Tracing tumorigenesis in a solid tumor model at single-cell resolution

Interleukin-12 signaling drives Alzheimer's disease pathology through disrupting neuronal and oligodendrocyte homeostasis

Neuroinflammation including interleukin (IL)-12/IL-23-signaling is central to Alzheimer's disease (AD) pathology. Inhibition of p40, a subunit of IL-12/IL-23, attenuates pathology in AD-like mice; however, its signaling mechanism and expression pattern remained elusive. Here we show that IL-12 receptors are predominantly expressed in neurons and oligodendrocytes in AD-like APPPS1 mice and in patients with AD, whereas IL-23 receptor transcripts are barely detectable. Consistently, deletion of the IL-12 receptor in neuroectodermal cells ameliorated AD pathology in APPPS1 mice, whereas removal of IL-23 receptors had no effect. Genetic ablation of IL-12 signaling alone reverted the loss of mature oligodendrocytes, restored myelin homeostasis, rescued the amyloid-β-dependent reduction of parvalbumin-positive interneurons and restored phagocytosis-related changes in microglia of APPPS1 mice. Furthermore, IL-12 protein expression was increased in human AD brains compared to healthy age-matched controls, and human oligodendrocyte-like cells responded profoundly to IL-12 stimulation. We conclude that oligodendroglial and neuronal IL-12 signaling, but not IL-23 signaling, are key in orchestrating AD-related neuroimmune crosstalk and that IL-12 represents an attractive therapeutic target in AD.

LTA4H improves the tumor microenvironment and prevents HCC progression via targeting the HNRNPA1/LTBP1/TGF-β axis

Leukotriene A4 hydrolase (LTA4H), an inflammatory mediator, has garnered attention for its role in the development of chronic lung diseases and various cancers. Our study highlights the protective role of LTA4H in hepatocellular carcinoma (HCC) occurrence and progression. LTA4H is downregulated in clinical and mouse HCC tumors. LTA4H deficiency exacerbates hepatocyte damage by restraining JNK activation and promotes CD206+ macrophage polarization through the upregulation of LTBP1 expression and downstream transforming growth factor β (TGF-β) secretion and activation. Mechanistically, LTA4H induces heterogeneous nuclear ribonucleoprotein A1 (HNRNPA1) phosphorylation, enhancing their interaction and leading to the functional inhibition of HNRNPA1 in regulating Ltbp1 mRNA maturation and processing in the nucleus. LTA4H-deficient patients exhibit poor prognosis and immunotherapy resistance. Combination therapy targeting TGF-β and PD-1 significantly improves the immunotherapy resistance of LTA4H-knockout Hepa1-6 tumors. Our findings reveal the previously unreported role of LTA4H in regulating the tumor microenvironment and provide insights into potential diagnostic and therapeutic strategies for patients with LTA4H-deficient HCC.

A spatiotemporal comparative analysis on tumor immune microenvironment characteristics between neoadjuvant chemotherapy and preoperative immunotherapy for ESCC

The average five-year survival rate for esophageal cancer, a common malignant tumor of the digestive system, is barely 20%. The majority of esophageal squamous cell carcinoma (ESCC) patients had already progressed to a locally advanced or even advanced stage at initial diagnosis, making routine surgery ineffective. Chemotherapy and immunotherapy are important neoadjuvant treatments for ESCC, however, it remains unknown how treatment will affect the immunological microenvironment, especially at the spatial level. Here, we presented the TME characters of ESCC from the temporal and spatial dimensions using scRNA-seq and ST, investigated the changes of immune cell clusters in the TME under neoadjuvant chemotherapy and preoperative immunotherapy, and explored the potential mechanisms. It was found that compared with chemotherapy, immunotherapy combined with chemotherapy increased the level of T cell proliferation, partially restored the function of exhausted T cells, induced the expansion of specific exhausted CD8 T cells, increased the production of dendritic cells (DCs), and supported the immune hot microenvironment of the tumor. We also found that CD52 and ID3 have potential as biomarkers of ESCC. Particularly, CD52 may be served as a predictor of the efficacy to screen the advantaged population of different regimens. Through multiple pathways, CAF2 and CAF5's antigen-presenting role affected the other fibroblast clusters, resulting in malignant transformation. We analyzed the immune microenvironment differences between the two regimens to provide a more thorough description of the ESCC microenvironment profile and serve as a foundation for customized neoadjuvant treatment of ESCC.

DUSP1 and SOX2 expression determine squamous cell carcinoma of the salivary gland progression

Salivary gland squamous cell carcinomas (SG-SCCs) constitute a rare type of head and neck cancer which is linked to poor prognosis. Due to their low frequency, the molecular mechanisms responsible for their aggressiveness are poorly understood. In this work we studied the role of the phosphatase DUSP1, a negative regulator of MAPK activity, in controlling SG-SCC progression. We generated DUSP1 KO clones in A253 human cells. These clones showed a reduced ability to grow in 2D, self-renew in ECM matrices and to form tumors in immunodeficient mice. This was caused by an overactivation of the stress and apoptosis kinase JNK1/2 in DUSP1-/+ clones. Interestingly, RNAseq analysis revealed that the expression of SOX2, a well-known self-renewal gene was decreased at the mRNA and protein levels in DUSP1-/+ cells. Unexpectedly, CRISPR-KO of SOX2 did not recapitulate DUSP1-/+ phenotype, and SOX2-null cells had an enhanced ability to self-renew and to form tumors in mice. Gene expression analysis demonstrated that SOX2-null cells have a decreased squamous differentiation profile -losing TP63 expression- and an increased migratory phenotype, with an enhanced epithelial to mesenchymal transition signature. In summary, our data indicates that DUSP1 and SOX2 have opposite functions in SG-SCC, being DUSP1 necessary for tumor growth and SOX2 dispensable showing a tumor suppressor function. Our data suggest that the combined expression of SOX2 and DUSP1 could be a useful biomarker to predict progression in patients with SG-SCCs.

Current landscape and future trends in salivary gland oncology research-a bibliometric evaluation

Background

The salivary glands are susceptible to both endogenous and exogenous influences, potentially resulting in the development of oncology. With the wide application of various technologies, research in this area has experienced rapid growth. Therefore, researchers must identify and characterize the current research hot topics to grasp the forefront of developments in the dynamic field of salivary gland oncology. The objective of this study was to thoroughly assess the current status and identify potential future research directions in salivary gland oncology.

Methods

The relevant salivary gland oncology dataset was obtained from the Web of Science Core Collection (WOSCC) database. Subsequently, VoSviewer and CiteSpace were employed for further evaluation.

Results

A total of 9,695 manuscripts were extracted and downloaded from the WOSCC database. Our findings revealed a substantial surge in research volume over the past 12 years. The researchers' analysis revealed that Abbas Agami showed unparalleled dedication, with over 180 publications, and that RH Spiro had the highest cocitation count, confirming its status as a key figure in the field. The detection of bursts in secretory carcinoma and the integration of artificial intelligence in salivary oncology have attracted increasing interest. Notably, there is a discernible trend towards increased research engagement in the study of salivary gland malignancies.

Conclusions

This study not only evaluated the current research landscape in salivary gland oncology but also anticipates future trends. These insights could contribute to the advancement of knowledge and policymaking in salivary gland oncology.

The crosstalk role of CDKN2A between tumor progression and cuproptosis resistance in colorectal cancer

BACKGROUND

Cuproptosis is a type of cell death characterized by excessive copper-lipid reactions in the tricarboxylic acid cycle, resulting in protein toxicity stress and cell death. Although known as a cuproptosis inhibitor through CRISPR-Cas9 screening, the role of cyclin-dependent kinase inhibitor 2A (CDKN2A) in cuproptosis resistance and its connection to tumor development remains unclear.

METHODS

In this study, we combined single-cell sequencing, spatial transcriptomics, pathological image analysis, TCGA multi-omics analysis and in vitro experimental validation to comprehensively investigate CDKN2A distribution, expression, epigenetic modification, regulation and genomic features in colorectal cancer cells. We further explored the associations between CDKN2A and cellular pathway, immune infiltration and spatial signal communication.

RESULTS

Our findings showed an increasing trend in cuproptosis in the trajectory of tumor progression, accompanied by an upward trend of CDKN2A. CDKN2A underwent transcriptional activation by MEF2D and via the SNHG7/miR-133b axis, upregulating glycolysis, copper metabolism and copper ion efflux. CDKN2A likely drives epithelial-mesenchymal transition (EMT) and progression by activating Wnt signaling. CDKN2A is associated with high genomic instability and sensitivity to radiation and chemotherapy. Tumor regions expressing CDKN2A exhibit distinctive SPP1+ tumor-associated macrophage (TAM) infiltration and MMP7 enrichment, along with unique signaling crosstalk with adjacent areas.

CONCLUSIONS

CDKN2A mediates cuproptosis resistance through regulating glycolysis and copper homeostasis, accompanied by a malignant phenotype and pro-tumor niche. Radiation and chemotherapy are expected to potentially serve as therapeutic approaches for cuproptosis-resistant colorectal cancer with high CDKN2A expression.

Up-regulation of RAN by MYBL2 maintains osteosarcoma cancer stem-like cells population during heterogeneous tumor generation

Intratumor heterogeneity is one of the major features of cancers, leading to aggressive disease and treatment failure. Cancer stem-like cells (CSCs) are believed to give rise to the heterogeneous cell types within tumors. Hence, understanding the regulatory mechanism underlying the recurrence process of heterogeneous tumor by CSCs could facilitate the development of CSC-targeted therapies. Here, utilizing single-cell transcriptomics, we present the molecular profile of osteosarcoma CSCs-derived heterogeneous tumors consisting of CSC clusters, osteoprogenitor and differentiated cell types, such as pre-osteoblasts, osteoblasts and chondroblasts. Furthermore, by constructing the comprehensive map of modulated genes during CSCs self-renewal and differentiation, we identify RAN exhibiting specific peak expression in osteosarcoma CSCs clusters which is transcriptionally up-regulated by MYBL2. Functionality, MYBL2-RAN pathway promotes the CSCs self-renewal by enhancing the nuclear accumulation of MYC protein, which in turn boosts the overexpression of RAN as a positive feedback. Importantly, blockage of MYBL2-RAN pathway sensitizes CSCs to cisplatin treatment and synergistically enhanced the cisplatin-induced cytotoxicity. Both MYBL2 and RAN are highly expressed in clinical osteosarcoma tissues which indicate poor prognosis. Collectively, our study provides advanced insights into the regeneration process of heterogeneous tumor originating from CSCs and highlights the MYBL2-RAN pathway as a promising target for CSC-based therapy in osteosarcoma.

Single-cell analysis reveals the transcriptional alterations in the submandibular glands of aged mice

OBJECTIVES

Aging-related salivary gland changes, such as lymphocyte infiltration and acinar cell loss decrease saliva secretion, thereby affecting quality of life. The precise molecular mechanisms underlying these changes remain unclear.

METHODS

We here performed single-cell RNA sequencing to clarify gene expression changes in each cell type comprising the submandibular glands (SMGs) of adult and aged mice.

RESULTS

The proportion of acinar cells decreased in various epithelial clusters annotated with cell type-specific marker genes. Expression levels of the cellular senescence markers, Cdkn2a/p16 and Cdkn1a/p21, were increased in the basal and striated ducts of aged SMGs relative to their levels in those of adult SMGs. In contrast, senescence-associated secretory phenotype-related genes, except transforming growth factor-β, exhibited little change in expression in aged SMGs relative to adult SMGs.

CONCLUSIONS

Gene Ontology analysis revealed increased expression levels of genes encoding major histocompatibility complex (MHC) class I components in the ductal component cells of aged SMGs. MHC class I expression may thus be associated with salivary gland aging.

Application of single-cell RNA sequencing in head and neck squamous cell carcinoma

Single-cell RNA sequencing has been broadly applied to head and neck squamous cell carcinoma (HNSCC) for characterizing the heterogeneity and genomic mutations of HNSCC benefiting from the advantage of single-cell resolution. We summarized most of the current studies and aimed to explore their research methods and ideas, as well as how to transform them into clinical applications. Through single-cell RNA sequencing, we found the differences in tumor cells' expression programs and differentiation tracks. The studies of immune microenvironment allowed us to distinguish immune cell subpopulations, the extensive expression of immune checkpoints, and the complex crosstalk network between immune cells and non-immune cells. For cancer-associated fibroblasts (CAFs), single-cell RNA sequencing had made an irreplaceable contribution to the exploration of their differentiation status, specific CAFs markers, and the interaction with tumor cells and immune cells. In addition, we demonstrated in detail how single-cell RNA sequencing explored the HNSCC epithelial-to-mesenchymal transition (EMT) model and the mechanism of drug resistance, as well as its clinical value.

Genetically engineered mouse models of head and neck cancers

The head and neck region is one of the anatomic sites commonly afflicted by cancer, with ~1.5 million new diagnoses reported worldwide in 2020 alone. Remarkable progress has been made in understanding the underlying disease mechanisms, personalizing care based on each tumor's individual molecular characteristics, and even therapeutically exploiting the inherent vulnerabilities of these neoplasms. In this regard, genetically engineered mouse models (GEMMs) have played an instrumental role. While progress in the development of GEMMs has been slower than in other major cancer types, several GEMMs are now available that recapitulate most of the heterogeneous characteristics of head and neck cancers such as the tumor microenvironment. Different approaches have been employed in GEMM development and implementation, though each can generally recapitulate only certain disease aspects. As a result, appropriate model selection is essential for addressing specific research questions. In this review, we present an overview of all currently available head and neck cancer GEMMs, encompassing models for head and neck squamous cell carcinoma, nasopharyngeal carcinoma, and salivary and thyroid gland carcinomas.

Approaches for single-cell RNA sequencing across tissues and cell types

Single-cell sequencing of RNA (scRNA-seq) has advanced our understanding of cellular heterogeneity and signaling in developmental biology and disease. A large number of complementary assays have been developed to profile transcriptomes of individual cells, also in combination with other readouts, such as chromatin accessibility or antibody-based analysis of protein surface markers. As scRNA-seq technologies are advancing fast, it is challenging to establish robust workflows and up-to-date protocols that are best suited to address the large range of research questions. Here, we review scRNA-seq techniques from mRNA end-counting to total RNA in relation to their specific features and outline the necessary sample preparation steps and quality control measures. Based on our experience in dealing with the continuously growing portfolio from the perspective of a central single-cell facility, we aim to provide guidance on how workflows can be best automatized and share our experience in coping with the continuous expansion of scRNA-seq techniques.

Single-cell technologies in multiple myeloma: new insights into disease pathogenesis and translational implications

Multiple myeloma (MM) is a hematological malignancy characterized by clonal proliferation of plasma cells. Although therapeutic advances have been made to improve clinical outcomes and to prolong patients' survival in the past two decades, MM remains largely incurable. Single-cell sequencing (SCS) is a powerful method to dissect the cellular and molecular landscape at single-cell resolution, instead of providing averaged results. The application of single-cell technologies promises to address outstanding questions in myeloma biology and has revolutionized our understanding of the inter- and intra-tumor heterogeneity, tumor microenvironment, and mechanisms of therapeutic resistance in MM. In this review, we summarize the recently developed SCS methodologies and latest MM research progress achieved by single-cell profiling, including information regarding the cancer and immune cell landscapes, tumor heterogeneities, underlying mechanisms and biomarkers associated with therapeutic response and resistance. We also discuss future directions of applying transformative SCS approaches with contribution to clinical translation.

Evaluating the transcriptional landscape and cell-cell communication networks in chronically irradiated parotid glands

Understanding the transcriptional landscape that results in chronic salivary hypofunction after irradiation will help identify injury mechanisms and develop regenerative therapies. We present scRNA-seq analysis from control and irradiated murine parotid glands collected 10 months after irradiation. We identify a population of secretory cells defined by specific expression of Etv1, which may be an acinar cell precursor. Acinar and Etv1+ secretory express Ntrk2 and Erbb3, respectively while the ligands for these receptors are expressed in myoepithelial and stromal cells. Furthermore, our data suggests that secretory cells and CD4+CD8+T-cells are the most transcriptionally affected during chronic injury with radiation, suggesting active immune involvement. Lastly, evaluation of cell-cell communication networks predicts that neurotrophin, neuregulin, ECM, and immune signaling are dysregulated after irradiation, and thus may play a role in the lack of repair. This resource will be helpful to understand cell-specific pathways that may be targeted to repair chronic damage in irradiated glands.

A clustering method for small scRNA-seq data based on subspace and weighted distance

Background

Identifying the cell types using unsupervised methods is essential for scRNA-seq research. However, conventional similarity measures introduce challenges to single-cell data clustering because of the high dimensional, high noise, and high dropout.

Methods

We proposed a clustering method for small ScRNA-seq data based on Subspace and Weighted Distance (SSWD), which follows the assumption that the sets of gene subspace composed of similar density-distributing genes can better distinguish cell groups. To accurately capture the intrinsic relationship among cells or genes, a new distance metric that combines Euclidean and Pearson distance through a weighting strategy was proposed. The relative Calinski-Harabasz (CH) index was used to estimate the cluster numbers instead of the CH index because it is comparable across degrees of freedom.

Results

We compared SSWD with seven prevailing methods on eight publicly scRNA-seq datasets. The experimental results show that the SSWD has better clustering accuracy and the partitioning ability of cell groups. SSWD can be downloaded at https://github.com/ningzilan/SSWD.

scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data

Single cell RNA sequencing (scRNA-seq) enables researchers to characterize transcriptomic profiles at the single-cell resolution with increasingly high throughput. Clustering is a crucial step in single cell analysis. Clustering analysis of transcriptome profiled by scRNA-seq can reveal the heterogeneity and diversity of cells. However, single cell study still remains great challenges due to its high noise and dimension. Subspace clustering aims at discovering the intrinsic structure of data in unsupervised fashion. In this paper, we propose a deep sparse subspace clustering method scDSSC combining noise reduction and dimensionality reduction for scRNA-seq data, which simultaneously learns feature representation and clustering via explicit modelling of scRNA-seq data generation. Experiments on a variety of scRNA-seq datasets from thousands to tens of thousands of cells have shown that scDSSC can significantly improve clustering performance and facilitate the interpretability of clustering and downstream analysis. Compared to some popular scRNA-deq analysis methods, scDSSC outperformed state-of-the-art methods under various clustering performance metrics.

Immune related biomarkers for cancer metastasis to the brain

Brain metastasis accounts for a large number of cancer-related deaths. The host immune system, involved at each step of the metastatic cascade, plays an important role in both the initiation of the brain metastasis and their treatment responses to various modalities, through either local and or systemic effect. However, few reliable immune biomarkers have been identified in predicting the development and the treatment outcome in patients with cancer brain metastasis. Here, we provide a focused perspective of immune related biomarkers for cancer metastasis to the brain and a thorough discussion of the potential utilization of specific biomarkers such as tumor mutation burden (TMB), genetic markers, circulating and tumor-infiltrating immune cells, cytokines, in predicting the brain disease progression and regression after therapeutic intervention. We hope to inspire the field to extend the research and establish practical guidelines for developing and validating immune related biomarkers to provide personalized treatment and improve treatment outcomes in patients with metastatic brain cancers.

Cell Differentiation Trajectory Predicts Prognosis and Immunotherapeutic Response in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the main type of malignancy in kidney related to glucose metabolism. Primary single cell culture and single cell sequencing are novel research technologies. In this study, we explored the differentiation status of ccRCC cells and its significance in prognosis and immunotherapeutic response through bioinformatics. We characterized distinct differentiation states and differentiation-related genes (DRGs) in ccRCC cells through single cell RNA sequencing (scRNA-seq) analysis. Combined with bulk RNA-seq data, we classified patients into two clusters and found that this classification was closely correlated with patient prognosis and immunotherapeutic responses. Based on machine learning, we identified a prognostic risk model composed of 14 DRGs, including BTG2, CDKN1A, COL6A1, CPM, CYB5D2, FOSB, ID2, ISG15, PLCG2, SECISBP2, SOCS3, TES, ZBTB16, and ZNF704, to predict the survival rate of patients and then constructed a nomogram model integrating clinicopathological characteristics and risk score for clinical practice. In the study of immune checkpoints, we found that patients in the high-risk group had a disposition to get worse prognosis and better effects of immune checkpoint blocking therapies. Finally, we found the expression level of model DRGs was associated with a tumor-immune microenvironment (TIME) pattern and the response of 83 compounds or inhibitors was significantly different in the two risk groups. In a word, our study highlights the potential contribution of cell differentiation in prognosis judgment and immunotherapy response and offers promising therapeutic options for ccRCC patients.

S100A6 as a Constituent and Potential Marker of Adult and Cancer Stem Cells

Adult or tissue stem cells are present in various tissues of the organism where they reside in a specific environment called the niche. Owing to their ability to generate a progeny that can proliferate and differentiate into specialized cell types, adult stem cells constitute a source of new cells necessary for tissue maintenance and/or regeneration. Under normal conditions they divide with a frequency matching the pace of tissue renewal but, following tissue damage, they can migrate to the site of injury and expand/divide intensively to facilitate tissue repair. For this reason much hope is being placed on the use of adult stem cells in regenerative therapies, including tissue engineering. Identification and characterization of tissue stem cells has been a laborious process due to their scarcity and lack of universal markers. Nonetheless, recent studies, employing various types of transcriptomic analyses, revealed some common trends in gene expression pattern among stem cells derived from different tissues, suggesting the importance of certain genes/proteins for the unique properties of these cells. S100A6, a small calcium binding protein, has been recognized as an important factor influencing cell proliferation and differentiation. Accumulating results show that S100A6 is a constituent of adult stem cells and, in some cases, may even be considered as their marker. Thus, in this review we summarize literature data concerning the presence of S100A6 in adult and cancer stem cells and speculate on its potential role and usefulness as a marker of these cells.

Salivary gland function, development, and regeneration

Salivary glands produce and secrete saliva, which is essential for maintaining oral health and overall health. Understanding both the unique structure and physiological function of salivary glands, as well as how they are affected by disease and injury, will direct the development of therapy to repair and regenerate them. Significant recent advances, particularly in the OMICS field, increase our understanding of how salivary glands develop at the cellular, molecular, and genetic levels: the signaling pathways involved, the dynamics of progenitor cell lineages in development, homeostasis, and regeneration, and the role of the extracellular matrix microenvironment. These provide a template for cell and gene therapies as well as bioengineering approaches to repair or regenerate salivary function.

Characterization of gene cluster heterogeneity in single-cell transcriptomic data within and across cancer types

Despite the remarkable progress in probing tumor transcriptomic heterogeneity by single-cell RNA sequencing (sc-RNAseq) data, several gaps exist in prior studies. Tumor heterogeneity is frequently mentioned but not quantified. Clustering analyses typically target cells rather than genes, and differential levels of transcriptomic heterogeneity of gene clusters are not characterized. Relations between gene clusters inferred from multiple datasets remain less explored. We provided a series of quantitative methods to analyze cancer sc-RNAseq data. First, we proposed two quantitative measures to assess intra-tumoral heterogeneity/homogeneity. Second, we established a hierarchy of gene clusters from sc-RNAseq data, devised an algorithm to reduce the gene cluster hierarchy to a compact structure, and characterized the gene clusters with functional enrichment and heterogeneity. Third, we developed an algorithm to align the gene cluster hierarchies from multiple datasets to a small number of meta gene clusters. By applying these methods to nine cancer sc-RNAseq datasets, we discovered that cancer cell transcriptomes were more homogeneous within tumors than the accompanying normal cells. Furthermore, many gene clusters from the nine datasets were aligned to two large meta gene clusters, which had high and low heterogeneity and were enriched with distinct functions. Finally, we found the homogeneous meta gene cluster retained stronger expression coherence and associations with survival times in bulk level RNAseq data than the heterogeneous meta gene cluster, yet the combinatorial expression patterns of breast cancer subtypes in bulk level data were not preserved in single-cell data. The inference outcomes derived from nine cancer sc-RNAseq datasets provide insights about the contributing factors for transcriptomic heterogeneity of cancer cells and complex relations between bulk level and single-cell RNAseq data. They demonstrate the utility of our methods to enable a comprehensive characterization of co-expressed gene clusters in a wide range of sc-RNAseq data in cancers and beyond.

Summary: We propose quantitative methods to analyze cancer sc-RNAseq data: measures of intra-tumoral heterogeneity, characterization of a hierarchy of gene clusters, and alignment of gene cluster hierarchies from multiple datasets.

Deciphering the Immune-Tumor Interplay During Early-Stage Lung Cancer Development via Single-Cell Technology

Lung cancer is the leading cause of cancer-related death worldwide. Cancer immunotherapy has shown great success in treating advanced-stage lung cancer but has yet been used to treat early-stage lung cancer, mostly due to lack of understanding of the tumor immune microenvironment in early-stage lung cancer. The immune system could both constrain and promote tumorigenesis in a process termed immune editing that can be divided into three phases, namely, elimination, equilibrium, and escape. Current understanding of the immune response toward tumor is mainly on the "escape" phase when the tumor is clinically detectable. The detailed mechanism by which tumor progenitor lesions was modulated by the immune system during early stage of lung cancer development remains elusive. The advent of single-cell sequencing technology enables tumor immunologists to address those fundamental questions. In this perspective, we will summarize our current understanding and big gaps about the immune response during early lung tumorigenesis. We will then present the state of the art of single-cell technology and then envision how single-cell technology could be used to address those questions. Advances in the understanding of the immune response and its dynamics during malignant transformation of pre-malignant lesion will shed light on how malignant cells interact with the immune system and evolve under immune selection. Such knowledge could then contribute to the development of precision and early intervention strategies toward lung malignancy.

The role of salivary gland macrophages in infection, disease and repair

Macrophages are mononuclear innate immune cells which have become of increasing interest in the fields of disease and regeneration, as their non-classical functions have been elucidated in addition to their classical inflammatory functions. Macrophages can regulate tissue remodeling, by both mounting and reducing inflammatory responses; and exhibit direct communication with other cells to drive tissue turnover and cell replacement. Furthermore, macrophages have recently become an attractive therapeutic target to drive tissue regeneration. The major salivary glands are glandular tissues that are exposed to pathogens through their close connection with the oral cavity. Moreover, there are a number of diseases that preferentially destroy the salivary glands, causing irreversible injury, highlighting the need for a regenerative strategy. However, characterization of macrophages in the mouse and human salivary glands is sparse and has been mostly determined from studies in infection or autoimmune pathologies. In this review, we describe the current literature around salivary gland macrophages, and speculate about the niches they inhabit and how their role in development, regeneration and cancer may inform future therapeutic advances.

Utilizing graph machine learning within drug discovery and development

Graph machine learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets - amongst other data types. Herein, we present a multidisciplinary academic-industrial review of the topic within the context of drug discovery and development. After introducing key terms and modelling approaches, we move chronologically through the drug development pipeline to identify and summarize work incorporating: target identification, design of small molecules and biologics, and drug repurposing. Whilst the field is still emerging, key milestones including repurposed drugs entering in vivo studies, suggest GML will become a modelling framework of choice within biomedical machine learning.

The current knowledge concerning solid cancer and therapy

Solid cancers comprise a large number of new cases and deaths from cancer each year globally. There are a number of strategies for addressing tumors raised from solid organs including surgery, chemotherapy, radiotherapy, targeted therapy, immunotherapy, combinational therapy, and stem cell and extracellular vesicle (EV) therapy. Surgery, radiotherapy, and chemotherapy are the dominant cures, but are not always effective, in which even in a localized tumor there is a possibility of tumor relapse after surgical resection. Over half of the cancer patients will receive radiotherapy as a part of their therapeutic schedule. Radiotherapy can cause an abscopal response for boosting the activity of the immune system outside the local field of radiation, but it may also cause an unwanted bystander effect, predisposing nonradiated cells into carcinogenesis. In the context of immunotherapy, immune checkpoint inhibition is known as the standard-of-care, but the major concern is in regard with cold cancers that show low responses to such therapy. Stem-cell therapy can be used to send prodrugs toward the tumor area; this strategy, however, has its own predicaments, such as unwanted attraction toward the other sites including healthy tissues and its instability. A substitute to such therapy and quite a novel strategy is to use EVs, by virtue of their stability and potential to cross biological barriers and long-term storage of contents. Combination therapy is the current focus. Despite advances in the field, there are still unmet concerns in the area of effective cancer therapy, raising challenges and opportunities for future investigations.

Current Insights and Advancements in Head and Neck Cancer: Emerging Biomarkers and Therapeutics with Cues from Single Cell and 3D Model Omics Profiling

Head and neck cancer (HNC) is among the ten leading malignancies worldwide, with India solely contributing one-third of global oral cancer cases. The current focus of all cutting-edge strategies against this global malignancy are directed towards the heterogeneous tumor microenvironment that obstructs most treatment blueprints. Subsequent to the portrayal of established information, the review details the application of single cell technology, organoids and spheroid technology in relevance to head and neck cancer and the tumor microenvironment acknowledging the resistance pattern of the heterogeneous cell population in HNC. Bioinformatic tools are used for study of differentially expressed genes and further omics data analysis. However, these tools have several challenges and limitations when analyzing single-cell gene expression data that are discussed briefly. The review further examines the omics of HNC, through comprehensive analyses of genomics, transcriptomics, proteomics, metabolomics, and epigenomics profiles. Patterns of alterations vary between patients, thus heterogeneity and molecular alterations between patients have driven the clinical significance of molecular targeted therapies. The analyses of potential molecular targets in HNC are discussed with connotation to the alteration of key pathways in HNC followed by a comprehensive study of protein kinases as novel drug targets including its ATPase and additional binding pockets, non-catalytic domains and single residues. We herein review, the therapeutic agents targeting the potential biomarkers in light of new molecular targeted therapies. In the final analysis, this review suggests that the development of improved target-specific personalized therapies can combat HNC's global plight.

Temporal omics analysis in Syrian hamsters unravel cellular effector responses to moderate COVID-19

In COVID-19, immune responses are key in determining disease severity. However, cellular mechanisms at the onset of inflammatory lung injury in SARS-CoV-2 infection, particularly involving endothelial cells, remain ill-defined. Using Syrian hamsters as a model for moderate COVID-19, we conduct a detailed longitudinal analysis of systemic and pulmonary cellular responses, and corroborate it with datasets from COVID-19 patients. Monocyte-derived macrophages in lungs exert the earliest and strongest transcriptional response to infection, including induction of pro-inflammatory genes, while epithelial cells show weak alterations. Without evidence for productive infection, endothelial cells react, depending on cell subtypes, by strong and early expression of anti-viral, pro-inflammatory, and T cell recruiting genes. Recruitment of cytotoxic T cells as well as emergence of IgM antibodies precede viral clearance at day 5 post infection. Investigating SARS-CoV-2 infected Syrian hamsters thus identifies cell type-specific effector functions, providing detailed insights into pathomechanisms of COVID-19 and informing therapeutic strategies.

Exploiting Single-Cell Tools in Gene and Cell Therapy

Single-cell molecular tools have been developed at an incredible pace over the last five years as sequencing costs continue to drop and numerous molecular assays have been coupled to sequencing readouts. This rapid period of technological development has facilitated the delineation of individual molecular characteristics including the genome, transcriptome, epigenome, and proteome of individual cells, leading to an unprecedented resolution of the molecular networks governing complex biological systems. The immense power of single-cell molecular screens has been particularly highlighted through work in systems where cellular heterogeneity is a key feature, such as stem cell biology, immunology, and tumor cell biology. Single-cell-omics technologies have already contributed to the identification of novel disease biomarkers, cellular subsets, therapeutic targets and diagnostics, many of which would have been undetectable by bulk sequencing approaches. More recently, efforts to integrate single-cell multi-omics with single cell functional output and/or physical location have been challenging but have led to substantial advances. Perhaps most excitingly, there are emerging opportunities to reach beyond the description of static cellular states with recent advances in modulation of cells through CRISPR technology, in particular with the development of base editors which greatly raises the prospect of cell and gene therapies. In this review, we provide a brief overview of emerging single-cell technologies and discuss current developments in integrating single-cell molecular screens and performing single-cell multi-omics for clinical applications. We also discuss how single-cell molecular assays can be usefully combined with functional data to unpick the mechanism of cellular decision-making. Finally, we reflect upon the introduction of spatial transcriptomics and proteomics, its complementary role with single-cell RNA sequencing (scRNA-seq) and potential application in cellular and gene therapy.

Applications of single-cell sequencing in cancer research: progress and perspectives

Single-cell sequencing, including genomics, transcriptomics, epigenomics, proteomics and metabolomics sequencing, is a powerful tool to decipher the cellular and molecular landscape at a single-cell resolution, unlike bulk sequencing, which provides averaged data. The use of single-cell sequencing in cancer research has revolutionized our understanding of the biological characteristics and dynamics within cancer lesions. In this review, we summarize emerging single-cell sequencing technologies and recent cancer research progress obtained by single-cell sequencing, including information related to the landscapes of malignant cells and immune cells, tumor heterogeneity, circulating tumor cells and the underlying mechanisms of tumor biological behaviors. Overall, the prospects of single-cell sequencing in facilitating diagnosis, targeted therapy and prognostic prediction among a spectrum of tumors are bright. In the near future, advances in single-cell sequencing will undoubtedly improve our understanding of the biological characteristics of tumors and highlight potential precise therapeutic targets for patients.

Co-delivery of IOX1 and doxorubicin for antibody-independent cancer chemo-immunotherapy

Anti-programmed cell death-1 (PD-1)/programmed cell death-ligand 1 (PD-L1) antibodies are currently used in the clinic to interupt the PD-1/PD-L1 immune checkpoint, which reverses T cell dysfunction/exhaustion and shows success in treating cancer. Here, we report a histone demethylase inhibitor, 5-carboxy-8-hydroxyquinoline (IOX1), which inhibits tumour histone demethylase Jumonji domain-containing 1A (JMJD1A) and thus downregulates its downstream β-catenin and subsequent PD-L1, providing an antibody-independent paradigm interrupting the PD-1/PD-L1 checkpoint. Synergistically, IOX1 inhibits cancer cells’ P-glycoproteins (P-gp) through the JMJD1A/β-catenin/P-gp pathway and greatly enhances doxorubicin (DOX)-induced immune-stimulatory immunogenic cell death. As a result, the IOX1 and DOX combination greatly promotes T cell infiltration and activity and significantly reduces tumour immunosuppressive factors. Their liposomal combination reduces the growth of various murine tumours, including subcutaneous, orthotopic, and lung metastasis tumours, and offers a long-term immunological memory function against tumour rechallenging. This work provides a small molecule-based potent cancer chemo-immunotherapy.

Some chemotherapeutic drugs, such as doxorubicin, induce immunogenic cell death (ICD) and promote anti-tumor immune responses. Here the authors report that the histone demethylase inhibitor 5-carboxy-8-hydroxyquinoline (IOX1) reduces the expression of PD-L1 in cancer cells and enhances doxorubicin-induced ICD, promoting T cell infiltration and reducing tumor growth in preclinical models.

Non-genetic heterogeneity, altered cell fate and differentiation therapy

Altered capacity for self-renewal and differentiation is a hallmark of cancer, and many tumors are composed of cells with a developmentally immature phenotype. Among the malignancies where processes that govern cell fate decisions have been studied most extensively is acute myeloid leukemia (AML), a disease characterized by the presence of large numbers of "blasts" that resemble myeloid progenitors. Classically, the defining properties of AML cells were said to be aberrant self-renewal and a block of differentiation, and the term "differentiation therapy" was coined to describe drugs that promote the maturation of leukemic blasts. Notionally however, the simplistic view that such agents "unblock" differentiation is at odds with the cancer stem cell (CSC) hypothesis that posits that tumors are hierarchically organized and that CSCs, which underpin cancer growth, retain the capacity to progress to a developmentally more mature state. Herein, we will review recent developments that are providing unprecedented insights into non-genetic heterogeneity both at steady state and in response to treatment, and propose a new conceptual framework for therapies that aim to alter cell fate decisions in cancer.

Joint probabilistic modeling of single-cell multi-omic data with totalVI

The paired measurement of RNA and surface proteins in single cells with cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) is a promising approach to connect transcriptional variation with cell phenotypes and functions. However, combining these paired views into a unified representation of cell state is made challenging by the unique technical characteristics of each measurement. Here we present Total Variational Inference (totalVI; https://scvi-tools.org ), a framework for end-to-end joint analysis of CITE-seq data that probabilistically represents the data as a composite of biological and technical factors, including protein background and batch effects. To evaluate totalVI's performance, we profiled immune cells from murine spleen and lymph nodes with CITE-seq, measuring over 100 surface proteins. We demonstrate that totalVI provides a cohesive solution for common analysis tasks such as dimensionality reduction, the integration of datasets with different measured proteins, estimation of correlations between molecules and differential expression testing.

Entropy subspace separation-based clustering for noise reduction (ENCORE) of scRNA-seq data

Single-cell RNA sequencing enables us to characterize the cellular heterogeneity in single cell resolution with the help of cell type identification algorithms. However, the noise inherent in single-cell RNA-sequencing data severely disturbs the accuracy of cell clustering, marker identification and visualization. We propose that clustering based on feature density profiles can distinguish informative features from noise. We named such strategy as 'entropy subspace' separation and designed a cell clustering algorithm called ENtropy subspace separation-based Clustering for nOise REduction (ENCORE) by integrating the 'entropy subspace' separation strategy with a consensus clustering method. We demonstrate that ENCORE performs superiorly on cell clustering and generates high-resolution visualization across 12 standard datasets. More importantly, ENCORE enables identification of group markers with biological significance from a hard-to-separate dataset. With the advantages of effective feature selection, improved clustering, accurate marker identification and high-resolution visualization, we present ENCORE to the community as an important tool for scRNA-seq data analysis to study cellular heterogeneity and discover group markers.

DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations.

(Oligo)metastasis as a Spectrum of Disease

Cancer metastasis is the leading cause of cancer-related mortality, and most patients with metastases from solid tumors have historically been considered incurable. Here, we discuss the evolution of our understanding of the oligometastatic state with an emphasis on the view that cancer metastasis represents a spectrum of disease. We highlight several recently published prospective clinical trials demonstrating improvements in cancer-specific outcomes with the utilization of metastasis-directed local therapies. We discuss biological aspects of oligometastases, including genetic, epigenetic, and immune determinants of the metastatic spectrum. Finally, we propose future considerations regarding clinical trial design for patients with oligometastatic disease.

Generation of a Single-Cell RNAseq Atlas of Murine Salivary Gland Development

Understanding the dynamic transcriptional landscape throughout organ development will provide a template for regenerative therapies. Here, we generated a single-cell RNA sequencing atlas of murine submandibular glands identifying transcriptional profiles that revealed cellular heterogeneity during landmark developmental events: end bud formation, branching morphogenesis, cytodifferentiation, maturation, and homeostasis. Trajectory inference analysis suggests plasticity among acinar and duct populations. We identify transcription factors correlated with acinar differentiation including Spdef, Etv1, and Xbp1, and loss of Ybx1, Eno1, Sox11, and Atf4. Furthermore, we characterize two intercalated duct populations defined by either Gfra3 and Kit, or Gstt1. This atlas can be used to investigate specific cell functions and comparative studies predicting common mechanisms involved in development of branching organs.

Benchmarking algorithms for pathway activity transformation of single-cell RNA-seq data

Biological pathway analysis provides new insights for cell clustering and functional annotation from single-cell RNA sequencing (scRNA-seq) data. Many pathway analysis algorithms have been developed to transform gene-level scRNA-seq data into functional gene sets representing pathways or biological processes. Here, we collected seven widely-used pathway activity transformation algorithms and 32 available datasets based on 16 scRNA-seq techniques. We proposed a comprehensive framework to evaluate their accuracy, stability and scalability. The assessment of scRNA-seq preprocessing showed that cell filtering had the less impact on scRNA-seq pathway analysis, while data normalization of sctransform and scran had a consistent well impact across all tools. We found that Pagoda2 yielded the best overall performance with the highest accuracy, scalability, and stability. Meanwhile, the tool PLAGE exhibited the highest stability, as well as moderate accuracy and scalability.

Glioblastoma cell differentiation trajectory predicts the immunotherapy response and overall survival of patients

Glioblastoma (GBM) is the most common and lethal primary brain tumor. In this study, we aimed to investigate the differentiation states of GBM cells and their clinical relevance. Integrated single-cell RNA-sequencing (scRNA-seq) data and bulk RNA-seq data from GBM samples were used for analysis. Two subsets of GBM cells in distinct differentiation states were characterized, and 498 GBM cell differentiation-related genes (GDRGs) were identified. GDRGs were significantly correlated with immune regulation and metabolic pathways. We classified the GBM patients into two groups based on the expression of GDRGs in tumors and found that the cell differentiation-based classification successfully predicted patient overall survival (OS), immune checkpoint expression and likelihood of immunotherapy response in GBMs. FN1, APOE, RPL7A and GSTM2 were the 4 most significant survival-predicting GDRGs, and patients with different expression levels of each of these genes had distinct survival outcomes. Finally, a nomogram composed of the GDRG signature, age, pharmacotherapy, radiotherapy, IDH mutations and MGMT promoter methylation was generated and validated in two large GBM cohorts to predict GBM prognosis. This study highlights the significant roles of cell differentiation in predicting the clinical outcomes of GBM patients and their potential response to immunotherapy, suggesting promising therapeutic targets for GBM.

Elucidation of Biological Networks across Complex Diseases Using Single-Cell Omics

Single-cell multimodal omics (scMulti-omics) technologies have made it possible to trace cellular lineages during differentiation and to identify new cell types in heterogeneous cell populations. The derived information is especially promising for computing cell-type-specific biological networks encoded in complex diseases and improving our understanding of the underlying gene regulatory mechanisms. The integration of these networks could, therefore, give rise to a heterogeneous regulatory landscape (HRL) in support of disease diagnosis and drug therapeutics. In this review, we provide an overview of this field and pay particular attention to how diverse biological networks can be inferred in a specific cell type based on integrative methods. Then, we discuss how HRL can advance our understanding of regulatory mechanisms underlying complex diseases and aid in the prediction of prognosis and therapeutic responses. Finally, we outline challenges and future trends that will be central to bringing the field of HRL in complex diseases forward.

Epithelial-mesenchymal plasticity: emerging parallels between tissue morphogenesis and cancer metastasis

Many cells possess epithelial–mesenchymal plasticity (EMP), which allows them to shift reversibly between adherent, static and more detached, migratory states. These changes in cell behaviour are driven by the programmes of epithelial–mesenchymal transition (EMT) and mesenchymal–epithelial transition (MET), both of which play vital roles during normal development and tissue homeostasis. However, the aberrant activation of these processes can also drive distinct stages of cancer progression, including tumour invasiveness, cell dissemination and metastatic colonization and outgrowth. This review examines emerging common themes underlying EMP during tissue morphogenesis and malignant progression, such as the context dependence of EMT transcription factors, a central role for partial EMTs and the nonlinear relationship between EMT and MET.

This article is part of a discussion meeting issue ‘Contemporary morphogenesis'.

Computational Oncology in the Multi-Omics Era: State of the Art

Cancer is the quintessential complex disease. As technologies evolve faster each day, we are able to quantify the different layers of biological elements that contribute to the emergence and development of malignancies. In this multi-omics context, the use of integrative approaches is mandatory in order to gain further insights on oncological phenomena, and to move forward toward the precision medicine paradigm. In this review, we will focus on computational oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. We will discuss the current roles of computation in oncology in the context of multi-omic technologies, which include: data acquisition and processing; data management in the clinical and research settings; classification, diagnosis, and prognosis; and the development of models in the research setting, including their use for therapeutic target identification. We will discuss the machine learning and network approaches as two of the most promising emerging paradigms, in computational oncology. These approaches provide a foundation on how to integrate different layers of biological description into coherent frameworks that allow advances both in the basic and clinical settings.