Defining Cell Identity with Single-Cell Omics

FIND-seq: high-throughput nucleic acid cytometry for rare single-cell transcriptomics

Rare cells have an important role in development and disease, and methods for isolating and studying cell subsets are therefore an essential part of biology research. Such methods traditionally rely on labeled antibodies targeted to cell surface proteins, but large public databases and sophisticated computational approaches increasingly define cell subsets on the basis of genomic, epigenomic and transcriptomic sequencing data. Methods for isolating cells on the basis of nucleic acid sequences powerfully complement these approaches by providing experimental access to cell subsets discovered in cell atlases, as well as those that cannot be otherwise isolated, including cells infected with pathogens, with specific DNA mutations or with unique transcriptional or splicing signatures. We recently developed a nucleic acid cytometry platform called 'focused interrogation of cells by nucleic acid detection and sequencing' (FIND-seq), capable of isolating rare cells on the basis of RNA or DNA markers, followed by bulk or single-cell transcriptomic analysis. This platform has previously been used to characterize the splicing-dependent activation of the transcription factor XBP1 in astrocytes and HIV persistence in memory CD4 T cells from people on long-term antiretroviral therapy. Here, we outline the molecular and microfluidic steps involved in performing FIND-seq, including protocol updates that allow detection and whole transcriptome sequencing of rare HIV-infected cells that harbor genetically intact virus genomes. FIND-seq requires knowledge of microfluidics, optics and molecular biology. We expect that FIND-seq, and this comprehensive protocol, will enable mechanistic studies of rare HIV+ cells, as well as other cell subsets that were previously difficult to recover and sequence.

Single-cell classification, analysis, and its application using deep learning techniques

Single-cell analysis (SCA) improves the detection of cancer, the immune system, and chronic diseases from complicated biological processes. SCA techniques generate high-dimensional, innovative, and complex data, making traditional analysis difficult and impractical. In the different cell types, conventional cell sequencing methods have signal transformation and disease detection limitations. To overcome these challenges, various deep learning techniques (DL) have outperformed standard state-of-the-art computer algorithms in SCA techniques. This review discusses DL application in SCA and presents a detailed study on improving SCA data processing and analysis. Firstly, we introduced fundamental concepts and critical points of cell analysis techniques, which illustrate the application of SCA. Secondly, various effective DL strategies apply to SCA to analyze data and provide significant results from complex data sources. Finally, we explored DL as a future direction in SCA and highlighted new challenges and opportunities for the rapidly evolving field of single-cell omics.

Preparation of polypeptide-metal complexes-coated Hosenkoside A and its inhibitory effect in cervical cancer

We reported the anti-cervical cancer effect of proprietary saponin content from seeds of Impatiens balsamina L., Hosenkoside A. Our study found that Hosenkoside A significantly promotes cell apoptosis and cell cycle arrest after administration, exhibiting anti-tumor effects. Then the transcriptome sequencing results after administration showed that Hosenkoside A had a significant inhibitory effect on Histone deacetylase 3 (HDAC3). After sufficient administration time, the inhibition of HDAC3 expression level leads to a significant decrease in lysine acetylation at histone 3 sites 4 and 9, blocking the activation of Signal transducer and activator of transcription 3 (STAT3) and achieving anti-tumor effects. In addition, we encapsulated Hosenkoside A into polypeptide metal complexes (PMC) to form slow-release spheres. This material breaks down in the tumor environment, not only does it solve the problem of low drug solubility, but it also achieves targeted sustained-release drug delivery. Under the same concentration of stimulation, the PMC complex group showed better anti-tumor effects in both in vitro and in vivo experiments.

Understanding Cell Differentiation Through Single-Cell Approaches: Conceptual Challenges of the Systemic Approach

The cells of a multicellular organism are derived from a single zygote and genetically almost identical. Yet, they are phenotypically very different. This difference is the result of a process commonly called cell differentiation. How the phenotypic diversity emerges during ontogenesis or regeneration is a central and intensely studied but still unresolved issue in biology. Cell biology is facing conceptual challenges that are frequently confused with methodological difficulties. How to define a cell type? What stability or change means in the context of cell differentiation and how to deal with the ubiquitous molecular variations seen in the living cells? What are the driving forces of the change? We propose to reframe the problem of cell differentiation in a systemic way by incorporating different theoretical approaches. The new conceptual framework is able to capture the insights made at different levels of cellular organization and considered previously as contradictory. It also provides a formal strategy for further experimental studies.

Optofluidic Flow Cytometer with In-Plane Spherical Mirror for Signal Enhancement

Statistical analysis of the properties of single microparticles, such as cells, bacteria or plastic slivers, has attracted increasing interest in recent years. In this regard, field flow cytometry is considered the gold standard technique, but commercially available instruments are bulky, expensive, and not suitable for use in point-of-care (PoC) testing. Microfluidic flow cytometers, on the other hand, are small, cheap and can be used for on-site analyses. However, in order to detect small particles, they require complex geometries and the aid of external optical components. To overcome these limitations, here, we present an opto-fluidic flow cytometer with an integrated 3D in-plane spherical mirror for enhanced optical signal collection. As a result, the signal-to-noise ratio is increased by a factor of six, enabling the detection of particle sizes down to 1.5 µm. The proposed optofluidic detection scheme enables the simultaneous collection of particle fluorescence and scattering using a single optical fiber, which is crucial to easily distinguishing particle populations with different optical properties. The devices have been fully characterized using fluorescent polystyrene beads of different sizes. As a proof of concept for potential real-world applications, signals from fluorescent HEK cells and Escherichia coli bacteria were analyzed.

Advanced Omics Techniques for Understanding Cochlear Genome, Epigenome, and Transcriptome in Health and Disease

Advanced genomics, transcriptomics, and epigenomics techniques are providing unprecedented insights into the understanding of the molecular underpinnings of the central nervous system, including the neuro-sensory cochlea of the inner ear. Here, we report for the first time a comprehensive and updated overview of the most advanced omics techniques for the study of nucleic acids and their applications in cochlear research. We describe the available in vitro and in vivo models for hearing research and the principles of genomics, transcriptomics, and epigenomics, alongside their most advanced technologies (like single-cell omics and spatial omics), which allow for the investigation of the molecular events that occur at a single-cell resolution while retaining the spatial information.

Development of a Microfluidic Flow Cytometer with a Uniform Optical Field (Uni-μFCM) Enabling Quantitative Analysis of Single-Cell Proteins and Its Applications in Leukemia Gating, Tumor Classification, and Hierarchy of Cancer Stem Cells

Fast and quantitative estimation of single-cell proteins with various distribution patterns remains a technical challenge. Here, a microfluidic flow cytometer with a uniform optical field (Uni-μFCM) was developed, which enabled the translation of multicolor fluorescence signals of bound antibodies into targeted protein numbers with arbitrary distributions of biological cells. As the core of Uni-μFCM, a uniform optical field for optical excitation and fluorescence detection was realized by adopting a microfabricated metal window to shape the optical beam for excitation, which was modeled and validated by both numerical simulation and experimental characterization. After the validation of Uni-μFCM in single-cell protein quantification by measuring single-cell expressions of three transcriptional factors from four cell lines of variable sizes and origins, Uni-μFCM was applied to (1) quantify membrane and cytoplasmic markers of myeloid and lymphocytic leukocytes to classify cell lines and normal and patient blood samples; (2) measure single-cell expressions of key cytokines affiliated with gene stabilities, differentiating paired oral and colon tumor cell lines with varied malignancies, and (3) quantify single-cell stemming markers of liver tumor cell lines, cell subtypes, and liver patient samples to determine a variety of lineage hierarchy. By quantitatively assessing complex cellular phenotypes, Uni-μFCM substantially expanded the phenotypic space accessible to single-cell applications in leukemia gating, tumor classification, and hierarchy determination of cancer stem cells.

Spatial transcriptomics in human skin research

Spatial transcriptomics is a revolutionary technique that enables researchers to characterise tissue architecture and localisation of gene expression. A plethora of technologies that map gene expression are currently being developed, aiming to facilitate spatially resolved, high-dimensional assessment of gene transcription in the context of human skin research. Knowing which gene is expressed by which cell and in which location within skin, facilitates understanding of skin function and dysfunction in both health and disease. In this review, we summarise the available spatial transcriptomic methods and we describe their application to a broad spectrum of dermatological diseases.

A rapid and sensitive single-cell proteomic method based on fast liquid-chromatography separation, retention time prediction and MS1-only acquisition

Single-cell analysis has received much attention in recent years for elucidating the widely existing cellular heterogeneity in biological systems. However, the ability to measure the proteome in single cells is still far behind that of transcriptomics due to the lack of sensitive and high-throughput mass spectrometry methods. Herein, we report an integrated strategy termed "SCP-MS1" that combines fast liquid chromatography (LC) separation, deep learning-based retention time (RT) prediction and MS1-only acquisition for rapid and sensitive single-cell proteome analysis. In SCP-MS1, the peptides were identified via four-dimensional MS1 feature (m/z, RT, charge and FAIMS CV) matching, therefore relieving MS acquisition from the time consuming and information losing MS2 step and making this method particularly compatible with fast LC separation. By completely omitting the MS2 step, all the MS analysis time was utilized for MS1 acquisition in SCP-MS1 and therefore led to 65%-138% increased MS1 feature collection. Unlike "match between run" methods that still needed MS2 information for RT alignment, SCP-MS1 used deep learning-based RT prediction to transfer the measured RTs in long gradient bulk analyses to short gradient single cell analyses, which was the key step to enhance both identification scale and matching accuracy. Using this strategy, more than 2000 proteins were obtained from 0.2 ng of peptides with a 14-min active gradient at a false discovery rate (FDR) of 0.8%. Comparing with the DDA method, improved quantitative performance was also observed for SCP-MS1 with approximately 50% decreased median coefficient of variation of quantified proteins. For single-cell analysis, 1715 ± 204 and 1604 ± 224 proteins were quantified in single 293T and HeLa cells, respectively. Finally, SCP-MS1 was applied to single-cell proteome analysis of sorafenib resistant and non-resistant HepG2 cells and revealed clear cellular heterogeneity in the resistant population that may be masked in bulk studies.

Deciphering endothelial heterogeneity in health and disease at single-cell resolution: progress and perspectives

Endothelial cells (ECs) constitute the inner lining of vascular beds in mammals and are crucial for homeostatic regulation of blood vessel physiology, but also play a key role in pathogenesis of many diseases, thereby representing realistic therapeutic targets. However, it has become evident that ECs are heterogeneous, encompassing several subtypes with distinct functions, which makes EC targeting and modulation in diseases challenging. The rise of the new single-cell era has led to an emergence of studies aimed at interrogating transcriptome diversity along the vascular tree, and has revolutionized our understanding of EC heterogeneity from both a physiological and pathophysiological context. Here, we discuss recent landmark studies aimed at teasing apart the heterogeneous nature of ECs. We cover driving (epi)genetic, transcriptomic, and metabolic forces underlying EC heterogeneity in health and disease, as well as current strategies used to combat disease-enriched EC phenotypes, and propose strategies to transcend largely descriptive heterogeneity towards prioritization and functional validation of therapeutically targetable drivers of EC diversity. Lastly, we provide an overview of the most recent advances and hurdles in single EC OMICs.

Learning cell identity in immunology, neuroscience, and cancer

Suspension and imaging cytometry techniques that simultaneously measure hundreds of cellular features are powering a new era of cell biology and transforming our understanding of human tissues and tumors. However, a central challenge remains in learning the identities of unexpected or novel cell types. Cell identification rubrics that could assist trainees, whether human or machine, are not always rigorously defined, vary greatly by field, and differentially rely on cell intrinsic measurements, cell extrinsic tissue measurements, or external contextual information such as clinical outcomes. This challenge is especially acute in the context of tumors, where cells aberrantly express developmental programs that are normally time, location, or cell-type restricted. Well-established fields have contrasting practices for cell identity that have emerged from convention and convenience as much as design. For example, early immunology focused on identifying minimal sets of protein features that mark individual, functionally distinct cells. In neuroscience, features including morphology, development, and anatomical location were typical starting points for defining cell types. Both immunology and neuroscience now aim to link standardized measurements of protein or RNA to informative cell functions such as electrophysiology, connectivity, lineage potential, phospho-protein signaling, cell suppression, and tumor cell killing ability. The expansion of automated, machine-driven methods for learning cell identity has further created an urgent need for a harmonized framework for distinguishing cell identity across fields and technology platforms. Here, we compare practices in the fields of immunology and neuroscience, highlight concepts from each that might work well in the other, and propose ways to implement these ideas to study neural and immune cell interactions in brain tumors and associated model systems.

Single-cell genomics reveals population structures from in vitro evolutionary studies of Salmonella

Single-cell DNA sequencing has the potential to reveal detailed hierarchical structures in evolving populations of cells. Single cell approaches are increasingly used to study clonal evolution in human ageing and cancer but have not yet been deployed to study evolving clonal microbial populations. Here, we present an approach for single bacterial genomic analysis for in vitro evolution experiments using FACS isolation of individual bacteria followed by whole-genome amplification and sequencing. We apply this to the experimental evolution of a hypermutator strain of Salmonella in response to antibiotic stress (ciprofloxacin). By analysing sequence polymorphisms in individual cells from populations we identified the presence and prevalence of sub-populations which have acquired polymorphisms in genes previously demonstrated to be associated with ciprofloxacin susceptibility. We were also able to identify that the population exposed to antibiotic stress was able to develop resistance whilst maintaining diversity. This population structure could not be resolved from bulk sequence data, and our results show how high-throughput single-cell sequencing can enhance experimental studies of bacterial evolution.

Protein Adsorption Loss─The Bottleneck of Single-Cell Proteomics

Single-cell proteomics is a promising field to provide direct yet comprehensive molecular insights into cellular functions without averaging effects. Here, we address a grand technical challenge impeding the maturation of single-cell proteomics─protein adsorption loss (PAL). Even though widely known, there is currently no quantitation on how profoundly and selectively PAL has affected single-cell proteomics. Therefore, the mitigations to this challenge have been generic, and their efficacy was only evaluated by the size of the resolved proteome with no specificity on individual proteins. We use the existing knowledge of PAL, protein expression, and the typical surface area used in single-cell proteomics to discuss the severity of protein loss. We also summarize the current solutions to this challenge and briefly review the available methods to characterize the physical and chemical properties of protein surface adsorption. By citing successful strategies in single-cell genomics for measurement errors in individual transcripts, we pinpoint the urgency to benchmark PAL at the proteome scale with individual protein resolution. Finally, orthogonal single-cell proteomic techniques that have the potential to cross validate PAL are proposed. We hope these efforts can promote the fruition of single-cell proteomics in the near future.

Developments of Conventional and Microfluidic Flow Cytometry Enabling High-Throughput Characterization of Single Cells

This article first reviews scientific meanings of single-cell analysis by highlighting two key scientific problems: landscape reconstruction of cellular identities during dynamic immune processes and mechanisms of tumor origin and evolution. Secondly, the article reviews clinical demands of single-cell analysis, which are complete blood counting enabled by optoelectronic flow cytometry and diagnosis of hematologic malignancies enabled by multicolor fluorescent flow cytometry. Then, this article focuses on the developments of optoelectronic flow cytometry for the complete blood counting by comparing conventional counterparts of hematology analyzers (e.g., DxH 900 of Beckman Coulter, XN-1000 of Sysmex, ADVIA 2120i of Siemens, and CELL-DYN Ruby of Abbott) and microfluidic counterparts (e.g., microfluidic impedance and imaging flow cytometry). Future directions of optoelectronic flow cytometry are indicated where intrinsic rather than dependent biophysical parameters of blood cells must be measured, and they can replace blood smears as the gold standard of blood analysis in the near future.

Human Embryos, Induced Pluripotent Stem Cells, and Organoids: Models to Assess the Effects of Environmental Plastic Pollution

For a long time, animal models were used to mimic human biology and diseases. However, animal models are not an ideal solution due to numerous interspecies differences between humans and animals. New technologies, such as human-induced pluripotent stem cells and three-dimensional (3D) cultures such as organoids, represent promising solutions for replacing, refining, and reducing animal models. The capacity of organoids to differentiate, self-organize, and form specific, complex, biologically suitable structures makes them excellent in vitro models of development and disease pathogenesis, as well as drug-screening platforms. Despite significant potential health advantages, further studies and considerable nuances are necessary before their clinical use. This article summarizes the definition of embryoids, gastruloids, and organoids and clarifies their appliance as models for early development, diseases, environmental pollution, drug screening, and bioinformatics.

Single-cell omics: Overview, analysis, and application in biomedical science

Single-cell sequencing methods provide the highest resolution insight into cellular heterogeneity. Owing to their rapid growth and decreasing cost, they are now widely accessible to scientists worldwide. Single-cell technologies enable analysis of a large number of cells, making them powerful tools to characterise rare cell types and refine our understanding of diverse cell states. Moreover, single-cell application in biomedical sciences helps to unravel mechanisms related to disease pathogenesis and outcome. In this Viewpoint, we briefly describe existing single-cell methods (genomics, transcriptomics, epigenomics, proteomics, and mulitomics), comment on available analysis tools, and give examples of method applications in the biomedical field.

Exploring tissue architecture using spatial transcriptomics

Deciphering the principles and mechanisms by which gene activity orchestrates complex cellular arrangements in multicellular organisms has far-reaching implications for research in the life sciences. Recent technological advances in next-generation sequencing- and imaging-based approaches have established the power of spatial transcriptomics to measure expression levels of all or most genes systematically throughout tissue space, and have been adopted to generate biological insights in neuroscience, development and plant biology as well as to investigate a range of disease contexts, including cancer. Similar to datasets made possible by genomic sequencing and population health surveys, the large-scale atlases generated by this technology lend themselves to exploratory data analysis for hypothesis generation. Here we review spatial transcriptomic technologies and describe the repertoire of operations available for paths of analysis of the resulting data. Spatial transcriptomics can also be deployed for hypothesis testing using experimental designs that compare time points or conditions-including genetic or environmental perturbations. Finally, spatial transcriptomic data are naturally amenable to integration with other data modalities, providing an expandable framework for insight into tissue organization.

Single-Cell Analysis Reveals Heterogeneity of Virus Infection, Pathogenicity, and Host Responses: HIV as a Pioneering Example

While analyses of cell populations provide averaged information about viral infections, single-cell analyses offer individual consideration, thereby revealing a broad spectrum of diversity as well as identifying extreme phenotypes that can be exploited to further understand the complex virus-host interplay. Single-cell technologies applied in the context of human immunodeficiency virus (HIV) infection proved to be valuable tools to help uncover specific biomarkers as well as novel candidate players in virus-host interactions. This review aims at providing an updated overview of single-cell analyses in the field of HIV and acquired knowledge on HIV infection, latency, and host response. Although HIV is a pioneering example, similar single-cell approaches have proven to be valuable for elucidating the behavior and virus-host interplay in a range of other viruses.

Unraveling Root Development Through Single-Cell Omics and Reconstruction of Gene Regulatory Networks

Over the last decades, research on postembryonic root development has been facilitated by "omics" technologies. Among these technologies, microarrays first, and RNA sequencing (RNA-seq) later, have provided transcriptional information on the underlying molecular processes establishing the basis of System Biology studies in roots. Cell fate specification and development have been widely studied in the primary root, which involved the identification of many cell type transcriptomes and the reconstruction of gene regulatory networks (GRN). The study of lateral root (LR) development has not been an exception. However, the molecular mechanisms regulating cell fate specification during LR formation remain largely unexplored. Recently, single-cell RNA-seq (scRNA-seq) studies have addressed the specification of tissues from stem cells in the primary root. scRNA-seq studies are anticipated to be a useful approach to decipher cell fate specification and patterning during LR formation. In this review, we address the different scRNA-seq strategies used both in plants and animals and how we could take advantage of scRNA-seq to unravel new regulatory mechanisms and reconstruct GRN. In addition, we discuss how to integrate scRNA-seq results with previous RNA-seq datasets and GRN. We also address relevant findings obtained through single-cell based studies and how LR developmental studies could be facilitated by scRNA-seq approaches and subsequent GRN inference. The use of single-cell approaches to investigate LR formation could help to decipher fundamental biological mechanisms such as cell memory, synchronization, polarization, or pluripotency.

α-Catenin levels determine direction of YAP/TAZ response to autophagy perturbation

The factors regulating cellular identity are critical for understanding the transition from health to disease and responses to therapies. Recent literature suggests that autophagy compromise may cause opposite effects in different contexts by either activating or inhibiting YAP/TAZ co-transcriptional regulators of the Hippo pathway via unrelated mechanisms. Here, we confirm that autophagy perturbation in different cell types can cause opposite responses in growth-promoting oncogenic YAP/TAZ transcriptional signalling. These apparently contradictory responses can be resolved by a feedback loop where autophagy negatively regulates the levels of α-catenins, LC3-interacting proteins that inhibit YAP/TAZ, which, in turn, positively regulate autophagy. High basal levels of α-catenins enable autophagy induction to positively regulate YAP/TAZ, while low α-catenins cause YAP/TAZ activation upon autophagy inhibition. These data reveal how feedback loops enable post-transcriptional determination of cell identity and how levels of a single intermediary protein can dictate the direction of response to external or internal perturbations.

Haematopoietic ageing through the lens of single-cell technologies

Human lifespan is now longer than ever and, as a result, modern society is getting older. Despite that, the detailed mechanisms behind the ageing process and its impact on various tissues and organs remain obscure. In general, changes in DNA, RNA and protein structure throughout life impair their function. Haematopoietic ageing refers to the age-related changes affecting a haematopoietic system. Aged blood cells display different functional aberrations depending on their cell type, which might lead to the development of haematologic disorders, including leukaemias, anaemia or declining immunity. In contrast to traditional bulk assays, which are not suitable to dissect cell-to-cell variation, single-cell-level analysis provides unprecedented insight into the dynamics of age-associated changes in blood. In this Review, we summarise recent studies that dissect haematopoietic ageing at the single-cell level. We discuss what cellular changes occur during haematopoietic ageing at the genomic, transcriptomic, epigenomic and metabolomic level, and provide an overview of the benefits of investigating those changes with single-cell precision. We conclude by considering the potential clinical applications of single-cell techniques in geriatric haematology, focusing on the impact on haematopoietic stem cell transplantation in the elderly and infection studies, including recent COVID-19 research.

HPV+ve/-ve oral-tongue cancer stem cells: A potential target for relapse-free therapy

The tongue squamous cell carcinoma (TSCC) is a highly prevalent head and neck cancer often associated with tobacco and/or alcohol abuse or high-risk human papillomavirus (HR-HPV) infection. HPV positive TSCCs present a unique mechanism of tumorigenesis as compared to tobacco and alcohol-induced TSCCs and show a better prognosis when treated. The poor prognosis and/or recurrence of TSCC is due to presence of a small subpopulation of tumor-initiating tongue cancer stem cells (TCSCs) that are intrinsically resistant to conventional chemoradio-therapies enabling cancer to relapse. Therefore, targeting TCSCs may provide efficient therapeutic strategy for relapse-free survival of TSCC patients. Indeed, the development of new TCSC targeting therapeutic approaches for the successful elimination of HPV+ve/-ve TCSCs could be achieved either by targeting the self-renewal pathways, epithelial mesenchymal transition, vascular niche, nanoparticles-based therapy, induction of differentiation, chemoradio-sensitization of TCSCs or TCSC-derived exosome-based drug delivery and inhibition of HPV oncogenes or by regulating epigenetic pathways. In this review, we have discussed all these potential approaches and highlighted several important signaling pathways/networks involved in the formation and maintenance of TCSCs, which are targetable as novel therapeutic targets to sensitize/eliminate TCSCs and to improve survival of TSCC patients.

Mass Cytometry Defines Virus-Specific CD4+ T Cells in Influenza Vaccination

The antiviral response to influenza virus is complex and multifaceted, involving many immune cell subsets. There is an urgent need to understand the role of CD4⁺ T cells, which orchestrate an effective antiviral response, to improve vaccine design strategies. In this study, we analyzed PBMCs from human participants immunized with influenza vaccine, using high-dimensional single-cell proteomic immune profiling by mass cytometry. Data were analyzed using a novel clustering algorithm, denoised ragged pruning, to define possible influenza virus–specific clusters of CD4⁺ T cells. Denoised ragged pruning identified six clusters of cells. Among these, one cluster (Cluster 3) was found to increase in abundance following stimulation with influenza virus peptide ex vivo. A separate cluster (Cluster 4) was found to expand in abundance between days 0 and 7 postvaccination, indicating that it is vaccine responsive. We examined the expression profiles of all six clusters to characterize their lineage, functionality, and possible role in the response to influenza vaccine. Clusters 3 and 4 consisted of effector memory cells, with high CD154 expression. Cluster 3 expressed cytokines like IL-2, IFN-γ, and TNF-α, whereas Cluster 4 expressed IL-17. Interestingly, some participants had low abundance of Clusters 3 and 4, whereas others had higher abundance of one of these clusters compared with the other. Taken together, we present an approach for identifying novel influenza virus–reactive CD4⁺ T cell subsets, a method that could help advance understanding of the immune response to influenza, predict responsiveness to vaccines, and aid in better vaccine design.

Single-cell sequencing in hematology

PURPOSE OF REVIEW

In this review, we highlight key recent insights into hematopoiesis and hematological malignancies through the application of novel single-cell approaches. We particularly focus on biological insights made through the study of stem/progenitors cells in myeloid malignancy at single-cell resolution.

RECENT FINDINGS

Bulk molecular profiling of hematological malignancies by next generation sequencing techniques has provided major insights into the molecular pathogenesis of blood cancers. This technology is now routinely implemented in advanced clinical diagnostics, leading to the development of novel targeted therapies. However, bulk genetic analysis can obscure key aspects of intratumoral heterogeneity which underlies critical disease events, such as treatment resistance and clonal evolution. The past few years have seen an explosion of novel techniques to analyze RNA, DNA, and protein expression at the single-cell level, providing unprecedented insight into cellular heterogeneity.

SUMMARY

Given the ease of accessibility of liquid tumor biopsies, hematology is well positioned to move novel single-cell techniques towards routine application in the clinic. The present review sets out to discuss current and potential future applications for this technology in the management of patients with hematological cancers.

Advances of single-cell genomics and epigenomics in human disease: where are we now?

Cellular heterogeneity is revolutionizing the way to study, monitor and dissect complex diseases. This has been possible with the technological and computational advances associated to single-cell genomics and epigenomics. Deeper understanding of cell-to-cell variation and its impact on tissue function will open new avenues for early disease detection, accurate diagnosis and personalized treatments, all together leading to the next generation of health care. This review focuses on the recent discoveries that single-cell genomics and epigenomics have facilitated in the context of human health. It highlights the potential of single-cell omics to further advance the development of personalized treatments and precision medicine in cancer, diabetes and chronic age-related diseases. The promise of single-cell technologies to generate new insights about the differences in function between individual cells is just emerging, and it is paving the way for identifying biomarkers and novel therapeutic targets to tackle age, complex diseases and understand the effect of life style interventions and environmental factors.

Cellular plasticity: A mechanism for homeostasis in the kidney

Cellular plasticity is a topical subject with interest spanning a wide range of fields from developmental biology to regenerative medicine. Even the nomenclature is a subject of debate, and the underlying mechanisms are still under investigation. On top of injury repair, cell plasticity is a constant physiological process in adult organisms and tissues, in response to homeostatic challenges. In this review we discuss two examples of plasticity for the maintenance of homeostasis in the renal system-namely the renin-producing juxtaglomerular cells (JG cells) and cortical collecting duct (CCD) cells. JG cells show plasticity through recruitment mechanisms, answering the demand for an increase in renin production. In the CCD, cells appear to have the ability to transdifferentiate between principal and intercalated cells to help maintain the highly regulated solute transport levels of that segment. These two cases highlight the complexity of plasticity processes and the role they can play in the kidney.

National Cancer Institute Think-Tank Meeting Report on Proteomic Cartography and Biomarkers at the Single-Cell Level: Interrogation of Premalignant Lesions

A Think-Tank Meeting was convened by the National Cancer Institute (NCI) to solicit experts' opinion on the development and application of multiomic single-cell analyses, and especially single-cell proteomics, to improve the development of a new generation of biomarkers for cancer risk, early detection, diagnosis, and prognosis as well as to discuss the discovery of new targets for prevention and therapy. It is anticipated that such markers and targets will be based on cellular, subcellular, molecular, and functional aberrations within the lesion and within individual cells. Single-cell proteomic data will be essential for the establishment of new tools with searchable and scalable features that include spatial and temporal cartographies of premalignant and malignant lesions. Challenges and potential solutions that were discussed included (i) The best way/s to analyze single-cells from fresh and preserved tissue; (ii) Detection and analysis of secreted molecules and from single cells, especially from a tissue slice; (iii) Detection of new, previously undocumented cell type/s in the premalignant and early stage cancer tissue microenvironment; (iv) Multiomic integration of data to support and inform proteomic measurements; (v) Subcellular organelles-identifying abnormal structure, function, distribution, and location within individual premalignant and malignant cells; (vi) How to improve the dynamic range of single-cell proteomic measurements for discovery of differentially expressed proteins and their post-translational modifications (PTM); (vii) The depth of coverage measured concurrently using single-cell techniques; (viii) Quantitation - absolute or semiquantitative? (ix) Single methodology or multiplexed combinations? (x) Application of analytical methods for identification of biologically significant subsets; (xi) Data visualization of N-dimensional data sets; (xii) How to construct intercellular signaling networks in individual cells within premalignant tumor microenvironments (TME); (xiii) Associations between intrinsic cellular processes and extrinsic stimuli; (xiv) How to predict cellular responses to stress-inducing stimuli; (xv) Identification of new markers for prediction of progression from precursor, benign, and localized lesions to invasive cancer, based on spatial and temporal changes within individual cells; (xvi) Identification of new targets for immunoprevention or immunotherapy-identification of neoantigens and surfactome of individual cells within a lesion.

Single-Cell Analysis of Foot-and-Mouth Disease Virus

With the rapid development of single-cell technologies, the mechanisms underlying viral infections and the interactions between hosts and viruses are starting to be explored at the single-cell level. The foot-and-mouth-disease (FMD) virus (FMDV) causes an acute and persistent infection that can result in the break-out of FMD, which can have serious effects on animal husbandry. Single-cell techniques have emerged as powerful approaches to analyze virus infection at the resolution of individual cells. In this review, the existing single-cell studies examining FMDV will be systematically summarized, and the central themes of these studies will be presented.

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.

Genetic Hallmarks and Heterogeneity of Glioblastoma in the Single-Cell Omics Era

Glioblastoma multiforme is the most common and aggressive malignant primary brain tumor. As implied by its name, the disease displays impressive intrinsic heterogeneity. Among other complications, inter- and intratumoral diversity hamper glioblastoma research and therapy, typically leaving patients with little hope for long-term survival. Extensive genetic analyses, including omics, characterize several recurrent mutations. However, confounding factors mask crucial aspects of the pathology to conventional bulk approaches. In recent years, single-cell omics have made their first appearance in cancer research, and the methodology is about to reach its full potential for glioblastoma too. Here, recent glioblastoma single-cell omics investigations are reviewed, and most promising routes toward less grim prognoses and more efficient therapeutics are discussed.

Spatially resolved transcriptomics reveals plant host responses to pathogens

BACKGROUND

Thorough understanding of complex model systems requires the characterisation of processes in different cell types of an organism. This can be achieved with high-throughput spatial transcriptomics at a large scale. However, for plant model systems this is still challenging as suitable transcriptomics methods are sparsely available. Here we present GaST-seq (Grid-assisted, Spatial Transcriptome sequencing), an easy to adopt, micro-scale spatial-transcriptomics workflow that allows to study expression profiles across small areas of plant tissue at a fraction of the cost of existing sequencing-based methods.

RESULTS

We compare the GaST-seq method with widely used library preparation methods (Illumina TruSeq). In spatial experiments we show that the GaST-seq method is sensitive enough to identify expression differences across a plant organ. We further assess the spatial transcriptome response of Arabidopsis thaliana leaves exposed to the bacterial molecule flagellin-22, and show that with eukaryotic (Albugo laibachii) infection both host and pathogen spatial transcriptomes are obtained.

CONCLUSION

We show that our method can be used to identify known, rapidly flagellin-22 elicited genes, plant immune response pathways to bacterial attack and spatial expression patterns of genes associated with these pathways.

Sialylation is involved in cell fate decision during development, reprogramming and cancer progression

Sialylation, or the covalent addition of sialic acid to the terminal end of glycoproteins, is a biologically important modification that is involved in embryonic development, neurodevelopment, reprogramming, oncogenesis and immune responses. In this review, we have given a comprehensive overview of the current literature on the involvement of sialylation in cell fate decision during development, reprogramming and cancer progression. Sialylation is essential for early embryonic development and the deletion of UDP-GlcNAc 2-epimerase, a rate-limiting enzyme in sialic acid biosynthesis, is embryonically lethal. Furthermore, the sialyltransferase ST6GAL1 is required for somatic cell reprogramming, and its downregulation is associated with decreased reprogramming efficiency. In addition, sialylation levels and patterns are altered during cancer progression, indicating the potential of sialylated molecules as cancer biomarkers. Taken together, the current evidences demonstrate that sialylation is involved in crucial cell fate decision.

Application of single-cell RNA sequencing methodologies in understanding haematopoiesis and immunology

The blood and immune system are characterised by utmost diversity in its cellular components. This heterogeneity can solely be resolved with the application of single-cell technologies that enable precise examination of cell-to-cell variation. Single-cell transcriptomics is continuously pushing forward our understanding of processes driving haematopoiesis and immune responses in physiological settings as well as in disease. Remarkably, in the last five years, a number of studies involving single-cell RNA sequencing (scRNA-seq) allowed the discovery of new immune cell types and revealed that haematopoiesis is a continuous rather than a stepwise process, thus challenging the classical haematopoietic lineage tree model. This review summarises the most recent studies which applied scRNA-seq to answer outstanding questions in the fields of haematology and immunology and discusses the present challenges and future directions.

GPCRomics: An Approach to Discover GPCR Drug Targets

G protein-coupled receptors (GPCRs) are targets for ∼35% of approved drugs but only ∼15% of the ∼800 human GPCRs are currently such targets. GPCRomics, the use of unbiased, hypothesis-generating methods [e.g., RNA-sequencing (RNA-seq)], with tissues and cell types to identify and quantify GPCR expression, has led to the discovery of previously unrecognized GPCRs that contribute to functional responses and pathophysiology and that may be therapeutic targets. The combination of GPCR expression data with validation studies (e.g., signaling and functional activities) provides opportunities for the discovery of disease-relevant GPCR targets and therapeutics. Here, we review insights from GPCRomic approaches, gaps in knowledge, and future directions by which GPCRomics can advance GPCR biology and the discovery of new GPCR-targeted drugs.

Selection and optimization of enzyme reporters for chemical cytometry

Chemical cytometry, sensitive analytical measurements of single cells, reveals inherent heterogeneity of cells within a population which is masked or averaged out when using bulk analysis techniques. A particular challenge of chemical cytometry is the development of a suitable reporter or probe for the desired measurement. These reporters must be sufficiently specific for measuring the desired process; possess a lifetime long enough to accomplish the measurement; and have the ability to be loaded into single cells. This chapter details our approach to rationally design and improve peptide substrates as reporters of enzyme activity utilizing chemical cytometry. This method details the iterative approach used to design, characterize, and identify a peptidase-resistant peptide reporter which acts as a kinase substrate within intact cells. Small-scale, rationally designed peptide libraries are generated to rapidly and economically screen candidate reporter peptides for substrate suitability and peptidase resistance. Also detailed are strategies to characterize and validate the designed reporters by determining kinetic parameters, intracellular substrate specificity, resistance to degradation by intracellular peptidases, and behavior within lysates and intact cells.

Dissecting human disease with single-cell omics: application in model systems and in the clinic

Probing cellular population diversity at single-cell resolution became possible only in recent years. The popularity of single-cell 'omic' approaches, which allow researchers to dissect sample heterogeneity and cell-to-cell variation, continues to grow. With continuous technological improvements, single-cell omics are becoming increasingly prevalent and contribute to the discovery of new and rare cell types, and to the deciphering of disease pathogenesis and outcome. Animal models of human diseases have significantly facilitated our understanding of the mechanisms driving pathologies and resulted in the development of more efficient therapies. The application of single-cell omics to animal models improves the precision of the obtained insights, and brings single-cell technology closer to the clinical field. This Review focuses on the use of single-cell omics in cellular and animal models of diseases, as well as in samples from human patients. It also highlights the potential of these approaches to further improve the diagnosis and treatment of various pathologies, and includes a discussion of the advantages and remaining challenges in implementing these technologies into clinical practice.

New MiniPromoter Ple345 (NEFL) Drives Strong and Specific Expression in Retinal Ganglion Cells of Mouse and Primate Retina

Retinal gene therapy is leading the neurological gene therapy field, with 32 ongoing clinical trials of recombinant adeno-associated virus (rAAV)–based therapies. Importantly, over 50% of those trials are using restricted promoters from human genes. Promoters that restrict expression have demonstrated increased efficacy and can limit the therapeutic to the target cells thereby reducing unwanted off-target effects. Retinal ganglion cells are a critical target in ocular gene therapy; they are involved in common diseases such as glaucoma, rare diseases such as Leber's hereditary optic neuropathy, and in revolutionary optogenetic treatments. Here, we used computational biology and mined the human genome for the best genes from which to develop a novel minimal promoter element(s) designed for expression in restricted cell types (MiniPromoter) to improve the safety and efficacy of retinal ganglion cell gene therapy. Gene selection included the use of the first available droplet-based single-cell RNA sequencing (Drop-seq) dataset, and promoter design was bioinformatically driven and informed by a wide range of genomics datasets. We tested seven promoter designs from four genes in rAAV for specificity and quantified expression strength in retinal ganglion cells in mouse, and then the single best in nonhuman primate retina. Thus, we developed a new human-DNA MiniPromoter, Ple345 (NEFL), which in combination with intravitreal delivery in rAAV9 showed specific and robust expression in the retinal ganglion cells of the nonhuman-primate rhesus macaque retina. In mouse, we also developed MiniPromoters expressing in retinal ganglion cells, the hippocampus of the brain, a pan neuronal pattern in the brain, and peripheral nerves. As single-cell transcriptomics such as Drop-seq become available for other cell types, many new opportunities for additional novel restricted MiniPromoters will present.

An epigenetic perspective on tumorigenesis: Loss of cell identity, enhancer switching, and NamiRNA network

Various tumorigenic theories have been proposed in the past century, which contribute to the prevention and treatment of cancer clinically. However, the underlying mechanisms of the initiation of cancer, drug resistance, neoplasm relapse, and metastasis are still challenging to be panoramically addressed. Based on the abundant evidence provided by others and us, we postulate that Tumor Initiated by Loss of Cell Identity (LOCI), which is an inevitable initiating event of tumorigenesis. As a result, normal cells are transformed into the cancerous cell. In this process, epigenetic regulatory program, especially NamiRNA (Nuclear activating miRNA)-enhancer-gene activation network, is vital for the cell identity. The disorganization of NamiRNA-enhancer-gene activation network is a causal predisposition to the cell identity loss, and the altered cell identity is stabilized by genetic variations of the NamiRNA-enhancer-gene activation network. Furthermore, the additional genetic or epigenetic abnormities confer those cells to carcinogenic characteristics, such as growth advantage over normal cells, and finally yield cancer. In this review, we literally explain our tumor initiation hypothesis based on the corresponding evidence, which will not only help to refresh our understanding of tumorigenesis but also bring benefits to developing "cell identity reversing" based therapies.

An epigenetic perspective on tumorigenesis: Loss of cell identity, enhancer switching, and NamiRNA network

Various tumorigenic theories have been proposed in the past century, which contribute to the prevention and treatment of cancer clinically. However, the underlying mechanisms of the initiation of cancer, drug resistance, neoplasm relapse, and metastasis are still challenging to be panoramically addressed. Based on the abundant evidence provided by others and us, we postulate that Tumor Initiated by Loss of Cell Identity (LOCI), which is an inevitable initiating event of tumorigenesis. As a result, normal cells are transformed into the cancerous cell. In this process, epigenetic regulatory program, especially NamiRNA (Nuclear activating miRNA)-enhancer-gene activation network, is vital for the cell identity. The disorganization of NamiRNA-enhancer-gene activation network is a causal predisposition to the cell identity loss, and the altered cell identity is stabilized by genetic variations of the NamiRNA-enhancer-gene activation network. Furthermore, the additional genetic or epigenetic abnormities confer those cells to carcinogenic characteristics, such as growth advantage over normal cells, and finally yield cancer. In this review, we literally explain our tumor imitation hypothesis based on the corresponding evidence, which will not only help to refresh our understanding of tumorigenesis but also bring benefits to developing "cell identity reversing" based therapies.

Defining Cell Identity with Single-Cell Omics

Cells are a fundamental unit of life, and the ability to study the phenotypes and behaviors of individual cells is crucial to understanding the workings of complex biological systems. Cell phenotypes (epigenomic, transcriptomic, proteomic, and metabolomic) exhibit dramatic heterogeneity between and within the different cell types and states underlying cellular functional diversity. Cell genotypes can also display heterogeneity throughout an organism, in the form of somatic genetic variation-most notably in the emergence and evolution of tumors. Recent technical advances in single-cell isolation and the development of omics approaches sensitive enough to reveal these aspects of cell identity have enabled a revolution in the study of multicellular systems. In this review, we discuss the technologies available to resolve the genomes, epigenomes, transcriptomes, proteomes, and metabolomes of single cells from a wide variety of living systems.