Single-Cell Genomics: A Stepping Stone for Future Immunology Discoveries

The network structural entropy for single-cell RNA sequencing data during skin aging

Aging is a complex and heterogeneous biological process at cellular, tissue, and individual levels. Despite extensive effort in scientific research, a comprehensive understanding of aging mechanisms remains lacking. This study analyzed aging-related gene networks, using single-cell RNA sequencing data from >15 000 cells. We constructed a gene correlation network, integrating gene expressions into the weights of network edges, and ranked gene importance using a random walk model to generate a gene importance matrix. This unsupervised method improved the clustering performance of cell types. To further quantify the complexity of gene networks during aging, we introduced network structural entropy. The findings of our study reveal that the overall network structural entropy increases in the aged cells compared to the young cells. However, network entropy changes varied greatly within different cell subtypes. Specifically, the network structural entropy among various cell types may increase, remain unchanged, or decrease. This wide range of changes may be closely related to their individual functions, highlighting the cellular heterogeneity and potential key network reconfigurations. Analyzing gene network entropy provides insights into the molecular mechanisms behind aging. This study offers new scientific evidence and theoretical support for understanding the changes in cell functions during aging.

Age-associated modulation of TREM1/2- expressing macrophages promotes melanoma progression and metastasis

Aging is a known risk factor for melanoma, yet mechanisms underlying melanoma progression and metastasis in older populations remain largely unexplored. Among the current knowledge gaps is how aging alters phenotypes of cells in the melanoma microenvironment. Here we demonstrate that age enriches the immunosuppressor tumor microenvironment, which is linked to phenotypes associated with melanoma metastasis. Among cellular populations enriched by aging were macrophages with a tolerogenic phenotype expressing TREM2 and dysfunctional CD8-positive cells with an exhausted phenotype, while macrophages with profibrotic phenotype expressing TREM1 were depleted. Notably, TREM1 inhibition decreased melanoma growth in young but not old mice, whereas TREM2 inhibition prevented lung metastasis in aged mice. These data identify novel targets associated with melanoma metastasis and may guide aged-dependent immunotherapies.

Whispering Gallery Mode Micro/Nanolasers for Intracellular Probing at Single Cell Resolution

Intracellular probing at single cell resolution is key to revealing the heterogeneity of cells, learning new cell subtypes and functions, understanding the pathophysiology of disease, and ensuring precise diagnosis and treatment. Despite the best efforts, an enormous challenge remains due to the very small size, extremely low content, and dynamic microenvironment of a single cell. Whispering gallery mode (WGM) micro/nanolasers (active WGM) offer unique advantages of small mode volume, high quality factors, bright and low threshold laser emission, and narrow line width, particularly suitable for integration within a single cell. In this review, we provide a focused overview of WGM micro/nanolasers for intracellular probing. We deliver information on WGM micro/nanolaser concepts, sensing mechanism, and biocompatibility, as well as recent progress in intracellular probing applications mainly covering cellular-level sensing, molecular-level detection, and feasibility for cellular imaging. At the end, challenges and prospects of WGM micro/nanolasers for intracellular applications are discussed.

Immunophenotyping myelodysplastic neoplasms: the role of flow cytometry in the molecular classification era

The unique heterogenous landscape of myelodysplastic syndromes/neoplasms (MDS) has resulted in continuous redefinition of disease sub-entities, in view of the novel translational research data that have clarified several areas of the pathogenesis and the progression of the disease. The new international classifications (WHO 2022, ICC 2022) have incorporated genomic data defining phenotypical alterations, that guide clinical management of specific patient subgroups. On the other hand, for over a decade, multiparameter flow cytometry (MFC) has proven its value as a complementary diagnostic tool for these diseases and although it has never been established as a mandatory test for the baseline evaluation of MDS patients in international guidelines, it is almost universally adopted in everyday clinical practice for the assessment of suspected cytopenias through simplified scoring systems or elaborate analytical strategies for the detection of immunophenotypical dysplastic features in every hematopoietic cell lineage in the bone marrow (BM). In this review, we explore the clinically meaningful interplay of MFC data and genetic profiles of MDS patients, to reveal the currently existing and the potential future role of each methodology for routine clinical practice, and the benefit of the patients. We reviewed the existing knowledge and recent advances in the field and discuss how an integrated approach could lead to patient re-stratification and guide personalized management.

Single-cell transcriptome analysis identifies a novel tumor-associated macrophage subtype predicting better prognosis in pancreatic ductal adenocarcinoma

Background

Characterized by an immune-suppressive tumor microenvironment (TME), pancreatic ductal adenocarcinoma (PDAC) is well-known for its poor prognosis. Tumor associated macrophages (TAMs) play a critical role in PDAC TME. An in-depth understanding of TAMs is helpful to develop new strategies for immunotherapy.

Methods

A large number of single-cell RNA sequencing data and bulk RNA sequencing data of PDAC were collected for systematic bioinformatics analysis. Characterize subtypes of TAMs at single-cell resolution and its effect on prognosis. Differential gene analysis and cell-cell communication were used to describe the effect on prognosis and validated by the TCGA dataset.

Results

We used two prognosis-favorable genes, SLC12A5 and ENPP2, to identify a benign M2-like TAMs (bM2-like TAMs), which shared similarities with C1QC + TAMs, CXCL9+ TAMs and CD169+ TAMs, by analyzing scRNA-seq data and bulk RNA data of PDAC. The bM2-like TAMs were revealed to promote T cell activation and proliferation through ALCAM/CD6 interaction. Meanwhile, the bM2-like TAMs were responsible for stroma modeling by altering αSMA+/αSMA-cell ratio. On the contrast, the rest of the M2-like TAMs were defined as malignant M2-like TAMs (mM2-like TAMs), partly overlapping with SPP1+ TAMs. mM2-like TAMs were revealed to promote tumor progression by secretion of MIF and SPP1.

Conclusion

Our study used two prognosis-favorable genes to divide M2-like TAMs of PDAC into anti-tumor bM2-like TAMs and pro-tumor mM2-like TAMs. The bM2-like TAMs activate T cells through ALCAM/CD6 and generate prognosis-favorable αSMA+ myofibroblasts through secreting TGFβ, which brings insight into heterogeneity of TAMs, prognosis prediction and immunotherapy of PDAC.

Regulator of nonsense transcripts 3B is a prognostic biomarker and associated with immune cell infiltration in lung squamous cell and hepatocellular carcinoma

PURPOSE

The characteristic of RENT3B in cancer remains ambiguous. We aimed to study the relationship between RENT3B and immune infiltration in liver hepatocellular carcinoma (LIHC) and lung squamous cell carcinoma (LUSC).

PATIENTS AND METHODS

We investigated the expression levels of RENT3B using ONCOMINE and TIMER databases, and assessed the interrelationship between RENT3B expression and survival using PrognoScan, GEPIA, and Kaplan-Meier plotter. Additionally, we examined the association between RENT3B and immune cells in the tumor microenvironment (TME), as well as markers of immune cells, using TIMER. Subsequently, we performed prognostic analysis based on the expression level of RENT3B within specific immune cell subgroups. Furthermore, we evaluated the promoter methylation profile of RENT3B between tumor and normal tissues in LIHC and LUSC using the DNMIVD database.

RESULTS

RENT3B exhibited increased levels in both in LIHC and LUSC. High RENT3B expression was associated with unfavorable prognosis in LIHC, whereas it indicated a beneficial prognosis in LUSC. In LIHC, the expression of RENT3B positively correlated with immune infiltration levels of B cells, CD4 + T cells, CD8 + T cells, neutrophils, macrophages, and dendritic cells. However, in LUSC, the expression of RENT3B showed a negative correlation with immune infiltration levels of B cells, CD8 + T cells, neutrophils, macrophages, and dendritic cells. RENT3B exhibited positive correlations with 42 immune markers in LIHC, while it displayed negative associations with 10 immune markers in LUSC. Despite variations in immune cell enrichment and reduction subgroups, high RENT3B expression consistently indicated poor prognosis in LIHC, whereas it remained favorable in LUSC. Additionally, there were no significant differences observed in RENT3B promoter methylation between tumor and normal tissues in both LIHC and LUSC.

CONCLUSION

RENT3B can affect the overall tumor prognosis and is associated with immune infiltration, especially in LIHC and LUSC. Consequently, RENT3B can become a prognostic biomarker for LIHC and LUSC.

Monocyte Single-Cell Multimodal Profiling in Cardiovascular Disease Risk States

BACKGROUND

Monocytes are a critical innate immune system cell type that serves homeostatic and immunoregulatory functions. They have been identified historically by the cell surface expression of CD14 and CD16. However, recent single-cell studies have revealed that they are much more heterogeneous than previously realized.

METHODS

We utilized cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and single-cell RNA sequencing to describe the comprehensive transcriptional and phenotypic landscape of 437 126 monocytes.

RESULTS

This high-dimensional multimodal approach identified vast phenotypic diversity and functionally distinct subsets, including IFN-responsive, MHCIIhi (major histocompatibility complex class II), monocyte-platelet aggregates, as well as nonclassical, and several subpopulations of classical monocytes. Using flow cytometry, we validated the existence of MHCII+CD275+ MHCIIhi, CD42b+ monocyte-platelet aggregates, CD16+CD99- nonclassical monocytes, and CD99+ classical monocytes. Each subpopulation exhibited unique characteristics, developmental trajectories, transcriptional regulation, and tissue distribution. In addition, alterations associated with cardiovascular disease risk factors, including race, smoking, and hyperlipidemia were identified. Moreover, the effect of hyperlipidemia was recapitulated in mouse models of elevated cholesterol.

CONCLUSIONS

This integrative and cross-species comparative analysis provides a new perspective on the comparison of alterations in monocytes in pathological conditions and offers insights into monocyte-driven mechanisms in cardiovascular disease and the potential for monocyte subpopulation targeted therapies.

An overview of multi-omics technologies in rheumatoid arthritis: applications in biomarker and pathway discovery

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease with a complex pathological mechanism involving autoimmune response, local inflammation and bone destruction. Metabolic pathways play an important role in immune-related diseases and their immune responses. The pathogenesis of rheumatoid arthritis may be related to its metabolic dysregulation. Moreover, histological techniques, including genomics, transcriptomics, proteomics and metabolomics, provide powerful tools for comprehensive analysis of molecular changes in biological systems. The present study explores the molecular and metabolic mechanisms of RA, emphasizing the central role of metabolic dysregulation in the RA disease process and highlighting the complexity of metabolic pathways, particularly metabolic remodeling in synovial tissues and its association with cytokine-mediated inflammation. This paper reveals the potential of histological techniques in identifying metabolically relevant therapeutic targets in RA; specifically, we summarize the genetic basis of RA and the dysregulated metabolic pathways, and explore their functional significance in the context of immune cell activation and differentiation. This study demonstrates the critical role of histological techniques in decoding the complex metabolic network of RA and discusses the integration of histological data with other types of biological data.

A temporal perspective for tumor-associated macrophage identities and functions

Cancer is a progressive disease that can develop and evolve over decades, with inflammation playing a central role at each of its stages, from tumor initiation to metastasis. In this context, macrophages represent well-established bridges reciprocally linking inflammation and cancer via an array of diverse functions that have spurred efforts to classify them into subtypes. Here, we discuss the intertwines between macrophages, inflammation, and cancer with an emphasis on temporal dynamics of macrophage diversity and functions in pre-malignancy and cancer. By instilling temporal dynamism into the more static classic view of tumor-associated macrophage biology, we propose a new framework to better contextualize their significance in the inflammatory processes that precede and result from the onset of cancer and shape its evolution.

Integrating single-cell RNA sequencing data to genome-wide association analysis data identifies significant cell types in influenza A virus infection and COVID-19

With the global pandemic of COVID-19, the research on influenza virus has entered a new stage, but it is difficult to elucidate the pathogenesis of influenza disease. Genome-wide association studies (GWASs) have greatly shed light on the role of host genetic background in influenza pathogenesis and prognosis, whereas single-cell RNA sequencing (scRNA-seq) has enabled unprecedented resolution of cellular diversity and in vivo following influenza disease. Here, we performed a comprehensive analysis of influenza GWAS and scRNA-seq data to reveal cell types associated with influenza disease and provide clues to understanding pathogenesis. We downloaded two GWAS summary data, two scRNA-seq data on influenza disease. After defining cell types for each scRNA-seq data, we used RolyPoly and LDSC-cts to integrate GWAS and scRNA-seq. Furthermore, we analyzed scRNA-seq data from the peripheral blood mononuclear cells (PBMCs) of a healthy population to validate and compare our results. After processing the scRNA-seq data, we obtained approximately 70 000 cells and identified up to 13 cell types. For the European population analysis, we determined an association between neutrophils and influenza disease. For the East Asian population analysis, we identified an association between monocytes and influenza disease. In addition, we also identified monocytes as a significantly related cell type in a dataset of healthy human PBMCs. In this comprehensive analysis, we identified neutrophils and monocytes as influenza disease-associated cell types. More attention and validation should be given in future studies.

A novel variable neighborhood search approach for cell clustering for spatial transcriptomics

This paper introduces a new approach to cell clustering using the Variable Neighborhood Search (VNS) metaheuristic. The purpose of this method is to cluster cells based on both gene expression and spatial coordinates. Initially, we confronted this clustering challenge as an Integer Linear Programming minimization problem. Our approach introduced a novel model based on the VNS technique, demonstrating the efficacy in navigating the complexities of cell clustering. Notably, our method extends beyond conventional cell-type clustering to spatial domain clustering. This adaptability enables our algorithm to orchestrate clusters based on information gleaned from gene expression matrices and spatial coordinates. Our validation showed the superior performance of our method when compared to existing techniques. Our approach advances current clustering methodologies and can potentially be applied to several fields, from biomedical research to spatial data analysis.

Digital PCR for Single-Cell Analysis

Single-cell analysis provides an overwhelming strategy for revealing cellular heterogeneity and new perspectives for understanding the biological function and disease mechanism. Moreover, it promotes the basic and clinical research in many fields at a single-cell resolution. A digital polymerase chain reaction (dPCR) is an absolute quantitative analysis technology with high sensitivity and precision for DNA/RNA or protein. With the development of microfluidic technology, digital PCR has been used to achieve absolute quantification of single-cell gene expression and single-cell proteins. For single-cell specific-gene or -protein detection, digital PCR has shown great advantages. So, this review will introduce the significance and process of single-cell analysis, including single-cell isolation, single-cell lysis, and single-cell detection methods, mainly focusing on the microfluidic single-cell digital PCR technology and its biological application at a single-cell level. The challenges and opportunities for the development of single-cell digital PCR are also discussed.

Evolution of Microglia

Microglial cells are unique tissue-resident macrophages located in the parenchyma of the central nervous system (CNS). A recent comparative transcriptional study on microglia across more than 20 species from leach across chicken and many more up to humans revealed multiple conserved features. The results indicate the imperative role of microglia over the last 500 million years (Geirsdottir et al. Cell 181:746, 2020). Improved understanding of microglial evolution provides essential insights into conserved and divergent microglial pathways and will have implications for future development of microglia-based therapies to treat CNS disorders. Not only therapeutic approaches may be rethought, but also the understanding of sex specificity of the immune system within the CNS needs to be renewed. Besides revealing the highly detailed characteristics of microglia, the former paradigm of microglia being the only CNS-resident immune cells was outdated by the identification of CNS-associated macrophages (CAMs) as CNS interface residents, who, most likely, accompanied microglia in evolution over the past million years.

Secretory GFP reconstitution labeling of neighboring cells interrogates cell-cell interactions in metastatic niches

Cancer cells inevitably interact with neighboring host tissue-resident cells during the process of metastatic colonization, establishing a metastatic niche to fuel their survival, growth, and invasion. However, the underlying mechanisms in the metastatic niche are yet to be fully elucidated owing to the lack of methodologies for comprehensively studying the mechanisms of cell-cell interactions in the niche. Here, we improve a split green fluorescent protein (GFP)-based genetically encoded system to develop secretory glycosylphosphatidylinositol-anchored reconstitution-activated proteins to highlight intercellular connections (sGRAPHIC) for efficient fluorescent labeling of tissue-resident cells that neighbor on and putatively interact with cancer cells in deep tissues. The sGRAPHIC system enables the isolation of metastatic niche-associated tissue-resident cells for their characterization using a single-cell RNA sequencing platform. We use this sGRAPHIC-leveraged transcriptomic platform to uncover gene expression patterns in metastatic niche-associated hepatocytes in a murine model of liver metastasis. Among the marker genes of metastatic niche-associated hepatocytes, we identify Lgals3, encoding galectin-3, as a potential pro-metastatic factor that accelerates metastatic growth and invasion.

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.

MitoTracer facilitates the identification of informative mitochondrial mutations for precise lineage reconstruction

Mitochondrial (MT) mutations serve as natural genetic markers for inferring clonal relationships using single cell sequencing data. However, the fundamental challenge of MT mutation-based lineage tracing is automated identification of informative MT mutations. Here, we introduced an open-source computational algorithm called "MitoTracer", which accurately identified clonally informative MT mutations and inferred evolutionary lineage from scRNA-seq or scATAC-seq samples. We benchmarked MitoTracer using the ground-truth experimental lineage sequencing data and demonstrated its superior performance over the existing methods measured by high sensitivity and specificity. MitoTracer is compatible with multiple single cell sequencing platforms. Its application to a cancer evolution dataset revealed the genes related to primary BRAF-inhibitor resistance from scRNA-seq data of BRAF-mutated cancer cells. Overall, our work provided a valuable tool for capturing real informative MT mutations and tracing the lineages among cells.

Teaser

MitoTracer enables automatically and accurately discover informative mitochondrial mutations for lineage tracing.

Single-cell transcriptomics reveals prominent expression of IL-14, IL-18, and IL-32 in psoriasis

RATIONALE

Psoriasis is a chronic inflammatory skin disease involving different cytokines and chemokines.

OBJECTIVES

Here we use single-cell transcriptomic analyses to identify relevant immune cell and nonimmune cell populations for an in-depth characterization of cell types and inflammatory mediators in this disease.

METHODS

Psoriasis skin lesions of eight patients are analyzed using single-cell technology. Data are further validated by in situ hybridization (ISH) of human tissues, serum analyses of human samples and tissues of a murine model of psoriasis, and by in vitro cell culture experiments.

RESULTS

Several different immune-activated cell types with particular cytokine patterns are identified such as keratinocytes, T-helper cells, dendritic cells, macrophages, and fibroblasts. Apart from well-known factors, IL-14 (TXLNA), IL-18, and IL-32 are identified with prominent expression in individual cell types in psoriasis. The percentage of inflammatory cellular subtypes expressing IL-14, IL-18, and IL-32 was significantly higher in psoriatic skin compared with healthy control skin. These findings were confirmed by ISH of human skin samples, in a murine model of psoriasis, in human serum samples, and in in vitro experiments.

CONCLUSIONS

Taken together, we provide a differentiated view of psoriasis immune-cell phenotypes that support the role of IL-14, IL-18, and IL-32 in psoriasis pathogenesis.

Single-Cell RNA-Sequencing in Astrocyte Development, Heterogeneity, and Disease

Astrocytes are the most plentiful cell type in the central nervous system (CNS) and perform complicated functions in health and disease. It is obvious that different astrocyte subpopulations, or activation states, are relevant with specific genomic programs and functions. In recent years, the emergence of new technologies such as single-cell RNA sequencing (scRNA-seq) has made substantial advance in the characterization of astrocyte heterogeneity, astrocyte developmental trajectory, and its role in CNS diseases which has had a significant impact on neuroscience. In this review, we present an overview of astrocyte development, heterogeneity, and its essential role in the physiological and pathological environments of the CNS. We focused on the critical role of single-cell sequencing in revealing astrocyte development, heterogeneity, and its role in different CNS diseases.

Transcriptome-Wide Analysis of Neutrophil-Related Circ_22232 in Neuroinflammation from Ischemic Stroke Mice

Ischemic stroke (IS) often leads to high rates of disability and mortality worldwide with secondary damage due to neuroinflammation. Identification of potential therapeutic targets via the novel circular RNAs (circRNAs) would advance the field and provide a better treatment option for neuroinflammation after IS. Gene Ontology Term Enrichment (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were applied to identify differentially expressed genes/miRNAs/circRNAs in the genome-wide RNA-seq profiles of ischemic mice. Meanwhile, relevant circRNAs were screened by differential expression analysis and coexpression RNA regulation network analysis. To explore the function of circ_22232 (Specc1l), we generated circ_22232 knockdown mice and applied middle cerebral artery occlusion (MCAO) to study IS. Cytokine levels were detected by enzyme-linked immunosorbent assay. Morphological changes were observed with immunohistochemical staining and hematoxylin-eosin staining. The circ_22232/miR-847-3p/Bmp1 axis was found to be highly correlated with neutrophil-associated neuroinflammation in cerebral tissue of mice. Immunohistochemical showed a progressive increase in the proportion of neutrophils after IS. In in vivo experiments, the circ_22232 knockdown alleviated cerebral injury by reducing the activation of neutrophils and inflammatory cytokine production. This suggests that circ_22232 is associated with inflammation, which may serve as a potential therapeutic target for IS.

Roles of macrophages in tumor development: a spatiotemporal perspective

Macrophages are critical regulators of tissue homeostasis but are also abundant in the tumor microenvironment (TME). In both primary tumors and metastases, such tumor-associated macrophages (TAMs) seem to support tumor development. While we know that TAMs are the dominant immune cells in the TME, their vast heterogeneity and associated functions are only just being unraveled. In this review, we outline the various known TAM populations found thus far and delineate their specialized roles associated with the main stages of cancer progression. We discuss how macrophages may prime the premetastatic niche to enable the growth of a metastasis and then how subsequent metastasis-associated macrophages can support secondary tumor growth. Finally, we speculate on the challenges that remain to be overcome in TAM research.

Cross-species single-cell transcriptomic analysis of animal gastric antrum reveals intense porcine mucosal immunity

As an important part of the stomach, gastric antrum secretes gastrin which can regulate acid secretion and gastric emptying. Although most cell types in the gastric antrum are identified, the comparison of cell composition and gene expression in the gastric antrum among different species are not explored. In this study, we collected antrum epithelial tissues from human, pig, rat and mouse for scRNA-seq and compared cell types and gene expression among species. In pig antral epithelium, we identified a novel cell cluster, which is marked by high expression of AQP5, F3, CLCA1 and RRAD. We also discovered that the porcine antral epithelium has stronger immune function than the other species. Further analysis revealed that this may be due to the insufficient function of porcine immune cells. Together, our results replenish the information of multiple species of gastric antral epithelium at the single cell level and provide resources for understanding the homeostasis maintenance and regeneration of gastric antrum epithelium.

In Silico Evaluation of HN-N07 Small Molecule as an Inhibitor of Angiogenesis and Lymphangiogenesis Oncogenic Signatures in Non-Small Cell Lung Cancer

Tumor angiogenesis and lymphangiogenesis pathways have been identified as important therapeutic targets in non-small cell lung cancer (NSCLC). Bevacizumab, which is a monoclonal antibody, was the initial inhibitor of angiogenesis and lymphangiogenesis that received approval for use in the treatment of advanced non-small cell lung cancer (NSCLC) in combination with chemotherapy. Despite its usage, patients may still develop resistance to the treatment, which can be attributed to various histological subtypes and the initiation of treatment at advanced stages of cancer. Due to their better specificity, selectivity, and safety compared to chemotherapy, small molecules have been approved for treating advanced NSCLC. Based on the development of multiple small-molecule antiangiogenic drugs either in house and abroad or in other laboratories to treat NSCLC, we used a quinoline-derived small molecule-HN-N07-as a potential target drug for NSCLC. Accordingly, we used computational simulation tools and evaluated the drug-likeness properties of HN-N07. Moreover, we identified target genes, resulting in the discovery of the target BIRC5/HIF1A/FLT4 pro-angiogenic genes. Furthermore, we used in silico molecular docking analysis to determine whether HN-N07 could potentially inhibit BIRC5/HIF1A/FLT4. Interestingly, the results of docking HN-N07 with the BIRC5, FLT4, and HIF1A oncogenes revealed unique binding affinities, which were significantly higher than those of standard inhibitors. In summary, these results indicate that HN-N07 shows promise as a potential inhibitor of oncogenic signaling pathways in NSCLC. Ongoing studies that involve in vitro experiments and in vivo investigations using tumor-bearing mice are in progress, aiming to evaluate the therapeutic effectiveness of the HN-N07 small molecule.

Single-cell profiling to explore pancreatic cancer heterogeneity, plasticity and response to therapy

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer entity characterized by a heterogeneous genetic landscape and an immunosuppressive tumor microenvironment. Recent advances in high-resolution single-cell sequencing and spatial transcriptomics technologies have enabled an in-depth characterization of both malignant and host cell types and increased our understanding of the heterogeneity and plasticity of PDAC in the steady state and under therapeutic perturbation. In this Review we outline single-cell analyses in PDAC, discuss their implications on our understanding of the disease and present future perspectives of multimodal approaches to elucidate its biology and response to therapy at the single-cell level.

Landscape of Exhausted T Cells in Tuberculosis Revealed by Single-Cell Sequencing

Tuberculosis, a contagious bacterial infection caused by Mycobacterium tuberculosis, is a substantial global health problem, impacting millions of lives annually. Exhausted T-cell signatures are critical for predicting clinical responses to tuberculosis infection. To obtain a panoramic transcriptional profile of T cells, we performed single-cell RNA-sequencing analysis of CD4+ T and CD8+ T cells isolated from peripheral blood mononuclear cells of healthy individuals and patients with tuberculosis. We identified seven subsets in CD8+ T cells and eight subsets in CD4+ T cells and elucidated the transcriptomic landscape changes and characteristics of each subset. We further investigated the cell-to-cell relationship of each subgroup of the two cell types. Different signature genes and pathways of exhausted CD4+ and CD8+ T cells were examined. We identified 12 genes with potential associations of T-cell exhaustion after tuberculosis infection. We also identified five genes as potential exhaustion marker genes. The CD8-EX3 subcluster in CD8+ T-exhausted cells was identified as an exhaustion-specific subcluster. The identified gene module further clarified the key factors influencing CD8+ T cell exhaustion. These data provide new insights into T-cell signatures in tuberculosis-exhausted populations. IMPORTANCE Identifying the changes in immune cells in response to infection can provide a better understanding of the effects of Mycobacterium tuberculosis on the host immune system. We performed single-cell RNA-sequencing analysis of CD4+ T and CD8+ T cells isolated from peripheral blood mononuclear cells of healthy individuals and patients with tuberculosis to reveal the cellular characteristics. Different signature genes and pathways of exhausted CD4+ and CD8+ T cells were examined. These will facilitate a more comprehensive understanding of the onset and underlying mechanism of T-cell exhaustion during active Mtb infection.

Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data

Remarkable advances in single cell genomics have presented unique challenges and opportunities for interrogating a wealth of biomedical inquiries. High dimensional genomic data are inherently complex because of intertwined relationships among the genes. Existing methods, including emerging deep learning-based approaches, do not consider the underlying biological characteristics during data processing, which greatly compromises the performance of data analysis and hinders the maximal utilization of state-of-the-art genomic techniques. In this work, we develop an entropy-based cartography strategy to contrive the high dimensional gene expression data into a configured image format, referred to as genomap, with explicit integration of the genomic interactions. This unique cartography casts the gene-gene interactions into the spatial configuration of genomaps and enables us to extract the deep genomic interaction features and discover underlying discriminative patterns of the data. We show that, for a wide variety of applications (cell clustering and recognition, gene signature extraction, single cell data integration, cellular trajectory analysis, dimensionality reduction, and visualization), the proposed approach drastically improves the accuracies of data analyses as compared to the state-of-the-art techniques.

Single-cell profiling identifies ACE+ granuloma macrophages as a nonpermissive niche for intracellular bacteria during persistent Salmonella infection

Macrophages mediate key antimicrobial responses against intracellular bacterial pathogens, such as Salmonella enterica. Yet, they can also act as a permissive niche for these pathogens to persist in infected tissues within granulomas, which are immunological structures composed of macrophages and other immune cells. We apply single-cell transcriptomics to investigate macrophage functional diversity during persistent S. enterica serovar Typhimurium (STm) infection in mice. We identify determinants of macrophage heterogeneity in infected spleens and describe populations of distinct phenotypes, functional programming, and spatial localization. Using an STm mutant with impaired ability to polarize macrophage phenotypes, we find that angiotensin-converting enzyme (ACE) defines a granuloma macrophage population that is nonpermissive for intracellular bacteria, and their abundance anticorrelates with tissue bacterial burden. Disruption of pathogen control by neutralizing TNF is linked to preferential depletion of ACE+ macrophages in infected tissues. Thus, ACE+ macrophages have limited capacity to serve as cellular niche for intracellular bacteria to establish persistent infection.

Single cell and immunity: Better understanding immune cell heterogeneities with single cell sequencing

Single-cell sequencing has scientific impacts on better understanding the immunity. There is a rapid development in single cell-based databases and analytic tools to provide the potential of clinical and translational discovery. The understanding of single-cell based immunity needs a strong program and solid evidence of preclinical and clinical validation and evaluation. The current special topic issue on single cell and immunity aimed to provide a strong communication for the progress of single cell-based studies on immune cell functional diversity in development and disease. The topic has a clear scope on the application of single cell sequencing to better understand immune cell heterogeneities, functions, cell-cell interactions, responses and regulatory roles in systems immunology and diseases.

Neuroimaging is the new "spatial omic": multi-omic approaches to neuro-inflammation and immuno-thrombosis in acute ischemic stroke

Ischemic stroke (IS) is the leading cause of acquired disability and the second leading cause of dementia and mortality. Current treatments for IS are primarily focused on revascularization of the occluded artery. However, only 10% of patients are eligible for revascularization and 50% of revascularized patients remain disabled at 3 months. Accumulating evidence highlight the prognostic significance of the neuro- and thrombo-inflammatory response after IS. However, several randomized trials of promising immunosuppressive or immunomodulatory drugs failed to show positive results. Insufficient understanding of inter-patient variability in the cellular, functional, and spatial organization of the inflammatory response to IS likely contributed to the failure to translate preclinical findings into successful clinical trials. The inflammatory response to IS involves complex interactions between neuronal, glial, and immune cell subsets across multiple immunological compartments, including the blood-brain barrier, the meningeal lymphatic vessels, the choroid plexus, and the skull bone marrow. Here, we review the neuro- and thrombo-inflammatory responses to IS. We discuss how clinical imaging and single-cell omic technologies have refined our understanding of the spatial organization of pathobiological processes driving clinical outcomes in patients with an IS. We also introduce recent developments in machine learning statistical methods for the integration of multi-omic data (biological and radiological) to identify patient-specific inflammatory states predictive of IS clinical outcomes.

Prognostic value and immunological role of Kruppel-like transcription factor 9 gene in pan-carcinoma

OBJECTIVE

To investigate the correlation between the expression of Kruppel-like transcription factor 9 (KLF9) and the prognostic value of tumors as well as its relationship with tumor immune invasion.

METHODS

A series of bioinformatics methods were used to analyze the relationship between KLF9 and tumor prognosis, tumor mutation burden, microsatellite instability (MSI), and immune cell infiltration in multiple carcinomas.

RESULTS

In multiple tumor tissues, the expression of KLF9 was lower compared with paracancerous tissues. Therefore, KLF9 can serve as a protective factor to improve the prognosis of carcinoma patients with certain tumor types. KLF9 was closely related to the clinical staging of various carcinomas. The expression of KLF9 was not only associated with tumor mutation burden and MSI in some tumor types, but also positively correlated with immune and stromal cells in multiple tumors. Further studies have found that, the level of immune cell infiltration was significantly related to the expression of KLF9.

CONCLUSION

KLF9 can affect the prognosis of pan-carcinoma, which is related to immune invasion. Therefore, KLF9 can be used as a potential biomarker for the prognosis of pan-carcinoma.

Multiplexed high-throughput immune cell imaging reveals molecular health-associated phenotypes

Phenotypic plasticity is essential to the immune system, yet the factors that shape it are not fully understood. Here, we comprehensively analyze immune cell phenotypes including morphology across human cohorts by single-round multiplexed immunofluorescence, automated microscopy, and deep learning. Using the uncertainty of convolutional neural networks to cluster the phenotypes of eight distinct immune cell subsets, we find that the resulting maps are influenced by donor age, gender, and blood pressure, revealing distinct polarization and activation-associated phenotypes across immune cell classes. We further associate T cell morphology to transcriptional state based on their joint donor variability and validate an inflammation-associated polarized T cell morphology and an age-associated loss of mitochondria in CD4⁺ T cells. Together, we show that immune cell phenotypes reflect both molecular and personal health information, opening new perspectives into the deep immune phenotyping of individual people in health and disease.

TISCH2: expanded datasets and new tools for single-cell transcriptome analyses of the tumor microenvironment

The Tumor Immune Single Cell Hub 2 (TISCH2) is a resource of single-cell RNA-seq (scRNA-seq) data from human and mouse tumors, which enables comprehensive characterization of gene expression in the tumor microenvironment (TME) across multiple cancer types. As an increasing number of datasets are generated in the public domain, in this update, TISCH2 has included 190 tumor scRNA-seq datasets covering 6 million cells in 50 cancer types, with 110 newly collected datasets and almost tripling the number of cells compared with the previous release. Furthermore, TISCH2 includes several new functions that allow users to better utilize the large-scale scRNA-seq datasets. First, in the Dataset module, TISCH2 provides the cell-cell communication results in each dataset, facilitating the analyses of interacted cell types and the discovery of significant ligand-receptor pairs between cell types. TISCH2 also includes the transcription factor analyses for each dataset and visualization of the top enriched transcription factors of each cell type. Second, in the Gene module, TISCH2 adds functions for identifying correlated genes and providing survival information for the input genes. In summary, TISCH2 is a user-friendly, up-to-date and well-maintained data resource for gene expression analyses in the TME. TISCH2 is freely available at http://tisch.comp-genomics.org/.

Anti-CTLA-4 antibodies drive myeloid activation and reprogram the tumor microenvironment through FcγR engagement and type I interferon signaling

Despite the clinical success of checkpoint inhibitors, a substantial gap still exists in our understanding of their mechanism of action. While antibodies to cytotoxic T lymphocyte-associated protein-4 (CTLA-4) were developed to block inhibitory signals in T cells, several recent studies have demonstrated that Fcγ receptor (FcγR)-dependent depletion of regulatory T cells (Treg) is critical for antitumor activity. Here, using single-cell RNA sequencing, we dissect the impact of anti-CTLA-4-blocking, Treg cell-depleting and FcR-engaging activity on the immune response within tumors. We observed a rapid remodeling of the innate immune landscape as early as 24 h after treatment. Using genetic Treg cell ablation models, we show that immune remodeling was not driven solely by Treg cell depletion or CTLA-4 blockade but mainly through FcγR engagement, downstream activation of type I interferon signaling and reduction of suppressive macrophages. Our findings indicate that FcγR engagement and innate immune remodeling are involved in successful anti-CTLA-4 treatment, supporting the development of optimized immunotherapy agents bearing these features.

Natural killer cell awakening: unleash cancer-immunity cycle against glioblastoma

Due to the negligence of the complex tumor immune microenvironment, traditional treatment for glioblastoma has reached its limitation and cannot achieve a satisfying outcome in the past decade. The emergence of immunotherapy based on the theory of cancer-immunity cycle has brought a new dawn to glioblastoma patients. However, the results of most phase II and phase III clinical trials are not optimistic due to the simple focus on T cells activation rather than other immune cells involved in anti-tumor immunity. NK cells play a critical role in both innate and adaptive immunity, having the ability to coordinate immune response in inflammation, autoimmune disease and cancer. They are expected to cooperate with T cells to maximize the anti-tumor immune effect and have great potential in treating glioblastoma. Here, we describe the traditional treatment methods and current immunotherapy strategies for glioblastoma. Then, we list a microenvironment map and discuss the reasons for glioblastoma inhibitory immunity from multiple perspectives. More importantly, we focus on the advantages of NK cells as potential immune regulatory cells and the ways to maximize their anti-tumor immune effect. Finally, our outlook on the directions and potential applications of NK cell-based therapy combining with the advance technologies is presented. This review depicts NK cell awakening as the precondition to unleash the cancer-immunity cycle against glioblastoma and elaborate this idea from biology to clinical treatment.

Exploring mechanisms of Chaihu-Shugan-San against liver fibrosis by integrated multi-omics and network pharmacology approach

Chaihu-Shugan-San (CHSGS), a noted traditional Chinese medicine formula, has been used as a complementary and alternative therapy for liver fibrosis. However, the antifibrotic mechanisms of CHSGS still remain unclear. Thus, we used network pharmacology approach in combination with single cell and bulk transcriptomics to elucidate the antifibrotic mechanisms of CHSGS. We first screened out 134 bioactive ingredients of CHSGS through the defined criteria. Then, 1,150 genes were predicted to be targets for CHSGS, while 625 liver fibrosis-associated genes were identified by single cell transcriptomics analysis. Next, 71 intersecting genes of CHSGS and liver fibrosis were defined as the therapeutic targets in CHSGS against liver fibrosis. Further, 21 core targets and 12 core ingredients of CHSGS against liver fibrosis were also identified. Meanwhile, enrichment analyses of core targets highlighted that the key mechanisms of CHSGS against liver fibrosis include modulation of inflammation responses, inhibition of angiogenesis, and regulation of ECM remodeling, of which the most important mechanism was the regulation of ECM remodeling. The molecular docking simulation validated strong binding affinity between the core targets and core ingredients. Furthermore, 62-gene signature may be used for determining the prognosis in cirrhotic patients based on the results of ssGSEA-Cox analysis. In conclusion, this study revealed the multiple pharmacological targets and therapeutic mechanisms of CHSGS against liver fibrosis, which may thus serve as an effective antifibrotic therapy. Meanwhile, CHSGS may improve survival of patients with liver cirrhosis by the interaction of 62-gene signature.

Mitochondrial variant enrichment from high-throughput single-cell RNA sequencing resolves clonal populations

The combination of single-cell transcriptomics with mitochondrial DNA variant detection can be used to establish lineage relationships in primary human cells, but current methods are not scalable to interrogate complex tissues. Here, we combine common 3' single-cell RNA-sequencing protocols with mitochondrial transcriptome enrichment to increase coverage by more than 50-fold, enabling high-confidence mutation detection. The method successfully identifies skewed immune-cell expansions in primary human clonal hematopoiesis.

Lessons From Transcriptome Analysis of Autoimmune Diseases

Various immune cell types, including monocytes, macrophages, and adaptive immune T and B cells, play major roles in inflammation in systemic autoimmune diseases. However, the precise contribution of these cells to autoimmunity remains elusive. Transcriptome analysis has added a new dimension to biology and medicine. It enables us to observe the dynamics of gene expression in different cell types in patients with diverse diseases as well as in healthy individuals, which cannot be achieved with genomic information alone. In this review, we summarize how transcriptome analysis has improved our understanding of the pathological roles of immune cells in autoimmune diseases with a focus on the ImmuNexUT database we reported. We will also discuss the common experimental and analytical design of transcriptome analyses. Recently, single-cell RNA-seq analysis has provided atlases of infiltrating immune cells, such as pro-inflammatory monocytes and macrophages, peripheral helper T cells, and age or autoimmune-associated B cells in various autoimmune disease lesions. With the integration of genomic data, expression quantitative trait locus (eQTL) analysis can help identify candidate causal genes and immune cells. Finally, we also mention how the information obtained from these analyses can be used practically to predict patient prognosis.

The Promise of Single-cell Technology in Providing New Insights Into the Molecular Heterogeneity and Management of Acute Lymphoblastic Leukemia

Drug resistance and treatment failure in pediatric acute lymphoblastic leukemia (ALL) are in part driven by tumor heterogeneity and clonal evolution. Although bulk tumor genomic analyses have provided some insight into these processes, single-cell sequencing has emerged as a powerful technique to profile individual cells in unprecedented detail. Since the introduction of single-cell RNA sequencing, we now have the capability to capture not only transcriptomic, but also genomic, epigenetic, and proteomic variation between single cells separately and in combination. This rapidly evolving field has the potential to transform our understanding of the fundamental biology of pediatric ALL and guide the management of ALL patients to improve their clinical outcome. Here, we discuss the impact single-cell sequencing has had on our understanding of tumor heterogeneity and clonal evolution in ALL and provide examples of how single-cell technology can be integrated into the clinic to inform treatment decisions for children with high-risk disease.

BSDE: barycenter single-cell differential expression for case-control studies

MOTIVATION

Single-cell sequencing brings about a revolutionarily high resolution for finding differentially expressed genes (DEGs) by disentangling highly heterogeneous cell tissues. Yet, such analysis is so far mostly focused on comparing between different cell types from the same individual. As single-cell sequencing becomes cheaper and easier to use, an increasing number of datasets from case-control studies are becoming available, which call for new methods for identifying differential expressions between case and control individuals.

RESULTS

To bridge this gap, we propose barycenter single-cell differential expression (BSDE), a nonparametric method for finding DEGs for case-control studies. Through the use of optimal transportation for aggregating distributions and computing their distances, our method overcomes the restrictive parametric assumptions imposed by standard mixed-effect-modeling approaches. Through simulations, we show that BSDE can accurately detect a variety of differential expressions while maintaining the type-I error at a prescribed level. Further, 1345 and 1568 cell type-specific DEGs are identified by BSDE from datasets on pulmonary fibrosis and multiple sclerosis, among which the top findings are supported by previous results from the literature.

AVAILABILITY AND IMPLEMENTATION

R package BSDE is freely available from doi.org/10.5281/zenodo.6332254. For real data analysis with the R package, see doi.org/10.5281/zenodo.6332566. These can also be accessed thorough GitHub at github.com/mqzhanglab/BSDE and github.com/mqzhanglab/BSDE_pipeline. The two single-cell sequencing datasets can be download with UCSC cell browser from cells.ucsc.edu/?ds=ms and cells.ucsc.edu/?ds=lung-pf-control.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-Cell Sequencing Facilitates Elucidation of HIV Immunopathogenesis: A Review of Current Literature

Knowledge gaps remain in the understanding of HIV disease establishment and progression. Scientists continue to strive in their endeavor to elucidate the precise underlying immunopathogenic mechanisms of HIV-related disease, in order to identify possible preventive and therapeutic targets. A useful tool in the quest to reveal some of the enigmas related to HIV infection and disease is the single-cell sequencing (scRNA-seq) technique. With its proven capacity to elucidate critical processes in cell formation and differentiation, to decipher critical hematopoietic pathways, and to understand the regulatory gene networks that predict immune function, scRNA-seq is further considered to be a potentially useful tool to explore HIV immunopathogenesis. In this article, we provide an overview of single-cell sequencing platforms, before delving into research findings gleaned from the use of single cell sequencing in HIV research, as published in recent literature. Finally, we describe two important avenues of research that we believe should be further investigated using the single-cell sequencing technique.

Single-cell immunology: Past, present, and future

The immune system is a complex, dynamic, and plastic ecosystem composed of multiple cell types that constantly sense and interact with their local microenvironment to protect from infection and maintain homeostasis. For over a century, great efforts and ingenuity have been applied to the characterization of immune cells and their microenvironments, but traditional marker-based and bulk technologies left key questions unanswered. In the past decade, the advent of single-cell genomic approaches has revolutionized our knowledge of the cellular and molecular makeup of the immune system. In this perspective, we outline the past, present, and future applications of single-cell genomics in immunology and discuss how the integration of multiomics at the single-cell level will pave the way for future advances in immunology research and clinical translation.

Single-Cell Technologies to Decipher the Immune Microenvironment in Myeloid Neoplasms: Perspectives and Opportunities

Myeloid neoplasms (MN) are heterogeneous clonal disorders arising from the expansion of hematopoietic stem and progenitor cells. In parallel with genetic and epigenetic dynamics, the immune system plays a critical role in modulating tumorigenesis, evolution and therapeutic resistance at the various stages of disease progression. Single-cell technologies represent powerful tools to assess the cellular composition of the complex tumor ecosystem and its immune environment, to dissect interactions between neoplastic and non-neoplastic components, and to decipher their functional heterogeneity and plasticity. In addition, recent progress in multi-omics approaches provide an unprecedented opportunity to study multiple molecular layers (DNA, RNA, proteins) at the level of single-cell or single cellular clones during disease evolution or in response to therapy. Applying single-cell technologies to MN holds the promise to uncover novel cell subsets or phenotypic states and highlight the connections between clonal evolution and immune escape, which is crucial to fully understand disease progression and therapeutic resistance. This review provides a perspective on the various opportunities and challenges in the field, focusing on key questions in MN research and discussing their translational value, particularly for the development of more efficient immunotherapies.

SkewC: Identifying cells with skewed gene body coverage in single-cell RNA sequencing data

The analysis and interpretation of single-cell RNA sequencing (scRNA-seq) experiments are compromised by the presence of poor-quality cells. For meaningful analyses, such poor-quality cells should be excluded as they introduce noise in the data. We introduce SkewC, a quality-assessment tool, to identify skewed cells in scRNA-seq experiments. The tool’s methodology is based on the assessment of gene coverage for each cell, and its skewness as a quality measure; the gene body coverage is a unique characteristic for each protocol, and different protocols yield highly different coverage profiles. This tool is designed to avoid misclustering or false clusters by identifying, isolating, and removing cells with skewed gene body coverage profiles. SkewC is capable of processing any type of scRNA-seq dataset, regardless of the protocol. We envision SkewC as a distinctive QC method to be incorporated into scRNA-seq QC processing to preclude the possibility of scRNA-seq data misinterpretation.

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We developed a quality assessment method applicable to single-cell RNA-Seq data

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SkewC relies on the use of gene coverage profile of cells as a quality measure

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SkewC reveals two classes of cells: typical cells and skewed cells

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Typical with prototypical coverage profiles, and skewed with skewed profiles

Immune response after central nervous system injury

Traumatic injuries of the central nervous system (CNS) affect millions of people worldwide, and they can lead to severely damaging consequences such as permanent disability and paralysis. Multiple factors can obstruct recovery after CNS injury. One of the most significant is the progressive neuronal death that follows the initial mechanical impact, leading to the loss of undamaged cells via a process termed secondary neurodegeneration. Efforts to define treatments that limit the spread of damage, while important, have been largely ineffectual owing to gaps in the mechanistic understanding that underlies the persisting neuronal cell death. Inflammation, with its influx of immune cells that occurs shortly after injury, has been associated with secondary neurodegeneration. However, the role of the immune system after CNS injury is far more complex. Studies have indicated that the immune response after CNS injury is detrimental, owing to immune cell-produced factors (e.g., pro-inflammatory cytokines, free radicals, neurotoxic glutamate) that worsen tissue damage. Our lab and others have also demonstrated the beneficial immune response that occurs after CNS injury, with the release of growth factors such as brain-derived growth factor (BDNF) and interleukin (IL-10) and the clearance of apoptotic and myelin debris by immune cells^1-4. In this review, we first discuss the multifaceted roles of the immune system after CNS injury. We then speculate on how advancements in single-cell RNA technologies can dramatically change our understanding of the immune response, how the spinal cord meninges serve as an important site for hosting immunological processes critical for recovery, and how the origin of peripherally recruited immune cells impacts their function in the injured CNS.

PPP1R14B Is a Prognostic and Immunological Biomarker in Pan-Cancer

Recent studies have shown that PPP1R14B was highly expressed in tumor tissues and patients with high expression of PPP1R14B had poor survival rates. However, the function and mechanisms of PPP1R14B in tumor progression remain ill defined. There was also lack of pan-cancer evidence for the relationship between PPP1R14B and various tumor types based on abundant clinical data. We used the TCGA project and GEO databases to perform pan-cancer analysis of PPP1R14B, including expression differences, correlations between expression levels and survival, genetic alteration, immune infiltration, and relevant cellular pathways, to investigate the functions and potential mechanisms of PPP1R14B in the pathogenesis or clinical prognosis of different cancers. Herein, we found that PPP1R14B was involved in the prognosis of pan-cancer and closely related to immune infiltration. Increased PPP1R14B expression correlated with poor prognosis and increased immune infiltration levels in myeloid-derived suppressor cells (MDSCs). Our studies suggest that PPP1R14B can be used as a prognostic biomarker for pan-cancer. Our findings may provide an antitumor strategy targeting PPP1R14B, including manipulation of tumor cell growth or the tumor microenvironment, especially myeloid-derived suppressor cell infiltration.

Prognostic and immunological value of LTB4R in pan-cancer

BACKGROUND

LTB4 receptor 1 (LTB4R), as the high affinity leukotriene B4 receptor, is rapidly revealing its function in malignancies. However, it is still uncertain.

METHODS

We investigated the expression pattern and prognostic significance of LTB4R in pan-cancer across different databases, including ONCOMINE, PrognoScan, GEPIA, and Kaplan-Meier Plotter, in this study. Meanwhile, we explored the significance of LTB4R in tumor metastasis by HCMDB. Then functional enrichment analysis of related genes was performed using GeneMANIA and DAVID. Lastly, utilizing the TIMER datasets, we looked into the links between LTB4R expression and immune infiltration in malignancies.

RESULTS

In general, tumor tissue displayed higher levels of LTB4R expression than normal tissue. Although LTB4R had a negative influence on pan-cancer, a high expression level of LTB4R was protective of LIHC (liver hepatocellular carcinoma) patients' survival. There was no significant difference in the distribution of LTB4R between non-metastatic and metastatic tumors. Based on Gene Set Enrichment Analysis, LTB4R was implicated in pathways involved in inflammation, immunity, metabolism, and cancer diseases. The correlation between immune cells and LTB4R was found to be distinct across cancer types. Furthermore, markers of infiltrating immune cells, such as Treg, T cell exhaustion and T helper cells, exhibited different LTB4R-related immune infiltration patterns.

CONCLUSION

The LTB4R is associated with immune infiltrates and can be used as a prognostic biomarker in pan-cancer.

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.

Exploiting dynamic enhancer landscapes to decode macrophage and microglia phenotypes in health and disease

The development and functional potential of metazoan cells is dependent on combinatorial roles of transcriptional enhancers and promoters. Macrophages provide exceptionally powerful model systems for investigation of mechanisms underlying the activation of cell-specific enhancers that drive transitions in cell fate and cell state. Here, we review recent advances that have expanded appreciation of the diversity of macrophage phenotypes in health and disease, emphasizing studies of liver, adipose tissue, and brain macrophages as paradigms for other tissue macrophages and cell types. Studies of normal tissue-resident macrophages and macrophages associated with cirrhosis, obese adipose tissue, and neurodegenerative disease illustrate the major roles of tissue environment in remodeling enhancer landscapes to specify the development and functions of distinct macrophage phenotypes. We discuss the utility of quantitative analysis of environment-dependent changes in enhancer activity states as an approach to discovery of regulatory transcription factors and upstream signaling pathways.

Deep immunophenotyping at the single-cell level identifies a combination of anti-IL-17 and checkpoint blockade as an effective treatment in a preclinical model of data-guided personalized immunotherapy

BACKGROUND

Although immune checkpoint blockade is effective for several malignancies, a substantial number of patients remain refractory to treatment. The future of immunotherapy will be a personalized approach adapted to each patient's cancer-immune interactions in the tumor microenvironment (TME) to prevent suppression of antitumor immune responses. To demonstrate the feasibility of this kind of approach, we developed combination therapy for a preclinical model guided by deep immunophenotyping of the TME.

METHODS

Gastric cancer cell lines YTN2 and YTN16 were subcutaneously inoculated into C57BL/6 mice. YTN2 spontaneously regresses, while YTN16 grows progressively. Bulk RNA-Seq, single-cell RNA-Seq (scRNA-Seq) and flow cytometry were performed to investigate the immunological differences in the TME of these tumors.

RESULTS

Bulk RNA-Seq demonstrated that YTN16 tumor cells produced CCL20 and that CD8⁺ T cell responses were impaired in these tumors relative to YTN2. We have developed a vertical flow array chip (VFAC) for targeted scRNA-Seq to identify unique subtypes of T cells by employing a panel of genes reflecting T cell phenotypes and functions. CD8⁺ T cell dysfunction (cytotoxicity, proliferation and the recruitment of interleukin-17 (IL-17)-producing cells into YTN16 tumors) was identified by targeted scRNA-Seq. The presence of IL-17-producing T cells in YTN16 tumors was confirmed by flow cytometry, which also revealed neutrophil infiltration. IL-17 blockade suppressed YTN16 tumor growth, while tumors were rejected by the combination of anti-IL-17 and anti-PD-1 (Programmed cell death protein 1) mAb treatment. Reduced neutrophil activation and enhanced expansion of neoantigen-specific CD8⁺ T cells were observed in tumors of the mice receiving the combination therapy.

CONCLUSIONS

Deep phenotyping of YTN16 tumors identified a sequence of events on the axis CCL20->IL-17-producing cells->IL-17-neutrophil-angiogenesis->suppression of neoantigen-specific CD8⁺ T cells which was responsible for the lack of tumor rejection. IL-17 blockade together with anti-PD-1 mAb therapy eradicated these YTN16 tumors. Thus, the deep immunological phenotyping can guide immunotherapy for the tailored treatment of each individual patient's tumor.

Shifting Gears in Precision Oncology-Challenges and Opportunities of Integrative Data Analysis

For decades, research relating to modification of host immunity towards antitumor response activation has been ongoing, with the breakthrough discovery of immune-checkpoint blockers. Several biomarkers with potential predictive value have been reported in recent studies for these novel therapies. However, with the plethora of therapeutic options existing for a given cancer entity, modern oncology is now being confronted with multifactorial interpretation to devise “the best therapy” for the individual patient. Into the bargain come the multiverse guidelines for established and emerging diagnostic biomarkers, as well as the complex interplay between cancer cells and tumor microenvironment, provoking immense challenges in the therapy decision-making process. Through this review, we present various molecular diagnostic modalities and techniques, such as genomics, immunohistochemistry and quantitative image analysis, which have the potential of becoming powerful tools in the development of an optimal treatment regime when analogized with patient characteristics. We will summarize the underlying complexities of these methods and shed light upon the necessary considerations and requirements for data integration. It is our hope to provide compelling evidence to emphasize on the need for inclusion of integrative data analysis in modern cancer therapy, and thereupon paving a path towards precision medicine and better patient outcomes.