Single-cell RNA sequencing reveals cell type-specific immune regulation associated with human neuromyelitis optica spectrum disorder.

Introduction: One rare type of autoimmune disease is called neuromyelitis optica spectrum disorder (NMOSD) and the peripheral immune characteristics of NMOSD remain unclear. Methods: Here, single-cell RNA sequencing (scRNA-seq) is used to characterize peripheral blood mononuclear cells from individuals with NMOSD. Results: The differentiation and activation of lymphocytes, expansion of myeloid cells, and an excessive inflammatory response in innate immunity are observed. Flow cytometry analyses confirm a significant increase in the percentage of plasma cells among B cells in NMOSD. NMOSD patients exhibit an elevated percentage of CD8+ T cells within the T cell population. Oligoclonal expansions of B cell receptors are observed after therapy. Additionally, individuals with NMOSD exhibit elevated expression of CXCL8, IL7, IL18, TNFSF13, IFNG, and NLRP3. Discussion: Peripheral immune response high-dimensional single-cell profiling identifies immune cell subsets specific to a certain disease and identifies possible new targets for NMOSD.

All-potassium channel CRISPR screening reveals a lysine-specific pathway of insulin secretion.

OBJECTIVE: Genome-scale CRISPR-Cas9 knockout coupled with single-cell RNA sequencing (scRNA-seq) has been used to identify function-related genes. However, this method may knock out too many genes, leading to low efficiency in finding genes of interest. Insulin secretion is controlled by several electrophysiological events, including fluxes of KATP depolarization and K+ repolarization. It is well known that glucose stimulates insulin secretion from pancreatic ß-cells, mainly via the KATP depolarization channel, but whether other nutrients directly regulate the repolarization K+ channel to promote insulin secretion is unknown. METHODS: We used a system involving CRISPR-Cas9-mediated knockout of all 83 K+ channels and scRNA-seq in a pancreatic ß cell line to identify genes associated with insulin secretion. RESULTS: The expression levels of insulin genes were significantly increased after all-K+ channel knockout. Furthermore, Kcnb1 and Kcnh6 were the two most important repolarization K+ channels for the increase in high-glucose-dependent insulin secretion that occurred upon application of specific inhibitors of the channels. Kcnh6 currents, but not Kcnb1 currents, were reduced by one of the amino acids, lysine, in both transfected cells, primary cells and mice with ß-cell-specific deletion of Kcnh6. CONCLUSIONS: Our function-related CRISPR screen with scRNA-seq identifies Kcnh6 as a lysine-specific channel.

Using graph-based model to identify cell specific synthetic lethal effects.

Synthetic lethal (SL) pairs are pairs of genes whose simultaneous loss-of-function results in cell death, while a damaging mutation of either gene alone does not affect the cell's survival. This makes SL pairs attractive targets for precision cancer therapies, as targeting the unimpaired gene of the SL pair can selectively kill cancer cells that already harbor the impaired gene. Limited by the difficulty of finding true SL pairs, especially on specific cell types, current computational approaches provide only limited insights because of overlooking the crucial aspects of cellular context dependency and mechanistic understanding of SL pairs. As a result, the identification of SL targets still relies on expensive, time-consuming experimental approaches. In this work, we applied cell-line specific multi-omics data to a specially designed deep learning model to predict cell-line specific SL pairs. Through incorporating multiple types of cell-specific omics data with a self-attention module, we represent gene relationships as graphs. Our approach achieves the prediction of SL pairs in a cell-specific manner and demonstrates the potential to facilitate the discovery of cell-specific SL targets for cancer therapeutics, providing a tool to unearth mechanisms underlying the origin of SL in cancer biology. The code and data of our approach can be found at https://github.com/promethiume/SLwise.

Single-cell transcriptomics reveals cell type-specific immune regulation associated with anti-NMDA receptor encephalitis in humans.

Introduction: Anti-N-methyl-D-aspartate receptor encephalitis (anti-NMDARE) is a rare autoimmune disease, and the peripheral immune characteristics associated with anti-NMDARE antibodies remain unclear. Methods: Herein, we characterized peripheral blood mononuclear cells from patients with anti-NMDARE and healthy individuals by single-cell RNA sequencing (scRNA-seq). Results: The transcriptional profiles of 129,217 cells were assessed, and 21 major cell clusters were identified. B-cell activation and differentiation, plasma cell expansion, and excessive inflammatory responses in innate immunity were all identified. Patients with anti-NMDARE showed higher expression levels of CXCL8, IL1B, IL6, TNF, TNFSF13, TNFSF13B, and NLRP3. We observed that anti-NMDARE patients in the acute phase expressed high levels of DC_CCR7 in human myeloid cells. Moreover, we observed that anti-NMDARE effects include oligoclonal expansions in response to immunizing agents. Strong humoral immunity and positive regulation of lymphocyte activation were observed in acute stage anti-NMDARE patients. Discussion: This high-dimensional single-cell profiling of the peripheral immune microenvironment suggests that potential mechanisms are involved in the pathogenesis and recovery of anti-NMDAREs.

Single-cell RNA sequencing reveals a pro-invasive cancer-associated fibroblast subgroup associated with poor clinical outcomes in patients with gastric cancer.

Background: The protumor activities of cancer-associated fibroblasts (CAFs) suggest that they are potential therapeutic targets for the treatment of cancer. The mechanism of CAF heterogeneity in gastric cancer (GC) remains unclear and has slowed translational advances in targeting CAFs. Therefore, a comprehensive understanding of the classification, function, activation stage, and spatial distribution of the CAF subsets in GC is urgently needed. Methods: In this study, the characteristics of the CAF subsets and the dynamic communication among the tumor microenvironment (TME) components regulated by the CAF subsets were analyzed by performing single-cell RNA sequencing of eight pairs of GC and adjacent mucosal (AM) samples. The spatial distribution of the CAF subsets in different Lauren subtypes of GC, as well as the neighborhood relations between these CAF subsets and the protumor immune cell subsets were evaluated by performing multistaining registration. Results: Tumor epithelial cells exhibited significant intratumor and intertumor variabilities, while CAFs mainly exhibited intratumor variability. Moreover, we identified four CAF subsets with different properties in GC. These four CAF subsets shared similar properties with their resident fibroblast counterparts in the adjacent mucosa but also exhibited enhanced protumor activities. Additionally, two CAF subsets, inflammatory CAFs (iCAFs) and extracellular matrix CAFs (eCAFs), communicated with adjacent immune cell subsets in the GC TME. iCAFs interacted with T cells by secreting interleukin (IL)-6 and C-X-C motif chemokine ligand 12 (CXCL12), while eCAFs correlated with M2 macrophages via the expression of periostin (POSTN). eCAFs, which function as a pro-invasive CAF subset, decreased the overall survival time of patients with GC. Conclusions: iCAFs and eCAFs not only exhibited enhanced pro-invasive activities but also mobilized the surrounding immune cells to construct a tumor-favorable microenvironment. Therefore, inhibiting their activation restrains the GC 'seed' and simultaneously improves the 'GC' soil, suggesting that it represents a promising therapeutic strategy for the treatment of GC.

Mesenchymal stem cell treatment improves outcome of COVID-19 patients via multiple immunomodulatory mechanisms.

The infusion of coronavirus disease 2019 (COVID-19) patients with mesenchymal stem cells (MSCs) potentially improves clinical symptoms, but the underlying mechanism remains unclear. We conducted a randomized, single-blind, placebo-controlled (29 patients/group) phase II clinical trial to validate previous findings and explore the potential mechanisms. Patients treated with umbilical cord-derived MSCs exhibited a shorter hospital stay (P = 0.0198) and less time required for symptoms remission (P = 0.0194) than those who received placebo. Based on chest images, both severe and critical patients treated with MSCs showed improvement by day 7 (P = 0.0099) and day 21 (P = 0.0084). MSC-treated patients had fewer adverse events. MSC infusion reduced the levels of C-reactive protein, proinflammatory cytokines, and neutrophil extracellular traps (NETs) and promoted the maintenance of SARS-CoV-2-specific antibodies. To explore how MSCs modulate the immune system, we employed single-cell RNA sequencing analysis on peripheral blood. Our analysis identified a novel subpopulation of VNN2+ hematopoietic stem/progenitor-like (HSPC-like) cells expressing CSF3R and PTPRE that were mobilized following MSC infusion. Genes encoding chemotaxis factors - CX3CR1 and L-selectin - were upregulated in various immune cells. MSC treatment also regulated B cell subsets and increased the expression of costimulatory CD28 in T cells in vivo and in vitro. In addition, an in vivo mouse study confirmed that MSCs suppressed NET release and reduced venous thrombosis by upregulating kindlin-3 signaling. Together, our results underscore the role of MSCs in improving COVID-19 patient outcomes via maintenance of immune homeostasis.

Single-cell heterogeneity analysis and CRISPR screens in MIN6 cell line reveal transcriptional regulators of insulin.

Diabetes mellitus is caused by either insulin resistance or insulin deficiency. The pancreatic ß cells are the primary producers of insulin. Large-scale CRISPR screens combined with single-cell RNA sequencing (scRNA-seq) on ß cells has identified novel insulin regulators and revealed the presence of a highly complex inner network. Here, we performed pooled CRISPR delivery with single-cell transcriptome analysis on the MIN6 cell line, a pancreatic ß-cell line. We have presented the scRNA-seq readout and demonstrated that the MIN6 cell line might develop genetic heterogeneity with increasing passage number based on GO and KEGG pathway analysis. Both computational and biological analyses revealed that the function of MIN6 cell lines could be divided into five clusters, including endocrine cells, basal cells, epithelial cells, and neuroendocrine cells. The fifth cluster was different from the other four clusters due to the differentially expressed insulin transcription and was called the lncRNA-enriched cluster. The experiments also confirmed that uncharacterized lncRNAs GM26917 and Cenpw were associated with insulin transcription. This study provides information that can be used to systematically characterize insulin regulator genes and other genes that control protein folding and vesicle trafficking.

The commensal consortium of the gut microbiome is associated with favorable responses to anti-programmed death protein 1 (PD-1) therapy in thoracic neoplasms.

OBJECTIVE: Immune checkpoint inhibitors have revolutionized cancer therapy for multiple types of solid tumors, but as expected, a large percentage of patients do not show durable responses. Biomarkers that can predict clinical responses to immunotherapies at diagnosis are therefore urgently needed. Herein, we determined the associations between baseline gut commensal microbes and the clinical treatment efficiencies of patients with thoracic neoplasms during anti-programmed death protein 1 (PD-1) therapy. METHODS: Forty-two patients with advanced thoracic carcinoma who received anti-PD-1 treatment were enrolled in the study. Baseline and time-serial stool samples were analyzed using 16S ribosomal RNA gene sequencing. Tumor responses, patient progression-free survival, and overall survival were used to measure clinical outcomes. RESULTS: The diversities of the baseline gut microbiota were similar between responders (n = 23) and nonresponders (n = 19). The relative abundances of the Akkermansiaceae, Enterococcaceae, Enterobacteriaceae, Carnobacteriaceae and Clostridiales Family XI bacterial families were significantly higher in the responder group. These 5 bacterial families acted as a commensal consortium and better stratified patients according to clinical responses (P = 0.014). Patients with a higher abundance of commensal microbes had prolonged PFS (P = 0.00016). Using multivariable analysis, the abundance of the commensal consortium was identified as an independent predictor of anti-PD-1 immunotherapy in thoracic neoplasms (hazard ratio: 0.17; 95% confidence interval: 0.05-0.55; P = 0.003). CONCLUSIONS: Baseline gut microbiota may have a critical impact on anti-PD-1 treatment in thoracic neoplasms. The abundance of gut commensal microbes at diagnosis might be useful for the early prediction of anti-PD-1 immunotherapy responses.

SCSA: A Cell Type Annotation Tool for Single-Cell RNA-seq Data.

Currently most methods take manual strategies to annotate cell types after clustering the single-cell RNA sequencing (scRNA-seq) data. Such methods are labor-intensive and heavily rely on user expertise, which may lead to inconsistent results. We present SCSA, an automatic tool to annotate cell types from scRNA-seq data, based on a score annotation model combining differentially expressed genes (DEGs) and confidence levels of cell markers from both known and user-defined information. Evaluation on real scRNA-seq datasets from different sources with other methods shows that SCSA is able to assign the cells into the correct types at a fully automated mode with a desirable precision.

Cytokines/Chemokines: Potential Biomarkers for Non-paraneoplastic Anti-N-Methyl-D-Aspartate Receptor Encephalitis.

Objective: Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is the most common type of autoimmune encephalitis. This study focuses on finding new biomarkers to evaluate the clinical condition and provide new directions for treatment. Methods: A total of 44 cytokines/chemokines in the cerebrospinal fluid of 10 non-paraneoplastic patients and nine controls were measured. We selected some of the cytokines/chemokines that significantly increased in patients. Six selected cytokines/chemokines, including IL-10, CXCL10, CCL22, CCL3, IL-7, TNF-α, and three previously reported (IL-2, IL-6, and IL-17A), were measured in seven other patients who provided repeat samples. We compared their levels and explored correlations with severity of disease and antibody titers. Results: The levels of Th1 axis (CXCL10, TNF-α, IFN-γ, CCL3), Th2 axis (CCL1, CCL8, CCL17, CCL22), Treg axis (IL-10), Th17 axis (IL-7), and B cell axis (CXCL13) cytokines, as well as IL-12 p40 and IL-16, were significantly higher in patients compared to those in controls. The level of IL-2 was significantly decreased at the intermediate stage of treatment compared with that before treatment. The severity of disease is positively correlated with levels of CXCL10, CCL3, IL-10, CCL22, and IL-6. The level of CCL3 in the high antibody titer group was greater than that in the low antibody titer group. Conclusion: The pathogenesis of anti-NMDAR encephalitis involves T cell and B cell cytokines. T cells likely assist B cells to produce antibodies. IL-2, CXCL10, CCL3, IL-10, CCL22, and IL-6 may represent new biomarkers in anti-NMDAR encephalitis. Given the lack of research on IL-10, CCL3, and CCL22 in this disease, it will be informative to explore their potential role in pathogenesis in larger studies.

A novel codon-based de Bruijn graph algorithm for gene construction from unassembled transcriptomes.

Most gene prediction methods detect coding sequences from transcriptome assemblies in the absence of closely related reference genomes. Such methods are of limited application due to high transcript fragmentation and extensive assembly errors, which may lead to redundant or false coding sequence predictions. We present inGAP-CDG, which can construct full-length and non-redundant coding sequences from unassembled transcriptomes by using a codon-based de Bruijn graph to simplify the assembly process and a machine learning-based approach to filter false positives. Compared with other methods, inGAP-CDG exhibits a significant increase in predicted coding sequence length and robustness to sequencing errors and varied read length.

Armet is an effector protein mediating aphid-plant interactions.

Aphid saliva is predicted to contain proteins that modulate plant defenses and facilitate feeding. Armet is a well-characterized bifunctional protein in mammalian systems. Here we report a new role of Armet, namely as an effector protein in the pea aphid, Acyrthosiphon pisum. Pea aphid Armet's physical and chemical properties and its intracellular role are comparable to those reported for mammalian Armets. Uniquely, we detected Armet in aphid watery saliva and in the phloem sap of fava beans fed on by aphids. Armet's transcript level is several times higher in the salivary gland when aphids feed on bean plants than when they feed on an artificial diet. Knockdown of the Armet transcript by RNA interference disturbs aphid feeding behavior on fava beans measured by the electrical penetration graph technique and leads to a shortened life span. Inoculation of pea aphid Armet protein into tobacco leaves induced a transcriptional response that included pathogen-responsive genes. The data suggest that Armet is an effector protein mediating aphid-plant interactions.