Targeting SIGLEC15 as an emerging immunotherapy for anaplastic thyroid cancer

Anaplastic thyroid carcinoma (ATC) is the most aggressive subtype of thyroid cancer with few effective therapies. Though immunotherapies such as targeting PD-1/PD-L1 axis have benefited patients with solid tumor, the druggable immune checkpoints are quite limited in ATC. In our study, we focused on the anti-tumor potential of sialic acid-binding Ig-like lectins (Siglecs) in ATC. Through screening by integrating microarray datasets including 216 thyroid-cancer tissues and single-cell RNA-sequencing, SIGLEC family members CD33, SIGLEC1, SIGLEC10 and SIGLEC15 were significantly overexpressed in ATC, among which SIGLEC15 increased highest and mainly expressed on cancer cells. SIGLEC15high ATC cells are characterized by high expression of serine protease PRSS23 and cancer stem cell marker CD44. Compared with SIGLEC15low cancer cells, SIGLEC15high ATC cells exhibited higher interaction frequency with tumor microenvironment cells. Further study showed that SIGLEC15high cancer cells mainly interacted with T cells by immunosuppressive signals such as MIF-TNFRSF14 and CXCL12-CXCR4. Notably, treatment of anti-SIGLEC15 antibody profoundly increased the cytotoxic ability of CD8+ T cells in a co-culture model and zebrafish-derived ATC xenografts. Consistently, administration of anti-SIGLEC15 antibody significantly inhibited tumor growth and prolonged mouse survival in an immunocompetent model of murine ATC, which was associated with increase of M1/M2, natural killer (NK) cells and CD8+ T cells, and decrease of myeloid-derived suppressor cells (MDSCs). SIGLEC15 inhibited T cell activation by reducing NFAT1, NFAT2, and NF-κB signals. Blocking SIGLEC15 increased the secretion of IFN-γ and IL-2 in vitro and in vivo. In conclusion, our finding demonstrates that SIGLEC15 is an emerging and promising target for immunotherapy in ATC.

lute: estimating the cell composition of heterogeneous tissue with varying cell sizes using gene expression

Relative cell type fraction estimates in bulk RNA-sequencing data are important to control for cell composition differences across heterogenous tissue samples. Current computational tools estimate relative RNA abundances rather than cell type proportions in tissues with varying cell sizes, leading to biased estimates. We present lute, a computational tool to accurately deconvolute cell types with varying sizes. Our software wraps existing deconvolution algorithms in a standardized framework. Using simulated and real datasets, we demonstrate how lute adjusts for differences in cell sizes to improve the accuracy of cell composition. Software is available from https://bioconductor.org/packages/lute.

Predicting cell population-specific gene expression from genomic sequence

Most regulatory elements, especially enhancer sequences, are cell population-specific. One could even argue that a distinct set of regulatory elements is what defines a cell population. However, discovering which non-coding regions of the DNA are essential in which context, and as a result, which genes are expressed, is a difficult task. Some computational models tackle this problem by predicting gene expression directly from the genomic sequence. These models are currently limited to predicting bulk measurements and mainly make tissue-specific predictions. Here, we present a model that leverages single-cell RNA-sequencing data to predict gene expression. We show that cell population-specific models outperform tissue-specific models, especially when the expression profile of a cell population and the corresponding tissue are dissimilar. Further, we show that our model can prioritize GWAS variants and learn motifs of transcription factor binding sites. We envision that our model can be useful for delineating cell population-specific regulatory elements.

Endosteal stem cells at the bone-blood interface: A double-edged sword for rapid bone formation: Bone marrow endosteal stem cells provide a robust source of bone-making osteoblasts both in normal and abnormal bone formation

Endosteal stem cells are a subclass of bone marrow skeletal stem cell populations that are particularly important for rapid bone formation occurring in growth and regeneration. These stem cells are strategically located near the bone surface in a specialized microenvironment of the endosteal niche. These stem cells are abundant in young stages but eventually depleted and replaced by other stem cell types residing in a non-endosteal perisinusoidal niche. Single-cell molecular profiling and in vivo cell lineage analyses play key roles in discovering endosteal stem cells. Importantly, endosteal stem cells can transform into bone tumor-making cells when deleterious mutations occur in tumor suppressor genes. The emerging hypothesis is that osteoblast-chondrocyte transitional identities confer a special subset of endosteal stromal cells with stem cell-like properties, which may make them susceptible for tumorigenic transformation. Endosteal stem cells are likely to represent an important therapeutic target of bone diseases caused by aberrant bone formation.

Single-cell transcriptome characteristics of testicular terminal epithelium lineages during aging in the Drosophila

Aging is a complex biological process leading to impaired functions, with a variety of hallmarks. In the testis of Drosophila, the terminal epithelium region is involved in spermatid release and maturation, while its functional diversity and regulatory mechanism remain poorly understood. In this study, we performed single-cell RNA-sequencing analysis (scRNA-seq) to characterize the transcriptomes of terminal epithelium in Drosophila testes at 2-, 10 and 40-Days. Terminal epithelium populations were defined with Metallothionein A (MtnA) and subdivided into six novel sub-cell clusters (EP0-EP5), and a series of marker genes were identified based on their expressions. The data revealed the functional characteristics of terminal epithelium populations, such as tight junction, focal adhesion, bacterial invasion, oxidative stress, mitochondrial function, proteasome, apoptosis and metabolism. Interestingly, we also found that disrupting genes for several relevant pathways in terminal epithelium led to male fertility disorders. Moreover, we also discovered a series of age-biased genes and pseudotime trajectory mediated state-biased genes during terminal epithelium aging. Differentially expressed genes during terminal epithelium aging were mainly participated in the regulation of several common signatures, e.g. mitochondria-related events, protein synthesis and degradation, and metabolic processes. We further explored the Drosophila divergence and selection in the functional constraints of age-biased genes during aging, revealing that age-biased genes in epithelial cells of 2 Days group evolved rapidly and were endowed with greater evolutionary advantages. scRNA-seq analysis revealed the diversity of testicular terminal epithelium populations, providing a gene target resource for further systematic research of their functions during aging.

Prognostic stratification of sepsis through DNA damage response based RiskScore system: insights from single-cell RNA-sequencing and transcriptomic profiling

Background

A novel risk scoring system, predicated on DNA damage response (DDR), was developed to enhance prognostic predictions and potentially inform the creation of more effective therapeutic protocols for sepsis.

Methods

To thoroughly delineate the expression profiles of DDR markers within the context of sepsis, an analytical approach utilizing single-cell RNA-sequencing (scRNA-seq) was implemented. Our study utilized single-cell analysis techniques alongside weighted gene co-expression network analysis (WGCNA) to pinpoint the genes that exhibit the most substantial associations with DNA damage response (DDR). Through Cox proportional hazards LASSO regression, we distinguished DDR-associated genes and established a risk model, enabling the stratification of patients into high- and low-risk groups. Subsequently, we carried out an analysis to determine our model's predictive accuracy regarding patient survival. Moreover, we examined the distinct biological characteristics, various signal transduction routes, and immune system responses in sepsis patients, considering different risk categories and outcomes related to survival. Lastly, we conducted experimental validation of the identified genes through in vivo and in vitro assays, employing RT-PCR, ELISA, and flow cytometry.

Results

Both single-cell RNA sequencing (scRNA-seq) and bulk transcriptomic analyses have demonstrated a strong correlation between DNA damage response (DDR) levels and sepsis prognosis. Specific cell subtypes, including monocytes, megakaryocytes, CD4+ T cells, and neutrophils, have shown elevated DDR activity. Cells with increased DDR scores exhibited more robust and numerous interactions with other cell populations. The weighted gene co-expression network analysis (WGCNA) and single-cell analyses revealed 71 DDR-associated genes. We developed a four-gene risk scoring system using ARL4C, CD247, RPL7, and RPL31, identified through univariate COX, LASSO COX regression, and log-rank (Mantel-Cox) tests. Nomograms, calibration plots, and decision curve analyses (DCA) regarding these specific genes have provided significant clinical benefits for individuals diagnosed with sepsis. The study suggested that individuals categorized as lower-risk demonstrated enhanced infiltration of immune cells, upregulated expression of immune regulators, and a more prolific presence of immune-associated functionalities and pathways. RT-qPCR analyses on a sepsis rat model revealed differential gene expression predominantly in the four targeted genes. Furthermore, ARL4C knockdown in sepsis model in vivo and vitro caused increased inflammatory response and a worse prognosis.

Conclusion

The delineated DDR expression landscape offers insights into sepsis pathogenesis, whilst our riskScore model, based on a robust four-gene signature, could underpin personalized sepsis treatment strategies.

Collagen induction of immune cells in the mammary glands during pregnancy

The mammary glands are dynamic tissues affected by pregnancy-related hormones during the pregnancy-lactation cycle. Collagen production and its dynamics are essential to the remodeling of the mammary glands. Alterations of the mammary microenvironment and stromal cells during the pregnancy-lactation cycle are important for understanding the physiology of the mammary glands and the development of breast tumors. In this study, we performed an evaluation of collagen dynamics in the mammary fat pad during the pregnancy-lactation cycle. Reanalysis of single-cell RNA-sequencing (scRNA-Seq) data showed the ectopic collagen expression in the immune cells and cell-cell interactions for collagens with single-cell resolution. The scRNA-Seq data showed that type I and type III collagen were produced not only by stromal fibroblasts but also by lymphoid and myeloid cell types in the pregnancy phase. Furthermore, the total cell-cell interaction score for collagen interactions was dramatically increased in the pregnancy tissue. The data presented in this study provide evidence that immune cells contribute, at least in part, to mammary collagen dynamics. Our findings suggest that immune cells, including lymphoid and myeloid cells, might be supportive members of the extracellular matrix orchestration in the pregnancy-lactation cycle of the mammary glands.NEW & NOTEWORTHY Our study evaluated mammary gland collagen dynamics during the pregnancy-lactation cycle using single-cell RNA-sequencing data. We found ectopic collagen expression in immune cells and an increase in collagen interactions during pregnancy. Type I and type III collagen were produced by lymphoid, myeloid, and stromal fibroblast cells during pregnancy. These findings suggest that immune cells, including lymphoid and myeloid cells, play a crucial role in supporting the extracellular matrix in mammary glands during pregnancy-lactation cycles.

Monocyte-derived alveolar macrophages are key drivers of smoke-induced lung inflammation and tissue remodeling

Smoking is a leading risk factor of chronic obstructive pulmonary disease (COPD), that is characterized by chronic lung inflammation, tissue remodeling and emphysema. Although inflammation is critical to COPD pathogenesis, the cellular and molecular basis underlying smoking-induced lung inflammation and pathology remains unclear. Using murine smoke models and single-cell RNA-sequencing, we show that smoking establishes a self-amplifying inflammatory loop characterized by an influx of molecularly heterogeneous neutrophil subsets and excessive recruitment of monocyte-derived alveolar macrophages (MoAM). In contrast to tissue-resident AM, MoAM are absent in homeostasis and characterized by a pro-inflammatory gene signature. Moreover, MoAM represent 46% of AM in emphysematous mice and express markers causally linked to emphysema. We also demonstrate the presence of pro-inflammatory and tissue remodeling associated MoAM orthologs in humans that are significantly increased in emphysematous COPD patients. Inhibition of the IRAK4 kinase depletes a rare inflammatory neutrophil subset, diminishes MoAM recruitment, and alleviates inflammation in the lung of cigarette smoke-exposed mice. This study extends our understanding of the molecular signaling circuits and cellular dynamics in smoking-induced lung inflammation and pathology, highlights the functional consequence of monocyte and neutrophil recruitment, identifies MoAM as key drivers of the inflammatory process, and supports their contribution to pathological tissue remodeling.

Synovium and infrapatellar fat pad share common mesenchymal progenitors and undergo coordinated changes in osteoarthritis

Osteoarthritis (OA) affects multiple tissues in the knee joint, including the synovium and intra-articular adipose tissue (IAAT) that are attached to each other. However, whether these two tissues share the same progenitor cells and hence function as a single unit in joint homeostasis and diseases is largely unknown. Single-cell transcriptomic profiling of synovium and infrapatellar fat pad (IFP), the largest IAAT, from control and OA mice revealed five mesenchymal clusters and predicted mesenchymal progenitor cells (MPCs) as the common progenitors for other cells: synovial lining fibroblasts (SLFs), myofibroblasts (MFs), and preadipocytes 1 and 2. Histologic examination of joints in reporter mice having Dpp4-CreER and Prg4-CreER that label MPCs and SLFs, respectively, demonstrated that Dpp4+ MPCs reside in the synovial sublining layer and give rise to Prg4+ SLFs and Perilipin+ adipocytes during growth and OA progression. After OA injury, both MPCs and SLFs gave rise to MFs, which remained in the thickened synovium at later stages of OA. In culture, Dpp4+ MPCs possessed mesenchymal progenitor properties, such as proliferation and multilineage differentiation. In contrast, Prg4+ SLFs did not contribute to adipocytes in IFP and Prg4+ cells barely grew in vitro. Taken together, we demonstrate that the synovium and joint fat pad are one integrated functional tissue sharing common mesenchymal progenitors and undergoing coordinated changes during OA progression.

Molecular features of luminal breast cancer defined through spatial and single-cell transcriptomics

BACKGROUND

Intratumour heterogeneity is a hallmark of most solid tumours, including breast cancers. We applied spatial transcriptomics and single-cell RNA-sequencing on patient-derived xenografts (PDXs) to profile spatially resolved cell populations within oestrogen receptor-positive (ER+ ) breast cancer and to elucidate their importance in oestrogen-dependent tumour growth.

METHODS

Two PDXs of 'ER-high' breast cancers with opposite oestrogen-mediated growth responses were investigated: oestrogen-suppressed GS3 (80-100% ER) and oestrogen-dependent SC31 (40-90% ER) models. The observation was validated via single-cell analyses on an 'ER-low' PDX, GS1 (5% ER). The results from our spatial and single-cell analyses were further supported by a public ER+ breast cancer single-cell dataset and protein-based dual immunohistochemistry (IHC) of SC31 examining important luminal cancer markers (i.e., ER, progesterone receptor and Ki67). The translational implication of our findings was assessed by clinical outcome analyses on publicly available cohorts.

RESULTS

Our space-gene-function study revealed four spatially distinct compartments within ER+ breast cancers. These compartments showed functional diversity (oestrogen-responsive, proliferative, hypoxia-induced and inflammation-related). The 'proliferative' population, rather than the 'oestrogen-responsive' compartment, was crucial for oestrogen-dependent tumour growth, leading to the acquisition of luminal B-like features. The cells expressing typical oestrogen-responsive genes like PGR were not directly linked to oestrogen-dependent proliferation. Dual IHC analyses demonstrated the distinct contribution of the Ki67+ proliferative cells toward oestrogen-mediated growth and their response to a CDK4/6 inhibitor. The gene signatures derived from the proliferative, hypoxia-induced and inflammation-related compartments were significantly correlated with worse clinical outcomes, while patients with the oestrogen-responsive signature showed better prognoses, suggesting that this compartment would not be directly associated with oestrogen-dependent tumour progression.

CONCLUSIONS

Our study identified the gene signature in our 'proliferative' compartment as an important determinant of luminal cancer subtypes. This 'proliferative' cell population is a causative feature of luminal B breast cancer, contributing toward its aggressive behaviours.

Single-cell profiling of prurigo nodularis demonstrates immune-stromal crosstalk driving profibrotic responses and reversal with nemolizumab

BACKGROUND

Prurigo nodularis (PN) is a chronic neuroimmune skin disease characterized by bilaterally distributed pruritic hyperkeratotic nodules on extremities and trunk. Neuroimmune dysregulation and chronic scratching are believed to both induce and maintain the characteristic lesions.

OBJECTIVES

This study sought to provide a comprehensive view of the molecular pathogenesis of PN at the single-cell level to identify and outline key pathologic processes and the cell types involved. Features that distinguish PN skin from the skin of patients with atopic dermatitis were of particular interest. We further aimed to determine the impact of the IL31RA antagonist, nemolizumab, and its specificity at the single-cell level.

METHODS

Single-cell RNA-sequencing of skin from 15 healthy donors and nonlesional and lesional skin from 6 patients each with PN and atopic dermatitis, combined with spatial-sequencing using the 10x Visium platform. Integration with bulk RNA-sequencing data from patients treated with nemolizumab.

RESULTS

This study demonstrates that PN is an inflammatory skin disease characterized by both keratinocyte proliferation and activation of profibrotic responses. This study also demonstrates that the COL11A1+ fibroblast subset is a major contributor to fibrosis and is predominantly found in the papillary dermis of PN skin. Activation of fibrotic responses is the main distinguishing feature between PN and atopic dermatitis skin. This study further shows the broad effect of nemolizumab on PN cell types, with a prominent effect driving COL11A1+ fibroblast and keratinocyte responses toward normal.

CONCLUSIONS

This study provides a high-resolution characterization of the cell types and cellular processes activated in PN skin, establishing PN as a chronic fibrotic inflammatory skin disease. It further demonstrates the broad effect of nemolizumab on pathological processes in PN skin.

Quantitative Modeling of Stemness in Single-Cell RNA Sequencing Data: A Nonlinear One-Class Support Vector Machine Method

Intratumoral heterogeneity and the presence of cancer stem cells are challenging issues in cancer therapy. An appropriate quantification of the stemness of individual cells for assessing the potential for self-renewal and differentiation from the cell of origin can define a measurement for quantifying different cell states, which is important in understanding the dynamics of cancer evolution, and might further provide possible targeted therapies aimed at tumor stem cells. Nevertheless, it is usually difficult to quantify the stemness of a cell based on molecular information associated with the cell. In this study, we proposed a stemness definition method with one-class Hadamard kernel support vector machine (OCHSVM) based on single-cell RNA sequencing (scRNA-seq) data. Applications of the proposed OCHSVM stemness are assessed by various data sets, including preimplantation embryo cells, induced pluripotent stem cells, or tumor cells. We further compared the OCHSVM model with state-of-the-art methods CytoTRACE, one-class logistic regression, or one-class SVM methods with different kernels. The computational results demonstrate that the OCHSVM method is more suitable for stemness identification using scRNA-seq data.

Integration of single-cell and bulk RNA-sequencing to analyze the heterogeneity of hepatocellular carcinoma and establish a prognostic model

BACKGROUND

The highly heterogeneous nature of hepatocellular carcinoma (HCC) results in different responses and prognoses to the same treatment in patients with similar clinical stages.

AIMS

Thus, it is imperative to investigate the association between HCC tumor heterogeneity and treatment response and prognosis.

METHODS AND RESULTS

At first, we downloaded scRNA-seq, bulk RNA-seq, and clinical data from TCGA and GEO databases. We conducted quality control, normalization using SCTransform, dimensionality reduction using PCA, batch effect removal using Harmony, dimensionality reduction using UMAP, and cell annotation-based marker genes on the scRNA-seq data. We recognized tumor cells, identified tumor-related genes (TRGs), and performed cell communication analysis. Next, we developed a prognostic model using univariable Cox, LASSO, and multivariate Cox analyses. The signature was evaluated using survival analysis, ROC curves, C-index, and nomogram. Last, we studied the predictability of the signature in terms of prognosis and immunotherapeutic response for HCC, assessed a variety of drugs for clinical treatment, and used the qRT-PCR analysis to validate the mRNA expression levels of prognostic TRGs.

CONCLUSION

To conclude, this study expounded upon the influence of tumor cell heterogeneity on the prediction of treatment outcomes and prognosis in HCC. This, in turn, enhances the predictive ability of the TNM staging system and furnishes novel perspectives on the prognostic assessment and therapy of HCC.

CXCL14 as a potential marker for immunotherapy response prediction in renal cell carcinoma

Background

Epigenetic mechanisms play vital roles in the activation, differentiation, and effector function of immune cells. The breast and kidney-expressed chemokine (CXCL14) mainly contributes to the regulation of immune cells. However, its role in shaping the tumor immune microenvironment (TIME) is yet to be elucidated in renal cell carcinoma (RCC).

Objectives

This study aimed to elucidate the role of CXCL14 in predicting the efficacy of immunotherapy in patients with RCC.

Methods

CXCL14 expression and RNA-sequencing, single-cell RNA-sequencing (scRNA-seq), and survival datasets of RCC from public databases were analyzed, and survival was compared between different CXCL14 levels. The correlation between CXCL14 and immune infiltration and human leukocyte antigen (HLA) gene expression was analyzed with TIMER2.0 and gene expression profiling interactive analysis. Institutional scRNA-seq and immunohistochemical staining analyses were used to verify the relationship between CXCL14 expression level and the efficacy of immunotherapy.

Results

CXCL14 was expressed in fibroblast and malignant cells in RCC, and higher expression was associated with better survival. Enrichment analysis revealed that CXCL14 is involved in immune activation, primarily in antigen procession, antigen presentation, and major histocompatibility complex assemble. CXCL14 expression was positively correlated with T-cell infiltration as well as HLA-related gene expression. Among the RCC cohort receiving nivolumab in Checkmate 025, the patients with CXCL14 high expression had better overall survival than those with CXCL14 low expression after immunotherapy. scRNA-seq revealed a cluster of CXCL14+ fibroblast in immunotherapy responders. Immunohistochemistry analysis verified that the patients with high CXCL14 expression had an increased proportion of high CD8 expression simultaneously. The expression level of CXCL14 was associated with CXCR4 expression in RCC.

Conclusion

CXCL14 expression is associated with immunotherapy response in RCC. It is a promising biomarker for immunotherapy response prediction and may be an effective epigenetic modulator in combination with immunotherapy approaches.

Single-cell transcriptomics stratifies organoid models of metabolic dysfunction-associated steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a growing cause of morbidity with limited treatment options. Thus, accurate in vitro systems to test new therapies are indispensable. While recently, human liver organoid models have emerged to assess steatotic liver disease, a systematic evaluation of their translational potential is still missing. Here, we evaluated human liver organoid models of MASLD, comparatively testing disease induction in three conditions: oleic acid, palmitic acid, and TGF-β1. Through single-cell analyses, we find that all three models induce inflammatory signatures, but only TGF-β1 promotes collagen production, fibrosis, and hepatic stellate cell expansion. In striking contrast, oleic acid ameliorates fibrotic signatures and reduces the hepatic stellate cell population. Linking data from each model to gene expression signatures associated with MASLD disease progression further demonstrates that palmitic acid and TGF-β1 more robustly model inflammation and fibrosis. Our findings highlight the importance of stratifying MASLD organoid models by signatures of clinical disease progression, provide a single-cell reference to benchmark future organoid injury models, and allow us to study evolving steatohepatitis, fibrosis, and HSC susceptibility to injury in a dynamic, multi-lineage human in vitro system.

Improved protocol for single-nucleus RNA-sequencing of frozen human bladder tumor biopsies

This paper provides a laboratory workflow for single-nucleus RNA-sequencing (snRNA-seq) including a protocol for gentle nuclei isolation from fresh frozen tumor biopsies, making it possible to analyze biobanked material. To develop this protocol, we used non-frozen and frozen human bladder tumors and cell lines. We tested different lysis buffers (IgePal and Nuclei EZ) and incubation times in combination with different approaches for tissue and cell dissection: sectioning, semi-automated dissociation, manual dissociation with pestles, and semi-automated dissociation combined with manual dissociation with pestles. Our results showed that a combination of IgePal lysis buffer, tissue dissection by sectioning, and short incubation time was the best conditions for gentle nuclei isolation applicable for snRNA-seq, and we found limited confounding transcriptomic changes based on the isolation procedure. This protocol makes it possible to analyze biobanked material from patients with well-described clinical and histopathological information and known clinical outcomes with snRNA-seq.

Single-Cell Profiling of Tumor-Associated Neutrophils in Advanced Non-Small Cell Lung Cancer

Purpose

Neutrophils act as a non-negligible regulator in the initiation and progression of malignancies, playing bifacial roles in the process. Thus, to understand the heterogeneity of tumor-associated neutrophils (TANs) comprehensively in advanced non-small cell lung cancer (NSCLC) at single-cell resolution is necessary and urgent.

Materials and Methods

We applied single-cell RNA-sequencing (scRNA-seq) to portray the subtype-specific transcriptome landscape of TANs in advanced NSCLC using nine freshly obtained specimens. The scRNA-seq data were further processed for pseudo-time analysis to depict the developmental trajectory of TANs. Meanwhile, the interplay between TANs and other cell types within tumor microenvironment (TME) was revealed by intercellular interaction analysis.

Results

Seven distinct TAN subtypes were defined, of which, the N3 cluster was considered inflammatory phenotype expressing genes encoding multiple chemotactic cytokines, and correlated with inferior overall survival, indicating that N3 might be a pro-tumorigenic TAN subtype. N1 and N5 clusters were considered to be well differentiated and mature neutrophils based on CXCR2 expression and pseudo-time patterns, and both accounted for relatively high proportions in lung adenocarcinoma. In addition, genes related to neutrophil differentiation were discovered. We also found that TAN subtypes interacted most closely with macrophages through chemokine signaling pathways within TME.

Conclusion

Our study refined TAN subtypes and mapped the transcriptome landscape of TANs at single-cell resolution in advanced NSCLC, collectively indicating the heterogeneity of TANs in NSCLC. Neutrophil differentiation- and maturation-related genes were also discovered, which shed light on different functions of TAN subclones in tumor immune escape, and may further provide novel targets for immunotherapy.

Single cell transcriptome analyses reveal the roles of B cells in fructose-induced hypertension

Rationale

While the immune system plays a crucial role in the development of hypertension, the specific contributions of distinct immune cell populations remain incompletely understood. The emergence of single-cell RNA-sequencing (scRNA-seq) technology enables us to analyze the transcriptomes of individual immune cells and to assess the significance of each immune cell type in hypertension development.

Objective

We aimed to investigate the hypothesis that B cells play a crucial role in the development of fructose-induced hypertension.

Methods and Results

Eight-week-old Dahl salt-sensitive (SS) male rats were divided into two groups and given either tap water (TW) or a 20% fructose solution (HFS) for 4 weeks. Systolic blood pressure was measured using the tail-cuff method. ScRNA-seq analysis was performed on lamina propria cells (LPs) and peripheral blood mononuclear cells (PBMCs) obtained from SS rats subjected to either TW or HFS. The HFS treatment induced hypertension in the SS rats. The analysis revealed 27 clusters in LPs and 28 clusters in PBMCs, allowing for the identification and characterization of various immune cell types within each cluster. Specifically, B cells and follicular helper T (Tfh) cells were prominent in LPs, while B cells and M1 macrophages dominated PBMCs in the HFS group. Moreover, the HFS treatment triggered an increase in the number of B cells in both LPs and PBMCs, accompanied by activation of the interferon pathway.

Conclusions

The significant involvement of B cells in intestinal and PBMC responses indicates their pivotal contribution to the development of hypertension. This finding suggests that targeting B cells could be a potential strategy to mitigate high blood pressure in fructose-induced hypertension. Moreover, the simultaneous increase in follicular B cells and Tfh cells in LPs, along with the upregulation of interferon pathway genes in B cells, underscores a potential autoimmune factor contributing to the pathogenesis of fructose-induced hypertension in the intestine.

Creation of a High-Throughput Microfluidic Platform for Single-Cell Transcriptome Sequencing of Cell-Cell Interactions

Cell-cell interaction is one of the major modalities for transmitting information between cells and activating the effects of functional cells. However, the construction of high-throughput analysis technologies from cell omics focusing on the impact of interactions of functional cells on targets has been relatively unexplored. Here, they propose a droplet-based microfluidic platform for cell-cell interaction sequencing (c-c-seq) and screening in vitro to address this challenge. A class of interacting cells is pre-labeled using cell molecular tags, and additional single-cell sequencing reagents are introduced to quickly form functional droplet mixes. Lastly, gene expression analysis is used to deduce the impact of the interaction, while molecular sequence tracing identifies the type of interaction. Research into the active effect between antigen-presenting cells and T cells, one of the most common cell-to-cell interactions, is crucial for the advancement of cancer therapy, particularly T cell receptor-engineered T cell therapy. As it allows for high throughput, this platform is superior to well plates as a research platform for cell-to-cell interactions. When combined with the next generation of sequencing, the platform may be able to more accurately evaluate interactions between epitopes and receptors and verify their functional relevance.

Single-cell transcriptional gene signature analysis identifies IL-17 signaling pathway as the key pathway in sepsis

Sepsis is a multiple dysregulated systemic inflammatory response with high mortality and leads to public concern. This study was designed to identify possible critical pathways associated with sepsis clinical severity and outcome, which offer potential biomarkers and therapeutic targets for sepsis diagnosis and treatment. Single-cell transcriptome profiles of human peripheral blood mononuclear (PBMC) in the healthy control population and sepsis patients were downloaded from the sepsis database GSE167363 and performed quality control before subsequent analysis. The bulk-RNA sequencing of blood samples in the sepsis-associated databases GSE100159 and GSE133822 was also used to confirm the association between critical pathways and sepsis pathology after processing raw data. We found there was a total of 18 distinct clusters in PBMC of sepsis, which was identified by the t-SNE and UMAP dimension reduction analysis. Meanwhile, the main cell types including B, NK, T, and monocyte cells were identified via the cell maker website and the "Single R" package cell-type annotation analysis. Subsequently, GO and KEGG enrichment analysis of differential expression genes in each cluster found that DEGs between healthy control and sepsis patients were significantly enriched in the IL-17 signaling pathway in monocyte, NK, and T cells. Finally, GSE100159 and GSE133822 confirmed IL-17 signaling pathway-associated genes including IL-17R, TRAF6, RELB, TRAF5, CEBPB, JUNB, CXCL1, CXCL3, CXCL8, CXCR1, and CXCR2 were significantly up-regulated in sepsis blood samples compared with the age-matched healthy control population. Taken together, we concluded that the IL-17 signaling pathway serves as a significant potential mechanism of sepsis and provides a promising therapeutic target for sepsis treatment. This research will further deepen our understanding of sepsis development.

Responsiveness to Vedolizumab Therapy in Ulcerative Colitis is Associated With Alterations in Immune Cell-Cell Communications

BACKGROUND

Ulcerative colitis (UC) and Crohn's disease are 2 types of inflammatory bowel disease (IBD), a group of chronic digestive disorders caused by aberrant immune responses to intestinal microbes. Although changes in the composition of immune cell subsets in the context of IBD have been previously described, the interactions and communication among cells are less well understood. Moreover, the precise mechanisms of action underlying many biologic therapies, including the anti-α4β7 integrin antagonist vedolizumab, remain incompletely understood. Our study aimed to explore possible additional mechanisms through which vedolizumab acts.

METHODS

We performed cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) on peripheral blood and colon immune cells derived from patients with ulcerative colitis treated with the anti-α4β7 integrin antagonist vedolizumab. We applied a previously published computational approach, NicheNet, to predict immune cell-cell interactions, revealing putative ligand-receptor pairs and key transcriptional changes downstream of these cell-cell communications (CCC).

RESULTS

We observed decreased proportions of T helper 17 (TH17) cells in UC patients who responded to vedolizumab and therefore focused the study on identifying cell-cell communications and signals of TH17 cells with other immune cells. For example, we observed that colon TH17 cells from vedolizumab nonresponders were predicted to have a greater degree of interactions with classical monocytes compared with responders, whereas colon TH17 cells from vedolizumab responders exhibited more interactions with myeloid dendritic cells compared with nonresponders.

CONCLUSIONS

Overall, our results indicate that efforts to elucidate cell-cell communications among immune and nonimmune cell types may increase the mechanistic understanding of current and investigational therapies for IBD.

Phase II trial of hypomethylating agent combined with nivolumab for acute myeloid leukaemia relapse after allogeneic haematopoietic cell transplantation-Immune signature correlates with response

Acute myeloid leukaemia (AML) relapse after allogeneic haematopoietic cell transplantation (allo-HCT) is often driven by immune-related mechanisms and associated with poor prognosis. Immune checkpoint inhibitors combined with hypomethylating agents (HMA) may restore or enhance the graft-versus-leukaemia effect. Still, data about using this combination regimen after allo-HCT are limited. We conducted a prospective, phase II, open-label, single-arm study in which we treated patients with haematological AML relapse after allo-HCT with HMA plus the anti-PD-1 antibody nivolumab. The response was correlated with DNA-, RNA- and protein-based single-cell technology assessments to identify biomarkers associated with therapeutic efficacy. Sixteen patients received a median number of 2 (range 1-7) nivolumab applications. The overall response rate (CR/PR) at day 42 was 25%, and another 25% of the patients achieved stable disease. The median overall survival was 15.6 months. High-parametric cytometry documented a higher frequency of activated (ICOS+ , HLA-DR+ ), low senescence (KLRG1- , CD57- ) CD8+ effector T cells in responders. We confirmed these findings in a preclinical model. Single-cell transcriptomics revealed a pro-inflammatory rewiring of the expression profile of T and myeloid cells in responders. In summary, the study indicates that the post-allo-HCT HMA/nivolumab combination induces anti-AML immune responses in selected patients and could be considered as a bridging approach to a second allo-HCT. Trial-registration: EudraCT-No. 2017-002194-18.

Regulatory T cells are associated with the tumor immune microenvironment and immunotherapy response in triple-negative breast cancer

Introduction

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer with a high risk of distant metastasis, an extremely poor prognosis, and a high risk of death. Regulatory T cells (Tregs) contribute to the formation of a tumor immunosuppressive microenvironment, which plays an important role in the progression and treatment resistance of TNBC.

Methods

A public single-cell sequencing dataset demonstrated increased infiltration of Tregs in TNBC tissues relative to normal breast tissue. Weighted gene co-expression network analysis was used to identify Treg infiltration-related modules for METABRIC TNBC samples. Subsequently, we obtained two Treg infiltration-associated clusters of TNBC by applying consensus clustering and further constructed a prognostic model based on this Treg infiltration-associated gene module. The ability of the selected gene in the prognostic model, thymidine kinase-1 (TK1), to promote the progression of TNBC was evaluated in vitro.

Results

We concluded that two Treg infiltration-associated clusters had different prognoses and sensitivities to drugs commonly used in breast cancer treatment, and multi-omics analysis revealed that the two clusters had different copy number variations of key tumor progression genes. The 7-gene risk score based on TNBC Treg infiltration was a reliable prognostic indicator both in the training and validation cohorts. Moreover, patients with TNBC with high Treg infiltration-related scores lacked the activation of immune activation pathways and exhibited resistance to anti-PD1 immunotherapy. Knocking down TK1 led to impaired proliferation, migration, and invasion of TNBC cells in vitro. In addition, specimens from patients with TNBC with high TK1 expression showed significantly higher Treg infiltration in tumors. Results of spatial transcriptome analysis showed that TK1 positive cells mainly localize in tumor area, and Treg cell infiltration in TNBC tissues was associated with high expression of TK1. Pan-cancer analysis also demonstrated that TK1 is associated with poor prognosis and activation of proliferation pathways in multiple cancers.

Discussion

We established a prognostic model related to Treg infiltration and this model can be used to establish a clinically relevant classification of TNBC progression. Additionally, our work revealed the underestimable potential of TK1 as a tumor biomarker and immunotherapeutic target.

Single-cell and genetic multi-omics analysis combined with experiments confirmed the signature and potential targets of cuproptosis in hepatocellular carcinoma

Background: Cuproptosis, as a recently discovered type of programmed cell death, occupies a very important role in hepatocellular carcinoma (HCC) and provides new methods for immunotherapy; however, the functions of cuproptosis in HCC are still unclear. Methods: We first analyzed the transcriptome data and clinical information of 526 HCC patients using multiple algorithms in R language and extensively described the copy number variation, prognostic and immune infiltration characteristics of cuproptosis related genes (CRGs). Then, the hub CRG related genes associated with prognosis through LASSO and Cox regression analyses and constructed a prognostic prediction model including multiple molecular markers and clinicopathological parameters through training cohorts, then this model was verified by test cohorts. On the basis of the model, the clinicopathological indicators, immune infiltration and tumor microenvironment characteristics of HCC patients were further explored via bioinformation analysis. Then, We further explored the key gene biological function by single-cell analysis, cell viability and transwell experiments. Meantime, we also explored the molecular docking of the hub genes. Results: We have screened 5 hub genes associated with HCC prognosis and constructed a prognosis prediction scoring model. And the model results showed that patients in the high-risk group had poor prognosis and the expression levels of multiple immune markers, including PD-L1, CD276 and CTLA4, were higher than those patients in the low-risk group. We found a significant correlation between risk score and M0 macrophages and memory CD4+ T cells. And the single-cell analysis and molecular experiments showed that BEX1 were higher expressed in HCC tissues and deletion inhibited the proliferation, invasion and migration and EMT pathway of HCC cells. Finally, it was observed that BEX1 could bind to sorafenib to form a stable conformation. Conclusion: The study not only revealed the multiomics characteristics of CRGs in HCC but also constructed a new high-accuracy prognostic prediction model. Meanwhile, BEX1 were also identified as hub genes that can mediate the cuproptosis of hepatocytes as potential therapeutic targets for HCC.

Single-Cell RNA-Sequencing Reveals Peripheral T Helper Cells Promoting the Development of IgG4-Related Disease by Enhancing B Cell Activation and Differentiation

Abnormal B cell differentiation plays a critical role in IgG4-related disease (IgG4-RD), but the underlying mechanism remains largely unknown. We investigated the cell landscape from three IgG4-RD retroperitoneal tissues and three control tissues using single-cell RNA-sequencing. Critical cell type or markers were further validated in the peripheral blood from the patients with IgG4-RD and healthy controls via flow cytometry as well as in the IgG4-RD and control tissue via immunofluorescence staining. The increases in B cells, plasma cells, and CD4+ T cells were found in IgG4-RD retroperitoneal tissue. Importantly, among CD4+ T cells, an increase in CD4+CXCR5-PD1hi peripheral T helper (Tph) cells with a high expression of IL-21 and TIGIT was discovered in IgG4-RD tissue, which was further validated in peripheral blood of the patients with IgG4-RD. The Tph cell and TIGIT+ Tph cell proportion were remarkably higher in active IgG4-RD patients and correlated with disease activity. Moreover, TIGIT+CD4+ cells were able to promote B cell differentiation via IL-21. Our study revealed that Tph cells are increased in IgG4-RD and probably play critical roles in B cell differentiation through TIGIT-IL-21 axis. Peripheral Tph cell and TIGIT+Tph cell are potential markers for IgG4-RD disease activity.

ScRNA-seq revealed disruption in CD8+ NKG2A+ natural killer T cells in patients after liver transplantation and immunosuppressive therapy

BACKGROUND

Liver transplantation (LT) offers a good survival chance for both the patient in short or long term, but still faces many challenges in the treatment of LT, such as the side effects associated with long-term immunosuppression, which is one of the side effects that occurs in most patients. However, the dynamics of the cellular immune system composition over time during immune tolerance to LT after immunosuppressive therapy are not known.

METHODS

Using single-cell transcriptome sequencing, we analyzed five peripheral blood samples (one normal individual and four patients who underwent LT and received immunosuppressive therapy for 2 months, 1 year, 3 years, and 7 years, respectively) for immune cell composition and gene expression.

RESULTS

A total of 17,462 peripheral blood mononuclear cells were acquired from a normal individual without LT and patients who underwent LT and received immunosuppressive therapy for 2 months, 1 year, 3 years, and 7 years, respectively. A total of 24 cell clusters were obtained and categorized into four different cell types based on gene expression characteristics as follows: eight clusters of T cells, two clusters of B cells, two clusters of neutrophils, two clusters of monocytes, natural killer cells, and natural killer T (NKT) cells (n = 4), and six other cell clusters. Cell subset analysis, pseudotime analysis, and intercellular communication analysis revealed that the CD8+ NKT cells specifically expressed NKG2A (KLRC1, CD159A), which may be an important cell group for CD8+ NKG2A+ NKT cells in LT, thereby highlighting the heterogeneity and functional diversity in patients who undergo LT.

CONCLUSIONS

We comprehensively analyzed single-cell RNA sequencing data from a normal individual and patients who underwent LT and elucidated the mechanism underlying the development of immune tolerance in LT. CD8+ NKT cells specifically expressing KLRC1 play a crucial role in LT, and dynamic monitoring of these cells may provide novel avenues for the diagnosis and treatment of LT-related immune rejection.

Construction of stemness gene score by bulk and single-cell transcriptome to characterize the prognosis of breast cancer

Breast cancer (BC) is a heterogeneous disease characterized by significant differences in prognosis and therapy response. Numerous prognostic tools have been developed for breast cancer. Usually these tools are based on bulk RNA-sequencing (RNA-Seq) and ignore tumor heterogeneity. Consequently, the goal of this study was to construct a single-cell level tool for predicting the prognosis of BC patients. In this study, we constructed a stemness-risk gene score (SGS) model based on single-sample gene set enrichment analysis (ssGSEA). Patients were divided into two groups based on the median SGS. Patients with a high SGS scores had a significantly worse prognosis than those with a low SGS, and these groups exhibited differences in several tumor characteristics, such as immune infiltration, gene mutations, and copy number variants. Our results indicate that the SGS is a reliable tool for predicting prognosis and response to immunotherapy in BC patients.

The CD58-CD2 axis is co-regulated with PD-L1 via CMTM6 and shapes anti-tumor immunity

The cell-autonomous balance of immune-inhibitory and -stimulatory signals is a critical process in cancer immune evasion. Using patient-derived co-cultures, humanized mouse models, and single-cell RNA-sequencing of patient melanomas biopsied before and on immune checkpoint blockade, we find that intact cancer cell-intrinsic expression of CD58 and ligation to CD2 is required for anti-tumor immunity and is predictive of treatment response. Defects in this axis promote immune evasion through diminished T cell activation, impaired intratumoral T cell infiltration and proliferation, and concurrently increased PD-L1 protein stabilization. Through CRISPR-Cas9 and proteomics screens, we identify and validate CMTM6 as critical for CD58 stability and upregulation of PD-L1 upon CD58 loss. Competition between CD58 and PD-L1 for CMTM6 binding determines their rate of endosomal recycling over lysosomal degradation. Overall, we describe an underappreciated yet critical axis of cancer immunity and provide a molecular basis for how cancer cells balance immune inhibitory and stimulatory cues.

Multiple Fibroblast Subtypes Contribute to Matrix Deposition in Pulmonary Fibrosis

Progressive pulmonary fibrosis results from a dysfunctional tissue repair response and is characterized by fibroblast proliferation, activation, and invasion and extracellular matrix accumulation. Lung fibroblast heterogeneity is well recognized. With single-cell RNA sequencing, fibroblast subtypes have been reported by recent studies. However, the roles of fibroblast subtypes in effector functions in lung fibrosis are not well understood. In this study, we incorporated the recently published single-cell RNA-sequencing datasets on murine lung samples of fibrosis models and human lung samples of fibrotic diseases and analyzed fibroblast gene signatures. We identified and confirmed the novel fibroblast subtypes we reported recently across all samples of both mouse models and human lung fibrotic diseases, including idiopathic pulmonary fibrosis, systemic sclerosis-associated interstitial lung disease, and coronavirus disease (COVID-19). Furthermore, we identified specific cell surface proteins for each fibroblast subtype through differential gene expression analysis, which enabled us to isolate primary cells representing distinct fibroblast subtypes by flow cytometry sorting. We compared matrix production, including fibronectin, collagen, and hyaluronan, after profibrotic factor stimulation and assessed the invasive capacity of each fibroblast subtype. Our results suggest that in addition to myofibroblasts, lipofibroblasts and Ebf1+ (Ebf transcription factor 1+) fibroblasts are two important fibroblast subtypes that contribute to matrix deposition and also have enhanced invasive, proliferative, and contraction phenotypes. The histological locations of fibroblast subtypes are identified in healthy and fibrotic lungs by these cell surface proteins. This study provides new insights to inform approaches to targeting lung fibroblast subtypes to promote the development of therapeutics for lung fibrosis.

Single-cell transcriptomics of NRAS-mutated melanoma transitioning to drug resistance reveals P2RX7 as an indicator of early drug response

Treatment options for patients with NRAS-mutant melanoma are limited and lack an efficient targeted drug combination that significantly increases overall and progression-free survival. In addition, targeted therapy success is hampered by the inevitable emergence of drug resistance. A thorough understanding of the molecular processes driving cancer cells' escape mechanisms is crucial to tailor more efficient follow-up therapies. We performed single-cell RNA sequencing of NRAS-mutant melanoma treated with MEK1/2 plus CDK4/6 inhibitors to decipher transcriptional transitions during the development of drug resistance. Cell lines resuming full proliferation (FACs [fast-adapting cells]) and cells that became senescent (SACs [slow-adapting cells]) over prolonged treatment were identified. The early drug response was characterized by transitional states involving increased ion signaling, driven by upregulation of the ATP-gated ion channel P2RX7. P2RX7 activation was associated with improved therapy responses and, in combination with targeted drugs, could contribute to the delayed onset of acquired resistance in NRAS-mutant melanoma.

Single-cell profiling reveals a potent role of quercetin in promoting hair regeneration

Hair loss affects millions of people at some time in their life, and safe and efficient treatments for hair loss are a significant unmet medical need. We report that topical delivery of quercetin (Que) stimulates resting hair follicles to grow with rapid follicular keratinocyte proliferation and replenishes perifollicular microvasculature in mice. We construct dynamic single-cell transcriptome landscape over the course of hair regrowth and find that Que treatment stimulates the differentiation trajectory in the hair follicles and induces an angiogenic signature in dermal endothelial cells by activating HIF-1α in endothelial cells. Skin administration of a HIF-1α agonist partially recapitulates the pro-angiogenesis and hair-growing effects of Que. Together, these findings provide a molecular understanding for the efficacy of Que in hair regrowth, which underscores the translational potential of targeting the hair follicle niche as a strategy for regenerative medicine, and suggest a route of pharmacological intervention that may promote hair regrowth.

Crafting a Personalized Prognostic Model for Malignant Prostate Cancer Patients Using Risk Gene Signatures Discovered through TCGA-PRAD Mining, Machine Learning, and Single-Cell RNA-Sequencing

BACKGROUND

Prostate cancer is a significant clinical issue, particularly for high Gleason score (GS) malignancy patients. Our study aimed to engineer and validate a risk model based on the profiles of high-GS PCa patients for early identification and the prediction of prognosis.

METHODS

We conducted differential gene expression analysis on patient samples from The Cancer Genome Atlas (TCGA) and enriched our understanding of gene functions. Using the least absolute selection and shrinkage operator (LASSO) regression, we established a risk model and validated it using an independent dataset from the International Cancer Genome Consortium (ICGC). Clinical variables were incorporated into a nomogram to predict overall survival (OS), and machine learning was used to explore the risk factor characteristics' impact on PCa prognosis. Our prognostic model was confirmed using various databases, including single-cell RNA-sequencing datasets (scRNA-seq), the Cancer Cell Line Encyclopedia (CCLE), PCa cell lines, and tumor tissues.

RESULTS

We identified 83 differentially expressed genes (DEGs). Furthermore, WASIR1, KRTAP5-1, TLX1, KIF4A, and IQGAP3 were determined to be significant risk factors for OS and progression-free survival (PFS). Based on these five risk factors, we developed a risk model and nomogram for predicting OS and PFS, with a C-index of 0.823 (95% CI, 0.766-0.881) and a 10-year area under the curve (AUC) value of 0.788 (95% CI, 0.633-0.943). Additionally, the 3-year AUC was 0.759 when validating using ICGC. KRTAP5-1 and WASIR1 were found to be the most influential prognosis factors when using the optimized machine learning model. Finally, the established model was interrelated with immune cell infiltration, and the signals were found to be differentially expressed in PCa cells when using scRNA-seq datasets and tissues.

CONCLUSIONS

We engineered an original and novel prognostic model based on five gene signatures through TCGA and machine learning, providing new insights into the risk of scarification and survival prediction for PCa patients in clinical practice.

Hes1 marks peri-condensation mesenchymal cells that generate both chondrocytes and perichondrial cells in early bone development

Bone development starts with condensations of undifferentiated mesenchymal cells that set a framework for future bones within the primordium. In the endochondral pathway, mesenchymal cells inside the condensation differentiate into chondrocytes and perichondrial cells in a SOX9-dependent mechanism. However, the identity of mesenchymal cells outside the condensation and how they participate in developing bones remain undefined. Here we show that mesenchymal cells surrounding the condensation contribute to both cartilage and perichondrium, robustly generating chondrocytes, osteoblasts, and marrow stromal cells in developing bones. Single-cell RNA-seq analysis of Prrx1-cre-marked limb bud mesenchymal cells at E11.5 reveals that Notch effector Hes1 is expressed in a mutually exclusive manner with Sox9 that is expressed in pre-cartilaginous condensations. Analysis of a Notch signaling reporter CBF1:H2B-Venus reveals that peri-condensation mesenchymal cells are active for Notch signaling. In vivo lineage-tracing analysis using Hes1-creER identifies that Hes1+ early mesenchymal cells surrounding the SOX9+ condensation at E10.5 contribute to both cartilage and perichondrium at E13.5, subsequently becoming growth plate chondrocytes, osteoblasts of trabecular and cortical bones, and marrow stromal cells in postnatal bones. In contrast, Hes1+ cells in the perichondrium at E12.5 or E14.5 do not generate chondrocytes within cartilage, contributing to osteoblasts and marrow stromal cells only through the perichondrial route. Therefore, Hes1+ peri-condensation mesenchymal cells give rise to cells of the skeletal lineage through cartilage-dependent and independent pathways, supporting the theory that early mesenchymal cells outside the condensation also play important roles in early bone development.

Single-cell RNA sequencing reveals a mechanism underlying the susceptibility of the left atrial appendage to intracardiac thrombogenesis during atrial fibrillation

BACKGROUND

Atrial fibrillation (AF) is associated with an increased risk of thrombosis of the left atrial appendage (LAA). However, the molecular mechanisms underlying this site-specificity remain poorly understood. Here, we present a comparative single-cell transcriptional profile of paired atrial appendages from patients with AF and illustrate the chamber-specific properties of the main cell types.

METHODS

Single-cell RNA sequencing analysis of matched atrial appendage samples from three patients with persistent AF was evaluated by 10× genomics. The AF mice model was created using Tbx5 knockout mice. Validation experiments were performed by glutathione S-transferase pull-down assays, coimmunoprecipitation (Co-IP), cleavage assays and shear stress experiments in vitro.

RESULTS

In LAA, phenotype switching from endothelial cells to fibroblasts and inflammation associated with proinflammatory macrophage infiltration were observed. Importantly, the coagulation cascade is highly enriched in LAA endocardial endothelial cells (EECs), accompanying the up-regulation of a disintegrin and metalloproteinase with thrombospondin motifs 1 (ADAMTS1) and the down-regulation of the tissue factor pathway inhibitor (TFPI) and TFPI2. Similar alterations were verified in an AF mouse model (Tbx5^+/- ) and EECs treated with simulated AF shear stress in vitro. Furthermore, we revealed that the cleavage of both TFPI and TFPI2 based on their interaction with ADAMTS1 would lead to loss of anticoagulant activities of EECs.

CONCLUSIONS

This study highlights the decrease in the anticoagulant status of EECs in LAA as a potential mechanism underlying the propensity for thrombosis, which may aid the development of anticoagulation therapeutic approaches targeting functionally distinct cell subsets or molecules during AF.

ScRNA-seq links dental fibroblasts heterogeneity with mechanoresponsiveness

BACKGROUND AND OBJECTIVE

Periodontal ligament (PDL) and dental pulp (DP) share a common origin but have distinct biological and mechanical functions. To what extent the mechanoresponsive property of PDL can be attributed to its unique transcriptional profiles of cellular heterogeneity is unclear. This study aims to decipher cellular heterogeneity and distinct mechanoresponsive characteristics of odontogenic soft tissues and their underlying molecular mechanisms.

MATERIALS AND METHODS

A single-cell comparison of digested human periodontal ligament (PDL) and dental pulp (DP) was performed using scRNA-seq. An in vitro loading model was constructed to measure mechanoresponsive ability. Dual-luciferase assay, overexpression, and shRNA knockdown were used to investigate the molecular mechanism.

RESULTS

Our results demonstrate striking fibroblast heterogeneity across and within human PDL and DP. We demonstrated that a tissue-specific subset of fibroblasts existed in PDL exhibiting high expression of mechanoresponsive extracellular matrix (ECM) genes, which was verified by an in vitro loading model. ScRNA-seq analysis indicated a particularly enriched regulator in PDL-specific fibroblast subtype, Jun Dimerization Protein 2 (JDP2). Overexpression and knockdown of JDP2 extensively regulated the downstream mechanoresponsive ECM genes in human PDL cells. The force loading model demonstrated that JDP2 responded to tension and that knockdown of JDP2 effectively inhibited the mechanical force-induced ECM remodeling.

CONCLUSIONS

Our study constructed the PDL and DP ScRNA-seq atlas to demonstrate PDL and DP fibroblast cellular heterogeneity and identify a PDL-specific mechanoresponsive fibroblast subtype and its underlying mechanism.

Navigating the immunometabolic heterogeneity of B cells in murine hepatocellular carcinoma at single cell resolution

Induction of antitumor immunity is critical for the therapeutic efficacy of hepatocellular carcinoma (HCC) immunotherapy. The cellular metabolic state underpins the effector function of immune cells, yet our understanding of the phenotypic and metabolic heterogeneity of B cells within HCC microenvironment is poorly developed. Herein, we investigated the composition, distribution, phenotype, function and metabolic profiles of B-cell subsets in HCC and adjacent liver tissues from an orthotopic HCC mouse model using single-cell RNA sequencing (scRNA-seq). Our results identified six B-cell clusters, which can be classified into plasma cells and activated and exhausted B cells according to marker expression, functional and temporal distribution. Exhausted B cells exhibited low metabolic activities and impaired effector functions. Activated B and plasma cells showed higher metabolic activity than exhausted B cells, but there were clear differences in their metabolic profiles. In addition, we found that the effector function of exhausted B cells was further diminished in HCC tissues compared with adjacent liver tissues, but their metabolic activity was significantly enhanced. Collectively, we comprehensively characterized the metabolic profile and alterations in B-cell subsets in HCC, which contributes to the understanding of B-cell immunology in HCC and lays the foundation for exploring novel targets in HCC immunotherapy.

High-Resolution Transcriptomic Landscape of the Human Submandibular Gland

Saliva-secreting and transporting cells are part of the complex cellular milieu of the human salivary gland, where they play important roles in normal glandular physiology and diseased states. However, comprehensive molecular characterization, particularly at single-cell resolution, is still incomplete, in part due to difficulty in procuring normal human tissues. Here, we perform an in-depth analysis of male and female adult human submandibular gland (SMG) samples by bulk RNA sequencing (RNA-seq) and examine the molecular underpinnings of the heterogeneous cell populations by single-cell (sc) RNA-seq. Our results from scRNA-seq highlight the remarkable diversity of clusters of epithelial and nonepithelial cells that reside in the SMG that is also faithfully recapitulated by deconvolution of the bulk-RNA data sets. Our analyses reveal complex transcriptomic heterogeneity within both the ductal and acinar subpopulations and identify atypical SMG cell types, such as mucoacinar cells that are unique to humans and ionocytes that have been recently described in the mouse. We use CellChat to explore ligand-receptor interactome predictions that likely mediate crucial cell-cell communications between the various cell clusters. Finally, we apply a trajectory inference method to investigate specific cellular branching points and topology that offers insights into the dynamic and complex differentiation process of the adult SMG. The data sets and the analyses herein comprise an extensive wealth of high-resolution information and a valuable resource for a deeper mechanistic understanding of human SMG biology and pathophysiology.

Somatic mutations alter the differentiation outcomes of iPSC-derived neurons

The use of induced pluripotent stem cells (iPSC) as models for development and human disease has enabled the study of otherwise inaccessible tissues. A remaining challenge in developing reliable models is our limited understanding of the factors driving irregular differentiation of iPSCs, particularly the impact of acquired somatic mutations. We leveraged data from a pooled dopaminergic neuron differentiation experiment of 238 iPSC lines profiled with single-cell RNA and whole-exome sequencing to study how somatic mutations affect differentiation outcomes. We found that deleterious somatic mutations in key developmental genes, notably the BCOR gene, are strongly associated with failure in dopaminergic neuron differentiation and a larger proliferation rate in culture. We further identified broad differences in cell type composition between incorrectly and successfully differentiating lines, as well as significant changes in gene expression contributing to the inhibition of neurogenesis. Our work calls for caution in interpreting differentiation-related phenotypes in disease-modeling experiments.

Organoids from mouse molar and incisor as new tools to study tooth-specific biology and development

Organoid models provide powerful tools to study tissue biology and development in a dish. Presently, organoids have not yet been developed from mouse tooth. Here, we established tooth organoids (TOs) from early-postnatal mouse molar and incisor, which are long-term expandable, express dental epithelium stem cell (DESC) markers, and recapitulate key properties of the dental epithelium in a tooth-type-specific manner. TOs display in vitro differentiation capacity toward ameloblast-resembling cells, even more pronounced in assembloids in which dental mesenchymal (pulp) stem cells are combined with the organoid DESCs. Single-cell transcriptomics supports this developmental potential and reveals co-differentiation into junctional epithelium- and odontoblast-/cementoblast-like cells in the assembloids. Finally, TOs survive and show ameloblast-resembling differentiation also in vivo. The developed organoid models provide new tools to study mouse tooth-type-specific biology and development and gain deeper molecular and functional insights that may eventually help to achieve future human biological tooth repair and replacement.

A Machine Learning Model to Predict Survival and Therapeutic Responses in Multiple Myeloma

Multiple myeloma (MM) is a highly heterogeneous hematologic tumor. Ubiquitin proteasome pathways (UPP) play a vital role in its initiation and development. We used cox regression analysis and least absolute shrinkage and selector operation (LASSO) to select ubiquitin proteasome pathway associated genes (UPPGs) correlated with the overall survival (OS) of MM patients in a Gene Expression Omnibus (GEO) dataset, and we formed this into ubiquitin proteasome pathway risk score (UPPRS). The association between clinical outcomes and responses triggered by proteasome inhibitors (PIs) and UPPRS were evaluated. MMRF CoMMpass was used for validation. We applied machine learning algorithms to MM clinical and UPPRS in the whole cohort to make a prognostic nomogram. Single-cell data and vitro experiments were performed to unravel the mechanism and functions of UPPRS. UPPRS consisting of 9 genes showed a strong ability to predict OS in MM patients. Additionally, UPPRS can be used to sort out the patients who would gain more benefits from PIs. A machine learning model incorporating UPPRS and International Staging System (ISS) improved survival prediction in both datasets compared to the revisions of ISS. At the single-cell level, high-risk UPPRS myeloma cells exhibited increased cell adhesion. Targeted UPPGs effectively inhibited myeloma cells in vitro. The UPP genes risk score is a helpful tool for risk stratification in MM patients, particularly those treated with PIs.

Integrating transcriptomic datasets across neurological disease identifies unique myeloid subpopulations driving disease-specific signatures

Microglia and bone marrow-derived monocytes are key elements of central nervous system (CNS) inflammation, both capable of enhancing and dampening immune-mediated pathology. However, the study-specific focus on individual cell types, disease models or experimental approaches has limited our ability to infer common and disease-specific responses. This meta-analysis integrates bulk and single-cell transcriptomic datasets of microglia and monocytes from disease models of autoimmunity, neurodegeneration, sterile injury, and infection to build a comprehensive resource connecting myeloid responses across CNS disease. We demonstrate that the bulk microglial and monocyte program is highly contingent on the disease environment, challenging the notion of a universal microglial disease signature. Integration of six single-cell RNA-sequencing datasets revealed that these disease-specific signatures are likely driven by differing proportions of unique myeloid subpopulations that were individually expanded in different disease settings. These subsets were functionally-defined as neurodegeneration-associated, inflammatory, interferon-responsive, phagocytic, antigen-presenting, and lipopolysaccharide-responsive cellular states, revealing a core set of myeloid responses at the single-cell level that are conserved across CNS pathology. Showcasing the predictive and practical value of this resource, we performed differential expression analysis on microglia and monocytes across disease and identified Cd81 as a new neuroinflammatory-stable gene that accurately identified microglia and distinguished them from monocyte-derived cells across all experimental models at both the bulk and single-cell level. Together, this resource dissects the influence of disease environment on shared immune response programmes to build a unified perspective of myeloid behavior across CNS pathology.

De novo mutations disturb early brain development more frequently than common variants in schizophrenia

Investigating functional, temporal, and cell-type expression features of mutations is important for understanding a complex disease. Here, we collected and analyzed common variants and de novo mutations (DNMs) in schizophrenia (SCZ). We collected 2,636 missense and loss-of-function (LoF) DNMs in 2,263 genes across 3,477 SCZ patients (SCZ-DNMs). We curated three gene lists: (a) SCZ-neuroGenes (159 genes), which are intolerant to LoF and missense DNMs and are neurologically important, (b) SCZ-moduleGenes (52 genes), which were derived from network analyses of SCZ-DNMs, and (c) SCZ-commonGenes (120 genes) from a recent GWAS as reference. To compare temporal gene expression, we used the BrainSpan dataset. We defined a fetal effect score (FES) to quantify the involvement of each gene in prenatal brain development. We further employed the specificity indexes (SIs) to evaluate cell-type expression specificity from single-cell expression data in cerebral cortices of humans and mice. Compared with SCZ-commonGenes, SCZ-neuroGenes and SCZ-moduleGenes were highly expressed in the prenatal stage, had higher FESs, and had higher SIs in fetal replicating cells and undifferentiated cell types. Our results suggested that gene expression patterns in specific cell types in early fetal stages might have impacts on the risk of SCZ during adulthood.

Involvement of Epithelial-Mesenchymal Transition Genes in Small Cell Lung Cancer Phenotypic Plasticity

Small cell lung cancer (SCLC) is an aggressive cancer recalcitrant to treatment, arising predominantly from epithelial pulmonary neuroendocrine (NE) cells. Intratumor heterogeneity plays critical roles in SCLC disease progression, metastasis, and treatment resistance. At least five transcriptional SCLC NE and non-NE cell subtypes were recently defined by gene expression signatures. Transition from NE to non-NE cell states and cooperation between subtypes within a tumor likely contribute to SCLC progression by mechanisms of adaptation to perturbations. Therefore, gene regulatory programs distinguishing SCLC subtypes or promoting transitions are of great interest. Here, we systematically analyze the relationship between SCLC NE/non-NE transition and epithelial to mesenchymal transition (EMT)-a well-studied cellular process contributing to cancer invasiveness and resistance-using multiple transcriptome datasets from SCLC mouse tumor models, human cancer cell lines, and tumor samples. The NE SCLC-A2 subtype maps to the epithelial state. In contrast, SCLC-A and SCLC-N (NE) map to a partial mesenchymal state (M1) that is distinct from the non-NE, partial mesenchymal state (M2). The correspondence between SCLC subtypes and the EMT program paves the way for further work to understand gene regulatory mechanisms of SCLC tumor plasticity with applicability to other cancer types.

scShapes: a statistical framework for identifying distribution shapes in single-cell RNA-sequencing data

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) methods have been advantageous for quantifying cell-to-cell variation by profiling the transcriptomes of individual cells. For scRNA-seq data, variability in gene expression reflects the degree of variation in gene expression from one cell to another. Analyses that focus on cell-cell variability therefore are useful for going beyond changes based on average expression and, instead, identifying genes with homogeneous expression versus those that vary widely from cell to cell.

RESULTS

We present a novel statistical framework, scShapes, for identifying differential distributions in single-cell RNA-sequencing data using generalized linear models. Most approaches for differential gene expression detect shifts in the mean value. However, as single-cell data are driven by overdispersion and dropouts, moving beyond means and using distributions that can handle excess zeros is critical. scShapes quantifies gene-specific cell-to-cell variability by testing for differences in the expression distribution while flexibly adjusting for covariates if required. We demonstrate that scShapes identifies subtle variations that are independent of altered mean expression and detects biologically relevant genes that were not discovered through standard approaches.

CONCLUSIONS

This analysis also draws attention to genes that switch distribution shapes from a unimodal distribution to a zero-inflated distribution and raises open questions about the plausible biological mechanisms that may give rise to this, such as transcriptional bursting. Overall, the results from scShapes help to expand our understanding of the role that gene expression plays in the transcriptional regulation of a specific perturbation or cellular phenotype. Our framework scShapes is incorporated into a Bioconductor R package (https://www.bioconductor.org/packages/release/bioc/html/scShapes.html).

Multi-Objective Genetic Algorithm for Cluster Analysis of Single-Cell Transcriptomes

Cells are the basic building blocks of human organisms, and the identification of their types and states in transcriptomic data is an important and challenging task. Many of the existing approaches to cell-type prediction are based on clustering methods that optimize only one criterion. In this paper, a multi-objective Genetic Algorithm for cluster analysis is proposed, implemented, and systematically validated on 48 experimental and 60 synthetic datasets. The results demonstrate that the performance and the accuracy of the proposed algorithm are reproducible, stable, and better than those of single-objective clustering methods. Computational run times of multi-objective clustering of large datasets were studied and used in supervised machine learning to accurately predict the execution times of clustering of new single-cell transcriptomes.

Single-cell analysis of myeloid cells in HPV+ tonsillar cancer

The incidence of human papillomavirus-positive (HPV⁺) tonsillar cancer has been sharply rising during the last decades. Myeloid cells represent an appropriate therapeutic target due to their proximity to virus-infected tumor cells, and their ability to orchestrate antigen-specific immunity, within the tonsil. However, the interrelationship of steady-state and inflammatory myeloid cell subsets, and their impact on patient survival remains unexplored. Here, we used single-cell RNA-sequencing to map the myeloid compartment in HPV⁺ tonsillar cancer. We observed an expansion of the myeloid compartment in HPV⁺ tonsillar cancer, accompanied by interferon-induced cellular responses both in dendritic cells (DCs) and monocyte-macrophages. Our analysis unveiled the existence of four DC lineages, two macrophage polarization processes, and their sequential maturation profiles. Within the DC lineages, we described a balance shift in the frequency of progenitor and mature cDC favoring the cDC1 lineage in detriment of cDC2s. Furthermore, we observed that all DC lineages apart from DC5s matured into a common activated DC transcriptional program involving upregulation of interferon-inducible genes. In turn, the monocyte-macrophage lineage was subjected to early monocyte polarization events, which give rise to either interferon-activated or CXCL-producing macrophages, the latter enriched in advanced tumor stages. We validated the existence of most of the single-cell RNA-seq clusters using 26-plex flow cytometry, and described a positive impact of cDC1 and interferon-activated DCs and macrophages on patient survival using gene signature scoring. The current study contributes to the understanding of myeloid ontogeny and dynamics in HPV-driven tonsillar cancer, and highlights myeloid biomarkers that can be used to assess patient prognosis.

Mapping the tumor microenvironment in bladder cancer and exploring the prognostic genes by single-cell RNA sequencing

Despite substantial advances in the treatment using immune checkpoint inhibitors (ICIs), the clinical expected therapeutic effect on bladder cancer has not been achieved, in which the tumor microenvironment (TME) occupies a notable position. In this research, 10X single-cell RNA-sequencing technology was conducted to analyze seven primary bladder tumor tissues (three non-muscle-invasive bladder cancer (NMIBC) and four muscle-invasive bladder cancer (MIBC)) and seven corresponding normal tissues adjacent to cancer; eight various cell types were identified in the bladder cancer (BC) TME, and a complete TME atlas in bladder cancer was made. Moreover, bladder cancer epithelial cells were further subdivided into 14 subgroups, indicating a high intra-tumoral heterogeneity. Additionally, the differences between NMIBC and MIBC were compared based on differential gene expression heatmap, copy number variation (CNV) distribution heatmap, Gene Ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) network mutual analysis, and the Kaplan-Meier survival prognosis analysis were used to identify six key genes associated with the prognosis of bladder cancer: VEGFA, ANXA1, HSP90B1, PSMA7, PRDX6, and PPP1CB. The dynamic change of the expression distribution of six genes on the pseudo-time axis was further verified by cell pseudo-time analysis.

Small intestine and colon tissue-resident memory CD8+ T cells exhibit molecular heterogeneity and differential dependence on Eomes

Tissue-resident memory CD8+ T (TRM) cells are a subset of memory T cells that play a critical role in limiting early pathogen spread and controlling infection. TRM cells exhibit differences across tissues, but their potential heterogeneity among distinct anatomic compartments within the small intestine and colon has not been well recognized. Here, by analyzing TRM cells from the lamina propria and epithelial compartments of the small intestine and colon, we showed that intestinal TRM cells exhibited distinctive patterns of cytokine and granzyme expression along with substantial transcriptional, epigenetic, and functional heterogeneity. The T-box transcription factor Eomes, which represses TRM cell formation in some tissues, exhibited unexpected context-specific regulatory roles in supporting the maintenance of established TRM cells in the small intestine, but not in the colon. Taken together, these data provide previously unappreciated insights into the heterogeneity and differential requirements for the formation vs. maintenance of intestinal TRM cells.

Single-cell transcriptomes and T cell receptors of vaccine-expanded apolipoprotein B-specific T cells

Atherosclerotic cardiovascular diseases are the major cause of death worldwide. CD4 T cells responding to Apolipoprotein B (ApoB), the core protein of most lipoproteins, have been identified as critical disease modulators. In healthy individuals, ApoB-reactive (ApoB+) CD4 T cells are mostly regulatory T cells (Tregs), which exert anti-inflammatory effects. Yet, they may obtain pro-inflammatory features and thus become proatherogenic. Evidence from animal studies suggests that vaccination against certain major histocompatibility complex (MHC) II-binding ApoB peptides induces an expansion of ApoB+ Tregs and thus confers atheroprotection. To date, in-depth phenotyping of vaccine-expanded ApoB+ T cells has not yet been performed. To this end, we vaccinated C57BL/6J mice with the ApoB-peptide P6 (ApoB978-993 TGAYSNASSTESASY) and performed single-cell RNA sequencing of tetramer-sorted P6+ T cells. P6+ cells were clonally expanded (one major, two minor clones) and formed a transcriptional cluster distinct from clusters mainly containing non-expanded P6+ and P6- cells. Transcriptomic profiling revealed that most expanded P6+ cells had a strong Treg signature and highly expressed genes mediating suppressive functions. Yet, some expanded P6+ cells only had a residual Treg signature and expressed genes related to T helper 1 (TH1) cells, which are proatherogenic. Modeling the T cell receptor (TCR) and P6:MHC-II interaction showed that only three amino acid residues in the α and β chain contact the P6 peptide in the MHC-II groove and thus determine the specificity of this TCR to P6. Our data begin to reveal the vaccination-induced response to an ApoB epitope.

Using Single-Cell RNA Sequencing and MicroRNA Targeting Data to Improve Colorectal Cancer Survival Prediction

Colorectal cancer has proven to be difficult to treat as it is the second leading cause of cancer death for both men and women worldwide. Recent work has shown the importance of microRNA (miRNA) in the progression and metastasis of colorectal cancer. Here, we develop a metric based on miRNA-gene target interactions, previously validated to be associated with colorectal cancer. We use this metric with a regularized Cox model to produce a small set of top-performing genes related to colon cancer. We show that using the miRNA metric and a Cox model led to a meaningful improvement in colon cancer survival prediction and correct patient risk stratification. We show that our approach outperforms existing methods and that the top genes identified by our process are implicated in NOTCH3 signaling and general metabolism pathways, which are essential to colon cancer progression.