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

Single-cell RNA sequencing reveals that targeting HSP90 suppresses PDAC progression by restraining mitochondrial bioenergetics

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers, which lacks effective treatment strategies. There is an urgent need for the development of new strategies for PDAC therapy. The genetic and phenotypic heterogeneity of PDAC cancer cell populations poses further challenges in the clinical management of PDAC. In this study, we performed single-cell RNA sequencing to characterize PDAC tumors from KPC mice. Functional studies and clinical analysis showed that PDAC cluster 2 cells with highly Hsp90 expression is much more aggressive than the other clusters. Genetic and pharmacologic inhibition of Hsp90 impaired tumor cell growth both in vitro and in vivo. Further mechanistic study revealed that HSP90 inhibition disrupted the interaction between HSP90 and OPA1, leading to a reduction in mitochondrial cristae amount and mitochondrial energy production. Collectively, our study reveals that HSP90 might be a potential therapeutic target for PDAC.

Single-cell RNA sequencing in cancer research

Single-cell RNA sequencing (scRNA-seq), a technology that analyzes transcriptomes of complex tissues at single-cell levels, can identify differential gene expression and epigenetic factors caused by mutations in unicellular genomes, as well as new cell-specific markers and cell types. scRNA-seq plays an important role in various aspects of tumor research. It reveals the heterogeneity of tumor cells and monitors the progress of tumor development, thereby preventing further cellular deterioration. Furthermore, the transcriptome analysis of immune cells in tumor tissue can be used to classify immune cells, their immune escape mechanisms and drug resistance mechanisms, and to develop effective clinical targeted therapies combined with immunotherapy. Moreover, this method enables the study of intercellular communication and the interaction of tumor cells and non-malignant cells to reveal their role in carcinogenesis. scRNA-seq provides new technical means for further development of tumor research and is expected to make significant breakthroughs in this field. This review focuses on the principles of scRNA-seq, with an emphasis on the application of scRNA-seq in tumor heterogeneity, pathogenesis, and treatment.

Single-Cell Profiling to Explore Immunological Heterogeneity of Tumor Microenvironment in Breast Cancer

Immune infiltrates in the tumor microenvironment (TME) of breast cancer (BRCA) have been shown to play a critical role in tumorigenesis, progression, invasion, and therapy resistance, and thereby will affect the clinical outcomes of BRCA patients. However, a wide range of intratumoral heterogeneity shaped by the tumor cells and immune cells in the surrounding microenvironment is a major obstacle in understanding and treating BRCA. Recent progress in single-cell technologies such as single-cell RNA sequencing (scRNA-seq), mass cytometry, and digital spatial profiling has enabled the detailed characterization of intratumoral immune cells and vastly improved our understanding of less-defined cell subsets in the tumor immune environment. By measuring transcriptomes or proteomics at the single-cell level, it provides an unprecedented view of the cellular architecture consist of phenotypical and functional diversities of tumor-infiltrating immune cells. In this review, we focus on landmark studies of single-cell profiling of immunological heterogeneity in the TME, and discuss its clinical applications, translational outlook, and limitations in breast cancer studies.

Established, New and Emerging Concepts in Brain Vascular Development

In this review, we discuss the state of our knowledge as it relates to embryonic brain vascular patterning in model systems zebrafish and mouse. We focus on the origins of endothelial cell and the distinguishing features of brain endothelial cells compared to non-brain endothelial cells, which is revealed by single cell RNA-sequencing methodologies. We also discuss the cross talk between brain endothelial cells and neural stem cells, and their effect on each other. In terms of mechanisms, we focus exclusively on Wnt signaling and the recent developments associated with this signaling network in brain vascular patterning, and the benefits and challenges associated with strategies for targeting the brain vasculature. We end the review with a discussion on the emerging areas of meningeal lymphatics, endothelial cilia biology and novel cerebrovascular structures identified in vertebrates.

eHSCPr discriminating the cell identity involved in endothelial to hematopoietic transition

MOTIVATION

Hematopoietic stem cells (HSCs) give rise to all blood cells and play a vital role throughout the whole lifespan through their pluripotency and self-renewal properties. Accurately identifying the stages of early HSCs is extremely important, as it may open up new prospects for extracorporeal blood research. Existing experimental techniques for identifying the early stages of HSCs development are time-consuming and expensive. Machine learning has shown its excellence in massive single-cell data processing and it is desirable to develop related computational models as good complements to experimental techniques.

RESULTS

In this study, we presented a novel predictor called eHSCPr specifically for predicting the early stages of HSCs development. To reveal the distinct genes at each developmental stage of HSCs, we compared F-score with three state-of-art differential gene selection methods (limma, DESeq2, edgeR) and evaluated their performance. F-score captured the more critical surface markers of endothelial cells and hematopoietic cells, and the area under receiver operating characteristic curve (ROC) value was 0.987. Based on SVM, the 10-fold cross-validation accuracy of eHSCpr in the independent dataset and the training dataset reached 94.84% and 94.19%, respectively. Importantly, we performed transcription analysis on the F-score gene set, which indeed further enriched the signal markers of HSCs development stages. eHSCPr can be a powerful tool for predicting early stages of HSCs development, facilitating hypothesis-driven experimental design and providing crucial clues for the in vitro blood regeneration studies.

AVAILABILITY

http://bioinfor.imu.edu.cn/ehscpr.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Cells That "Fire Together, Wire Together", but Do They Transcribe Together in Epilepsy?

Identification of Epilepsy-Associated Neuronal Subtypes and Gene Expression Underlying Epileptogenesis

Pfisterer U, Petukhov V, Demharter S, Meichsner J, Thompson JJ, Batiuk MY, Asenjo-Martinez A, Vasistha NA, Thakur A, Mikkelsen J, Adorjan I, Pinborg LH, Pers TH, von Engelhardt J, Kharchenko PV, Khodosevich K. Nat Commun. 2020;11(1):5038. doi:10.1038/s41467-020-18752-7 [published correction appears in Nat Commun. 2020;11(1):5988]

Epilepsy is one of the most common neurological disorders, yet its pathophysiology is poorly understood due to the high complexity of affected neuronal circuits. To identify dysfunctional neuronal subtypes underlying seizure activity in the human brain, we have performed single-nucleus transcriptomics analysis of >110 000 neuronal transcriptomes derived from temporal cortex samples of multiple temporal lobe epilepsy and nonepileptic subjects. We found that the largest transcriptomic changes occur in distinct neuronal subtypes from several families of principal neurons (L5-6_Fezf2 and L2-3_Cux2) and GABAergic interneurons (Sst and Pvalb), whereas other subtypes in the same families were less affected. Furthermore, the subtypes with the largest epilepsy-related transcriptomic changes may belong to the same circuit, since we observed coordinated transcriptomic shifts across these subtypes. Glutamate signaling exhibited one of the strongest dysregulations in epilepsy, highlighted by layer-wise transcriptional changes in multiple glutamate receptor genes and strong upregulation of genes coding for AMPA receptor auxiliary subunits. Overall, our data reveal a neuronal subtype-specific molecular phenotype of epilepsy.

JOINT for large-scale single-cell RNA-sequencing analysis via soft-clustering and parallel computing

Background

Single-cell RNA-Sequencing (scRNA-Seq) has provided single-cell level insights into complex biological processes. However, the high frequency of gene expression detection failures in scRNA-Seq data make it challenging to achieve reliable identification of cell-types and Differentially Expressed Genes (DEG). Moreover, with the explosive growth of single-cell data using 10x genomics protocol, existing methods will soon reach the computation limit due to scalability issues. The single-cell transcriptomics field desperately need new tools and framework to facilitate large-scale single-cell analysis.

Results

In order to improve the accuracy, robustness, and speed of scRNA-Seq data processing, we propose a generalized zero-inflated negative binomial mixture model, “JOINT,” that can perform probability-based cell-type discovery and DEG analysis simultaneously without the need for imputation. JOINT performs soft-clustering for cell-type identification by computing the probability of individual cells, i.e. each cell can belong to multiple cell types with different probabilities. This is drastically different from existing hard-clustering methods where each cell can only belong to one cell type. The soft-clustering component of the algorithm significantly facilitates the accuracy and robustness of single-cell analysis, especially when the scRNA-Seq datasets are noisy and contain a large number of dropout events. Moreover, JOINT is able to determine the optimal number of cell-types automatically rather than specifying it empirically. The proposed model is an unsupervised learning problem which is solved by using the Expectation and Maximization (EM) algorithm. The EM algorithm is implemented using the TensorFlow deep learning framework, dramatically accelerating the speed for data analysis through parallel GPU computing.

Conclusions

Taken together, the JOINT algorithm is accurate and efficient for large-scale scRNA-Seq data analysis via parallel computing. The Python package that we have developed can be readily applied to aid future advances in parallel computing-based single-cell algorithms and research in various biological and biomedical fields.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-020-07302-6.

STAB: a spatio-temporal cell atlas of the human brain

The human brain is the most complex organ consisting of billions of neuronal and non-neuronal cells that are organized into distinct anatomical and functional regions. Elucidating the cellular and transcriptome architecture underlying the brain is crucial for understanding brain functions and brain disorders. Thanks to the single-cell RNA sequencing technologies, it is becoming possible to dissect the cellular compositions of the brain. Although great effort has been made to explore the transcriptome architecture of the human brain, a comprehensive database with dynamic cellular compositions and molecular characteristics of the human brain during the lifespan is still not available. Here, we present STAB (a Spatio-Temporal cell Atlas of the human Brain), a database consists of single-cell transcriptomes across multiple brain regions and developmental periods. Right now, STAB contains single-cell gene expression profiling of 42 cell subtypes across 20 brain regions and 11 developmental periods. With STAB, the landscape of cell types and their regional heterogeneity and temporal dynamics across the human brain can be clearly seen, which can help to understand both the development of the normal human brain and the etiology of neuropsychiatric disorders. STAB is available at http://stab.comp-sysbio.org.

SCC: an accurate imputation method for scRNA-seq dropouts based on a mixture model

Background

Single-cell RNA sequencing (scRNA-seq) enables the possibility of many in-depth transcriptomic analyses at a single-cell resolution. It’s already widely used for exploring the dynamic development process of life, studying the gene regulation mechanism, and discovering new cell types. However, the low RNA capture rate, which cause highly sparse expression with dropout, makes it difficult to do downstream analyses.

Results

We propose a new method SCC to impute the dropouts of scRNA-seq data. Experiment results show that SCC gives competitive results compared to two existing methods while showing superiority in reducing the intra-class distance of cells and improving the clustering accuracy in both simulation and real data.

Conclusions

SCC is an effective tool to resolve the dropout noise in scRNA-seq data. The code is freely accessible at https://github.com/nwpuzhengyan/SCC.

Single-cell RNA sequencing of developing maize ears facilitates functional analysis and trait candidate gene discovery

Crop productivity depends on activity of meristems that produce optimized plant architectures, including that of the maize ear. A comprehensive understanding of development requires insight into the full diversity of cell types and developmental domains and the gene networks required to specify them. Until now, these were identified primarily by morphology and insights from classical genetics, which are limited by genetic redundancy and pleiotropy. Here, we investigated the transcriptional profiles of 12,525 single cells from developing maize ears. The resulting developmental atlas provides a single-cell RNA sequencing (scRNA-seq) map of an inflorescence. We validated our results by mRNA in situ hybridization and by fluorescence-activated cell sorting (FACS) RNA-seq, and we show how these data may facilitate genetic studies by predicting genetic redundancy, integrating transcriptional networks, and identifying candidate genes associated with crop yield traits.

Toward uncharted territory of cellular heterogeneity: advances and applications of single-cell RNA-seq

Among single-cell analysis technologies, single-cell RNA-seq (scRNA-seq) has been one of the front runners in technical inventions. Since its induction, scRNA-seq has been well received and undergone many fast-paced technical improvements in cDNA synthesis and amplification, processing and alignment of next generation sequencing reads, differentially expressed gene calling, cell clustering, subpopulation identification, and developmental trajectory prediction. scRNA-seq has been exponentially applied to study global transcriptional profiles in all cell types in humans and animal models, healthy or with diseases, including cancer. Accumulative novel subtypes and rare subpopulations have been discovered as potential underlying mechanisms of stochasticity, differentiation, proliferation, tumorigenesis, and aging. scRNA-seq has gradually revealed the uncharted territory of cellular heterogeneity in transcriptomes and developed novel therapeutic approaches for biomedical applications. This review of the advancement of scRNA-seq methods provides an exploratory guide of the quickly evolving technical landscape and insights of focused features and strengths in each prominent area of progress.

Single-cell RNA Sequencing: In-depth Decoding of Heart Biology and Cardiovascular Diseases

Background

The cardiac system is a combination of a complex structure, various cells, and versatile specified functions and sophisticated regulatory mechanisms. Moreover, cardiac diseases that encompass a wide range of endogenous conditions, remain a serious health burden worldwide. Recent genome-wide profiling techniques have taken the lead in uncovering a new realm of cell types and molecular programs driving physiological and pathological processes in various organs and diseases. In particular, the emerging technique single-cell RNA sequencing dominates a breakthrough in decoding the cell heterogeneity, phenotype transition, and developmental dynamics in cardiovascular science.

Conclusion

Herein, we review recent advances in single cellular studies of cardiovascular system and summarize new insights provided by single-cell RNA sequencing in heart developmental sciences, stem-cell researches as well as normal or disease-related working mechanisms.

An integrative atlas of chicken long non-coding genes and their annotations across 25 tissues

Long non-coding RNAs (LNC) regulate numerous biological processes. In contrast to human, the identification of LNC in farm species, like chicken, is still lacunar. We propose a catalogue of 52,075 chicken genes enriched in LNC (http://www.fragencode.org/), built from the Ensembl reference extended using novel LNC modelled here from 364 RNA-seq and LNC from four public databases. The Ensembl reference grew from 4,643 to 30,084 LNC, of which 59% and 41% with expression ≥ 0.5 and ≥ 1 TPM respectively. Characterization of these LNC relatively to the closest protein coding genes (PCG) revealed that 79% of LNC are in intergenic regions, as in other species. Expression analysis across 25 tissues revealed an enrichment of co-expressed LNC:PCG pairs, suggesting co-regulation and/or co-function. As expected LNC were more tissue-specific than PCG (25% vs. 10%). Similarly to human, 16% of chicken LNC hosted one or more miRNA. We highlighted a new chicken LNC, hosting miR155, conserved in human, highly expressed in immune tissues like miR155, and correlated with immunity-related PCG in both species. Among LNC:PCG pairs tissue-specific in the same tissue, we revealed an enrichment of divergent pairs with the PCG coding transcription factors, as for example LHX5, HXD3 and TBX4, in both human and chicken.

Strategies and technologies for exploring long noncoding RNAs in heart failure

Long non-coding RNA (lncRNA) was once considered to be the "noise" of genome transcription without biological function. However, increasing evidence shows that lncRNA is dynamically expressed in developmental stage or disease status, playing a regulatory role in the process of gene expression and translation. In recent years, lncRNA is considered to be a core node of functional regulatory networks that controls cardiac and also involves in multiple process of heart failure such as myocardial hypertrophy, fibrosis, angiogenesis, etc., which would be a therapeutic target for diseases. In fact, it is the development of technology that has improved our understanding of lncRNAs and broadened our perspective on heart failure. From transcriptional "noise" to star molecule, progress of lncRNAs can't be achieved without the combination of multidisciplinary technologies, especially the emergence of high-throughput approach. Thus, here, we review the strategies and technologies available for the exploration lncRNAs and try to yield insights into the prospect of lncRNAs in clinical diagnosis and treatment in heart failure.

Integrated multi-omics approaches to improve classification of chronic kidney disease

Chronic kidney diseases (CKDs) are currently classified according to their clinical features, associated comorbidities and pattern of injury on biopsy. Even within a given classification, considerable variation exists in disease presentation, progression and response to therapy, highlighting heterogeneity in the underlying biological mechanisms. As a result, patients and clinicians experience uncertainty when considering optimal treatment approaches and risk projection. Technological advances now enable large-scale datasets, including DNA and RNA sequence data, proteomics and metabolomics data, to be captured from individuals and groups of patients along the genotype-phenotype continuum of CKD. The ability to combine these high-dimensional datasets, in which the number of variables exceeds the number of clinical outcome observations, using computational approaches such as machine learning, provides an opportunity to re-classify patients into molecularly defined subgroups that better reflect underlying disease mechanisms. Patients with CKD are uniquely poised to benefit from these integrative, multi-omics approaches since the kidney biopsy, blood and urine samples used to generate these different types of molecular data are frequently obtained during routine clinical care. The ultimate goal of developing an integrated molecular classification is to improve diagnostic classification, risk stratification and assignment of molecular, disease-specific therapies to improve the care of patients with CKD.

Enzyme-Regulated Peptide-Liquid Metal Hybrid Hydrogels as Cell Amber for Single-Cell Manipulation

Current strategies to construct cell-based bioartificial tissues largely remain on a multicell level. Taking cell diversity into account, single-cell manipulation is urgently needed for delicate bioartificial tissue construction. Current single-cell isolation and profiling techniques involve invasive processes and thus are not applicable for single-cell manipulation. Here, we managed to fabricate peptide-liquid metal hybrid hydrogels as "cell ambers" which were suitable for single-cell isolation as well as further handling. The successful preparation of uniform liquid metal nanoparticles allowed the fabrication of peptide-liquid metal hydrogel with excellent recovery property upon mechanical destruction. The alkaline phosphatase-instructed supramolecular self-assembly process allowed the formation of microhydrogel post-filling in the PDMS template. The co-culture of the hydrogel precursor and mammalian cells realized the embedding of cells into elastic hydrogels which were the so-called cell ambers. The cell ambers turned out to be biocompatible and capable of supporting cell survival. Aided with the micro-operating system and a laser scanning confocal microscope, we could arrange these as-prepared 3D single-cell ambers into various patterns as desired. Our strategy provided the possibility to manipulate a single cell, which served as a prototype of cell architecture toward cell-based bioartificial tissue construction.

Divergent Resistance Mechanisms to Immunotherapy Explain Responses in Different Skin Cancers

The advent of immune checkpoint therapy for metastatic skin cancer has greatly improved patient survival. However, most skin cancer patients are refractory to checkpoint therapy, and furthermore, the intra-immune cell signaling driving response to checkpoint therapy remains uncharacterized. When comparing the immune transcriptome in the tumor microenvironment of melanoma and basal cell carcinoma (BCC), we found that the presence of memory B cells and macrophages negatively correlate in both cancers when stratifying patients by their response, with memory B cells more present in responders. Moreover, inhibitory immune signaling mostly decreases in melanoma responders and increases in BCC responders. We further explored the relationships between macrophages, B cells and response to checkpoint therapy by developing a stochastic differential equation model which qualitatively agrees with the data analysis. Our model predicts BCC to be more refractory to checkpoint therapy than melanoma and predicts the best qualitative ratio of memory B cells and macrophages for successful treatment.

Tissue engineering of the biliary tract and modelling of cholestatic disorders

Our insight into the pathogenesis of cholestatic liver disease remains limited, partly owing to challenges in capturing the multitude of factors that contribute to disease pathogenesis in vitro. Tissue engineering could address this challenge by combining cells, materials and fabrication strategies into dynamic modelling platforms, recapitulating the multifaceted aetiology of cholangiopathies. Herein, we review the advantages and limitations of platforms for bioengineering the biliary tree, looking at how these can be applied to model biliary disorders, as well as exploring future directions for the field.

Dynamic single-cell phenotyping of immune cells using the microfluidic platform DropMap

Characterization of immune responses is currently hampered by the lack of systems enabling quantitative and dynamic phenotypic characterization of individual cells and, in particular, analysis of secreted proteins such as cytokines and antibodies. We recently developed a simple and robust microfluidic platform, DropMap, to measure simultaneously the kinetics of secretion and other cellular characteristics, including endocytosis activity, viability and expression of cell-surface markers, from tens of thousands of single immune cells. Single cells are compartmentalized in 50-pL droplets and analyzed using fluorescence microscopy combined with an immunoassay based on fluorescence relocation to paramagnetic nanoparticles aligned to form beadlines in a magnetic field. The protocol typically takes 8-10 h after preparation of microfluidic chips and chambers, which can be done in advance. By contrast, enzyme-linked immunospot (ELISPOT), flow cytometry, time-of-flight mass cytometry (CyTOF), and single-cell sequencing enable only end-point measurements and do not enable direct, quantitative measurement of secreted proteins. We illustrate how this system can be used to profile downregulation of tumor necrosis factor-α (TNF-α) secretion by single monocytes in septic shock patients, to study immune responses by measuring rates of cytokine secretion from single T cells, and to measure affinity of antibodies secreted by single B cells.

Identification of cell-to-cell interactions by ligand-receptor pairs in human fetal heart

The heart is the first organ to form during embryogenesis and its development is a complex process. In this study, we identified 120 ligand-receptor pairs including 65 ligands and 58 receptors specifically expressed in one of the nine cell types. The correlation analysis of the cell proportions revealed that the cell-to-cell contact exhibited spatial patterns in human fetal heart. Specifically, the cardiomyocytes (CMs) proportion might have negative correlation with proportion of endothelial cell in left atrium and ventricle during the heart development. In contrast, fibroblast-like cells and macrophages were jointly increased with the gestation. Furthermore, the ligand in CM, NPPA (Natriuretic Peptide A), and receptor in endothelial cell (EC), NPR3 (Natriuretic Peptide Receptor 3), were specifically expressed in atrial CM and endocardial cells, respectively, indicating that the atrial CM might communicate with endocardial cells via NPPA-NRP3 interaction. Moreover, the interplay between fibroblast-like cell and macrophage was observed in both left and right atriums via the ligand-receptor interactions of COL1A1/COL1A2 (Collagen Type I Alpha 1/2 Chain)-CD36 and CTGF (connective tissue growth factor)-ITGB2 (Integrin Subunit Beta 2). Functional enrichment analysis revealed that the ligand-receptor interactions might be associated with the intracellular activation of cGMP-PKG signaling pathway in ECs, PDGF-beta signaling pathway in fibroblast-like cell, and Toll-like receptor signaling in macrophage, respectively. Collectively, the present study unveiled the potential cell-cell communication and underlying mechanism involved in cardiac development, which broadened our insights into developmental biology of heart.

Application of single-cell RNA sequencing in embryonic development

Embryonic development is a complex process that is regulated by a series of precise cellular behaviours. The limited number of cells in the early stages of embryonic development represents a challenge for studying early gene regulation and maintaining cell sternness. Single-cell sequencing is a new technology for high-throughput sequencing analysis at the single-cell level that not only reflects the heterogeneity between cells but also reveals gene expression characteristics in different cells from limited samples. Currently, the widespread application of single-cell RNA sequencing technology is gradually changing our understanding of disease pathogenesis. This article reviews the application of single-cell RNA sequencing in embryonic development in recent years and provides innovative ideas for research on embryonic development and the treatment of diseases related to embryonic development.

Principles and innovative technologies for decrypting noncoding RNAs: from discovery and functional prediction to clinical application

Noncoding RNAs (ncRNAs) are a large segment of the transcriptome that do not have apparent protein-coding roles, but they have been verified to play important roles in diverse biological processes, including disease pathogenesis. With the development of innovative technologies, an increasing number of novel ncRNAs have been uncovered; information about their prominent tissue-specific expression patterns, various interaction networks, and subcellular locations will undoubtedly enhance our understanding of their potential functions. Here, we summarized the principles and innovative methods for identifications of novel ncRNAs that have potential functional roles in cancer biology. Moreover, this review also provides alternative ncRNA databases based on high-throughput sequencing or experimental validation, and it briefly describes the current strategy for the clinical translation of cancer-associated ncRNAs to be used in diagnosis.

Single-cell RNA sequencing in cardiovascular development, disease and medicine

Advances in single-cell RNA sequencing (scRNA-seq) technologies in the past 10 years have had a transformative effect on biomedical research, enabling the profiling and analysis of the transcriptomes of single cells at unprecedented resolution and throughput. Specifically, scRNA-seq has facilitated the identification of novel or rare cell types, the analysis of single-cell trajectory construction and stem or progenitor cell differentiation, and the comparison of healthy and disease-related tissues at single-cell resolution. These applications have been critical in advances in cardiovascular research in the past decade as evidenced by the generation of cell atlases of mammalian heart and blood vessels and the elucidation of mechanisms involved in cardiovascular development and stem or progenitor cell differentiation. In this Review, we summarize the currently available scRNA-seq technologies and analytical tools and discuss the latest findings using scRNA-seq that have substantially improved our knowledge on the development of the cardiovascular system and the mechanisms underlying cardiovascular diseases. Furthermore, we examine emerging strategies that integrate multimodal single-cell platforms, focusing on future applications in cardiovascular precision medicine that use single-cell omics approaches to characterize cell-specific responses to drugs or environmental stimuli and to develop effective patient-specific therapeutics.

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.

Single-Cell Multimodal Analytical Approach by Integrating Raman Optical Tweezers and RNA Sequencing

Single-cell analysis has become a state-of-art approach to heterogeneity profiling in tumor cells. Herein, we realize a kind of single-cell multimodal analytical approach by combining single-cell RNA sequencing (scRNA-seq) with Raman optical tweezers (ROT), a label-free single-cell identification and isolation technique, and apply it to investigate drug sensitivity. The drug sensitivity of human BGC823 gastric cancer cells toward different drugs, paclitaxel and sodium dichloroacetate, was distinguished in the conjoint analytical way including morphology monitoring, Raman identification, and transcriptomic profiling. Each individual BGC823 cancer cell was measured by Raman spectroscopy, then nondestructively isolated out by ROT, and finally RNA-sequenced. Our results demonstrate each analytical mode can reflect cell response to the drugs from different perspectives and is consistent and complementary with each other. Therefore, we believe the multimodal analytical approach offers an access to comprehensive characterizations of the unicellular complexity, which especially makes sense for studying tumor heterogeneity or a desired special cell from a mixture cell sample such as whole blood.

Innate Lymphoid Cell-Epithelial Cell Modules Sustain Intestinal Homeostasis

The intestines have the essential but challenging mission of absorbing nutrients, restricting damage from food-derived toxins, promoting colonization by symbionts, and expelling pathogens. These processes are often incompatible with each other and must therefore be prioritized in view of the most crucial contemporary needs of the host. Recent work has shown that tissue-resident innate lymphoid cells (ILCs) constitute a central sensory module allowing adaptation of intestinal organ function to changing environmental input. Here, we propose a conceptual framework positing that the various types of ILC act in distinct modules with intestinal epithelial cells, collectively safeguarding organ function. Such homeostasis-promoting circuitry has high potential to be plumbed for new therapeutic approaches to the treatment of immune-mediated inflammatory diseases.

Single-cell RNA sequencing in breast cancer: Understanding tumor heterogeneity and paving roads to individualized therapy

Single‐cell RNA sequencing (scRNA‐seq) is a novel technology that allows transcriptomic analyses of individual cells. During the past decade, scRNA‐seq sensitivity, accuracy, and efficiency have improved due to innovations including more sensitive, automated, and cost‐effective single‐cell isolation methods with higher throughput as well as ongoing technological development of scRNA‐seq protocols. Among the variety of current approaches with distinct features, researchers can choose the most suitable method to carry out their research. By profiling single cells in a complex population mix, scRNA‐seq presents great advantages over traditional sequencing methods in dissecting heterogeneity in cell populations hidden in bulk analysis and exploring rare cell types associated with tumorigenesis and metastasis. scRNA‐seq studies in recent years in the field of breast cancer research have clustered breast cancer cell populations with different molecular subtypes to identify distinct populations that may correlate with poor prognosis and drug resistance. The technology has also been used to explain tumor microenvironment heterogeneity by identifying distinct immune cell subsets that may be associated with immunosurveillance and are potential immunotherapy targets. Moreover, scRNA‐seq has diverse applications in breast cancer research besides exploring heterogeneity, including the analysis of cell‐cell communications, regulatory single‐cell states, immune cell distributions, and more. scRNA‐seq is also a promising tool that can facilitate individualized therapy due to its ability to define cell subsets with potential treatment targets. Although scRNA‐seq studies of therapeutic selection in breast cancer are currently limited, the application of this technology in this field is prospective. Joint efforts and original ideas are needed to better implement scRNA‐seq technologies in breast cancer research to pave the way for individualized treatment management. This review provides a brief introduction on the currently available scRNA‐seq approaches along with their corresponding strengths and weaknesses and may act as a reference for the selection of suitable methods for research. We also discuss the current applications of scRNA‐seq in breast cancer research for tumor heterogeneity analysis, individualized therapy, and the other research directions mentioned above by reviewing corresponding published studies. Finally, we discuss the limitations of current scRNA‐seq technologies and technical problems that remain to be overcome.

Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows

BACKGROUND

Single-cell RNA sequencing has been widely adopted to estimate the cellular composition of heterogeneous tissues and obtain transcriptional profiles of individual cells. Multiple approaches for optimal sample dissociation and storage of single cells have been proposed as have single-nuclei profiling methods. What has been lacking is a systematic comparison of their relative biases and benefits.

RESULTS

Here, we compare gene expression and cellular composition of single-cell suspensions prepared from adult mouse kidney using two tissue dissociation protocols. For each sample, we also compare fresh cells to cryopreserved and methanol-fixed cells. Lastly, we compare this single-cell data to that generated using three single-nucleus RNA sequencing workflows. Our data confirms prior reports that digestion on ice avoids the stress response observed with 37 °C dissociation. It also reveals cell types more abundant either in the cold or warm dissociations that may represent populations that require gentler or harsher conditions to be released intact. For cell storage, cryopreservation of dissociated cells results in a major loss of epithelial cell types; in contrast, methanol fixation maintains the cellular composition but suffers from ambient RNA leakage. Finally, cell type composition differences are observed between single-cell and single-nucleus RNA sequencing libraries. In particular, we note an underrepresentation of T, B, and NK lymphocytes in the single-nucleus libraries.

CONCLUSIONS

Systematic comparison of recovered cell types and their transcriptional profiles across the workflows has highlighted protocol-specific biases and thus enables researchers starting single-cell experiments to make an informed choice.

Applications of single-cell sequencing for the field of otolaryngology: A contemporary review

OBJECTIVES

Single-cell RNA sequencing (scRNA-Seq) is a new technique used to interrogate the transcriptome of individual cells within native tissues that have already resulted in key discoveries in auditory basic science research. Rapid advances in scRNA-Seq make it likely that it will soon be translated into clinical medicine. The goal of this review is to inspire the use of scRNA-Seq in otolaryngology by giving examples of how it can be applied to patient samples and how this information can be used clinically.

METHODS

Studies were selected based on the scientific quality and relevance to scRNA-Seq. In addition to mouse auditory system (inner ear including hair cells and supporting cells, spiral ganglion neurons, and inner ear organoids), recent studies using human primary cell samples are discussed. We also perform our own analysis on publicly available, published scRNA-Seq data from oral head and neck squamous cell carcinoma (HNSCC) samples to serve as an example of a clinically relevant application of scRNA-Seq.

RESULTS

Studies focusing on patient tissues show that scRNA-Seq reveals tissue heterogeneity and rare-cell types responsible for disease pathogenesis. The heterogeneity detected by scRNA-Seq can result in both the identification of known or novel disease biomarkers and drug targets. Our analysis of HNSCC data gives an example for how otolaryngologists can use scRNA-Seq for clinical use.

CONCLUSIONS

Although there are limitations to the translation of scRNA-Seq to the clinic, we show that its use in otolaryngology can give physicians insight into the tissue heterogeneity within their patient's diseased tissue giving them information on disease pathogenesis, novel disease biomarkers or druggable targets, and aid in selecting patient-specific drug cocktails.

Unravelling the heterogeneity and dynamic relationships of tumor-infiltrating T cells by single-cell RNA sequencing analysis

T cells are crucial for the success of immune-based cancer therapy. Reinvigorating antitumor T cell activity by blocking checkpoint inhibitory receptors has provided clinical benefits for many cancer patients. However, the efficacy of these treatments varies in cancer patients and the mechanisms underlying these diverse responses remain elusive. The density and status of tumor-infiltrating T cells have been shown to positively correlate with patient response to checkpoint blockades. Therefore, further understanding of the heterogeneity, clonal expansion, migration, and effector functions of tumor-infiltrating T cells will provide fundamental insights into antitumor immune responses. To this end, recent advances in single-cell RNA sequencing technology have enabled profound and extensive characterization of intratumoral immune cells and have improved our understanding of their dynamic relationships. Here, we summarize recent progress in single-cell RNA sequencing technology and current strategies to uncover heterogeneous tumor-infiltrating T cell subsets. In particular, we discuss how the coupling of deep transcriptome information with T cell receptor (TCR)-based lineage tracing has furthered our understanding of intratumoral T cell populations. We also discuss the functional implications of various T cell subsets in tumors and highlight the identification of novel T cell markers with therapeutic or prognostic potential.

Structural Biology in the Multi-Omics Era

Rapid developments in cryogenic electron microscopy have opened new avenues to probe the structures of protein assemblies in their near native states. Recent studies have begun applying single -particle analysis to heterogeneous mixtures, revealing the potential of structural-omics approaches that combine the power of mass spectrometry and electron microscopy. Here we highlight advances and challenges in sample preparation, data processing, and molecular modeling for handling increasingly complex mixtures. Such advances will help structural-omics methods extend to cellular-level models of structural biology.

A new protocol for single-cell RNA-seq reveals stochastic gene expression during lag phase in budding yeast

Current methods for single-cell RNA sequencing (scRNA-seq) of yeast cells do not match the throughput and relative simplicity of the state-of-the-art techniques that are available for mammalian cells. In this study, we report how 10x Genomics' droplet-based single-cell RNA sequencing technology can be modified to allow analysis of yeast cells. The protocol, which is based on in-droplet spheroplasting of the cells, yields an order-of-magnitude higher throughput in comparison to existing methods. After extensive validation of the method, we demonstrate its use by studying the dynamics of the response of isogenic yeast populations to a shift in carbon source, revealing the heterogeneity and underlying molecular processes during this shift. The method we describe opens new avenues for studies focusing on yeast cells, as well as other cells with a degradable cell wall.

The effects of long non-coding ribonucleic acids on various cellular components in rheumatoid arthritis

RA is a chronic, autoimmune-mediated inflammatory pathology. Long non-coding RNAs (lncRNAs) are a novel group of non-coding RNAs with a length of >200 nucleotides. There are reports emerging that suggest that lncRNAs participate in establishing and sustaining autoimmune diseases, including RA. In this review article, we highlight the functions of lncRNAs in different cell types in RA. Our review indicates that lncRNAs affect various cellular components and are novel candidates that could constitute promising targets for the diagnosis and treatment of RA.

Understanding generation and regeneration of pancreatic β cells from a single-cell perspective

Understanding the mechanisms that underlie the generation and regeneration of β cells is crucial for developing treatments for diabetes. However, traditional research methods, which are based on populations of cells, have limitations for defining the precise processes of β-cell differentiation and trans-differentiation, and the associated regulatory mechanisms. The recent development of single-cell technologies has enabled re-examination of these processes at a single-cell resolution to uncover intermediate cell states, cellular heterogeneity and molecular trajectories of cell fate specification. Here, we review recent advances in understanding β-cell generation and regeneration, in vivo and in vitro, from single-cell technologies, which could provide insights for optimization of diabetes therapy strategies.

Cytokine analysis on a countable number of molecules from living single cells on nanofluidic devices

Analysis of proteins released from living single cells is strongly required in the fields of biology and medicine to elucidate the mechanism of gene expression, cell-cell communication and cytopathology. However, as living single-cell analysis involves fL sample volumes with ultra-small amounts of analyte, comprehensive integration of entire chemical processing for single cells and proteins into spaces smaller than single cells (pL) would be indispensable to prevent dispersion-associated analyte loss. In this study, we proposed and developed a living single-cell protein analysis device based on micro/nanofluidics and demonstrated analysis of cytokines released from living single B cells by enzyme-linked immunosorbent assay. Based on our integration method and technologies including top-down nanofabrication, surface modifications and pressure-driven flow control, we designed and prepared the device where pL-microfluidic- and fL-nanofluidic channels are hierarchically allocated for cellular and molecular processing, respectively, and succeeded in micro/nanofluidic control for manipulating single cells and molecules. 13-unit operations for pL-cellular processing including single-cell trapping and stimulation and fL-molecular processing including fL-volumetry, antigen-antibody reactions and detection were entirely integrated into a microchip. The results suggest analytical performances for countable interleukin (IL)-6 molecules at the limit of detection of 5.27 molecules and that stimulated single B cells secrete 3.41 IL-6 molecules per min. The device is a novel tool for single-cell targeted proteomics, and the methodology of device integration is applicable to other single-cell analyses such as single-cell shotgun proteomics. This study thus provides a general approach and technical breakthroughs that will facilitate further advances in micro/nanofluidics, single-cell life science research, and other fields.

Single cell transcriptomics identifies focal segmental glomerulosclerosis remission endothelial biomarker

To define cellular mechanisms underlying kidney function and failure, the KPMP analyzes biopsy tissue in a multicenter research network to build cell-level process maps of the kidney. This study aimed to establish a single cell RNA sequencing strategy to use cell-level transcriptional profiles from kidney biopsies in KPMP to define molecular subtypes in glomerular diseases. Using multiple sources of adult human kidney reference tissue samples, 22,268 single cell profiles passed KPMP quality control parameters. Unbiased clustering resulted in 31 distinct cell clusters that were linked to kidney and immune cell types using specific cell markers. Focusing on endothelial cell phenotypes, in silico and in situ hybridization methods assigned 3 discrete endothelial cell clusters to distinct renal vascular beds. Transcripts defining glomerular endothelial cells (GEC) were evaluated in biopsies from patients with 10 different glomerular diseases in the NEPTUNE and European Renal cDNA Bank (ERCB) cohort studies. Highest GEC scores were observed in patients with focal segmental glomerulosclerosis (FSGS). Molecular endothelial signatures suggested 2 distinct FSGS patient subgroups with α-2 macroglobulin (A2M) as a key downstream mediator of the endothelial cell phenotype. Finally, glomerular A2M transcript levels associated with lower proteinuria remission rates, linking endothelial function with long-term outcome in FSGS.

Microfluidic Single-Cell Omics Analysis

The commonly existing cellular heterogeneity plays a critical role in biological processes such as embryonic development, cell differentiation, and disease progress. Single-cell omics-based heterogeneous studies have great significance for identifying different cell populations, discovering new cell types, revealing informative cell features, and uncovering significant interrelationships between cells. Recently, microfluidics has evolved to be a powerful technology for single-cell omics analysis due to its merits of throughput, sensitivity, and accuracy. Herein, the recent advances of microfluidic single-cell omics analysis, including different microfluidic platform designs, lysis strategies, and omics analysis techniques, are reviewed. Representative applications of microfluidic single-cell omics analysis in complex biological studies are then summarized. Finally, a few perspectives on the future challenges and development trends of microfluidic-assisted single-cell omics analysis are discussed.

scCATCH: Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data

Recent advancements in single-cell RNA sequencing (scRNA-seq) have facilitated the classification of thousands of cells through transcriptome profiling, wherein accurate cell type identification is critical for mechanistic studies. In most current analysis protocols, cell type-based cluster annotation is manually performed and heavily relies on prior knowledge, resulting in poor replicability of cell type annotation. This study aimed to introduce a single-cell Cluster-based Automatic Annotation Toolkit for Cellular Heterogeneity (scCATCH, https://github.com/ZJUFanLab/scCATCH). Using three benchmark datasets, the feasibility of evidence-based scoring and tissue-specific cellular annotation strategies were demonstrated by high concordance among cell types, and scCATCH outperformed Seurat, a popular method for marker genes identification, and cell-based annotation methods. Furthermore, scCATCH accurately annotated 67%–100% (average, 83%) clusters in six published scRNA-seq datasets originating from various tissues. The present results show that scCATCH accurately revealed cell identities with high reproducibility, thus potentially providing insights into mechanisms underlying disease pathogenesis and progression.

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Construction of a comprehensive tissue-specific reference database of cell markers

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Paired comparisons to identify potential marker genes for clusters to ensure accuracy

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Evidence-based scoring and annotation for clustered cells from scRNA-seq data

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Accurate and replicable annotation on cell types of clusters without prior knowledge

Using single-cell technologies to map the human immune system - implications for nephrology

Advances in single-cell technologies are transforming our understanding of cellular identity. For instance, the application of single-cell RNA sequencing and mass cytometry technologies to the study of immune cell populations in blood, secondary lymphoid organs and the renal tract is helping researchers to map the complex immune landscape within the kidney, define cell ontogeny and understand the relationship of kidney-resident immune cells with their circulating counterparts. These studies also provide insights into the interactions of immune cell populations with neighbouring epithelial and endothelial cells in health, and across a range of kidney diseases and cancer. These data have translational potential and will aid the identification of drug targets and enable better prediction of off-target effects. The application of single-cell technologies to clinical renal biopsy samples, or even cells within urine, will improve diagnostic accuracy and assist with personalized prognostication for patients with various kidney diseases. A comparison of immune cell types in peripheral blood and secondary lymphoid organs in healthy individuals and in patients with systemic autoimmune diseases that affect the kidney will also help to unravel the mechanisms that underpin the breakdown in self-tolerance and propagation of autoimmune responses. Together, these immune cell atlases have the potential to transform nephrology.

SHARP: hyperfast and accurate processing of single-cell RNA-seq data via ensemble random projection

To process large-scale single-cell RNA-sequencing (scRNA-seq) data effectively without excessive distortion during dimension reduction, we present SHARP, an ensemble random projection-based algorithm that is scalable to clustering 10 million cells. Comprehensive benchmarking tests on 17 public scRNA-seq data sets show that SHARP outperforms existing methods in terms of speed and accuracy. Particularly, for large-size data sets (more than 40,000 cells), SHARP runs faster than other competitors while maintaining high clustering accuracy and robustness. To the best of our knowledge, SHARP is the only R-based tool that is scalable to clustering scRNA-seq data with 10 million cells.

Single-Cell Molecular and Cellular Architecture of the Mouse Neurohypophysis

The neurohypophysis (NH), located at the posterior lobe of the pituitary, is a major neuroendocrine tissue, which mediates osmotic balance, blood pressure, reproduction, and lactation by means of releasing the neurohormones oxytocin (OXT) and arginine-vasopressin (AVP) from the brain into the peripheral blood circulation. The major cellular components of the NH are hypothalamic axonal termini, fenestrated endothelia and pituicytes, the resident astroglia. However, despite the physiological importance of the NH, the exact molecular signature defining neurohypophyseal cell types and in particular the pituicytes, remains unclear. Using single-cell RNA sequencing (scRNA-Seq), we captured seven distinct cell types in the NH and intermediate lobe (IL) of adult male mouse. We revealed novel pituicyte markers showing higher specificity than previously reported. Bioinformatics analysis demonstrated that pituicyte is an astrocytic cell type whose transcriptome resembles that of tanycyte. Single molecule in situ hybridization revealed spatial organization of the major cell types implying intercellular communications. We present a comprehensive molecular and cellular characterization of neurohypophyseal cell types serving as a valuable resource for further functional research.

Mouse Models of Congenital Kidney Anomalies

Congenital anomalies of the kidney and urinary tract (CAKUT) are common birth defects, which cause the majority of chronic kidney diseases in children. CAKUT covers a wide range of malformations that derive from deficiencies in embryonic kidney and lower urinary tract development, including renal aplasia, hypodysplasia, hypoplasia, ectopia, and different forms of ureter abnormalities. The majority of the genetic causes of CAKUT remain unknown. Research on mutant mice has identified multiple genes that critically regulate renal differentiation. The data generated from this research have served as an excellent resource to identify the genetic bases of human kidney defects and have led to significantly improved diagnostics. Furthermore, genetic data from human CAKUT studies have also revealed novel genes regulating kidney differentiation.

The Current State of the Art in Acute Kidney Injury

Decades of pre-clinical research have revealed biologic pathways that have suggested potential therapies for acute kidney injury (AKI) in experimental models. However, translating these to human AKI has largely yielded disappointing results. Fortunately, recent discoveries in AKI molecular mechanisms are providing new opportunities for early detection and novel interventions. This review identifies technologies that are revealing the exceptionally complex nature of the normal kidney, the remarkable heterogeneity of the AKI syndrome, and the myriad responses of the kidney to AKI. Based on the current state of the art, novel approaches to improve the bench-to-bedside translation of novel discoveries are proposed. These strategies include the use of unbiased approaches to improve our understanding of human AKI, establishment of irrefutable biologic plausibility for proposed biomarkers and therapies, identification of patients at risk for AKI pre-injury using clinical scores and non-invasive biomarkers, initiation of safe, and effective preventive interventions of pre-injury in susceptible patients, identification of patients who may develop AKI post-injury using electronic triggers, clinical scores, and novel biomarkers, employment of sequential biomarkers to initiate appropriate therapies based on knowledge of the underlying pathophysiology, use of new biomarkers as criteria for enrollment in randomized clinical trials, assessing efficacy, and empowering the drug development process, and early initiation of anti-fibrotic therapies. These strategies are immediately actionable and hold tremendous promise for effective bench-to-bedside translation of novel discoveries that will change the current dismal prognosis of human AKI.

Next-Generation Liver Medicine Using Organoid Models

"Liver medicine" refers to all diagnostic and treatment strategies of diseases and conditions that cause liver failure directly or indirectly. Despite significant advances in the field of liver medicine in recent years, improved tools are needed to efficiently define the pathophysiology of liver diseases and provide effective therapeutic options to patients. Recently, organoid technology has been established as the state-of-the-art cell culture tool for studying human biology in health and disease. In general, organoids are simplified three-dimensional (3D) mini-organ structures that can be grown in a 3D matrix where the structural and functional aspects of real organs are efficiently recapitulated. The generation of organoids is facilitated by exogenous factors that regulate multiple signaling pathways and promote the self-renewal, proliferation, and differentiation of the cells to promote spontaneous self-organization and tissue-specific organogenesis. Newly established protocols suggest that liver-specific organoids can be derived from either pluripotent stem cells or liver-specific stem/progenitor cells. Today, robust and long-term cultures of organoids with the closest physiology to in vivo liver, in terms of cellular composition and function, open a new era in studying and understanding the disease pathology as well as high-throughput drug screening. Of note, these next-generation cell culture systems have immense potential to be further improved by genome editing and bioengineering technologies to foster the development of patient-specific therapeutic options for clinical applications. Here, we will discuss recent advances and challenges in the generation of human liver organoids and highlight emerging concepts for their potential applications in liver medicine.

Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for studying complex biological systems, such as tumor heterogeneity and tissue microenvironments. However, the sources of technical and biological variation in primary solid tumor tissues and patient-derived mouse xenografts for scRNA-seq are not well understood.

RESULTS

We use low temperature (6 °C) protease and collagenase (37 °C) to identify the transcriptional signatures associated with tissue dissociation across a diverse scRNA-seq dataset comprising 155,165 cells from patient cancer tissues, patient-derived breast cancer xenografts, and cancer cell lines. We observe substantial variation in standard quality control metrics of cell viability across conditions and tissues. From the contrast between tissue protease dissociation at 37 °C or 6 °C, we observe that collagenase digestion results in a stress response. We derive a core gene set of 512 heat shock and stress response genes, including FOS and JUN, induced by collagenase (37 °C), which are minimized by dissociation with a cold active protease (6 °C). While induction of these genes was highly conserved across all cell types, cell type-specific responses to collagenase digestion were observed in patient tissues.

CONCLUSIONS

The method and conditions of tumor dissociation influence cell yield and transcriptome state and are both tissue- and cell-type dependent. Interpretation of stress pathway expression differences in cancer single-cell studies, including components of surface immune recognition such as MHC class I, may be especially confounded. We define a core set of 512 genes that can assist with the identification of such effects in dissociated scRNA-seq experiments.