Single-cell transcriptomics to explore the immune system in health and disease

Cutaneous leishmaniasis: multiomics approaches to unravel the role of immune cells checkpoints

INTRODUCTION

Cutaneous leishmaniasis (CL) is the most frequent form of leishmaniases, associated with skin inflammation and ulceration. Understanding the interaction of different phagocytic cells in the recognition and uptake of different Leishmania species is critical for controlling the infection. Phagocytic cells have a pivotal role as professional antigen-presenting cells that bridge the innate and adaptive immunity and shape the outcome of the disease.

AREAS COVERED

Here we reviewed new technologies with high-throughput data collection capabilities along with systems biology approaches which are recently being used to decode the paradox of CL immunology.

EXPERT OPINION

We emphasized on the crosstalk between DC and T-cells while focusing on the immune checkpoints interactions between the human immune system and the Leishmania species. Further, we discussed omics technologies including bulk RNA sequencing, reverse transcriptase-multiplex ligation dependent probe amplification (RT-MLPA), and proximity extension assay (PEA) in studies on human blood or tissue-driven samples from CL patients in which we have so far been involved.

scLRTD : A Novel Low Rank Tensor Decomposition Method for Imputing Missing Values in Single-Cell Multi-Omics Sequencing Data

With the successful application of single-cell sequencing technology, a large number of single-cell multi-omics sequencing (scMO-seq)data have been generated, which enables researchers to study heterogeneity between individual cells. One prominent problem in single-cell data analysis is the prevalence of dropouts, caused by failures in amplification during the experiments. It is necessary to develop effective approaches for imputing the missing values. Different with general methods imputing single type of single-cell data, we propose an imputation method called scLRTD, using low-rank tensor decomposition based on nuclear norm to impute scMO-seq data and single-cell RNA-sequencing (scRNA-seq)data with different stages, tissues or conditions. Furthermore, four sets of simulated and two sets of real scRNA-seq data from mouse embryonic stem cells and hepatocellular carcinoma, respectively, are used to carry out numerical experiments and compared with other six published methods. Error accuracy and clustering results demonstrate the effectiveness of proposed method. Moreover, we clearly identify two cell subpopulations after imputing the real scMO-seq data from hepatocellular carcinoma. Further, Gene Ontology identifies 7 genes in Bile secretion pathway, which is related to metabolism in hepatocellular carcinoma. The survival analysis using the database TCGA also show that two cell subpopulations after imputing have distinguished survival rates.

Single-cell transcriptomics identifies Gadd45b as a regulator of herpesvirus-reactivating neurons

Single-cell RNA sequencing (scRNA-seq) is a powerful technique for dissecting the complexity of normal and diseased tissues, enabling characterization of cell diversity and heterogeneous phenotypic states in unprecedented detail. However, this technology has been underutilized for exploring the interactions between the host cell and viral pathogens in latently infected cells. Herein, we use scRNA-seq and single-molecule sensitivity fluorescent in situ hybridization (smFISH) technologies to investigate host single-cell transcriptome changes upon the reactivation of a human neurotropic virus, herpes simplex virus-1 (HSV-1). We identify the stress sensor growth arrest and DNA damage-inducible 45 beta (Gadd45b) as a critical antiviral host factor that regulates HSV-1 reactivation events in a subpopulation of latently infected primary neurons. We show that distinct subcellular localization of Gadd45b correlates with the viral late gene expression program, as well as the expression of the viral transcription factor, ICP4. We propose that a hallmark of a "successful" or "aborted" HSV-1 reactivation state in primary neurons is determined by a unique subcellular localization signature of the stress sensor Gadd45b.

Single cell transcriptomic landscape of diabetic foot ulcers

Diabetic foot ulceration (DFU) is a devastating complication of diabetes whose pathogenesis remains incompletely understood. Here, we profile 174,962 single cells from the foot, forearm, and peripheral blood mononuclear cells using single-cell RNA sequencing. Our analysis shows enrichment of a unique population of fibroblasts overexpressing MMP1, MMP3, MMP11, HIF1A, CHI3L1, and TNFAIP6 and increased M1 macrophage polarization in the DFU patients with healing wounds. Further, analysis of spatially separated samples from the same patient and spatial transcriptomics reveal preferential localization of these healing associated fibroblasts toward the wound bed as compared to the wound edge or unwounded skin. Spatial transcriptomics also validates our findings of higher abundance of M1 macrophages in healers and M2 macrophages in non-healers. Our analysis provides deep insights into the wound healing microenvironment, identifying cell types that could be critical in promoting DFU healing, and may inform novel therapeutic approaches for DFU treatment.

Gene Expression Nebulas (GEN): a comprehensive data portal integrating transcriptomic profiles across multiple species at both bulk and single-cell levels

Transcriptomic profiling is critical to uncovering functional elements from transcriptional and post-transcriptional aspects. Here, we present Gene Expression Nebulas (GEN, https://ngdc.cncb.ac.cn/gen/), an open-access data portal integrating transcriptomic profiles under various biological contexts. GEN features a curated collection of high-quality bulk and single-cell RNA sequencing datasets by using standardized data processing pipelines and a structured curation model. Currently, GEN houses a large number of gene expression profiles from 323 datasets (157 bulk and 166 single-cell), covering 50 500 samples and 15 540 169 cells across 30 species, which are further categorized into six biological contexts. Moreover, GEN integrates a full range of transcriptomic profiles on expression, RNA editing and alternative splicing for 10 bulk datasets, providing opportunities for users to conduct integrative analysis at both transcriptional and post-transcriptional levels. In addition, GEN provides abundant gene annotations based on value-added curation of transcriptomic profiles and delivers online services for data analysis and visualization. Collectively, GEN presents a comprehensive collection of transcriptomic profiles across multiple species, thus serving as a fundamental resource for better understanding genetic regulatory architecture and functional mechanisms from tissues to cells.

Advances in Single-Cell Printing

Single-cell analysis is becoming an indispensable tool in modern biological and medical research. Single-cell isolation is the key step for single-cell analysis. Single-cell printing shows several distinct advantages among the single-cell isolation techniques, such as precise deposition, high encapsulation efficiency, and easy recovery. Therefore, recent developments in single-cell printing have attracted extensive attention. We review herein the recently developed bioprinting strategies with single-cell resolution, with a special focus on inkjet-like single-cell printing. First, we discuss the common cell printing strategies and introduce several typical and advanced printing strategies. Then, we introduce several typical applications based on single-cell printing, from single-cell array screening and mass spectrometry-based single-cell analysis to three-dimensional tissue formation. In the last part, we discuss the pros and cons of the single-cell strategies and provide a brief outlook for single-cell printing.

Analysis of Single-Cell/Nucleus Transcriptome Data in Adipose Tissue

Adipose tissue is highly heterogeneous and plastic. Recent advances in single-cell/nucleus RNA sequencing technology have helped to study the cellular composition and dynamics of adipose tissue. In this protocol, I outline a typical workflow of analyzing single-cell/nucleus transcriptome data. Specifically, I show an example of how cellular populations are estimated and characterized from a single-nucleus RNAseq data set of frozen archived human adipose tissue.

Involvement of caspase-3 in apoptosis of human lymphocytes exposed to cadmium chloride

BACKGROUND

Lymphocytes are a group of white blood cells with a variety of roles their integrity is crucial for the body's immune responses. Cadmium, a heavy metal and environmental pollutant, is known as a toxicant to exert its adverse effects on some sort of cells including blood cells.

RESEARCH DESIGN

In this study, human lymphocytes were divided into 3 groups: (1) lymphocytes at 0-h, (2) lymphocytes at 24 h (control), (3) lymphocytes treated with cadmium chloride (15 μM). Lymphocyte viability and plasma membrane integrity were assessed in these groups. In addition, the occurrence of apoptosis was investigated by assessment of nucleus diameter and flow cytometry. Activation of caspase-3 was also detected by immunocytochemistry.

RESULTS

Result showed that lymphocyte's viability and plasma membrane integrity decreased in lymphocytes treated with cadmium as compared with the control group. Decreased nucleus diameter and result of flow cytometry demonstrated cadmium-induced apoptosis in human lymphocytes. Furthermore, lymphocytes treated with cadmium displayed intensely activated caspase-3 immunoreactivity in their cytoplasm.

CONCLUSION

In conclusion, cadmium not only negatively effect on viability and plasma membrane, but also induces caspase-dependent apoptosis in human lymphocytes.

Molecular mechanisms governing circulating immune cell heterogeneity across different species revealed by single-cell sequencing

BACKGROUND

Immune cells play important roles in mediating immune response and host defense against invading pathogens. However, insights into the molecular mechanisms governing circulating immune cell diversity among multiple species are limited.

METHODS

In this study, we compared the single-cell transcriptomes of immune cells from 12 species. Distinct molecular profiles were characterized for different immune cell types, including T cells, B cells, natural killer cells, monocytes, and dendritic cells.

RESULTS

Our data revealed the heterogeneity and compositions of circulating immune cells among 12 different species. Additionally, we explored the conserved and divergent cellular crosstalks and genetic regulatory networks among vertebrate immune cells. Notably, the ligand and receptor pair VIM-CD44 was highly conserved among the immune cells.

CONCLUSIONS

This study is the first to provide a comprehensive analysis of the cross-species single-cell transcriptome atlas for peripheral blood mononuclear cells (PBMCs). This research should advance our understanding of the cellular taxonomy and fundamental functions of PBMCs, with important implications in evolutionary biology, developmental biology, and immune system disorders.

Redefining intestinal immunity with single-cell transcriptomics

The intestinal immune system represents the largest collection of immune cells in the body and is continually exposed to antigens from food and the microbiota. Here we discuss the contribution of single-cell transcriptomics in shaping our understanding of this complex system. We consider the impact on resolving early intestine development, engagement with the neighbouring microbiota, diversity of intestinal immune cells, compartmentalisation within the intestines and interactions with non-immune cells. Finally, we offer a perspective on open questions about gut immunity that evolving single-cell technologies are well placed to address.

Heterogeneity in GnRH and kisspeptin neurons and their significance in vertebrate reproductive biology

Vertebrate reproduction is essentially controlled by the hypothalamus-pituitary-gonadal (HPG) axis, which is a central dogma of reproductive biology. Two major hypothalamic neuroendocrine cell groups containing gonadotropin-releasing hormone (GnRH) and kisspeptin are crucial for control of the HPG axis in vertebrates. GnRH and kisspeptin neurons exhibit high levels of heterogeneity including their cellular morphology, biochemistry, neurophysiology and functions. However, the molecular foundation underlying heterogeneities in GnRH and kisspeptin neurons remains unknown. More importantly, the biological and physiological significance of their heterogeneity in reproductive biology is poorly understood. In this review, we first describe the recent advances in the neuroendocrine functions of kisspeptin-GnRH pathways. We then view the recent emerging progress in the heterogeneity of GnRH and kisspeptin neurons using morphological and single-cell transcriptomic analyses. Finally, we discuss our views on the significance of functional heterogeneity of reproductive endocrine cells and their potential relevance to reproductive health.

Immune cell profiling in atherosclerosis: role in research and precision medicine

Inflammation is intimately involved at all stages of atherosclerosis and remains a substantial residual cardiovascular risk factor in optimally treated patients. The proof of concept that targeting inflammation reduces cardiovascular events in patients with a history of myocardial infarction has highlighted the urgent need to identify new immunotherapies to treat patients with atherosclerotic cardiovascular disease. Importantly, emerging data from new clinical trials show that successful immunotherapies for atherosclerosis need to be tailored to the specific immune alterations in distinct groups of patients. In this Review, we discuss how single-cell technologies - such as single-cell mass cytometry, single-cell RNA sequencing and cellular indexing of transcriptomes and epitopes by sequencing - are ideal for mapping the cellular and molecular composition of human atherosclerotic plaques and how these data can aid in the discovery of new precise immunotherapies. We also argue that single-cell data from studies in humans need to be rigorously validated in relevant experimental models, including rapidly emerging single-cell CRISPR screening technologies and mouse models of atherosclerosis. Finally, we discuss the importance of implementing single-cell immune monitoring tools in early phases of drug development to aid in the precise selection of the target patient population for data-driven translation into randomized clinical trials and the successful translation of new immunotherapies into the clinic.

Single-cell approaches to dissect adaptive immune responses involved in autoimmunity: the case of celiac disease

Single-cell analysis is a powerful technology that has found widespread use in recent years. For diseases with involvement of adaptive immunity, single-cell analysis of antigen-specific T cells and B cells is particularly informative. In autoimmune diseases, the adaptive immune system is obviously at play, yet the ability to identify the culprit T and B cells recognizing disease-relevant antigen can be difficult. Celiac disease, a widespread disorder with autoimmune components, is unique in that disease-relevant antigens for both T cells and B cells are well defined. Furthermore, the celiac disease gut lesion is readily accessible allowing for sampling of tissue-resident cells. Thus, disease-relevant T cells and B cells from the gut and blood can be studied at the level of single cells. Here we review single-cell studies providing information on such adaptive immune cells and outline some future perspectives in the area of single-cell analysis in autoimmune diseases.

Synthesis and application of POLYseq for profiling human liver organoids

Detailed herein is the protocol for synthesis, characterization, and application of POLYseq for cell pooling in single-cell sequencing runs. POLYseq is synthesized through commercially available reagents and is highly tailorable. Synthesis is easily performed in a two-step protocol, utilizing Michael addition only requiring a temperature-stable hot bath capable of holding 90°C. However, care must be taken when mixing reagents for synthesis, as the final product is sensitive to initial mixing ratios of POLYseq reagents. For complete details on the use and execution of this protocol, please refer to Dunn et al. (2021).

Single-cell transcriptome analysis identifies a unique tumor cell type producing multiple hormones in ectopic ACTH and CRH secreting pheochromocytoma

Ectopic Cushing’s syndrome due to ectopic ACTH&CRH-secreting by pheochromocytoma is extremely rare and can be fatal if not properly diagnosed. It remains unclear whether a unique cell type is responsible for multiple hormones secreting. In this work, we performed single-cell RNA sequencing to three different anatomic tumor tissues and one peritumoral tissue based on a rare case with ectopic ACTH&CRH-secreting pheochromocytoma. And in addition to that, three adrenal tumor specimens from common pheochromocytoma and adrenocortical adenomas were also involved in the comparison of tumor cellular heterogeneity. A total of 16 cell types in the tumor microenvironment were identified by unbiased cell clustering of single-cell transcriptomic profiles from all specimens. Notably, we identified a novel multi-functionally chromaffin-like cell type with high expression of both POMC (the precursor of ACTH) and CRH, called ACTH+&CRH + pheochromocyte. We hypothesized that the molecular mechanism of the rare case harbor Cushing’s syndrome is due to the identified novel tumor cell type, that is, the secretion of ACTH had a direct effect on the adrenal gland to produce cortisol, while the secretion of CRH can indirectly stimulate the secretion of ACTH from the anterior pituitary. Besides, a new potential marker (GAL) co-expressed with ACTH and CRH might be involved in the regulation of ACTH secretion. The immunohistochemistry results confirmed its multi-functionally chromaffin-like properties with positive staining for CRH, POMC, ACTH, GAL, TH, and CgA. Our findings also proved to some extent the heterogeneity of endothelial and immune microenvironment in different adrenal tumor subtypes.

Myeloid Heterogeneity in Kidney Disease as Revealed through Single-Cell RNA Sequencing

Kidney disease represents a global health burden of increasing prevalence and is an independent risk factor for cardiovascular disease. Myeloid cells are a major cellular compartment of the immune system; they are found in the healthy kidney and in increased numbers in the damaged and/or diseased kidney, where they act as key players in the progression of injury, inflammation, and fibrosis. They possess enormous plasticity and heterogeneity, adopting different phenotypic and functional characteristics in response to stimuli in the local milieu. Although this inherent complexity remains to be fully understood in the kidney, advances in single-cell genomics promise to change this. Specifically, single-cell RNA sequencing (scRNA-seq) has had a transformative effect on kidney research, enabling the profiling and analysis of the transcriptomes of single cells at unprecedented resolution and throughput, and subsequent generation of cell atlases. Moving forward, combining scRNA- and single-nuclear RNA-seq with greater-resolution spatial transcriptomics will allow spatial mapping of kidney disease of varying etiology to further reveal the patterning of immune cells and nonimmune renal cells. This review summarizes the roles of myeloid cells in kidney health and disease, the experimental workflow in currently available scRNA-seq technologies, and published findings using scRNA-seq in the context of myeloid cells and the kidney.

Single-Cell Sequencing Reveals the Novel Role of Ezh2 in NK Cell Maturation and Function

Natural killer (NK) cells are lymphocytes primarily involved in innate immunity and exhibit important functional properties in antimicrobial and antitumoral responses. Our previous work indicated that the enhancer of zeste homolog 2 (Ezh2) is a negative regulator of early NK cell differentiation and function through trimethylation of histone H3 lysine 27 (H3K27me3). Here, we deleted Ezh2 from immature NK cells and downstream progeny to explore its role in NK cell maturation by single-cell RNA sequencing (scRNA-seq). We identified six distinct NK stages based on the transcriptional signature during NK cell maturation. Conditional deletion of Ezh2 in NK cells resulted in a maturation trajectory toward NK cell arrest in CD11b SP stage 5, which was clustered with genes related to the activating function of NK cells. Mechanistically, we speculated that Ezh2 plays a critical role in NK development by activating AP-1 family gene expression independent of PRC2 function. Our results implied a novel role for the Ezh2-AP-1-Klrg1 axis in altering the NK cell maturation trajectory and NK cell-mediated cytotoxicity.

Targeting monocytes/macrophages in fibrosis and cancer diseases: Therapeutic approaches

Over almost 140 years since their identification, the knowledge about macrophages has unbelievably evolved. The 'big eaters' from being thought of as simple phagocytic cells have been recognized as master regulators in immunity, homeostasis, healing/repair and organ development. Long considered to originate exclusively from bone marrow-derived circulating monocytes, macrophages have been also demonstrated to be the first immune cells colonizing tissues in the developing embryo and persisting in adult life by self-renewal, as long-lived tissue resident macrophages. Therefore, heterogeneous populations of macrophages with different ontogeny and functions co-exist in tissues. Macrophages act as sentinels of homeostasis and are intrinsically programmed to lead the wound healing and repair processes that occur after injury. However, in certain pathological circumstances macrophages get dysfunctional, and impaired or aberrant macrophage activities become key features of diseases. For instance, in both fibrosis and cancer, that have been defined 'wounds that do not heal', dysfunctional monocyte-derived macrophages overall play a key detrimental role. On the other hand, due to their plasticity these cells can be 're-educated' and exert anti-fibrotic and anti-cancer functions. Therefore macrophages represent an important therapeutic target in both fibrosis and cancer diseases. The current review will illustrate new insights into the role of monocytes/macrophages in these devastating diseases and summarize new therapeutic strategies and applications of macrophage-targeted drug development in their clinical setting.

The effects of TNF-α/TNFR2 in regulatory T cells on the microenvironment and progression of gastric cancer

TNFR2⁺ regulatory T cells preferentially accumulate in the tumor microenvironment, express high levels of immunosuppressive molecules and possess strong suppressive activity. Our study aimed to explore the characteristics and role of TNFR2⁺ Tregs in the microenvironment and progression of gastric cancer via polychromatic immunofluorescence, single-cell RNA sequencing and flow cytometry assays. The TNFR2⁺ Treg infiltration level in the tumor microenvironment increased significantly as gastric cancer progressed and was demonstrated to be a prognostic marker. Single-cell RNA sequencing revealed high levels of TNFR2 in tumor-infiltrating Tregs. The TNF-α/TNFR2 signaling pathway was activated, accompanied by the upregulation of costimulatory molecules. Unlike blood Tregs, tumor-infiltrating Tregs existed in activated and effector states. In addition to expressing costimulatory molecules such as TNFR2, 4-1BB, OX40 and GITR, tumor-infiltrating Tregs were also characterized by high expression levels of immune checkpoints such as CTLA-4 and TIGIT and chemokines such as CCR6. In vitro studies showed that the TNF-α/TNFR2 pathway increased the Foxp3 expression in CD4⁺ CD25⁺ T cells and the latent TGF-β production in Tregs as well as enhanced the immunosuppressive function of Tregs. In summary, our study revealed high infiltration levels of TNFR2⁺ Tregs that were in activated and effector states in the tumor microenvironment. The infiltration level of TNFR2⁺ Tregs is a prognostic marker and an independent risk factor for gastric cancer. Activation of the TNF-α/TNFR2 pathway promotes the immunosuppressive phenotype and function of Tregs. Our study provides a new theoretical basis for TNFR2⁺ Tregs as a therapeutic target in gastric cancer.

A cell-cell atlas approach for understanding symbiotic interactions between microbes

Natural environments are composed of a huge diversity of microorganisms interacting with each other to form complex functional networks. Our understanding of the operative nature of host-symbiont associations is limited because propagating such associations in a laboratory is challenging. The advent of single-cell technologies applied to, for example, animal cells and apicomplexan parasites has revolutionized our understanding of development and disease. Such cell atlas approaches generate maps of cell-specific processes and variations within cellular populations. These methods can now be combined with cellular-imaging so that interaction stage versus transcriptome state can be quantized for microbe-microbe interactions. We predict that the combination of these methods applied to the study of symbioses will transform our understanding of many ecological interactions, including those sampled directly from natural environments.

Label-free single-cell isolation enabled by microfluidic impact printing and real-time cellular recognition

Analysis of cellular components at the single-cell level is important to reveal cellular heterogeneity. However, current technologies to isolate individual cells are either label-based or have low performance. Here, we present a novel technique by integrating real-time cellular recognition and microfluidic impact printing (MIP) to isolate single cells with high efficiency and high throughput in a label-free manner. Specifically, morphological characteristics of polystyrene beads and cells, computed by an efficient image processing algorithm, are utilized as selection criteria to identify target objects. Subsequently, each detected single-cell object in the suspension is ejected from the microfluidic channel by impact force. It has been demonstrated that the single-cell isolating system has the ability to encapsulate polystyrene beads in droplets with an efficiency of 95%, while for HeLa cells, this has been experimentally measured as 90.3%. Single-cell droplet arrays are generated at a throughput of 2 Hz and 96.6% of the cells remain alive after isolation. This technology has significant potential in various emerging applications, including single-cell omics, tissue engineering, and cell-line development.

LEARNING GENERAL TRANSFORMATIONS OF DATA FOR OUT-OF-SAMPLE EXTENSIONS

While generative models such as GANs have been successful at mapping from noise to specific distributions of data, or more generally from one distribution of data to another, they cannot isolate the transformation that is occurring and apply it to a new distribution not seen in training. Thus, they memorize the domain of the transformation, and cannot generalize the transformation out of sample. To address this, we propose a new neural network called a Neuron Transformation Network (NTNet) that isolates the signal representing the transformation itself from the other signals representing internal distribution variation. This signal can then be removed from a new dataset distributed differently from the original one trained on. We demonstrate the effectiveness of our NTNet on more than a dozen synthetic and biomedical single-cell RNA sequencing datasets, where the NTNet is able to learn the data transformation performed by genetic and drug perturbations on one sample of cells and successfully apply it to another sample of cells to predict treatment outcome.

Dice-XMBD: Deep Learning-Based Cell Segmentation for Imaging Mass Cytometry

Highly multiplexed imaging technology is a powerful tool to facilitate understanding the composition and interactions of cells in tumor microenvironments at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging method recently introduced, can measure up to 100 markers simultaneously in one tissue section by using a high-resolution laser with a mass cytometer. However, due to its high resolution and large number of channels, how to process and interpret the image data from IMC remains a key challenge to its further applications. Accurate and reliable single cell segmentation is the first and a critical step to process IMC image data. Unfortunately, existing segmentation pipelines either produce inaccurate cell segmentation results or require manual annotation, which is very time consuming. Here, we developed Dice-XMBD, a Deep learnIng-based Cell sEgmentation algorithm for tissue multiplexed imaging data. In comparison with other state-of-the-art cell segmentation methods currently used for IMC images, Dice-XMBD generates more accurate single cell masks efficiently on IMC images produced with different nuclear, membrane, and cytoplasm markers. All codes and datasets are available at https://github.com/xmuyulab/Dice-XMBD.

Single-cell RNA sequencing of peripheral blood mononuclear cells from acute Kawasaki disease patients

Kawasaki disease (KD) is the most common cause of acquired heart disease in children in developed countries. Although functional and phenotypic changes of immune cells have been reported, a global understanding of immune responses underlying acute KD is unclear. Here, using single-cell RNA sequencing, we profile peripheral blood mononuclear cells from seven patients with acute KD before and after intravenous immunoglobulin therapy and from three age-matched healthy controls. The most differentially expressed genes are identified in monocytes, with high expression of pro-inflammatory mediators, immunoglobulin receptors and low expression of MHC class II genes in acute KD. Single-cell RNA sequencing and flow cytometry analyses, of cells from an additional 16 KD patients, show that although the percentage of total B cells is substantially decreased after therapy, the percentage of plasma cells among the B cells is significantly increased. The percentage of CD8+ T cells is decreased in acute KD, notably effector memory CD8+ T cells compared with healthy controls. Oligoclonal expansions of both B cell receptors and T cell receptors are observed after therapy. We identify biological processes potentially underlying the changes of each cell type. The single-cell landscape of both innate and adaptive immune responses provides insights into pathogenesis and therapy of KD.

Understanding the pathogenesis of infectious diseases by single-cell RNA sequencing

Infections are highly orchestrated and dynamic processes, which involve both pathogen and host. Transcriptional profiling at the single-cell level enables the analysis of cell diversity, heterogeneity of the immune response, and detailed molecular mechanisms underlying infectious diseases caused by bacteria, viruses, fungi, and parasites. Herein, we highlight recent remarkable advances in single-cell RNA sequencing (scRNA-seq) technologies and their applications in the investigation of host-pathogen interactions, current challenges and potential prospects for disease treatment are discussed as well. We propose that with the aid of scRNA-seq, the mechanism of infectious diseases will be further revealed thus inspiring the development of novel interventions and therapies.

Single-cell RNA-seq reveals functionally distinct biomaterial degradation-related macrophage populations

Macrophages play crucial roles in host tissue reaction to biomaterials upon implantation in vivo. However, the complexity of biomaterial degradation-related macrophage subpopulations that accumulate around the implanted biomaterials in situ is not fully understood. Here, using single cell RNA-seq, we analyze the transcriptome profiles of the various cell types around the scaffold to map the scaffold-induced reaction, in an unbiased approach. This enables mapping of all biomaterial degradation-associated cells at high resolution, revealing distinct subpopulations of tissue-resident macrophages as the major cellular sources of biomaterial degradation in situ. We also find that scaffold architecture can affect the mechanotransduction and catabolic activity of specific material degradation-related macrophage subpopulations in an Itgav-Mapk1-Stat3 dependent manner, eventually leading to differences in scaffold degradation rate in vivo. Our work dissects unanticipated aspects of the cellular and molecular basis of biomaterial degradation at the single-cell level, and provides a conceptual framework for developing functional tissue engineering scaffolds in future.

Advances in single-cell sequencing: insights from organ transplantation

Single-cell RNA sequencing (scRNA-seq) is a comprehensive technical tool to analyze intracellular and intercellular interaction data by whole transcriptional profile analysis. Here, we describe the application in biomedical research, focusing on the immune system during organ transplantation and rejection. Unlike conventional transcriptome analysis, this method provides a full map of multiple cell populations in one specific tissue and presents a dynamic and transient unbiased method to explore the progression of allograft dysfunction, starting from the stress response to final graft failure. This promising sequencing technology remarkably improves individualized organ rejection treatment by identifying decisive cellular subgroups and cell-specific interactions.

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.

Cross-tissue single-cell landscape of human monocytes and macrophages in health and disease

Mononuclear phagocytes (MNPs) encompass dendritic cells, monocytes, and macrophages (MoMac), which exhibit antimicrobial, homeostatic, and immunoregulatory functions. We integrated 178,651 MNPs from 13 tissues across 41 datasets to generate a MNP single-cell RNA compendium (MNP-VERSE), a publicly available tool to map MNPs and define conserved gene signatures of MNP populations. Next, we generated a MoMac-focused compendium that revealed an array of specialized cell subsets widely distributed across multiple tissues. Specific pathological forms were expanded in cancer and inflammation. All neoplastic tissues contained conserved tumor-associated macrophage populations. In particular, we focused on IL4I1⁺CD274(PD-L1)⁺IDO1⁺ macrophages, which accumulated in the tumor periphery in a T cell-dependent manner via interferon-γ (IFN-γ) and CD40/CD40L-induced maturation from IFN-primed monocytes. IL4I1_Macs exhibited immunosuppressive characteristics through tryptophan degradation and promoted the entry of regulatory T cell into tumors. This integrated analysis provides a robust online-available platform for uniform annotation and dissection of specific macrophage functions in healthy and pathological states.

Diversity of Reactive Astrogliosis in CNS Pathology: Heterogeneity or Plasticity?

Astrocytes are essential for the development and homeostatic maintenance of the central nervous system (CNS). They are also critical players in the CNS injury response during which they undergo a process referred to as "reactive astrogliosis." Diversity in astrocyte morphology and gene expression, as revealed by transcriptional analysis, is well-recognized and has been reported in several CNS pathologies, including ischemic stroke, CNS demyelination, and traumatic injury. This diversity appears unique to the specific pathology, with significant variance across temporal, topographical, age, and sex-specific variables. Despite this, there is limited functional data corroborating this diversity. Furthermore, as reactive astrocytes display significant environmental-dependent plasticity and fate-mapping data on astrocyte subsets in the adult CNS is limited, it remains unclear whether this diversity represents heterogeneity or plasticity. As astrocytes are important for neuronal survival and CNS function post-injury, establishing to what extent this diversity reflects distinct established heterogeneous astrocyte subpopulations vs. environmentally dependent plasticity within established astrocyte subsets will be critical for guiding therapeutic development. To that end, we review the current state of knowledge on astrocyte diversity in the context of three representative CNS pathologies: ischemic stroke, demyelination, and traumatic injury, with the goal of identifying key limitations in our current knowledge and suggesting future areas of research needed to address them. We suggest that the majority of identified astrocyte diversity in CNS pathologies to date represents plasticity in response to dynamically changing post-injury environments as opposed to heterogeneity, an important consideration for the understanding of disease pathogenesis and the development of therapeutic interventions.

New Insights From Single-Cell Sequencing Data: Synovial Fibroblasts and Synovial Macrophages in Rheumatoid Arthritis

Single-cell RNA sequencing (scRNA-seq) technology can analyze the transcriptome expression level of cells with high-throughput from the single cell level, fully show the heterogeneity of cells, and provide a new way for the study of multicellular biological heterogeneity. Synovitis is the pathological basis of rheumatoid arthritis (RA). Synovial fibroblasts (SFs) and synovial macrophages are the core target cells of RA, which results in the destruction of articular cartilage, as well as bone. Recent scRNA-seq technology has made breakthroughs in the differentiation and development of two types of synovial cells, identification of subsets, functional analysis, and new therapeutic targets, which will bring remarkable changes in RA treatment.

CALLR: a semi-supervised cell-type annotation method for single-cell RNA sequencing data

Motivation

Single-cell RNA sequencing (scRNA-seq) technology has been widely applied to capture the heterogeneity of different cell types within complex tissues. An essential step in scRNA-seq data analysis is the annotation of cell types. Traditional cell-type annotation is mainly clustering the cells first, and then using the aggregated cluster-level expression profiles and the marker genes to label each cluster. Such methods are greatly dependent on the clustering results, which are insufficient for accurate annotation.

Results

In this article, we propose a semi-supervised learning method for cell-type annotation called CALLR. It combines unsupervised learning represented by the graph Laplacian matrix constructed from all the cells and supervised learning using sparse logistic regression. By alternately updating the cell clusters and annotation labels, high annotation accuracy can be achieved. The model is formulated as an optimization problem, and a computationally efficient algorithm is developed to solve it. Experiments on 10 real datasets show that CALLR outperforms the compared (semi-)supervised learning methods, and the popular clustering methods.

Availability and implementation

The implementation of CALLR is available at https://github.com/MathSZhang/CALLR.

Supplementary information

Supplementary data are available at Bioinformatics online.

In silico analysis of the potential mechanism of a preventive Chinese medicine formula on coronavirus disease 2019

ETHNOPHARMACOLOGICAL RELEVANCE

With the spread of Coronavirus Disease (2019) (COVID-19), combination with traditional Chinese medicine (TCM) has been widely used as a prevention and therapy strategy in China. Xin guan No.1 (XG-1) prescription is a preventive formula recommended by the Hunan Provincial Administration of TCM to prevent the pandemic of COVID-19.

AIM OF THE STUDY

To explore the potential preventive mechanisms of XG-1 against COVID-19 in the combination of network pharmacology approach, single-cell RNA expression profiling analysis, molecular docking and retrospective study.

MATERIALS AND METHODS

Encyclopedia of Traditional Chinese Medicine (ETCM) database was used to determine the meridian tropism, active components and target genes of XG-1. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis were conducted by R Cluster Profiler package (3.14.3). Single cell RNA sequencing (scRNA-seq) data of human lung (GSE122960) was downloaded from Gene Expression Omnibus (GEO) database and analyzed by R Seurat package (3.1.2). Cytoscape (3.7.2) was used to construct the interaction network. The main ingredients in XG-1 were identified by HPLC- Q-TOF- MS and used for molecular docking with COVID-19 3CL hydrolytic enzyme and angiotensin converting enzyme II (ACE2). A retrospective study of 47 close contact participants from Dongtang Community of Hunan Province was conducted to evaluated the preventive effect of XG-1.

RESULTS

According to the network pharmacology analysis, XG-1 formula was closely related to lung-, spleen- and stomach-meridians and include a total of 206 active components and 853 target genes. GO and KEGG pathway enrichment revealed that XG-1 mainly regulated cellular amino acid metabolism process and neuroactive ligand-receptors interaction. The scRNA-seq profiling showed that angiotensin converting enzyme 2 (ACE2) was principally expressed in alveolar type 2 epithelial cells (AT2). 153 genes were up-regulated in AT2 cells expressing ACE2 and 12 genes were obtained by intersecting with XG-1 target genes, of which 3 were related to immunity. Five main chemical ingredients were detected in XG-1 sample by HPLC-Q-TOF-MS. The molecular docking showed that Rutin, Liquiritin and Astragaloside Ⅳ had a good affinity with COVID-19 3CL hydrolytic enzyme and ACE2. Compared with participants who didn't take XG-1, preventive treatment with XG-1gradules resulted in a significant lower rate of testing positive for SARS-CoV-2 nucleic acid (P < 0.0001).

CONCLUSION

The present study showed that XG-1 exerts a preventive effect in close contacts against COVID-19. The underlying mechanism may be related to modulate immunity response through multiple components, pathways, and several target genes co-expressed with ACE2. These findings provide preliminary evidences and methodological reference for the potential preventive mechanism of XG-1 against COVID-19.

Single-cell transcriptome analysis of the zebrafish embryonic trunk

During embryonic development, cells differentiate into a variety of distinct cell types and subtypes with diverse transcriptional profiles. To date, transcriptomic signatures of different cell lineages that arise during development have been only partially characterized. Here we used single-cell RNA-seq to perform transcriptomic analysis of over 20,000 cells disaggregated from the trunk region of zebrafish embryos at the 30 hpf stage. Transcriptional signatures of 27 different cell types and subtypes were identified and annotated during this analysis. This dataset will be a useful resource for many researchers in the fields of developmental and cellular biology and facilitate the understanding of molecular mechanisms that regulate cell lineage choices during development.

Stem Cells and Innate Immunity in Aquatic Invertebrates: Bridging Two Seemingly Disparate Disciplines for New Discoveries in Biology

The scopes related to the interplay between stem cells and the immune system are broad and range from the basic understanding of organism's physiology and ecology to translational studies, further contributing to (eco)toxicology, biotechnology, and medicine as well as regulatory and ethical aspects. Stem cells originate immune cells through hematopoiesis, and the interplay between the two cell types is required in processes like regeneration. In addition, stem and immune cell anomalies directly affect the organism's functions, its ability to cope with environmental changes and, indirectly, its role in ecosystem services. However, stem cells and immune cells continue to be considered parts of two branches of biological research with few interconnections between them. This review aims to bridge these two seemingly disparate disciplines towards much more integrative and transformative approaches with examples deriving mainly from aquatic invertebrates. We discuss the current understanding of cross-disciplinary collaborative and emerging issues, raising novel hypotheses and comments. We also discuss the problems and perspectives of the two disciplines and how to integrate their conceptual frameworks to address basic equations in biology in a new, innovative way.

Deep feature extraction of single-cell transcriptomes by generative adversarial network

MOTIVATION

Single-cell RNA-sequencing (scRNA-seq) offers the opportunity to dissect heterogeneous cellular compositions and interrogate the cell-type-specific gene expression patterns across diverse conditions. However, batch effects such as laboratory conditions and individual-variability hinder their usage in cross-condition designs.

RESULTS

Here, we present a single-cell Generative Adversarial Network (scGAN) to simultaneously acquire patterns from raw data while minimizing the confounding effect driven by technical artifacts or other factors inherent to the data. Specifically, scGAN models the data likelihood of the raw scRNA-seq counts by projecting each cell onto a latent embedding. Meanwhile, scGAN attempts to minimize the correlation between the latent embeddings and the batch labels across all cells. We demonstrate scGAN on three public scRNA-seq datasets and show that our method confers superior performance over the state-of-the-art methods in forming clusters of known cell types and identifying known psychiatric genes that are associated with major depressive disorder.

AVAILABILITYAND IMPLEMENTATION

The scGAN code and the information for the public scRNA-seq datasets are available at https://github.com/li-lab-mcgill/singlecell-deepfeature.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-cell RNA-seq landscape midbrain cell responses to red spotted grouper nervous necrosis virus infection

Viral nervous necrosis (VNN) is an acute and serious fish disease caused by nervous necrosis virus (NNV) which has been reported massive mortality in more than fifty teleost species worldwide. VNN causes damage of necrosis and vacuolation to central nervous system (CNS) cells in fish. It is difficult to identify the specific type of cell targeted by NNV, and to decipher the host immune response because of the functional diversity and highly complex anatomical and cellular composition of the CNS. In this study, we found that the red spotted grouper NNV (RGNNV) mainly attacked the midbrain of orange-spotted grouper (Epinephelus coioides). We conducted single-cell RNA-seq analysis of the midbrain of healthy and RGNNV-infected fish and identified 35 transcriptionally distinct cell subtypes, including 28 neuronal and 7 non-neuronal cell types. An evaluation of the subpopulations of immune cells revealed that macrophages were enriched in RGNNV-infected fish, and the transcriptional profiles of macrophages indicated an acute cytokine and inflammatory response. Unsupervised pseudotime analysis of immune cells showed that microglia transformed into M1-type activated macrophages to produce cytokines to reduce the damage to nerve tissue caused by the virus. We also found that RGNNV targeted neuronal cell types was GLU1 and GLU3, and we found that the key genes and pathways by which causes cell cytoplasmic vacuoles and autophagy significant enrichment, this may be the major route viruses cause cell death. These data provided a comprehensive transcriptional perspective of the grouper midbrain and the basis for further research on how viruses infect the teleost CNS.

Integrated analysis of multimodal single-cell data

The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce "weighted-nearest neighbor" analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.

Cell landscape atlas for patients with chronic thromboembolic pulmonary hypertension after pulmonary endarterectomy constructed using single-cell RNA sequencing

This study aimed to construct an atlas of the cell landscape and comprehensively characterize the cellular repertoire of the pulmonary endarterectomized tissues of patients with chronic thromboembolic pulmonary hypertension (CTEPH). Five pulmonary endarterectomized tissues were collected. 10× Genomics single-cell RNA sequencing was performed, followed by the identification of cluster marker genes and cell types. Gene Ontology (GO) enrichment analysis was conducted. Seventeen cell clusters were characterized, corresponding to 10,518 marker genes, and then classified into eight cell types, including fibroblast/smooth muscle cell, endothelial cell, T cell/NK cell, macrophage, mast cell, cysteine rich secretory protein LCCL domain containing 2 (CRISPLD2)+ cell, cancer stem cell, and undefined. The specific marker genes of fibroblast/smooth muscle cell, endothelial cell, T cell/NK cell, macrophage, mast cell, and cancer stem cell were significantly enriched for multiple functions associated with muscle cell migration, endothelial cell migration, T cell activation, neutrophil activation, erythrocyte homeostasis, and tissue remodeling, respectively. No functions were significantly enriched for the marker gene of CRISPLD2+ cell. Our study, for the first time, provides an atlas of the cell landscape of the pulmonary endarterectomized tissues of CTEPH patients at single-cell resolution, which may serve as a valuable resource for further elucidation of disease pathophysiology.

Single-cell RNA sequencing reveals the mechanism of sonodynamic therapy combined with a RAS inhibitor in the setting of hepatocellular carcinoma

BACKGROUND

Ras activation is a frequent event in hepatocellular carcinoma (HCC). Combining a RAS inhibitor with traditional clinical therapeutics might be hampered by a variety of side effects, thus hindering further clinical translation. Herein, we report on integrating an IR820 nanocapsule-augmented sonodynamic therapy (SDT) with the RAS inhibitor farnesyl-thiosalicylic acid (FTS). Using cellular and tumor models, we demonstrate that combined nanocapsule-augmented SDT with FTS induces an anti-tumor effect, which not only inhibits tumor progression, and enables fluorescence imaging. To dissect the mechanism of a combined tumoricidal therapeutic strategy, we investigated the scRNA-seq transcriptional profiles of an HCC xenograft following treatment.

RESULTS

Integrative single-cell analysis identified several clusters that defined many corresponding differentially expressed genes, which provided a global view of cellular heterogeneity in HCC after combined SDT/FTS treatment. We conclude that the combination treatment suppressed HCC, and did so by inhibiting endothelial cells and a modulated immunity. Moreover, hepatic stellate secretes hepatocyte growth factor, which plays a key role in treating SDT combined FTS. By contrast, enrichment analysis estimated the functional roles of differentially expressed genes. The Gene Ontology terms "cadherin binding" and "cell adhesion molecule binding" and KEGG pathway "pathway in cancer" were significantly enriched by differentially expressed genes after combined SDT/FTS therapy.

CONCLUSIONS

Thus, some undefined mechanisms were revealed by scRNA-seq analysis. This report provides a novel proof-of-concept for combinatorial HCC-targeted therapeutics that is based on a non-invasive anti-tumor therapeutic strategy and a RAS inhibitor.

Single-cell RNA sequencing of human femoral head in vivo

The homeostasis of bone metabolism depends on the coupling and precise regulation of various types of cells in bone tissue. However, the communication and interaction between bone tissue cells at the single-cell level remains poorly understood. Thus, we performed single-cell RNA sequencing (scRNA-seq) on the primary human femoral head tissue cells (FHTCs). Nine cell types were identified in 26,574 primary human FHTCs, including granulocytes, T cells, monocytes, B cells, red blood cells, osteoblastic lineage cells, endothelial cells, endothelial progenitor cells (EPCs) and plasmacytoid dendritic cells. We identified serine protease 23 (PRSS23) and matrix remodeling associated protein 8 (MXRA8) as novel bone metabolism-related genes. Additionally, we found that several subtypes of monocytes, T cells and B cells were related to bone metabolism. Cell-cell communication analysis showed that collagen, chemokine, transforming growth factor and their ligands have significant roles in the crosstalks between FHTCs. In particular, EPCs communicated with osteoblastic lineage cells closely via the "COL2A1-ITGB1" interaction pair. Collectively, this study provided an initial characterization of the cellular composition of the human FHTCs and the complex crosstalks between them at the single-cell level. It is a unique starting resource for in-depth insights into bone metabolism.

On-Chip Multicolor Photoacoustic Imaging Flow Cytometry

On-chip imaging flow cytometry has been widely used in cancer biology, immunology, microbiology, and drug discovery. Pure optical imaging combined with flow cytometry to derive chemical, structural, and morphological features of cells provides systematic insights into biological processes. However, due to the high concentration and strong optical attenuation of red blood cells, preprocessing is necessary for optical flow cytometry while dealing with whole blood. In this study, we develop an on-chip photoacoustic imaging flow cytometry (PAIFC), which combines multicolor high-speed photoacoustic microscopy and microfluidics for cell imaging. The device employs a micro-optical scanner to achieve a miniaturized outer size of 30 × 17 × 24 mm³ and ultrafast cross-sectional imaging at a frame rate of 1758 Hz and provides lateral and axial resolutions of 2.2 and 33 μm, respectively. Using a multicolor strategy, PAIFC is able to differentiate cells labeled by external contrast agents, detect melanoma cells with an endogenous contrast in whole blood, and image melanoma cells in blood samples from tumor-bearing mice. The results suggest that PAIFC has sufficient sensitivity and specificity for future cell-on-chip applications.

Cell subtypes and immune dysfunction in peritoneal fluid of endometriosis revealed by single-cell RNA-sequencing

BACKGROUND

Endometriosis is a refractory and recurrent disease and it affects nearly 10% of reproductive-aged women and 40% of infertile patients. The commonly accepted theory for endometriosis is retrograde menstruation where endometrial tissues invade into peritoneal cavity and fail to be cleared due to immune dysfunction. Therefore, the comprehensive understanding of immunologic microenvironment of peritoneal cavity deserves further investigation for the previous studies mainly focus on one or several immune cells.

RESULTS

High-quality transcriptomes were from peritoneal fluid samples of patients with endometriosis and control, and firstly subjected to 10 × genomics single-cell RNA-sequencing. We acquired the single-cell transcriptomes of 10,280 cells from endometriosis sample and 7250 cells from control sample with an average of approximately 63,000 reads per cell. A comprehensive map of overall cells in peritoneal fluid was first exhibited. We unveiled the heterogeneity of immune cells and discovered new cell subtypes including T cell receptor positive (TCR+) macrophages, proliferating macrophages and natural killer dendritic cells in peritoneal fluid, which was further verified by double immunofluorescence staining and flow cytometry. Pseudo-time analysis showed that the response of macrophages to the menstrual debris might follow the certain differentiation trajectory after endometrial tissues invaded into the peritoneal cavity, that is, from antigen presentation to pro-inflammation, then to chemotaxis and phagocytosis. Our analyses also mirrored the dysfunctions of immune cells including decreased phagocytosis and cytotoxic activity and elevated pro-inflammatory and chemotactic effects in endometriosis.

CONCLUSION

TCR+ macrophages, proliferating macrophages and natural killer dendritic cells are firstly reported in human peritoneal fluid. Our results also revealed that immune dysfunction happens in peritoneal fluid of endometriosis, which may be responsible for the residues of invaded menstrual debris. It provided a large-scale and high-dimensional characterization of peritoneal microenvironment and offered a useful resource for future development of immunotherapy.

Biological controls for standardization and interpretation of adaptive immune receptor repertoire profiling

Use of adaptive immune receptor repertoire sequencing (AIRR-seq) has become widespread, providing new insights into the immune system with potential broad clinical and diagnostic applications. However, like many high-throughput technologies, it comes with several problems, and the AIRR Community was established to understand and help solve them. We, the AIRR Community's Biological Resources Working Group, have surveyed scientists about the need for standards and controls in generating and annotating AIRR-seq data. Here, we review the current status of AIRR-seq, provide the results of our survey, and based on them, offer recommendations for developing AIRR-seq standards and controls, including future work.