Single-cell RNA-sequencing analyses identify heterogeneity of CD8+ T cell subpopulations and novel therapy targets in melanoma

Urine scRNAseq reveals new insights into the bladder tumor immune microenvironment

Due to bladder tumors' contact with urine, urine-derived cells (UDCs) may serve as a surrogate for monitoring the tumor microenvironment (TME) in bladder cancer (BC). However, the composition of UDCs and the extent to which they mirror the tumor remain poorly characterized. We generated the first single-cell RNA-sequencing of BC patient UDCs with matched tumor and peripheral blood mononuclear cells (PBMC). BC urine was more cellular than healthy donor (HD) urine, containing multiple immune populations including myeloid cells, CD4+ and CD8+ T cells, natural killer (NK) cells, B cells, and dendritic cells (DCs) in addition to tumor and stromal cells. Immune UDCs were transcriptionally more similar to tumor than blood. UDCs encompassed cytotoxic and activated CD4+ T cells, exhausted and tissue-resident memory CD8+ T cells, macrophages, germinal-center-like B cells, tissue-resident and adaptive NK cells, and regulatory DCs found in tumor but lacking or absent in blood. Our findings suggest BC UDCs may be surrogates for the TME and serve as therapeutic biomarkers.

Identification of a metabolism-linked genomic signature for prognosis and immunotherapeutic efficiency in metastatic skin cutaneous melanoma

Metastatic skin cutaneous melanoma (MSCM) is the most rapidly progressing/invasive skin-based malignancy, with median survival rates of about 12 months. It appears that metabolic disorders accelerate disease progression. However, correlations between metabolism-linked genes (MRGs) and prognosis in MSCM are unclear, and potential mechanisms explaining the correlation are unknown. The Cancer Genome Atlas (TCGA) was utilized as a training set to develop a genomic signature based on the differentially expressed MRGs (DE-MRGs) between primary skin cutaneous melanoma (PSCM) and MSCM. The Gene Expression Omnibus (GEO) was utilized as a validation set to verify the effectiveness of genomic signature. In addition, a nomogram was established to predict overall survival based on genomic signature and other clinic-based characteristics. Moreover, this study investigated the correlations between genomic signature and tumor micro-environment (TME). This study established a genomic signature consisting of 3 genes (CD38, DHRS3, and TYRP1) and classified MSCM patients into low and high-risk cohorts based on the median risk scores of MSCM cases. It was discovered that cases in the high-risk cohort had significantly lower survival than cases in the low-risk cohort across all sets. Furthermore, a nomogram containing this genomic signature and clinic-based parameters was developed and demonstrated high efficiency in predicting MSCM case survival times. Interestingly, Gene Set Variation Analysis results indicated that the genomic signature was involved in immune-related physiological processes. In addition, this study discovered that risk scoring was negatively correlated with immune-based cellular infiltrations in the TME and critical immune-based checkpoint expression profiles, indicating that favorable prognosis may be influenced in part by immunologically protective micro-environments. A novel 3-genomic signature was found to be reliable for predicting MSCM outcomes and may facilitate personalized immunotherapy.

Tumor-infiltrating CD8+ sub-populations in primary and recurrent glioblastoma: An in-silico study

Background

Glioblastoma multiforme (GBM) remains an incurable primary brain tumor. CD8+ tumor-infiltrating lymphocytes (TILs) can target malignant cells; however, their anti-tumoral immune responses mostly do not lead to GBM rejection in GBM patients. We profiled the sub-populations of tumor-infiltrating CD8+ T-cells, i.e., naïve, cytotoxic, and exhausted cells, in primary and recurrent GBM tissues and provided a blueprint for future precision-based GBM immunotherapy.

Method

We re-analyzed the raw data of single-cell RNA sequencing on the cells residing in the GBM microenvironment and leveraged tumor bulk RNA analyses to study the significance of CD8+ TILs sub-populations in primary and recurrent GBM. We investigated cell-cell interaction between exhausted CD8+ TILs and other immune cells residing in the primary and recurrent GBM microenvironments and profiled the expression changes following CD8+ TILs' transition from primary GBM to recurrent GBM.

Results

Exhausted CD8+ TILs are the majority of CD8+ TILs sub-populations in primary and recurrent GBM, and cytotoxic CD8+ TILs display decreased expression of inhibitory immune checkpoint (IC) molecules in the primary and recurrent GBM. In the primary and recurrent GBM microenvironment, exhausted CD8+ TILs interact most with tumor-infiltrating dendritic cells.

Conclusion

This study demonstrates the profiles of CD8+ TILs sub-populations in primary and recurrent GBM and provides a proof-of-concept for future precision-based GBM immunotherapy.

Unlocking the potential of T-cell metabolism reprogramming: Advancing single-cell approaches for precision immunotherapy in tumour immunity

As single-cell RNA sequencing enables the detailed clustering of T-cell subpopulations and facilitates the analysis of T-cell metabolic states and metabolite dynamics, it has gained prominence as the preferred tool for understanding heterogeneous cellular metabolism. Furthermore, the synergistic or inhibitory effects of various metabolic pathways within T cells in the tumour microenvironment are coordinated, and increased activity of specific metabolic pathways generally corresponds to increased functional activity, leading to diverse T-cell behaviours related to the effects of tumour immune cells, which shows the potential of tumour-specific T cells to induce persistent immune responses. A holistic understanding of how metabolic heterogeneity governs the immune function of specific T-cell subsets is key to obtaining field-level insights into immunometabolism. Therefore, exploring the mechanisms underlying the interplay between T-cell metabolism and immune functions will pave the way for precise immunotherapy approaches in the future, which will empower us to explore new methods for combating tumours with enhanced efficacy.

Immune cell identifier and classifier (ImmunIC) for single cell transcriptomic readouts

Single cell RNA sequencing has a central role in immune profiling, identifying specific immune cells as disease markers and suggesting therapeutic target genes of immune cells. Immune cell-type annotation from single cell transcriptomics is in high demand for dissecting complex immune signatures from multicellular blood and organ samples. However, accurate cell type assignment from single-cell RNA sequencing data alone is complicated by a high level of gene expression heterogeneity. Many computational methods have been developed to respond to this challenge, but immune cell annotation accuracy is not highly desirable. We present ImmunIC, a simple and robust tool for immune cell identification and classification by combining marker genes with a machine learning method. With over two million immune cells and half-million non-immune cells from 66 single cell RNA sequencing studies, ImmunIC shows 98% accuracy in the identification of immune cells. ImmunIC outperforms existing immune cell classifiers, categorizing into ten immune cell types with 92% accuracy. We determine peripheral blood mononuclear cell compositions of severe COVID-19 cases and healthy controls using previously published single cell transcriptomic data, permitting the identification of immune cell-type specific differential pathways. Our publicly available tool can maximize the utility of single cell RNA profiling by functioning as a stand-alone bioinformatic cell sorter, advancing cell-type specific immune profiling for the discovery of disease-specific immune signatures and therapeutic targets.

Robust joint clustering of multi-omics single-cell data via multi-modal high-order neighborhood Laplacian matrix optimization

MOTIVATION

Simultaneous profiling of multi-omics single-cell data represents exciting technological advancements for understanding cellular states and heterogeneity. Cellular indexing of transcriptomes and epitopes by sequencing allowed for parallel quantification of cell-surface protein expression and transcriptome profiling in the same cells; methylome and transcriptome sequencing from single cells allows for analysis of transcriptomic and epigenomic profiling in the same individual cells. However, effective integration method for mining the heterogeneity of cells over the noisy, sparse, and complex multi-modal data is in growing need.

RESULTS

In this article, we propose a multi-modal high-order neighborhood Laplacian matrix optimization framework for integrating the multi-omics single-cell data: scHoML. Hierarchical clustering method was presented for analyzing the optimal embedding representation and identifying cell clusters in a robust manner. This novel method by integrating high-order and multi-modal Laplacian matrices would robustly represent the complex data structures and allow for systematic analysis at the multi-omics single-cell level, thus promoting further biological discoveries.

AVAILABILITY AND IMPLEMENTATION

Matlab code is available at https://github.com/jianghruc/scHoML.

A comprehensive assessment of cell type-specific differential expression methods in bulk data

Accounting for cell type compositions has been very successful at analyzing high-throughput data from heterogeneous tissues. Differential gene expression analysis at cell type level is becoming increasingly popular, yielding biomarker discovery in a finer granularity within a particular cell type. Although several computational methods have been developed to identify cell type-specific differentially expressed genes (csDEG) from RNA-seq data, a systematic evaluation is yet to be performed. Here, we thoroughly benchmark six recently published methods: CellDMC, CARseq, TOAST, LRCDE, CeDAR and TCA, together with two classical methods, csSAM and DESeq2, for a comprehensive comparison. We aim to systematically evaluate the performance of popular csDEG detection methods and provide guidance to researchers. In simulation studies, we benchmark available methods under various scenarios of baseline expression levels, sample sizes, cell type compositions, expression level alterations, technical noises and biological dispersions. Real data analyses of three large datasets on inflammatory bowel disease, lung cancer and autism provide evaluation in both the gene level and the pathway level. We find that csDEG calling is strongly affected by effect size, baseline expression level and cell type compositions. Results imply that csDEG discovery is a challenging task itself, with room to improvements on handling low signal-to-noise ratio and low expression genes.

The role of cuproptosis-related gene in the classification and prognosis of melanoma

Background

Melanoma, as one of the most aggressive and malignant cancers, ranks first in the lethality rate of skin cancers. Cuproptosis has been shown to paly a role in tumorigenesis, However, the role of cuproptosis in melanoma metastasis are not clear. Studying the correlation beteen the molecular subtypes of cuproptosis-related genes (CRGs) and metastasis of melanoma may provide some guidance for the prognosis of melanoma.

Methods

We collected 1085 melanoma samples in The Cancer Genome Atlas(TCGA) and Gene Expression Omnibus(GEO) databases, constructed CRGs molecular subtypes and gene subtypes according to clinical characteristics, and investigated the role of CRGs in melanoma metastasis. We randomly divide the samples into train set and validation set according to the ratio of 1:1. A prognostic model was constructed using data from the train set and then validated on the validation set. We performed tumor microenvironment analysis and drug sensitivity analyses for high and low risk groups based on the outcome of the prognostic model risk score. Finally, we established a metastatic model of melanoma.

Results

According to the expression levels of 12 cuproptosis-related genes, we obtained three subtypes of A₁, B₁, and C₁. Among them, C₁ subtype had the best survival outcome. Based on the differentially expressed genes shared by A₁, B₁, and C₁ genotypes, we obtained the results of three gene subtypes of A₂, B₂, and C₂. Among them, the B₂ group had the best survival outcome. Then, we constructed a prognostic model consisting of 6 key variable genes, which could more accurately predict the 1-, 3-, and 5-year overall survival rates of melanoma patients. Besides, 98 drugs were screened out. Finally, we explored the role of cuproptosis-related genes in melanoma metastasis and established a metastasis model using seven key genes.

Conclusions

In conclusion, CRGs play a role in the metastasis and prognosis of melanoma, and also provide new insights into the underlying pathogenesis of melanoma.

Unravelling the landscape of skin cancer through single-cell transcriptomics

The human skin is a complex organ that forms the first line of defense against pathogens and external injury. It is composed of a wide variety of cells that work together to maintain homeostasis and prevent disease, such as skin cancer. The exponentially rising incidence of skin malignancies poses a growing public health challenge, particularly when the disease course is complicated by metastasis and therapeutic resistance. Recent advances in single-cell transcriptomics have provided a high-resolution view of gene expression heterogeneity that can be applied to skin cancers to define cell types and states, understand disease evolution, and develop new therapeutic concepts. This approach has been particularly valuable in characterizing the contribution of immune cells in skin cancer, an area of great clinical importance given the increasing use of immunotherapy in this setting. In this review, we highlight recent skin cancer studies utilizing bulk RNA sequencing, introduce various single-cell transcriptomics approaches, and summarize key findings obtained by applying single-cell transcriptomics to skin cancer.

Recent developments in application of single-cell RNA sequencing in the tumour immune microenvironment and cancer therapy

The advent of single-cell RNA sequencing (scRNA-seq) has provided insight into the tumour immune microenvironment (TIME). This review focuses on the application of scRNA-seq in investigation of the TIME. Over time, scRNA-seq methods have evolved, and components of the TIME have been deciphered with high resolution. In this review, we first introduced the principle of scRNA-seq and compared different sequencing approaches. Novel cell types in the TIME, a continuous transitional state, and mutual intercommunication among TIME components present potential targets for prognosis prediction and treatment in cancer. Thus, we concluded novel cell clusters of cancer-associated fibroblasts (CAFs), T cells, tumour-associated macrophages (TAMs) and dendritic cells (DCs) discovered after the application of scRNA-seq in TIME. We also proposed the development of TAMs and exhausted T cells, as well as the possible targets to interrupt the process. In addition, the therapeutic interventions based on cellular interactions in TIME were also summarized. For decades, quantification of the TIME components has been adopted in clinical practice to predict patient survival and response to therapy and is expected to play an important role in the precise treatment of cancer. Summarizing the current findings, we believe that advances in technology and wide application of single-cell analysis can lead to the discovery of novel perspectives on cancer therapy, which can subsequently be implemented in the clinic. Finally, we propose some future directions in the field of TIME studies that can be aided by scRNA-seq technology.

Deciphering cell-cell interactions and communication in the tumor microenvironment and unraveling intratumoral genetic heterogeneity via single-cell genomic sequencing

A tumor's heterogeneity has important implications in terms of its clonal origin, progression, stemness, and drug resistance. Therefore, because of its significance in treatment, it is important to understand the gene expression pattern of a single cell, track gene expression or mutation in heterogeneous cells, evaluate the clonal origin of cancer cells, and determine the selective evolution of different subpopulations of cancer cells. Researchers are able to trace a cell's mutation and identify different types of tumor cells by measuring the whole transcriptome with single-cell sequencing (scRNA-seq). This technology provides a better understanding of the molecular mechanisms driving tumor growth than that offered by traditional RNA sequencing methods. In addition, it has revealed changes in the mutations and functions of somatic cells as a tumor evolves; it has also clarified immune cell infiltration and activation. Research on scRNA-seq technology has recently advanced significantly, suggesting new strategies for the treatment of cancer. In short, cancer researchers have become increasingly dependent on scRNA-seq. This paper reviews the development, detection principles, and processes of scRNA-seq technology and their application in tumor research. It also considers potential clinical applications.

A deep matrix factorization based approach for single-cell RNA-seq data clustering

The rapid development of single-cell sequencing technologies makes it possible to analyze cellular heterogeneity at the single-cell level. Cell clustering is one of the most fundamental and common steps in the heterogeneity analysis. However, due to the high noise level, high dimensionality and high sparsity, accurate cell clustering is still challengeable. Here, we present DeepCI, a new clustering approach for scRNA-seq data. Using two autoencoders to obtain cell embedding and gene embedding, DeepCI can simultaneously learn cell low-dimensional representation and clustering. In addition, the recovered gene expression matrix can be obtained by the matrix multiplication of cell and gene embedding. To evaluate the performance of DeepCI, we performed it on several real scRNA-seq datasets for clustering and visualization analysis. The experimental results show that DeepCI obtains the overall better performance than several popular single cell analysis methods. We also evaluated the imputation performance of DeepCI by a dedicated experiment. The corresponding results show that the imputed gene expression of known specific marker gene can greatly improve the accuracy of cell type classification.

Single-Cell Sequencing Identifies the Heterogeneity of CD8+ T Cells and Novel Biomarker Genes in Hepatocellular Carcinoma

CD8+ T cells are required for the establishment of antitumor immunity, and their substantial infiltration is associated with a good prognosis. However, CD8+ T cell subsets in the tumor microenvironment may play distinct roles in tumor progression, prognosis, and immunotherapy. In this study, we used the scRNA-seq data of hepatocellular carcinoma (HCC) to reveal the heterogeneity of different CD8+ T cell subsets. The scRNA-seq data set GSE149614 was obtained from the GEO database, and the transcriptome and sample phenotypic data of TCGA-LIHC were obtained from the TCGA database. CD8+ T cell subtypes and metabolic gene sets were obtained from published reports. The data processing and analysis of CD8+ T cell groups was performed by R language. The PPI network was constructed to obtain the hub genes, and the KM survival curve of the hub genes was further plotted to determine the hub genes with differences in survival. CD8+ T cells in HCC were divided into 7 subsets, and the cytotoxic CD8 T cells 4 subset showed considerable differences between the TP53-mutant and nonmutant groups, as well as between different degrees of cirrhosis, HCC grades, stages, ages, and body weights. Cytotoxic CD8 T cells 4 differential genes were analyzed by TCGA-LIHC data and single-cell sequencing data set. 10 hub genes were found: FGA, ApoA1, ApoH, AHSG, FGB, HP, TTR, TF, HPX, and APOC3. Different subsets of CD8+ T cells were found to contribute to heterogeneous prognosis and pathway activity in HCC. Alterations in the cytotoxic and immune checkpoint gene expression during CD8+ T cell differentiation were also identified. We found that cytotoxic CD8 T cells 4 is closely associated with survival and prognosis of HCC and identified four differential genes that can be used as biological markers for survival, prognosis, and clinically relevant characteristics of HCC. Results of this study could help finding targets for immunotherapy of HCC and aid in the accelerated development of immunotherapy for HCC.

Single-cell transcriptomic landscape identifies the expansion of peripheral blood monocytes as an indicator of HIV-1-TB co-infection

Certain circulating cell subsets are involved in immune dysregulation in human immunodeficiency virus type 1 (HIV-1) and tuberculosis (TB) co-infection; however, the characteristics and role of these subclusters are unknown. Peripheral blood mononuclear cells (PBMCs) of patients with HIV-1 infection alone (HIV-pre) and those with HIV-1-TB co-infection without anti-TB treatment (HIV-pre & TB-pre) and with anti-TB treatment for 2 weeks (HIV-pre & TB-pos) were subjected to single-cell RNA sequencing (scRNA-seq) to characterize the transcriptome of different immune cell subclusters. We obtained > 60,000 cells and identified 32 cell subclusters based on gene expression. The proportion of immune-cell subclusters was altered in HIV-1-TB co-infected individuals compared with that in HIV-pre-group, indicating immune dysregulation corresponding to different disease states. The proportion of an inflammatory CD14+CD16+ monocyte subset was higher in the HIV-pre & TB-pre group than in the HIV-pre group; this was validated in an additional cohort (n = 80) via a blood cell differential test, which also demonstrated a good discriminative performance (area under the curve, 0.8046). These findings depicted the atlas of immune PBMC subclusters in HIV-1-TB co-infection and demonstrate that monocyte subsets in peripheral blood might serve as a discriminating biomarker for diagnosis of HIV-1-TB co-infection.

A Systematic Review on PD-1 Blockade and PD-1 Gene-Editing of CAR-T Cells for Glioma Therapy: From Deciphering to Personalized Medicine

Background

Programmed cell death protein 1 (PD-1) can attenuate chimeric antigen receptor-T (CAR-T) cell-mediated anti-tumoral immune responses. In this regard, co-administration of anti-PD-1 with CAR-T cells and PD-1 gene-editing of CAR-T cells have been suggested to disrupt this inhibitory axis. Herein, we aim to investigate the advantages and disadvantages of these two approaches and propose a novel strategy to ameliorate the prognosis of glioma patients.

Methods

Scopus, Embase, and Web of Science were systematically searched to obtain relevant peer-reviewed studies published before March 7, 2021. Then, the current study was conducted based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statements. The random-effect model was applied to evaluate the effect size of administrated agents on the survival of animal models bearing gliomas using RevMan version 5.4. The Cochran Q test and I2 were performed to assess the possible between-study heterogeneity. Egger's and Begg and Mazumdar's tests were performed to objectively assess potential asymmetry and publication bias using CMA version 2.

Results

Anti-PD-1 can substantially increase the survival of animal models on second-generation CAR-T cells. Also, PD-1 knockdown can remarkably prolong the survival of animal models on third-generation CAR-T cells. Regardless of the CAR-T generations, PD-1 gene-edited CAR-T cells can considerably enhance the survival of animal-bearing gliomas compared to the conventional CAR-T cells.

Conclusions

The single-cell sequencing of tumoral cells and cells residing in the tumor microenvironment can provide valuable insights into the patient-derived neoantigens and the expression profile of inhibitory immune checkpoint molecules in tumor bulk. Thus, single-cell sequencing-guided fourth-generation CAR-T cells can cover patient-derived neoantigens expressed in various subpopulations of tumoral cells and inhibit related inhibitory immune checkpoint molecules. The proposed approach can improve anti-tumoral immune responses, decrease the risk of immune-related adverse events, reduce the risk of glioma relapse, and address the vast inter-and intra-heterogeneity of gliomas.

Immunotherapy of cancer in single-cell RNA sequencing era: A precision medicine perspective

Immunotherapy has revolutionized cancer treatment and brought new aspects into tumor immunology. Effective immunotherapy will require using the suitable target antigens, optimizing the interaction between the antigenic peptide, the APC, and the T cell, and the simultaneous inhibitor of the negative regulatory process that inhibits immunotherapeutic effects and develop resistance. Tumor heterogeneity and its microenvironment is the leading cause of resistance in patients. Recently by emerging the single-cell RNA sequencing technology and its combination with immunotherapy, now we can specifically evaluate the mechanism of tumors in the face of immunotherapy agents at the single-cell resolution by detecting the transcriptional activity of immune checkpoints, screening neoantigens with high transcription levels, identifying rare cells, and other important processes. This review focuses on scRNA-seq, particularly on its application in cancer immunotherapy.

High-Dimensional Single-Cell Transcriptomics in Melanoma and Cancer Immunotherapy

Recent advances in single-cell transcriptomics have greatly improved knowledge of complex transcriptional programs, rapidly expanding our knowledge of cellular phenotypes and functions within the tumour microenvironment and immune system. Several new single-cell technologies have been developed over recent years that have enabled expanded understanding of the mechanistic cells and biological pathways targeted by immunotherapies such as immune checkpoint inhibitors, which are now routinely used in patient management with high-risk early-stage or advanced melanoma. These technologies have method-specific strengths, weaknesses and capabilities which need to be considered when utilising them to answer translational research questions. Here, we provide guidance for the implementation of single-cell transcriptomic analysis platforms by reviewing the currently available experimental and analysis workflows. We then highlight the use of these technologies to dissect the tumour microenvironment in the context of cancer patients treated with immunotherapy. The strategic use of single-cell analytics in clinical settings are discussed and potential future opportunities are explored with a focus on their use to rationalise the design of novel immunotherapeutic drug therapies that will ultimately lead to improved cancer patient outcomes.

A Systematic Review and Meta-Analysis on the Significance of TIGIT in Solid Cancers: Dual TIGIT/PD-1 Blockade to Overcome Immune-Resistance in Solid Cancers

Preclinical studies have indicated that T-cell immunoglobulin and ITIM domain (TIGIT) can substantially attenuate anti-tumoral immune responses. Although multiple clinical studies have evaluated the significance of TIGIT in patients with solid cancers, their results remain inconclusive. Thus, we conducted the current systematic review and meta-analysis based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) to determine its significance in patients with solid cancers. We systematically searched the Web of Science, Embase, PubMed, and Scopus databases to obtain peer-reviewed studies published before September 20, 2020. Our results have shown that increased TIGIT expression has been significantly associated with inferior overall survival (OS) (HR = 1.42, 95% CI: 1.11–1.82, and p-value = 0.01). Besides, the level of tumor-infiltrating TIGIT⁺CD8⁺ T-cells have been remarkably associated inferior OS and relapse-free survival (RFS) of affected patients (HR = 2.17, 95% CI: 1.43–3.29, and p-value < 0.001, and HR = 1.89, 95% CI: 1.36–2.63, and p-value < 0.001, respectively). Also, there is a strong positive association between TIGIT expression with programmed cell death-1 (PD-1) expression in these patients (OR = 1.71, 95% CI: 1.10–2.68, and p-value = 0.02). In summary, increased TIGIT expression and increased infiltration of TIGIT⁺CD8⁺ T-cells can substantially worsen the prognosis of patients with solid cancers. Besides, concerning the observed strong association between TIGIT and PD-1, ongoing clinical trials, and promising preclinical results, PD-1/TIGIT dual blockade can potentially help overcome the immune-resistance state seen following monotherapy with a single immune checkpoint inhibitor in patients with solid cancers.

Dissecting the single-cell transcriptome network underlying esophagus non-malignant tissues and esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is among the most prevalent causes of cancer-related death in adults. Tumor microenvironment (TME) has been associated with therapeutic failure and lethal outcomes for patients. However, published reports on the heterogeneity and TME in ESCC are scanty.

METHODS

Five tumor samples and five corresponding non-malignant samples were subjected to scRNA-seq analysis. Bulk RNA sequencing data were retrieved in publicly available databases.

FINDINGS

From the scRNA-seq data, a total of 128,688 cells were enrolled for subsequent analyses. Gene expression and CNV status exhibited high heterogeneity of tumor cells. We further identified a list of tumor-specific genes and four malignant signatures, which are potential new markers for ESCC. Metabolic analysis revealed that energy supply-related pathways are pivotal in cancer metabolic reprogramming. Moreover, significant differences were found in stromal and immune cells between the esophagus normal and tumor tissues, which promoted carcinogenesis at both cellular and molecular levels in ESCC. Immune checkpoints, regarded as potential targets for immunotherapy in ESCC were significantly highly expressed in ESCC, including LAG3 and HAVCR2. Eventually, we constructed a cell-to-cell communication atlas based on cancer cells and immune cells and performed the flow cytometry, qRT-PCR, immunofluorescence, and immunohistochemistry analyses to validate the results.

INTERPRETATION

This study demonstrates a widespread reprogramming across multiple cellular elements within the TME in ESCC, particularly in transcriptional states, cellular functions, and cell-to-cell interactions. The findings offer an insight into the exploration of TME and heterogeneity in the ESCC and provide new therapeutic targets for its clinical management in the future.

FUNDING

The work was supported by the Shanghai Pujiang Program (2020PJD009) and Research Development Fund of Zhongshan Hospital, Fudan University (2019ZSFZ002 and 2019ZSFZ19).

Single-cell immune checkpoint landscape of PBMCs stimulated with Candida albicans

Immune checkpoints play various important roles in tumour immunity, which usually contribute to T cells’ exhaustion, leading to immunosuppression in the tumour microenvironment. However, the roles of immune checkpoints in infectious diseases, especially fungal infection, remain elusive. Here, we reanalyzed a recent published single-cell RNA-sequencing (scRNA-seq) data of peripheral blood mononuclear cells (PBMCs) stimulated with Candida albicans (C. albicans), to explore the expression patterns of immune checkpoints after C. albicans bloodstream infection. We characterized the heterogeneous pathway activities among different immune cell subpopulations after C. albicans infection. The CTLA-4 pathway was up-regulated in stimulated CD4⁺ and CD8⁺ T cells, while the PD-1 pathway showed high activity in stimulated plasmacytoid dendritic cell (pDC) and monocytes. Importantly, we found that immunosuppressive checkpoints HAVCR2 and LAG3 were only expressed in stimulated NK and CD8⁺ T cells, respectively. Their viabilities were validated by flow cytometry. We also identified three overexpressed genes (ISG20, LY6E, ISG15) across all stimulated cells. Also, two monocyte-specific overexpressed genes (SNX10, IDO1) were screened out in this study. Together, these results supplemented the landscape of immune checkpoints in fungal infection, which may serve as potential therapeutic targets for C. albicans infection. Moreover, the genes with the most relevant for C. albicans infection were identified in this study.

Identification of 15 lncRNAs Signature for Predicting Survival Benefit of Advanced Melanoma Patients Treated with Anti-PD-1 Monotherapy

The blockade of programmed cell death protein 1 (PD-1) as monotherapy has been widely used in melanoma, but to identify melanoma patients with survival benefit from anti-PD-1 monotherapy is still a big challenge. There is an urgent need for prognostic signatures improving the prediction of immunotherapy responses of these patients. We analyzed transcriptomic data of pre-treatment tumor biopsies and clinical profiles in advanced melanoma patients receiving only anti-PD-1 monotherapy (nivolumab or pembrolizumab) from the PRJNA356761 and PRJEB23709 data sets as the training and validation cohort, respectively. Weighted gene co-expression network analysis was used to identify the key module, then least absolute shrinkage and selection operator was conducted to determine prognostic-related long noncoding RNAs (lncRNAs). Subsequently, the differentially expressed genes between different clusters were identified, and their function and pathway annotation were performed. In this investigation, 92 melanoma patients with complete survival information (51 from training cohort and 41 from validation cohort) were included in our analyses. We initiallyidentified the key module (skyblue) by weighted gene co-expression network analysis, and then identified a 15 predictive lncRNAs (AC010904.2, LINC01126, AC012360.1, AC024933.1, AL442128.2, AC022211.4, AC022211.2, AC127496.5, NARF-AS1, AP000919.3, AP005329.2, AC023983.1, AC023983.2, AC139100.1, and AC012615.4) signature in melanoma patients treated with anti-PD-1 monotherapy by least absolute shrinkage and selection operator in the training cohort. These results were then validated in the validation cohort. Finally, enrichment analysis showed that the functions of differentially expressed genes between two consensus clusters were mainly related to the immune process and treatment. In summary, the 15 lncRNAs signature is a novel effective predictor for prognosis in advanced melanoma patients treated with anti-PD-1 monotherapy.