligodendrocytes support neurons with energy-rich lactate/pyruvate via monocarboxylate transporters (MCTs). The presence of such transporters in the PNS, in both Schwann cells and neurons, has prompted us to question if a similar interaction may be present. Here we describe the generation and characterization of conditional knockout mouse models where MCT1 or MCT4 is specifically deleted in Schwann cells (named MCT1 and MCT4 cKO). We show that MCT1 cKO and MCT4 cKO mice develop normally and that myelin in the PNS is preserved. However, MCT1 expressed by Schwann cells is necessary for long-term maintenance of motor end-plate integrity as revealed by disrupted neuromuscular innervation in mutant mice, while MCT4 appears largely dispensable for the support of motor neurons. Concomitant to detected structural alterations, lumbar motor neurons from MCT1 cKO mice show transcriptional changes affecting cytoskeletal components, transcriptional regulators, and mitochondria related transcripts, among others. Together, our data indicate that MCT1 plays a role in Schwann cell-mediated maintenance of motor end-plate innervation thus providing further insight into the emerging picture of the biology of the axon-glia metabolic crosstalk.

Unraveling Heterogeneity in Transcriptome and Its Regulation Through Single-Cell Multi-Omics Technologies

Cellular heterogeneity plays a pivotal role in tissue homeostasis and the disease development of multicellular organisms. To deconstruct the heterogeneity, a multitude of single-cell toolkits measuring various cellular contents, including genome, transcriptome, epigenome, and proteome, have been developed. More recently, multi-omics single-cell techniques enable the capture of molecular footprints with a higher resolution by simultaneously profiling various cellular contents within an individual cell. Integrative analysis of multi-omics datasets unravels the relationships between cellular modalities, builds sophisticated regulatory networks, and provides a holistic view of the cell state. In this review, we summarize the major developments in the single-cell field and review the current state-of-the-art single-cell multi-omic techniques and the bioinformatic tools for integrative analysis.

Breathing fresh air into respiratory research with single-cell RNA sequencing

The complex cellular heterogeneity of the lung poses a unique challenge to researchers in the field. While the use of bulk RNA sequencing has become a ubiquitous technology in systems biology, the technique necessarily averages out individual contributions to the overall transcriptional landscape of a tissue. Single-cell RNA sequencing (scRNA-seq) provides a robust, unbiased survey of the transcriptome comparable to bulk RNA sequencing while preserving information on cellular heterogeneity. In just a few years since this technology was developed, scRNA-seq has already been adopted widely in respiratory research and has contributed to impressive advancements such as the discoveries of the pulmonary ionocyte and of a profibrotic macrophage population in pulmonary fibrosis. In this review, we discuss general technical considerations when considering the use of scRNA-seq and examine how leading investigators have applied the technology to gain novel insights into respiratory biology, from development to disease. In addition, we discuss the evolution of single-cell technologies with a focus on spatial and multi-omics approaches that promise to drive continued innovation in respiratory research.

Defining the Neural Kinome: Strategies and Opportunities for Small Molecule Drug Discovery to Target Neurodegenerative Diseases

Kinases are highly tractable drug targets that have reached unparalleled success in fields such as cancer but whose potential has not yet been realized in neuroscience. There are currently 55 approved small molecule kinase-targeting drugs, 48 of which have an anticancer indication. The intrinsic complexity linked to central nervous system (CNS) drug development and a lack of validated targets has hindered progress in developing kinase inhibitors for CNS disorders when compared to other therapeutic areas such as oncology. Identification and/or characterization of new kinases as potential drug targets for neurodegenerative diseases will create opportunities for the development of CNS drugs in the future. The track record of kinase inhibitors in other disease indications supports the idea that with the best targets identified small molecule kinase modulators will become impactful therapeutics for neurodegenerative diseases. This Review highlights the imminent need for new therapeutics to treat the most prevalent neurodegenerative diseases as well as the promise of kinase inhibitors to address this need. With a focus on kinases that remain largely unexplored after decades of dedicated research in the kinase field, we offer specific examples of understudied kinases that are supported by patient-derived data as linked to Alzheimer's disease, Parkinson's disease, and/or amyotrophic lateral sclerosis. Finally, we show literature-reported high-quality inhibitors for several understudied kinases and suggest other kinases that merit additional medicinal chemistry efforts to elucidate their therapeutic potential.

Midbrain Dopaminergic Neuron Development at the Single Cell Level: In vivo and in Stem Cells

Parkinson's disease (PD) is a progressive neurodegenerative disorder that predominantly affects dopaminergic (DA) neurons of the substantia nigra. Current treatment options for PD are symptomatic and typically involve the replacement of DA neurotransmission by DA drugs, which relieve the patients of some of their motor symptoms. However, by the time of diagnosis, patients have already lost about 70% of their substantia nigra DA neurons and these drugs offer only temporary relief. Therefore, cell replacement therapy has garnered much interest as a potential treatment option for PD. Early studies using human fetal tissue for transplantation in PD patients provided proof of principle for cell replacement therapy, but they also highlighted the ethical and practical difficulties associated with using human fetal tissue as a cell source. In recent years, advancements in stem cell research have made human pluripotent stem cells (hPSCs) an attractive source of material for cell replacement therapy. Studies on how DA neurons are specified and differentiated in the developing mouse midbrain have allowed us to recapitulate many of the positional and temporal cues needed to generate DA neurons in vitro. However, little is known about the developmental programs that govern human DA neuron development. With the advent of single-cell RNA sequencing (scRNA-seq) and bioinformatics, it has become possible to analyze precious human samples with unprecedented detail and extract valuable high-quality information from large data sets. This technology has allowed the systematic classification of cell types present in the human developing midbrain along with their gene expression patterns. By studying human development in such an unbiased manner, we can begin to elucidate human DA neuron development and determine how much it differs from our knowledge of the rodent brain. Importantly, this molecular description of the function of human cells has become and will increasingly be a reference to define, evaluate, and engineer cell types for PD cell replacement therapy and disease modeling.

The Role of Single-Cell Technology in the Study and Control of Infectious Diseases

The advent of single-cell research in the recent decade has allowed biological studies at an unprecedented resolution and scale. In particular, single-cell analysis techniques such as Next-Generation Sequencing (NGS) and Fluorescence-Activated Cell Sorting (FACS) have helped show substantial links between cellular heterogeneity and infectious disease progression. The extensive characterization of genomic and phenotypic biomarkers, in addition to host-pathogen interactions at the single-cell level, has resulted in the discovery of previously unknown infection mechanisms as well as potential treatment options. In this article, we review the various single-cell technologies and their applications in the ongoing fight against infectious diseases, as well as discuss the potential opportunities for future development.

Uncovering an Organ's Molecular Architecture at Single-Cell Resolution by Spatially Resolved Transcriptomics

Revealing fine-scale cellular heterogeneity among spatial context and the functional and structural foundations of tissue architecture is fundamental within biological research and pharmacology. Unlike traditional approaches involving single molecules or bulk omics, cutting-edge, spatially resolved transcriptomics techniques offer near-single-cell or even subcellular resolution within tissues. Massive information across higher dimensions along with position-coordinating labels can better map the whole 3D transcriptional landscape of tissues. In this review, we focus on developments and strategies in spatially resolved transcriptomics, compare the cell and gene throughput and spatial resolution in detail for existing methods, and highlight the enormous potential in biomedical research.

Transcriptomics: a Solution for Renal Osteodystrophy?

PURPOSE OF REVIEW

The molecular mechanisms of the bone disease associated with chronic kidney disease (CKD), called renal osteodystrophy (ROD), are poorly understood. New transcriptomics technologies may provide clinically relevant insights into the pathogenesis of ROD. This review summarizes current progress and limitations in the study and treatment of ROD, and in transcriptomics analyses of skeletal tissues.

RECENT FINDINGS

ROD is characterized by poor bone quality and strength leading to increased risk of fracture. Recent studies indicate permanent alterations in bone cell populations during ROD. Single-cell transcriptomics analyses, successful at identifying specialized cell subpopulations in bone, have not yet been performed in ROD. ROD is a widespread poorly understood bone disease with limited treatment options. Transcriptomics analyses of bone are needed to identify the bone cell subtypes and their role in the pathogenesis of ROD, and to develop adequate diagnosis and treatment strategies.

Identification of non-cancer cells from cancer transcriptomic data

Interactions between cancer cells and non-cancer cells composing the tumour microenvironment play a primary role in determining cancer progression and shaping the response to therapy. The qualitative and quantitative characterisation of the different cell populations in the tumour microenvironment is therefore crucial to understand its role in cancer. In recent years, many experimental and computational approaches have been developed to identify the cell populations composing heterogeneous tissue samples, such as cancer. In this review, we describe the state-of-the-art approaches for the quantification of non-cancer cells from bulk and single-cell cancer transcriptomic data, with a focus on immune cells. We illustrate the main features of these approaches and highlight their applications for the analysis of the tumour microenvironment in solid cancers. We also discuss techniques that are complementary and alternative to RNA sequencing, particularly focusing on approaches that can provide spatial information on the distribution of the cells within the tumour in addition to their qualitative and quantitative measurements. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Subcellular Spatial Transcriptomes: Emerging Frontier for Understanding Gene Regulation

RNAs are trafficked and localized with exquisite precision inside the cell. Studies of candidate messenger RNAs have shown the vital importance of RNA subcellular location in development and cellular function. New sequencing- and imaging-based methods are providing complementary insights into subcellular localization of RNAs transcriptome-wide. APEX-seq and ribosome profiling as well as proximity-labeling approaches have revealed thousands of transcript isoforms are localized to distinct cytotopic locations, including locations that defy biochemical fractionation and hence were missed by prior studies. Sequences in the 3' and 5' untranslated regions (UTRs) serve as "zip codes" to direct transcripts to particular locales, and it is clear that intronic and retrotransposable sequences within transcripts have been co-opted by cells to control localization. Molecular motors, nuclear-to-cytosol RNA export, liquid-liquid phase separation, RNA modifications, and RNA structure dynamically shape the subcellular transcriptome. Location-based RNA regulation continues to pose new mysteries for the field, yet promises to reveal insights into fundamental cell biology and disease mechanisms.

SpatialDB: a database for spatially resolved transcriptomes

Spatially resolved transcriptomic techniques allow the characterization of spatial organization of cells in tissues, which revolutionize the studies of tissue function and disease pathology. New strategies for detecting spatial gene expression patterns are emerging, and spatially resolved transcriptomic data are accumulating rapidly. However, it is not convenient for biologists to exploit these data due to the diversity of strategies and complexity in data analysis. Here, we present SpatialDB, the first manually curated database for spatially resolved transcriptomic techniques and datasets. The current version of SpatialDB contains 24 datasets (305 sub-datasets) from 5 species generated by 8 spatially resolved transcriptomic techniques. SpatialDB provides a user-friendly web interface for visualization and comparison of spatially resolved transcriptomic data. To further explore these data, SpatialDB also provides spatially variable genes and their functional enrichment annotation. SpatialDB offers a repository for research community to investigate the spatial cellular structure of tissues, and may bring new insights into understanding the cellular microenvironment in disease. SpatialDB is freely available at https://www.spatialomics.org/SpatialDB.

Single-cell sequencing of miRNAs: A modified technology

The recent development of next-generation sequencing technologies has offered valuable insights into individual cells. This technology is centered on the characterization of single cells for epigenomics, genomics, and transcriptomics. Ever since the first report appeared in 2009, the single-cell RNA-sequencing saga started to explore deeper into the mechanics intrigued within a single cell. microRNA (miRNA) has been increasingly recognized as an essential molecule triggering an additional layer for gene regulation. Therefore, single-cell sequencing of miRNAs is crucial to explore the logical riddles surrounding the epigenomics, genomics, and transcriptomics of an individual cell. Scientists from the Vienna Biocenter Campus have lately performed single-cell sequencing of miRNAs in the fly, Drosophila, and nematode, Caenorhabditis elegans. In this review, we present the latest scientific explorations supported by all-inclusive data on this novel subject matter of next-generation sequencing.

ALS and Parkinson's disease genes CHCHD10 and CHCHD2 modify synaptic transcriptomes in human iPSC-derived motor neurons

Mitochondrial intermembrane space proteins CHCHD2 and CHCHD10 have roles in motor neuron diseases such as amyotrophic lateral sclerosis, spinal muscular atrophy and axonal neuropathy and in Parkinson's disease. They form a complex of unknown function. Here we address the importance of these two proteins in human motor neurons. We show that gene edited human induced pluripotent stem cells (iPSC) lacking either CHCHD2 or CHCHD10 are viable and can be differentiated into functional motor neurons that fire spontaneous and evoked action potentials. Mitochondria in knockout iPSC and motor neurons sustain ultrastructure but show increased proton leakage and respiration, and reciprocal compensatory increases in CHCHD2 or CHCHD10. Knockout motor neurons have largely overlapping transcriptome profiles compared to isogenic control line, in particular for synaptic gene expression. Our results show that the absence of either CHCHD2 or CHCHD10 alters mitochondrial respiration in human motor neurons, inducing similar compensatory responses. Thus, pathogenic mechanisms may involve loss of synaptic function resulting from defective energy metabolism.

Synaptotagmin 13 is neuroprotective across motor neuron diseases

In amyotrophic lateral sclerosis (ALS) and spinal muscular atrophy (SMA), spinal and lower brainstem motor neurons degenerate, but some motor neuron subtypes are spared, including oculomotor neurons (OMNs). The mechanisms responsible for this selective degeneration are largely unknown, but the molecular signatures of resistant and vulnerable motor neurons are distinct and offer clues to neuronal resilience and susceptibility. Here, we demonstrate that healthy OMNs preferentially express Synaptotagmin 13 (SYT13) compared to spinal motor neurons. In end-stage ALS patients, SYT13 is enriched in both OMNs and the remaining relatively resilient spinal motor neurons compared to controls. Overexpression of SYT13 in ALS and SMA patient motor neurons in vitro improves their survival and increases axon lengths. Gene therapy with Syt13 prolongs the lifespan of ALS mice by 14% and SMA mice by 50% by preserving motor neurons and delaying muscle denervation. SYT13 decreases endoplasmic reticulum stress and apoptosis of motor neurons, both in vitro and in vivo. Thus, SYT13 is a resilience factor that can protect motor neurons and a candidate therapeutic target across motor neuron diseases.

Single-Cell Multi-Omics and Its Prospective Application in Cancer Biology

In recent years, the emergence of single-cell omics technologies, which can profile genomics, transcriptomics, epigenomics, and proteomics, has provided unprecedented insights into characteristics of cancer, enabling higher resolution and accuracy to decipher the cellular and molecular mechanisms relating to tumorigenesis, evolution, metastasis, and immune responses. Single-cell multi-omics technologies, which are developed based on the combination of multiple single-cell mono-omics technologies, can simultaneously analyze RNA expression, single nucleotide polymorphism, epigenetic modification, or protein abundance, enabling the in-depth understanding of gene expression regulatory mechanisms. In this review, the state-of-the-art single-cell multi-omics technologies are summarized and the prospects of their application in cancer biology are discussed.

Effective microtissue RNA extraction coupled with Smart-seq2 for reproducible and robust spatial transcriptome analysis

Spatial transcriptomics is useful for understanding the molecular organization of a tissue and providing insights into cellular function in a morphological context. In order to obtain reproducible results in spatial transcriptomics, we have to maintain tissue morphology and RNA molecule stability during the image acquisition and biomolecule collection processes. Here, we developed a tissue processing method for robust and reproducible RNA-seq from tissue microdissection samples. In this method, we suppressed RNA degradation in fresh-frozen tissue specimens by dehydration fixation and effectively collected a small amount of RNA molecules from microdissection samples by magnetic beads. We demonstrated the spatial transcriptome analysis of the mouse liver and brain in serial microdissection samples (100 μm in a diameter and 10 μm in thickness) produced by a microdissection punching system. Using our method, we could prevent RNA degradation at room temperature and effectively produce a sequencing library with Smart-seq2. This resulted in reproducible sequence read mapping in exon regions and the detection of more than 2000 genes compared to non-fixed samples in the RNA-seq analysis. Our method would be applied to various transcriptome analyses, providing the information for region specific gene expression in tissue specimens.

Advancing Cancer Research and Medicine with Single-Cell Genomics

Single-cell sequencing (SCS) has impacted many areas of cancer research and improved our understanding of intratumor heterogeneity, the tumor microenvironment, metastasis, and therapeutic resistance. The development and refinement of SCS technologies has led to massive reductions in costs, increased cell throughput, and improved reproducibility, paving the way for clinical applications. However, before translational applications can be realized, there are a number of logistical and technical challenges that must be overcome. This review discusses past cancer research studies, emerging technologies, and future clinical applications that are bound to transform cancer medicine.

Macrophage subsets in atherosclerosis as defined by single-cell technologies

Macrophages play a major role in the pathogenesis of atherosclerosis. Many studies have shone light on the different phenotypes and functions that macrophages can acquire upon exposure to local cues. The microenvironment of the atherosclerotic plaque contains a plethora of macrophage-controlling factors, such as cytokines, oxidised low-density lipoproteins and cell debris. Previous research has determined macrophage function within the plaque mainly by using immunohistochemistry and bulk analysis. The recent development and rapid progress of single-cell technologies, such as cytometry by time of flight and single-cell RNA sequencing, now enable comprehensive mapping of the wide range of cell types and their phenotypes present in atherosclerotic plaques. In this review we discuss recent advances applying these technologies in defining macrophage subsets residing in the atherosclerotic arterial wall of mice and men. Resulting from these studies, we describe three main macrophage subsets: resident-like, pro-inflammatory and anti-inflammatory foamy TREM2hi macrophages, which are found in both mouse and human atherosclerotic plaques. Furthermore, we discuss macrophage subset-specific markers and functions. More insights into the characteristics and phenotype of immune cells within the atherosclerotic plaque may guide future clinical approaches to treat disease. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Reliable approaches to extract high-integrity RNA from skin and other pertinent tissues used in pain research

Introduction

Comprehensive mRNA sequencing is a powerful tool for conducting unbiased, quantitative differential gene expression analysis. However, the reliability of these data is contingent on the extraction of high-quality RNA from samples. Preserving RNA integrity during extraction can be problematic, especially in tissues such as skin with dense, connective matrices and elevated ribonuclease expression. This is a major barrier to understanding the influences of altered gene expression in many preclinical pain models and clinical pain disorders where skin is the site of tissue injury.

Objective

This study developed and evaluated extraction protocols for skin and other tissues to maximize recovery of high-integrity RNA needed for quantitative mRNA sequencing.

Methods

Rodent and human tissue samples underwent one of the several different protocols that combined either RNA-stabilizing solution or snap-freezing with bead milling or cryosectioning. Indices of RNA integrity and purity were assessed for all samples.

Results

Extraction of high-integrity RNA is highly dependent on the methods used. Bead-milling skin collected in RNA-stabilizing solution resulted in extensive RNA degradation. Snap-freezing in liquid nitrogen was required for skin and highly preferable for other tissues. Skin also required cryosectioning to achieve effective penetration of RNA-stabilizing solution to preserve RNA integrity, whereas bead milling could be used instead with other tissues. Each method was reproducible across multiple experimenters. Electrophoretic anomalies that skewed RNA integrity value assignment required manual correction and often resulted in score reduction.

Conclusion

To achieve the potential of quantitative differential gene expression analysis requires verification of tissue-dependent extraction methods that yield high-integrity RNA.

Visualization of cardiovascular development, physiology and disease at the single-cell level: Opportunities and future challenges

Single-cell RNA sequencing (scRNA-seq), a method of transcriptome sequencing at the single-cell level, has recently emerged as a revolutionary technology in the field of biomedical research. Compared to conventional gene expression profiling in bulk, scRNA-seq resolves biological differences among individual cells and enables the identification of rare cell populations that are easily overlooked. This review introduces the method of scRNA-seq, summarizes its applications in the field of cardiovascular disease research, and discusses existing limitations and prospects for future applications.