Understanding cell-cell communication and signaling in the colorectal cancer microenvironment

Integration of single-nucleus and exosome RNA sequencing dissected inter-cellular communication and biomarkers in pancreatic ductal adenocarcinoma

Background

Mounting evidence underscores the importance of cell communication within the tumor microenvironment, which is pivotal in tumor proliferation, invasion, and metastasis. Exosomes play a crucial role in cell-to-cell communication. Although single-cell RNA sequencing (scRNA-seq) provides insights into individual cell transcriptional characteristics, it falls short of comprehensively capturing exosome-mediated intercellular communication.

Method

We analyzed Pancreatic Ductal Adenocarcinoma (PDAC) tissues, separating supernatant and precipitate for exosome purification and single-cell nucleus suspension. We then constructed Single-nucleus RNA sequencing (snRNA-seq) and small RNA-seq libraries from these components. Our bioinformatic analysis integrated these sequences with ligand-receptor analysis and public miRNA data to map the cell communication network.

Results

We established intercellular communication networks using bioinformatic analysis to track exosome miRNA effects and ligand-receptor pairs. Significantly, hsa-miR-1293 emerged as a prognostic biomarker for pancreatic cancer, linked to immune evasion, increased myeloid-derived suppressor cells, and poorer prognosis. Targeting this miRNA may enhance anti-tumor immunity and improve outcomes.

Conclusion

Our study offers a novel approach to constructing intercellular communication networks using snRNA-seq and exosome-small RNA sequencing. By integrating miRNA tracing with ligand-receptor analysis, we illuminate the complex interactions in the pancreatic cancer microenvironment, highlighting the pivotal role of miRNAs and identifying potential biomarkers and therapeutic targets.

Single-cell analysis of tumor microenvironment and cell adhesion reveals that interleukin-1 beta promotes cancer cell proliferation in breast cancer

BACKGROUND

Triple-negative breast cancer (TNBC), which is so called because of the lack of estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptor 2 (HER2) receptors on the cancer cells, accounts for 10%-15% of all breast cancers. The heterogeneity of the tumor microenvironment is high. However, the role of plasma cells controlling the tumor migration progression in TNBC is still not fully understood.

METHODS

We analyzed single-cell RNA sequencing data from five HER2 positive, 12 ER positive/PR positive, and nine TNBC samples. The potential targets were validated by immunohistochemistry.

RESULTS

Plasma cells were enriched in TNBC samples, which was consistent with validation using data from The Cancer Genome Atlas. Cell communication analysis revealed that plasma cells interact with T cells through the intercellular adhesion molecule 2-integrin-aLb2 complex, and then release interleukin 1 beta (IL1B), as verified by immunohistochemistry, ultimately promoting tumor growth.

CONCLUSION

Our results revealed the role of plasma cells in TNBC and identified IL1B as a new prognostic marker for TNBC.

Exploration of the shared pathways and common biomarker in adamantinomatous craniopharyngioma and type 2 diabetes using integrated bioinformatics analysis

Craniopharyngiomas are rare tumors of the central nervous system that typically present with symptoms such as headache and visual impairment, and those reflecting endocrine abnormalities, which seriously affect the quality of life of patients. Patients with craniopharyngiomas are at higher cardiometabolic risk, defined as conditions favoring the development of type 2 diabetes and cardiovascular disease. However, the underlying common pathogenic mechanisms of craniopharyngiomas and type 2 diabetes are not clear. Especially due to the difficulty of conducting in vitro or in vivo experiments on craniopharyngioma, we thought the common pathway analysis between craniopharyngioma and type 2 diabetes based on bioinformatics is a powerful and feasible method. In the present study, using public datasets (GSE94349, GSE68015, GSE38642 and GSE41762) obtained from the GEO database, the gene expression associated with adamantinomatous craniopharyngioma, a subtype of craniopharyngioma, and type 2 diabetes were analyzed using a bioinformatic approach. We found 11 hub genes using a protein-protein interaction network analysis. Of these, seven (DKK1, MMP12, KRT14, PLAU, WNT5B, IKBKB, and FGF19) were also identified by least absolute shrinkage and selection operator analysis. Finally, single-gene validation and receptor operating characteristic analysis revealed that four of these genes (MMP12, PLAU, KRT14, and DKK1) may be involved in the common pathogenetic mechanism of adamantinomatous craniopharyngioma and type 2 diabetes. In addition, we have characterized the differences in immune cell infiltration that characterize these two diseases, providing a reference for further research.

Integrative network analysis of transcriptomics data reveals potential prognostic biomarkers for colorectal cancer

INTRODUCTION

Cross-talk among biological pathways is essential for normal biological function and plays a significant role in cancer progression. Through integrated network analysis, this study explores the significance of pathway cross-talk in colorectal cancer (CRC) development at both the pathway and gene levels.

METHODS

In this study, we integrated the gene expression data with domain knowledge to construct state-dependent pathway cross-talk networks. The significance of the genes involved in pathway cross-talk was assessed by analyzing their association with cancer hallmarks, disease-gene relation, genetic alterations, and survival analysis. We also analyzed the gene regulatory network to identify the dysregulated genes and their role in CRC progression.

RESULTS

Cross-talk was observed between immune-related pathways and pathways associated with cell communication and signaling. The PTPRC gene was identified as a mediator, facilitating interactions within the immune system and other signaling pathways. The rewired interactions of ITGA7 were identified as influential in the epithelial-mesenchymal transition in CRC. This study also highlighted the crucial link between cell communication and vascular smooth muscle contraction pathway in CRC progression. The survival analysis of identified gene clusters showed their significant prognostic value in distinguishing high-risk from low-risk CRC groups, and L1000CDS2 revealed seven potential drug molecules in CRC. Nine dysregulated genes (CTNNB1, EP300, JUN, MYC, NFKB1, RELA, SP1, STAT1, and TP53) emerge as transcription factors acting as common regulators across various pathways.

CONCLUSIONS

This study highlights the crucial role of pathway cross-talk in CRC progression and identified the potential prognostic biomarkers and potential drug molecules.

Pancreatic cancer environment: from patient-derived models to single-cell omics

Pancreatic cancer (PC) is a highly malignant cancer characterized by poor prognosis, high heterogeneity, and intricate heterocellular systems. Selecting an appropriate experimental model for studying its progression and treatment is crucial. Patient-derived models provide a more accurate representation of tumor heterogeneity and complexity compared to cell line-derived models. This review initially presents relevant patient-derived models, including patient-derived xenografts (PDXs), patient-derived organoids (PDOs), and patient-derived explants (PDEs), which are essential for studying cell communication and pancreatic cancer progression. We have emphasized the utilization of these models in comprehending intricate intercellular communication, drug responsiveness, mechanisms underlying tumor growth, expediting drug discovery, and enabling personalized medical approaches. Additionally, we have comprehensively summarized single-cell analyses of these models to enhance comprehension of intercellular communication among tumor cells, drug response mechanisms, and individual patient sensitivities.

Single-cell and bulk RNA-sequencing reveal SPP1 and CXCL12 as cell-to-cell communication markers to predict prognosis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) generally presents as an immunosuppressive microenvironment. The characteristics of cell-to-cell communication in the LUAD microenvironment has been unclear. In this study, the LUAD bulk RNA-seq data and single-cell RNA-seq data were retrieved from public dataset. Differential expression genes (DEGs) between LUAD tumor and adjacent non-tumor tissues were calculated by limma algorithm, and then detected by PPI, KEGG, and GO analysis. Cell-cell interactions were explored using the single-cell RNA-seq data. Finally, the first 15 CytoHubba genes were used to establish related pathways and these pathways were used to characterize the immune-related ligands and their receptors in LUAD. Our analyses showed that monocytes or macrophages interact with tissue stem cells and NK cells via SPP1 signaling pathway and tissue stem cells interact with T and B cells via CXCL signaling pathway in different states. Hub genes of SPP1 participated in SPP1 signaling pathway, which was negatively correlated with CD4+ T cell and CD8+ T cell. The expression of SPP1 in LUAD tumor tissues was negatively correlated with the prognosis. While CXCL12 participated in CXCL signaling pathway, which was positively correlated with CD4+ T cell and CD8+ T cell. The role of CXCL12 in LUAD tumor tissues exhibits an opposite effect to that of SPP1. This study reveals that tumor-associated monocytes or macrophages may affect tumor progression. Moreover, the SPP1 and CXCL12 may be the critic genes of cell-to-cell communication in LUAD, and targeting these pathways may provide a new molecular mechanism for the treatment of LUAD.

Multi-omics cluster defines the subtypes of CRC with distinct prognosis and tumor microenvironment

BACKGROUND

Colorectal cancer (CRC) is a complex malignancy characterized by diverse molecular profiles, clinical outcomes, and limited precision in prognostic markers. Addressing these challenges, this study utilized multi-omics data to define consensus molecular subtypes in CRC and elucidate their association with clinical outcomes and underlying biological processes.

METHODS

Consensus molecular subtypes were obtained by applying ten integrated multi-omics clustering algorithms to analyze TCGA-CRC multi-omics data, including mRNA, lncRNA, miRNA, DNA methylation CpG sites, and somatic mutation data. The association of subtypes with prognoses, enrichment functions, immune status, and genomic alterations were further analyzed. Next, we conducted univariate Cox and Lasso regression analyses to investigate the potential prognostic application of biomarkers associated with multi-omics subtypes derived from weighted gene co-expression network analysis (WGCNA). The function of one of the biomarkers MID2 was validated in CRC cell lines.

RESULTS

Two CRC subtypes linked to distinct clinical outcomes were identified in TCGA-CRC cohort and validated with three external datasets. The CS1 subtype exhibited a poor prognosis and was characterized by higher tumor-related Hallmark pathway activity and lower metabolism pathway activity. In addition, the CS1 was predicted to have less immunotherapy responder and exhibited more genomic alteration compared to CS2. Then a prognostic model comprising five genes was established, with patients in the high-risk group showing substantial concordance with the CS1 subtype, and those in the low-risk group with the CS2 subtype. The gene MID2, included in the prognostic model, was found to be correlated with epithelial-mesenchymal transition (EMT) pathway and distinct DNA methylation patterns. Knockdown of MID2 in CRC cells resulted in reduced colony formation, migration, and invasion capacities.

CONCLUSION

The integrative multi-omics subtypes proposed potential biomarkers for CRC and provided valuable knowledge for precision oncology.

Unlocking the power of nanomedicine: Cell membrane-derived biomimetic cancer nanovaccines for cancer treatment

Over the past decades, nanomedicine researchers have dedicated their efforts to developing nanoscale platforms capable of more precisely delivering drug payloads to attack tumors. Cancer nanovaccines are exhibiting a distinctive capability in inducing tumor-specific antitumor responses. Nevertheless, there remain numerous challenges that must be addressed for cancer nanovaccines to evoke sufficient therapeutic effects. Cell membrane-derived nanovaccines are an emerging class of cancer vaccines that comprise a synthetic nanoscale core camouflaged by naturally derived cell membranes. The specific cell membrane has a biomimetic nanoformulation with several distinctive abilities, such as immune evasion, enhanced biocompatibility, and tumor targeting, typically associated with a source cell. Here, we discuss the advancements of cell membrane-derived nanovaccines and how these vaccines are used for cancer therapeutics. Translational endeavors are currently in progress, and additional research is also necessary to effectively address crucial areas of demand, thereby facilitating the future successful translation of these emerging vaccine platforms.

Spatial transcriptomics data and analytical methods: An updated perspective

Spatial transcriptomics (ST) is a newly emerging field that integrates high-resolution imaging and transcriptomic data to enable the high-throughput analysis of the spatial localization of transcripts in diverse biological systems. The rapid progress in this field necessitates the development of innovative computational methods to effectively tackle the distinct challenges posed by the analysis of ST data. These platforms, integrating AI techniques, offer a promising avenue for understanding disease mechanisms and expediting drug discovery. Despite significant advances in the development of ST data analysis techniques, there is an ongoing need to enhance these models for increased biological relevance. In this review, we briefly discuss the ST-related databases and current deep-learning-based models for spatial transcriptome data analyses and highlight their roles and future perspectives in biomedical applications.

Single-cell and Bulk RNA-Seq reveal angiogenic heterogeneity and microenvironmental features to evaluate prognosis and therapeutic response in lung adenocarcinoma

Background

Angiogenesis stands as a pivotal hallmark in lung adenocarcinoma (LUAD), intricately shaping the tumor microenvironment (TME) and influencing LUAD progression. It emerges as a promising therapeutic target for LUAD, affecting patients' prognosis. However, its role in TME, LUAD prognosis, and its clinical applicability remain shrouded in mystery.

Methods

We employed integrated single-cell and bulk transcriptome sequencing to unravel the heterogeneity of angiogenesis within LUAD cells. Through "consensus clustering", we delineated distinct angiogenic clusters and deciphered their TME features. "Monocle2" was used to unravel divergent trajectories within malignant cell subpopulations of LUAD. Additionally, regulon submodules and specific cellular communication patterns of cells in different angiogenic states were analyzed by "pyscenic" and "Cellchat" algorithms. The "univariate Cox" and "LASSO" algorithms were applied to build angiogenic prognostic models. Immunohistochemistry (IHC) on clinical samples validated the role of model factors in LUAD angiogenesis. We utilized CTRP 2.0 and PRISM databases for pinpointing sensitive drugs against lung adenocarcinoma.

Results

Two clusters for the activation of angiogenesis were identified, with Cluster 1 showing a poor prognosis and a pro-cancerous TME. Three differentiated states of malignant epithelial LUAD cells were identified, which had different degrees of angiogenic activation, were regulated by three different regulon submodules, and had completely different crosstalk from other cells in TME. The experiments validate that SLC2A1 promotes angiogenesis in LUAD. ARS (Angiogenesis related score) had a high prognostic value; low ARSs showed immunotherapy benefits, whereas high ARSs were sensitive to 15 chemotherapeutic agents.

Conclusion

The assessment of angiogenic clusters helps to determine the prognostic and TME characteristics of LUAD. Angiogenic prognostic models can be used to assess the prognosis, immunotherapeutic response, and chemotherapeutic drug sensitivity of LUAD.

Single-cell landscape identifies the immunophenotypes and microenvironments of HBV-positive and HBV-negative liver cancer

BACKGROUND

HBV infection leads to HCC and affects immunotherapy. We are exploring the tumor ecosystem in HCC to help gain a deeper understanding and design more effective immunotherapy strategies for patients with HCC with or without HBV infection.

METHODS

Single-cell RNA sequencing series were integrated as a discovery cohort to interrogate the tumor microenvironment of HBV-positive (HBV+) HCC and HBV-negative (HBV-) HCC. We further dissect the intratumoral immune status of HBV+ HCC and HBV- HCC. An independent cohort, including samples treated with immune checkpoint blockade therapy, was used to validate the major finding and investigate the effect of HBV infection on response to immunotherapy.

RESULTS

The interrogation of tumor microenvironment indicated that regulatory T cells, exhausted CD8+ T cells, and M1-like Macrophage_MMP9 were enriched in HBV+ HCC, while mucosa-associated invariant T cells were enriched in HBV- HCC. All subclusters of T cells showed high expression of immune checkpoint genes in HBV+ HCC. Regulatory T cells enriched in HBV+ HCC also showed more robust immunosuppressive properties, which was confirmed by cross talk between immune cell subsets. The ability of antigen presentation with major histocompatibility complex-II was downregulated in HBV+ HCC and this phenomenon can be reversed by immunotherapy. Two types of HCC also present different responses to immunotherapy.

CONCLUSIONS

There is a more immunosuppressive and exhausted tumor microenvironment in HBV+ HCC than in HBV- HCC. This in-depth immunophenotyping strategy is critical to understanding the impact of HBV and the HCC immune microenvironment and helping develop more effective treatments in patients with HCC.

Decoding leukemia at the single-cell level: clonal architecture, classification, microenvironment, and drug resistance

Leukemias are refractory hematological malignancies, characterized by marked intrinsic heterogeneity which poses significant obstacles to effective treatment. However, traditional bulk sequencing techniques have not been able to effectively unravel the heterogeneity among individual tumor cells. With the emergence of single-cell sequencing technology, it has bestowed upon us an unprecedented resolution to comprehend the mechanisms underlying leukemogenesis and drug resistance across various levels, including the genome, epigenome, transcriptome and proteome. Here, we provide an overview of the currently prevalent single-cell sequencing technologies and a detailed summary of single-cell studies conducted on leukemia, with a specific focus on four key aspects: (1) leukemia's clonal architecture, (2) frameworks to determine leukemia subtypes, (3) tumor microenvironment (TME) and (4) the drug-resistant mechanisms of leukemia. This review provides a comprehensive summary of current single-cell studies on leukemia and highlights the markers and mechanisms that show promising clinical implications for the diagnosis and treatment of leukemia.

MACC: a visual interactive knowledgebase of metabolite-associated cell communications

Metabolite-associated cell communications play critical roles in maintaining the normal biological function of human through coordinating cells, organs and physiological systems. Though substantial information of MACCs has been continuously reported, no relevant database has become available so far. To address this gap, we here developed the first knowledgebase (MACC), to comprehensively describe human metabolite-associated cell communications through curation of experimental literatures. MACC currently contains: (a) 4206 carefully curated metabolite-associated cell communications pairs involving 244 human endogenous metabolites and reported biological effects in vivo and in vitro; (b) 226 comprehensive cell subtypes and 296 disease states, such as cancers, autoimmune diseases, and pathogenic infections; (c) 4508 metabolite-related enzymes and transporters, involving 542 pathways; (d) an interactive tool with user-friendly interface to visualize networks of multiple metabolite-cell interactions. (e) overall expression landscape of metabolite-associated gene sets derived from over 1500 single-cell expression profiles to infer metabolites variations across different cells in the sample. Also, MACC enables cross-links to well-known databases, such as HMDB, DrugBank, TTD and PubMed etc. In complement to ligand-receptor databases, MACC may give new perspectives of alternative communication between cells via metabolite secretion and adsorption, together with the resulting biological functions. MACC is publicly accessible at: http://macc.badd-cao.net/.

Combined signature of G protein-coupled receptors and tumor microenvironment provides a prognostic and therapeutic biomarker for skin cutaneous melanoma

BACKGROUND

G protein-coupled receptors (GPCRs) have been shown to have an important role in tumor development and metastasis, and abnormal expression of GPCRs is significantly associated with poor prognosis of tumor patients. In this study, we analyzed the GPCRs-related gene (GPRGs) and tumor microenvironment (TME) in skin cutaneous melanoma (SKCM) to construct a prognostic model to help SKCM patients obtain accurate clinical treatment strategies.

METHODS

SKCM expression data and clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differential expression analysis, LASSO algorithm, and univariate and multivariate cox regression analysis were used to screen prognosis-related genes (GPR19, GPR146, S1PR2, PTH1R, ADGRE5, CXCR3, GPR143, and OR2I1P) and multiple prognosis-good immune cells; the data set was analyzed according to above results and build up a GPR-TME classifier. The model was further subjected to immune infiltration, functional enrichment, tumor mutational load, immunotherapy prediction, and scRNA-seq data analysis. Finally, cellular experiments were conducted to validate the functionality of the key gene GPR19 in the model.

RESULTS

The findings indicate that high expression of GPRGs is associated with a poor prognosis in patients with SKCM, highlighting the significant role of GPRGs and the tumor microenvironment (TME) in SKCM development. Notably, the group characterized by low GPR expression and a high TME exhibited the most favorable prognosis and immunotherapeutic efficacy. Furthermore, cellular assays demonstrated that knockdown of GPR19 significantly reduced the proliferation, migration, and invasive capabilities of melanoma cells in A375 and A2058 cell lines.

CONCLUSION

This study provides novel insights for the prognosis evaluation and treatment of melanoma, along with the identification of a new biomarker, GPR19.

Integrin beta1 mediates the effect of telocytes on mesenchymal stem cell proliferation and migration in the treatment of acute lung injury

Co-transplantation of mesenchymal stem cells (MSCs) with telocytes (TCs) was found to have therapeutic effects, although the mechanism of intercellular communication is still unknown. Our current studies aim at exploring the potential molecular mechanisms of TCs interaction and communication with MSCs with a focus on integrin beta1 (ITGB1) in TCs. We found that the co-culture of MSCs with ITGB1-deleted TCs (TCITGB1-ko ) changed the proliferation, differentiation and growth dynamics ability of MSC in responses to LPS or PI3K inhibitor. Changes of MSC proliferation and apoptosis were accompanied with the dysregulation of cytokine mRNA expression in MSCs co-cultured with TCITGB1-ko during the exposure of PI3Kα/δ/β inhibitor, of which IL-1β, IL-6 and TNF-α increased, while IFN-γ, IL-4 and IL-10 decreased. The responses of PI3K p85, PI3K p110 and pAKT of MSCs co-cultured with TCITGB1-ko to LPS or PI3K inhibitor were opposite to those with ITGB1-presented TCs. The intraperitoneal injection of TCITGB1-ko , TCvector or MSCs alone, as well as the combination of MSCs with TCITGB1-ko or TCvector exhibited therapeutic effects on LPS-induced acute lung injury. Thus, our data indicate that telocyte ITGB1 contributes to the interaction and intercellular communication between MSCs and TCs, responsible for influencing other cell phenomes and functions.

Cordycepin remodels the tumor microenvironment of colorectal cancer by down-regulating the expression of PD-L1

PURPOSE

Colorectal cancer, as a common malignant tumor, poses a serious threat to human life. Cordycepin, derived from Cordyceps militaris extract, which was established as a capable inhibitor of tumor growth. Nevertheless, the precise antitumor mechanism of cordycepin in colorectal cancer cells remains elusive.

METHODS

Herein, our initial focus was to explore the tumor-suppressive impact of cordycepin through its influence on various biological functions in murine colorectal cancer cells, conducted by an in vitro setting. First, we investigated the tumor-suppressive effect of cordycepin on the regulation of biological functions in murine colorectal cancer cells in vitro. Furthermore, we evaluated the in vivo antitumor potential of cordycepin using a mouse preclinical tumor model, and further explored the antitumor mechanism.

RESULTS

Our findings revealed that cordycepin effectively inhibit the proliferation, invasion, and migration of murine colon cancer cells. Moreover, there is a substantial reduction in the expression of PD-L1 observed in tumor cells, in response to cordycepin treatment. Collectively, these results demonstrate the significant tumor-suppressive attributes of cordycepin against colorectal cancer. Consequently, our study lays a solid foundation for the potential clinical utilization of cordycepin in cancer therapy.

CONCLUSION

Cordycepin inhibits the biological functions of colorectal cancer cells and suppresses tumor growth by reducing the expression of PD-L1.

Cancer-Associated Fibroblast-Induced Remodeling of Tumor Microenvironment in Recurrent Bladder Cancer

Bladder carcinoma (BC) recurrence is a major clinical challenge, and targeting the tumor microenvironment (TME) is a promising therapy. However, the relationship between individual TME components, particularly cancer-associated fibroblasts (CAFs), and tumor recurrence is unclear. Here, TME heterogeneity in primary and recurrent BC is investigated using single-cell RNA sequence profiling of 62 460 cells. Two cancer stem cell (CSC) subtypes are identified in recurrent BC. An inflammatory CAF subtype, ICAM1+ iCAFs, specifically associated with BC recurrence is also identified. iCAFs are found to secrete FGF2, which acts on the CD44 receptor of rCSC-M, thereby maintaining tumor stemness and epithelial-mesenchymal transition. Additionally, THBS1+ monocytes, a group of myeloid-derived suppressor cells (MDSCs), are enriched in recurrent BC and interacted with CAFs. ICAM1+ iCAFs are found to secrete CCL2, which binds to CCR2 in MDSCs. Moreover, elevated STAT3, NFKB2, VEGFA, and CTGF levels in iCAFs reshape the TME in recurrent tumors. CCL2 inhibition in an in situ BC mouse model suppressed tumor growth, decreased MDSCs and Tregs, and fostered tumor immune suppression. The study results highlight the role of iCAFs in TME cell-cell crosstalk during recurrent BC. The identification of pivotal signaling factors driving BC relapse is promising for the development of novel therapies.

Notch receptor/ligand diversity: contribution to colorectal cancer stem cell heterogeneity

Cancer cell heterogeneity is a key contributor to therapeutic failure and post-treatment recurrence. Targeting cell subpopulations responsible for chemoresistance and recurrence seems to be an attractive approach to improve treatment outcome in cancer patients. However, this remains challenging due to the complexity and incomplete characterization of tumor cell subpopulations. The heterogeneity of cells exhibiting stemness-related features, such as self-renewal and chemoresistance, fuels this complexity. Notch signaling is a known regulator of cancer stem cell (CSC) features in colorectal cancer (CRC), though the effects of its heterogenous signaling on CRC cell stemness are only just emerging. In this review, we discuss how Notch ligand-receptor specificity contributes to regulating stemness, self-renewal, chemoresistance and cancer stem cells heterogeneity in CRC.

CellEnBoost: A Boosting-Based Ligand-Receptor Interaction Identification Model for Cell-to-Cell Communication Inference

Cell-to-cell communication (CCC) plays important roles in multicellular organisms. The identification of communication between cancer cells themselves and one between cancer cells and normal cells in tumor microenvironment helps understand cancer genesis, development and metastasis. CCC is usually mediated by Ligand-Receptor Interactions (LRIs). In this manuscript, we developed a Boosting-based LRI identification model (CellEnBoost) for CCC inference. First, potential LRIs are predicted by data collection, feature extraction, dimensional reduction, and classification based on an ensemble of Light gradient boosting machine and AdaBoost combining convolutional neural network. Next, the predicted LRIs and known LRIs are filtered. Third, the filtered LRIs are applied to CCC elucidation by combining CCC strength measurement and single-cell RNA sequencing data. Finally, CCC inference results are visualized using heatmap view, Circos plot view, and network view. The experimental results show that CellEnBoost obtained the best AUCs and AUPRs on the collected four LRI datasets. Case study in human head and neck squamous cell carcinoma (HNSCC) tissues demonstrates that fibroblasts were more likely to communicate with HNSCC cells, which is in accord with the results from iTALK. We anticipate that this work can contribute to the diagnosis and treatment of cancers.

Deciphering ligand-receptor-mediated intercellular communication based on ensemble deep learning and the joint scoring strategy from single-cell transcriptomic data

BACKGROUND

Cell-cell communication in a tumor microenvironment is vital to tumorigenesis, tumor progression and therapy. Intercellular communication inference helps understand molecular mechanisms of tumor growth, progression and metastasis.

METHODS

Focusing on ligand-receptor co-expressions, in this study, we developed an ensemble deep learning framework, CellComNet, to decipher ligand-receptor-mediated cell-cell communication from single-cell transcriptomic data. First, credible LRIs are captured by integrating data arrangement, feature extraction, dimension reduction, and LRI classification based on an ensemble of heterogeneous Newton boosting machine and deep neural network. Next, known and identified LRIs are screened based on single-cell RNA sequencing (scRNA-seq) data in certain tissues. Finally, cell-cell communication is inferred by incorporating scRNA-seq data, the screened LRIs, a joint scoring strategy that combines expression thresholding and expression product of ligands and receptors.

RESULTS

The proposed CellComNet framework was compared with four competing protein-protein interaction prediction models (PIPR, XGBoost, DNNXGB, and OR-RCNN) and obtained the best AUCs and AUPRs on four LRI datasets, elucidating the optimal LRI classification ability. CellComNet was further applied to analyze intercellular communication in human melanoma and head and neck squamous cell carcinoma (HNSCC) tissues. The results demonstrate that cancer-associated fibroblasts highly communicate with melanoma cells and endothelial cells strong communicate with HNSCC cells.

CONCLUSIONS

The proposed CellComNet framework efficiently identified credible LRIs and significantly improved cell-cell communication inference performance. We anticipate that CellComNet can contribute to anticancer drug design and tumor-targeted therapy.

Cancer Models on Chip: Paving the Way to Large-Scale Trial Applications

Cancer kills millions of individuals every year all over the world (Global Cancer Observatory). The physiological and biomechanical processes underlying the tumor are still poorly understood, hindering researchers from creating new, effective therapies. Inconsistent results of preclinical research, in vivo testing, and clinical trials decrease drug approval rates. 3D tumor-on-a-chip (ToC) models integrate biomaterials, tissue engineering, fabrication of microarchitectures, and sensory and actuation systems in a single device, enabling reliable studies in fundamental oncology and pharmacology. This review includes a critical discussion about their ability to reproduce the tumor microenvironment (TME), the advantages and drawbacks of existing tumor models and architectures, major components and fabrication techniques. The focus is on current materials and micro/nanofabrication techniques used to manufacture reliable and reproducible microfluidic ToC models for large-scale trial applications.

Regulatory roles of Osteopontin in lung epithelial inflammation and epithelial-telocyte interaction

BACKGROUND

Lung epithelial cells play important roles in lung inflammation and injury, although mechanisms remain unclear. Osteopontin (OPN) has essential roles in epithelial damage and repair and in lung cancer biological behaviours. Telocyte (TC) is a type of interstitial cell that interacts with epithelial cells to alleviate acute inflammation and lung injury. The present studies aim at exploring potential mechanisms by which OPN regulates the epithelial origin lung inflammation and the interaction of epithelial cells with TCs in acute and chronic lung injury.

METHODS

The lung disease specificity of OPN and epithelial inflammation were defined by bioinformatics. We evaluated the regulatory roles of OPN in OPN-knockdown or over-expressed bronchial epithelia (HBEs) challenged with cigarette smoke extracts (CSE) or in animals with genome OPN knockout (gKO) or lung conditional OPN knockout (cKO). Acute lung injury and chronic obstructive pulmonary disease (COPD) were induced by smoking or lipopolysaccharide (LPS). Effects of OPN on PI3K subunits and ERK were assessed using the inhibitors. Spatialization and distribution of OPN, OPN-positive epithelial subtypes, and TCs were defined by spatial transcriptomics. The interaction between HBEs and TCs was assayed by the co-culture system.

RESULTS

Levels of OPN expression increased in smokers, smokers with COPD, and smokers with COPD and lung cancer, as compared with healthy nonsmokers. LPS and/or CSE induced over-production of cytokines from HBEs, dependent upon the dysfunction of OPN. The severity of lung inflammation and injury was significantly lower in OPN-gKO or OPN-cKO mice. HBEs transferred with OPN enhanced the expression of phosphoinositide 3-kinase (PI3K)CA/p110α, PIK3CB/p110β, PIK3CD/p110δ, PIK3CG/p110γ, PIK3R1, PIK3R2 or PIK3R3. Spatial locations of OPN and OPN-positive epithelial subtypes showed the tight contact of airway epithelia and TCs. Epithelial OPN regulated the epithelial communication with TCs, and the down-regulation of OPN induced more alterations in transcriptomic profiles than the up-regulation.

CONCLUSION

Our data evidenced that OPN regulated lung epithelial inflammation, injury, and cell communication between epithelium and TCs in acute and chronic lung injury. The conditional control of lung epithelial OPN may be an alternative for preventing and treating epithelial-origin lung inflammation and injury.

The Combination of R848 with Sorafenib Enhances Antitumor Effects by Reprogramming the Tumor Immune Microenvironment and Facilitating Vascular Normalization in Hepatocellular Carcinoma

Novel promising strategies for combination with sorafenib are urgently needed to enhance its clinical benefit and overcome toxicity in hepatocellular carcinoma (HCC). the molecular and immunomodulatory antitumor effects of sorafenib alone and in combination with the new immunotherapeutic agent R848 are presented. Syngeneic HCC mouse model is presented to explore the antitumor effect and safety of three sorafenib doses alone, R848 alone, or their combination in vivo. R848 significantly enhances the sorafenib antitumor activity at a low subclinical dose with no obvious toxic side effects. Furthermore, the combination therapy reprograms the tumor immune microenvironment by increasing antitumor macrophages and neutrophils and preventing immunosuppressive signaling. Combination treatment promotes classical M1 macrophage-to-FTH1high M1 macrophage transition. The close interaction between neutrophils/classical M1 macrophages and dendritic cells promotes tumor antigen presentation to T cells, inducing cytotoxic CD8+ T cell-mediated antitumor immunity. Additionally, low-dose sorafenib, alone or combined with R848, normalizes the tumor vasculature, generating a positive feedback loop to support the antitumor immune environment. Therefore, the combination therapy reprograms the HCC immune microenvironment and normalizes the vasculature, improving the therapeutic benefit of low-dose sorafenib and minimizing toxicity, suggesting a promising novel immunotherapy (R848) and targeted therapy (tyrosine kinase inhibitors) combination strategy for HCC treatment.

GRACE: a comprehensive web-based platform for integrative single-cell transcriptome analysis

Large-scale single-cell RNA sequencing (scRNA-seq) has emerged as a robust method for dissecting cellular heterogeneity at single-cell resolution. However, to meet the increasingly high computational demands of non-programming experts, a user-friendly, scalable, and accessible online platform for analyzing scRNA-seq data is urgently needed. Here, we have developed a web-based platform GRACE (GRaphical Analyzing Cell Explorer) (http://grace.flowhub.com.cn or http://grace.jflab.ac.cn:28080) that enables online massive single-cell transcriptome analysis, improving interactivity and reproducibility using high-quality visualization frameworks. GRACE provides easy access to interactive visualization, customized parameters, and publication-quality graphs. Furthermore, it comprehensively integrates preprocessing, clustering, developmental trajectory inference, cell-cell communication, cell-type annotation, subcluster analysis, and pathway enrichment. In addition to the website platform, we also provide a Docker version that can be easily deployed on private servers. The source code for GRACE is freely available at (https://github.com/th00516/GRACE). Documentation and video tutorials are accessible from website homepage (http://grace.flowhub.com.cn). GRACE can analyze massive scRNA-seq data more flexibly and be accessible to the scientific community. This platform fulfills the major gap that exists between experimental (wet lab) and bioinformatic (dry lab) research.

Comprehensive Analysis of the Expression, Prognostic Value, and Immune Infiltration Activities of GABRD in Colon Adenocarcinoma

Colon adenocarcinoma (COAD) is one of the tumors with the highest mortality rates. It is of the utmost significance to make an accurate prognostic assessment and to tailor one's treatment to the specific needs of the patient. Multiple lines of evidence point to the possibility that genetic variables and clinicopathological traits are connected to the onset and development of cancer. In the past, a number of studies have revealed that gamma-aminobutyric acid type A receptor subunit delta (GABRD) plays a role in the advancement of a number of different cancers. However, its function in COAD was rarely reported. In this study, we analyzed TCGA datasets and identified 29 survival-related differentially expressed genes (DEGs) in COAD patients. In particular, GABRD expression was noticeably elevated in COAD specimens. There was a correlation between high GABRD expression and an advanced clinical stage. According to the results of the survival tests, patients whose GABRD expression was high had a lower overall survival time and progression-free survival time than those whose GABRD expression was low. GABRD expression was found to be an independent predictive predictor for overall survival, as determined by multivariate COX regression analysis. Additionally, the predictive nomogram model can accurately predict the fate of individuals with COAD. In addition, we observed that GABRD expressions were positively associated with the expression of T cells regulatory (Tregs), macrophages M0, while negatively associated with the expression of T cells CD8, T cells follicular helper, macrophages M1, dendritic cells activated, eosinophils, and T cells CD4 memory activated. The IC50 of BI-2536, bleomycin, embelin, FR-180204, GW843682X, LY317615, NSC-207895, rTRAIL, and VX-11e was higher in the GABRD high-expression group. In conclusion, we have shown evidence that GABRD is a novel biomarker that is connected with immune cell infiltration in COAD and may be utilized to predict the prognosis of COAD patients.

Single-Cell RNA Sequencing Analysis of Gene Regulatory Network Changes in the Development of Lung Adenocarcinoma

Lung cancer is a highly heterogeneous disease. Cancer cells and other cells within the tumor microenvironment interact to determine disease progression, as well as response to or escape from treatment. Understanding the regulatory relationship between cancer cells and their tumor microenvironment in lung adenocarcinoma is of great significance for exploring the heterogeneity of the tumor microenvironment and its role in the genesis and development of lung adenocarcinoma. This work uses public single-cell transcriptome data (distant normal, nLung; early LUAD, tLung; advanced LUAD, tL/B), to draft a cell map of lung adenocarcinoma from onset to progression, and provide a cell-cell communication view of lung adenocarcinoma in the different disease stages. Based on the analysis of cell populations, it was found that the proportion of macrophages was significantly reduced in the development of lung adenocarcinoma, and patients with lower proportions of macrophages exhibited poor prognosis. We therefore constructed a process to screen an intercellular gene regulatory network that reduces any error generated by single cell communication analysis and increases the credibility of selected cell communication signals. Based on the key regulatory signals in the macrophage-tumor cell regulatory network, we performed a pseudotime analysis of the macrophages and found that signal molecules (TIMP1, VEGFA, SPP1) are highly expressed in immunosuppression-associated macrophages. These molecules were also validated using an independent dataset and were significantly associated with poor prognosis. Our study provides an effective method for screening the key regulatory signals in the tumor microenvironment and the selected signal molecules may serve as a reference to guide the development of diagnostic biomarkers for risk stratification and therapeutic targets for lung adenocarcinoma.

Identification of hub genes and pathways in lung metastatic colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is one of the most prevalent types of malignant tumours. Metastasis is the leading cause of cancer-related mortality, with lung metastases accounting for 32.9% of all metastatic CRCs. However, since the biological mechanism of lung metastatic CRC is poorly understood, limited therapeutic targets are available. In the present study, we aimed to identify the key genes and molecular processes involved in CRC lung metastasis.

METHODS

The differentially expressed genes (DEGs) between primary and lung metastatic CRC patients were obtained from the Gene Expression Omnibus (GEO) database via the GEO2R tool. The enriched biological processes and pathways modulated by the DEGs were determined with Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome Gene Sets analyses. The search tool Retrieval of Interacting Genes (STRING) and Cytoscape were used to construct a protein-protein interaction (PPI) network among DEGs.

RESULTS

The DEGs were enriched in surfactant metabolism, cell-cell communication and chemokine signaling pathways. The defined hub genes were included CLU, SFTPD, CCL18, SPP1, APOE, BGN and MMP3. Among them, CLU, SFTPD and CCL18 might be associated with the specific lung tropism metastasis in CRC. In addition, the expression and prognostic values of the hub genes in CRC patients were verified in database of The Cancer Genome Atlas (TCGA) and GEO. Moreover, the protein levels of the hub genes were detected in primary and lung metastatic CRC cells, serum or tissues. Furthermore, SFTPD was confirmed to facilitate cellular proliferation and lung metastasis in CRC.

CONCLUSION

This bioinformatics study may provide a better understanding of the candidate therapeutic targets and molecular mechanisms for CRC lung metastasis.

Interplay between Signaling Pathways and Tumor Microenvironment Components: A Paradoxical Role in Colorectal Cancer

The study of the tumor microenvironment (TME) has become an important part of colorectal cancer (CRC) research. Indeed, it is now accepted that the invasive character of a primary CRC is determined not only by the genotype of the tumor cells, but also by their interactions with the extracellular environment, which thereby orchestrates the development of the tumor. In fact, the TME cells are a double-edged sword as they play both pro- and anti-tumor roles. The interaction of the tumor-infiltrating cells (TIC) with the cancer cells induces the polarization of the TIC, exhibiting an antagonist phenotype. This polarization is controlled by a plethora of interconnected pro- and anti-oncogenic signaling pathways. The complexity of this interaction and the dual function of these different actors contribute to the failure of CRC control. Thus, a better understanding of such mechanisms is of great interest and provides new opportunities for the development of personalized and efficient therapies for CRC. In this review, we summarize the signaling pathways linked to CRC and their implication in the development or inhibition of the tumor initiation and progression. In the second part, we enlist the major components of the TME and discuss the complexity of their cells functions.

Patient-derived xenograft model in colorectal cancer basic and translational research

Colorectal cancer (CRC) is one of the most popular malignancies globally, with 930 000 deaths in 2020. The evaluation of CRC-related pathogenesis and the discovery of potential therapeutic targets will be meaningful and helpful for improving CRC treatment. With huge efforts made in past decades, the systematic treatment regimens have been applied to improve the prognosis of CRC patients. However, the sensitivity of CRC to chemotherapy and targeted therapy is different from person to person, which is an important cause of treatment failure. The emergence of patient-derived xenograft (PDX) models shows great potential to alleviate the straits. PDX models possess similar genetic and pathological characteristics as the features of primary tumors. Moreover, PDX has the ability to mimic the tumor microenvironment of the original tumor. Thus, the PDX model is an important tool to screen precise drugs for individualized treatment, seek predictive biomarkers for prognosis supervision, and evaluate the unknown mechanism in basic research. This paper reviews the recent advances in constructed methods and applications of the CRC PDX model, aiming to provide new knowledge for CRC basic research and therapeutics.

New Frontiers in Colorectal Cancer Treatment Combining Nanotechnology with Photo- and Radiotherapy

Colorectal cancer is the third most common cancer worldwide. Despite recent advances in the treatment of this pathology, which include a personalized approach using radio- and chemotherapies in combination with advanced surgical techniques, it is imperative to enhance the performance of these treatments and decrease their detrimental side effects on patients' health. Nanomedicine is likely the pathway towards solving this challenge by enhancing both the therapeutic and diagnostic capabilities. In particular, plasmonic nanoparticles show remarkable potential due to their dual therapeutic functionalities as photothermal therapy agents and as radiosensitizers in radiotherapy. Their dual functionality, high biocompatibility, easy functionalization, and targeting capabilities make them potential agents for inducing efficient cancer cell death with minimal side effects. This review aims to identify the main challenges in the diagnosis and treatment of colorectal cancer. The heterogeneous nature of this cancer is also discussed from a single-cell point of view. The most relevant works in photo- and radiotherapy using nanotechnology-based therapies for colorectal cancer are addressed, ranging from in vitro studies (2D and 3D cell cultures) to in vivo studies and clinical trials. Although the results using nanoparticles as a photo- and radiosensitizers in photo- and radiotherapy are promising, preliminary studies showed that the possibility of combining both therapies must be explored to improve the treatment efficiency.

scFASTCORMICS: A Contextualization Algorithm to Reconstruct Metabolic Multi-Cell Population Models from Single-Cell RNAseq Data

Tumours are composed of various cancer cell populations with different mutation profiles, phenotypes and metabolism that cause them to react to drugs in diverse manners. Increasing the resolution of metabolic models based on single-cell expression data will provide deeper insight into such metabolic differences and improve the predictive power of the models. scFASTCORMICS is a network contextualization algorithm that builds multi-cell population genome-scale models from single-cell RNAseq data. The models contain a subnetwork for each cell population in a tumour, allowing to capture metabolic variations between these clusters. The subnetworks are connected by a union compartment that permits to simulate metabolite exchanges between cell populations in the microenvironment. scFASTCORMICS uses Pareto optimization to simultaneously maximise the compactness, completeness and specificity of the reconstructed metabolic models. scFASTCORMICS is implemented in MATLAB and requires the installation of the COBRA toolbox, rFASTCORMICS and the IBM CPLEX solver.

A SERPINE1-Based Immune Gene Signature Predicts Prognosis and Immunotherapy Response in Gastric Cancer

Immune checkpoint inhibitors (ICIs) therapy has been successfully utilized in the treatment of multiple tumors, but only a fraction of patients with gastric cancer (GC) could greatly benefit from it. A recent study has shown that the tumor microenvironment (TME) can greatly affect the effect of immunotherapy in GC. In this study, we established a novel immune risk signature (IRS) for prognosis and predicting response to ICIs in GC based on the TCGA-STAD dataset. Characterization of the TME was explored and further validated to reveal the underlying survival mechanisms and the potential therapeutic targets of GC. The GC patients were stratified into high- and low-risk groups based on the IRS. Patients in the high-risk group, associated with poorer outcomes, were characterized by significantly higher immune function. Further analysis showed higher T cell immune dysfunction and probability of potential immune escape. In vivo, we detected the expressions of SERPINE1 by the quantitative real-time polymerase chain reaction (qPCR)in tumor tissues and adjacent normal tissues. In vitro, knockdown of SERPINE1 significantly attenuated malignant biological behaviors of tumor cells in GC. Our signature can effectively predict the prognosis and response to immunotherapy in patients with GC.

Extracellular vesicles microRNA-592 of melanoma stem cells promotes metastasis through activation of MAPK/ERK signaling pathway by targeting PTPN7 in non-stemness melanoma cells

Melanoma, one of the most aggressive malignancies, its high mortality and low survival rates are associated with effective metastatic colonization. Melanoma metastasis hinges on the bidirectional cell-cell communication within the complex metastatic microenvironments (MME). Extracellular vesicles (EVs) are recognized as a new class of molecular mediator in MME programing. Published studies show that melanoma EVs can educate MME stromal cells to acquire the pro-metastatic phenotype to enhance metastatic colonization. Whether EVs can mediate the interactions between heterogenous cancer cells within the MME that alter the course of metastasis has not been investigated at the mechanistic level. In this study, melanoma parental cells (MPCs) and paired derivative cancer stem cell line melanoma stem cells (MSCs) that were derived from melanoma cell line M14 were used. We demonstrate that the EVs-mediated crosstalk between the MSCs and the MPCs is a novel mechanism for melanoma metastasis. We characterized miR-592, a relatively novel microRNA of prognostic potential, in mediation of such intercellular crosstalk. EVs can encapsulate and deliver miR-592 to target MPCs. Upon entering, miR-592 inhibits the expression of its gene target protein tyrosine phosphatase non-receptor type7 (PTPN7), a phosphatase targeting MAPKs. This leads to the relief of the inhibitory effect of PTPN7 on MAPK/ERK signaling and consequently the augmentation of metastatic colonization of MPCs. Thus, via the extracellular vesicle miR-592/PTPN7/MAPK axis, melanoma-CSCs can transfer their metastatic ability to the low-metastatic non-CSC melanoma cells.

Natural cell based biomimetic cellular transformers for targeted therapy of digestive system cancer

Digestive system cancer is the most common cause of cancer death in the world. Although cancer treatment options are increasingly diversified, the mortality rate of malignant cancer of the digestive system remains high. Therefore, it is necessary to explore effective cancer treatment methods. Recently, biomimetic nanoparticle delivery systems based on natural cells that organically integrate the low immunogenicity, high biocompatibility, cancer targeting, and controllable, versatile functionality of smart nanocarrier design with natural cells have been expected to break through the bottleneck of tumor targeted therapy. In this review, we focus on the dynamic changes and complex cellular communications that occur in vivo in natural cells based vehicles. Recent studies on the development of advanced targeted drug delivery systems using the dynamic behaviors such as specific surface protein affinity, morphological changes, and phenotypic polarization of natural cells are summarized. In addition to drug delivery mediated by dynamic behavior, functional “delivery” based on the natural cell themselves is also involved. Aiming to make the best use of the functions of cells, providing clues for the development of advanced drug delivery platforms.

Delving into the Heterogeneity of Different Breast Cancer Subtypes and the Prognostic Models Utilizing scRNA-Seq and Bulk RNA-Seq

Background: Breast cancer (BC) is the most common malignancy in women with high heterogeneity. The heterogeneity of cancer cells from different BC subtypes has not been thoroughly characterized and there is still no valid biomarker for predicting the prognosis of BC patients in clinical practice. Methods: Cancer cells were identified by calculating single cell copy number variation using the inferCNV algorithm. SCENIC was utilized to infer gene regulatory networks. CellPhoneDB software was used to analyze the intercellular communications in different cell types. Survival analysis, univariate Cox, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis were used to construct subtype specific prognostic models. Results: Triple-negative breast cancer (TNBC) has a higher proportion of cancer cells than subtypes of HER2+ BC and luminal BC, and the specifically upregulated genes of the TNBC subtype are associated with antioxidant and chemical stress resistance. Key transcription factors (TFs) of tumor cells for three subtypes varied, and most of the TF-target genes are specifically upregulated in corresponding BC subtypes. The intercellular communications mediated by different receptor–ligand pairs lead to an inflammatory response with different degrees in the three BC subtypes. We establish a prognostic model containing 10 genes (risk genes: ATP6AP1, RNF139, BASP1, ESR1 and TSKU; protective genes: RPL31, PAK1, STARD10, TFPI2 and SIAH2) for luminal BC, seven genes (risk genes: ACTR6 and C2orf76; protective genes: DIO2, DCXR, NDUFA8, SULT1A2 and AQP3) for HER2+ BC, and seven genes (risk genes: HPGD, CDC42 and PGK1; protective genes: SMYD3, LMO4, FABP7 and PRKRA) for TNBC. Three prognostic models can distinguish high-risk patients from low-risk patients and accurately predict patient prognosis. Conclusions: Comparative analysis of the three BC subtypes based on cancer cell heterogeneity in this study will be of great clinical significance for the diagnosis, prognosis and targeted therapy for BC patients.

Cell-cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies

Carcinomas are complex ecosystems composed of cancer, stromal and immune cells. Communication between these cells and their microenvironments induces cancer progression and causes therapy resistance. In order to improve the treatment of cancers, it is essential to quantify crosstalk between and within various cell types in a tumour microenvironment. Focusing on the coordinated expression patterns of ligands and cognate receptors, cell-cell communication can be inferred through ligand-receptor interactions (LRIs). In this manuscript, we carry out the following work: (i) introduce pipeline for ligand-receptor-mediated intercellular communication estimation from single-cell transcriptomics and list a few available LRI-related databases and visualization tools; (ii) demonstrate seven classical intercellular communication scoring strategies, highlight four types of representative intercellular communication inference methods, including network-based approaches, machine learning-based approaches, spatial information-based approaches and other approaches; (iii) summarize the evaluation and validation avenues for intercellular communication inference and analyze the advantages and limitations for the above four types of cell-cell communication methods; (iv) comment several major challenges while provide further research directions for intercellular communication analysis in the tumour microenvironments. We anticipate that this work helps to better understand intercellular crosstalk and to further develop powerful cell-cell communication estimation tools for tumor-targeted therapy.

Non-Coding RNAs Implicated in the Tumor Microenvironment of Colorectal Cancer: Roles, Mechanisms and Clinical Study

Colorectal cancer (CRC) is one of the most common malignant tumors. The morbidity and mortality rates have been increasing all over the world. It is critical to elucidate the mechanism of CRC occurrence and development. However, tumor microenvironment (TME) includes immune cells, fibroblasts, endothelial cells, cytokines, chemokines and other components that affect the progression of CRC and patients’ prognosis. Non-coding RNAs (ncRNAs) including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs) without protein-coding ability have been shown to engage in tumor microenvironment-mediated angiogenesis and metastasis. Therefore, clarifying the mechanism of ncRNAs regulating the microenvironment is very important to develop the therapeutic target of CRC and improve the survival time of patients. This review focuses on the role and mechanism of ncRNAs in the CRC microenvironment and puts forward possible clinical treatment strategies.

Forward single-cell sequencing into clinical application: Understanding of cancer microenvironment at single-cell solution

Single‐cell RNA sequencing (scRNA‐seq) is considered an important approach to understand the molecular mechanisms of cancer microenvironmental functions and has the potential for clinical and translational discovery and development. The recent concerns on the impact of scRNA‐seq for clinical practice are whether scRNA can be applied as a routine measurement of clinical biochemistry to assist in clinical decision‐making for diagnosis and therapy. Pushing single‐cell sequencing into clinical application is one of the important missions for clinical and translational medicine (CTM), although there still are a large number of challenges to be overcome. The present Editorial as one of serials aims at overviewing the history of scRNA‐seq publications in CTM, sharing the understanding and consideration of the cancer microenvironment at the single‐cell solution and emphasising the objective of translating scRNA‐seq into clinical application. The dynamic characteristics and patterns of single‐cell identity, regulatory networks, and intercellular communication play decisive roles in the properties of the microenvironment, malignancy and migrative capacity of cancer cells, and defensive capacity of immune cells. The microenvironmental single‐cell transcriptomic profiles and cell clusters defined by scRNA‐seq have great value for exploring the molecular mechanisms of diseases and predicting cell sensitivities to therapy and patient prognosis.

Clinical and translational values of spatial transcriptomics

The combination of spatial transcriptomics (ST) and single cell RNA sequencing (scRNA-seq) acts as a pivotal component to bridge the pathological phenomes of human tissues with molecular alterations, defining in situ intercellular molecular communications and knowledge on spatiotemporal molecular medicine. The present article overviews the development of ST and aims to evaluate clinical and translational values for understanding molecular pathogenesis and uncovering disease-specific biomarkers. We compare the advantages and disadvantages of sequencing- and imaging-based technologies and highlight opportunities and challenges of ST. We also describe the bioinformatics tools necessary on dissecting spatial patterns of gene expression and cellular interactions and the potential applications of ST in human diseases for clinical practice as one of important issues in clinical and translational medicine, including neurology, embryo development, oncology, and inflammation. Thus, clear clinical objectives, designs, optimizations of sampling procedure and protocol, repeatability of ST, as well as simplifications of analysis and interpretation are the key to translate ST from bench to clinic.

Single-Cell RNA Sequencing of Human Corpus Cavernosum Reveals Cellular Heterogeneity Landscapes in Erectile Dysfunction

PURPOSE

To assess the diverse cell populations of human corpus cavernosum in patients with severe erectile dysfunction (ED) at the single-cell level.

METHODS

Penile tissues collected from three patients were subjected to single-cell RNA sequencing using the BD Rhapsody™ platform. Common bioinformatics tools were used to analyze cellular heterogeneity and gene expression profiles from generated raw data, including the packages Seurat, Monocle, and CellPhoneDB.

RESULTS

Disease-related heterogeneity of cell types was determined in the cavernous tissue such as endothelial cells (ECs), smooth muscle cells, fibroblasts, and immune cells. Reclustering analysis of ECs identified an arteriole ECs subcluster and another one with gene signatures of fibroblasts. The proportion of fibroblasts was higher than the other cell populations and had the most significant cellular heterogeneity, in which a distinct subcluster co-expressed endothelial markers. The transition trajectory of differentiation from smooth muscle cells into fibroblasts was depicted using the pseudotime analysis, suggesting that the expansion of corpus cavernosum is possibly compromised as a result of fibrosis. Cell-cell communications among ECs, smooth muscle cells, fibroblasts, and macrophages were robust, which indicated that inflammation may also have a crucial role in the development of ED.

CONCLUSIONS

Our study has demonstrated a comprehensive single-cell atlas of cellular components in human corpus cavernosum of ED, providing in-depth insights into the pathogenesis. Future research is warranted to explore disease-specific alterations for individualized treatment of ED.

Clinical challenges of tissue preparation for spatial transcriptome

Spatial transcriptomics is considered as an important part of spatiotemporal molecular images to bridge molecular information with clinical images. Of those potentials and opportunities, the excellent quality of human sample preparation and handling will ensure the precise and reliable information generated from clinical spatial transcriptome. The present study aims at defining potential factors that might influence the quality of spatial transcriptomics in lung cancer, para-cancer, or normal tissues, pathological images of sections and the RNA integrity before spatial transcriptome sequencing. We categorised potential influencing factors from clinical aspects, including patient selection, pathological definition, surgical types, sample harvest, temporary preservation conditions and solutions, frozen approaches, transport and storage conditions and duration. We emphasis on the relationship between the combination of histological scores with RNA integrity number (RIN) and the unique molecular identifier (UMI), which is determines the quality of of spatial transcriptomics; however, we did not find significantly relevance between them. Our results showed that isolated times and dry conditions of sample are critical for the UMI and the quality of spatial transcriptomic samples. Thus, clinical procedures of sample preparation should be furthermore optimised and standardised as new standards of operation performance for clinical spatial transcriptome. Our data suggested that the temporary preservation time and condition of samples at operation room should be within 30 min and in 'dry' status. The direct cryo-preservation within OCT media for human lung sample is recommended. Thus, we believe that clinical spatial transcriptome will be a decisive approach and bridge in the development of spatiotemporal molecular images and provide new insights for understanding molecular mechanisms of diseases at multi-orientations.

Can single cell RNA sequencing reshape the clinical biochemistry of hematology: New clusters of circulating blood cells

scRNA‐seq is on track for use as a routine measurement of clinical biochemistry and to assist in clinical decision‐making and guide the performance of molecular medicine, but there are still a large number of challenges to be overcome. In conclusion, scRNA‐seq‐based clusters and differentiation of circulating blood cells have been examined and informative in patients with various diseases, although the information generated from scRNA‐seq varies between different conditions, technologies, and diseases. Most of the clinical studies published have focused on the landscape of circulating immune cells, disease‐specific patterns of new clusters, understanding of potential mechanisms, and potential correlation between cell clusters, differentiations, cell interactions, and circulating and migrated cells. It is clear that the information from scRNA‐seq advances the understanding of the disease, identifies disease‐specific target panels, and suggests new therapeutic strategies. The adaptation of scRNA‐seq as a routine clinical measurement will require standardization and normalization of scRNA‐seq‐based comprehensive information and validation in a large population of healthy and diseased patients. The integration of public databases on human circulating cell clusters and differentiations with an application of artificial intelligence and computational science will accelerate the application of scRNA‐seq for clinical practice. Thus, we call special attention from scientists and clinicians to the clinical and translational discovery, validation, and medicine opportunities of scRNA‐seq development.

A cellular census of human peripheral immune cells identifies novel cell states in lung diseases

Increasing evidence supports a central role of the immune system in lung diseases. Understanding how immunological alterations between lung diseases provide opportunities for immunotherapy. Exhausted T cells play a key role of immune suppression in lung cancer and chronic obstructive pulmonary disease was proved in our previous study. The present study aims to furthermore define molecular landscapes and heterogeneity of systemic immune cell target proteomic and transcriptomic profiles and interactions between circulating immune cells and lung residential cells in various lung diseases. We firstly measured target proteomic profiles of circulating immune cells from healthy volunteers and patients with stable pneumonia, stable asthma, acute asthma, acute exacerbation of chronic obstructive pulmonary disease, chronic obstructive pulmonary disease and lung cancer, using single-cell analysis by cytometry by time-of-flight with 42 antibodies. The nine immune cells landscape was mapped among those respiratory system diseases, including CD4+ T cells, CD8+ T cells, dendritic cells, B cells, eosinophil, γδT cells, monocytes, neutrophil and natural killer cells. The double-negative T cells and exhausted CD4+ central memory T cells subset were identified in patients with acute pneumonia. This T subset expressed higher levels of T-cell immunoglobulin and mucin domain-containing protein 3 (Tim3) and T-cell immunoreceptor with Ig and ITIM domains (TIGIT) in patients with acute pneumonia and stable pneumonia. Biological processes and pathways of immune cells including immune response activation, regulation of cell cycle and pathways in cancer in peripheral blood immune cells were defined by bulk RNA sequencing (RNA-seq). The heterogeneity among immune cells including CD4+ , CD8+ T cells and NK T cells by single immune cell RNA-seq with significant difference was found by single-cell sequencing. The effect of interstitial telocytes on the immune cell types and immune function was finally studied and the expressions of CD8a and chemokine C-C motif receptor 7 (CCR7) were increased significantly in co-cultured groups. Our data indicate that proteomic and transcriptomic profiles and heterogeneity of circulating immune cells provides new insights for understanding new molecular mechanisms of immune cell function, interaction and modulation as a source to identify and develop biomarkers and targets for lung diseases.

Three dimensional in vitro culture systems in anticancer drug discovery targeted on cancer stem cells

Worldwide, tumors are one of the most common causes of death. Every year 3.7 million new cases occur in Europe and more than 1.9 million patients die (WHO data). Most of the fields of research are focused on developing new therapeutic strategies that will be effective in eliminating the tumor, preventing its remission, and avoiding or reducing the side effects of therapy. In the past, generally classical 2D cell cultures or immunodeficient animal models had been used to cultivate and test drugs on human cancer cell lines. Nowadays, there are increasing interests in three-dimensional (3D) cell cultures, a method with significant differences from flat cultured cells, both considering gene expressions and cell-cell interactions. Various evidence suggests that high tumorigenic properties might be dependent on the occurrence of a small cell population, pointed out to be responsible for metastasis and recurrence. This population is called cancer stem cells (CSCs), hinted to have a lot of similarities with normal stem cells. CSCs are the main reason for chemotherapy failure as well as multi-drug resistance (MDR). CSCs can also interact through the cytokine network, with other cells like the macrophages of the inflammatory system. The big advantage of a 3D culture is the possibility to isolate and investigate the CSCs population surrounded by its environment. This article aims to sum up known 3D cell cultures, especially in the field of CSCs research due to the importance of the tumor's environment on stem cell's markers expression and their development.

Biomaterials for intestinal organoid technology and personalized disease modeling

Recent advances in intestinal organoid technologies have paved the way for in vitro recapitulation of the homeostatic renewal of adult tissues, tissue or organ morphogenesis during development, and pathogenesis of many disorders. In vitro modelling of individual patient diseases using organoid systems have been considered key in establishing rational design of personalized treatment strategies and in improving therapeutic outcomes. In addition, the transplantation of organoids into diseased tissues represents a novel approach to treat currently incurable diseases. Emerging evidence from intensive studies suggests that organoid systems' development and functional maturation depends on the presence of an extracellular matrix with suitable biophysical properties, where advanced synthetic hydrogels open new avenues for theoretical control of organoid phenotypes and potential applications of organoids in therapeutic purposes. In this review, we discuss the status, applications, challenges and perspectives of intestinal organoid systems emphasising on hydrogels and their properties suitable for intestinal organoid culture. We provide an overview of hydrogels used for intestinal organoid culture and key factors regulating their biological activity. The comparison of different hydrogels would be a theoretical basis for establishing design principles of synthetic niches directing intestinal cell fates and functions. STATEMENT OF SIGNIFICANCE: Intestinal organoid is an in vitro recapitulation of the gut, which self-organizes from intestinal stem cells and maintains many features of the native tissue. Since the development of this technology, intestinal organoid systems have made significant contribution to rapid progress in intestinal biology. Prevailing methodology for organoid culture, however, depends on animal-derived matrices and suffers from variability and potential risk for contamination of pathogens, limiting their therapeutic application. Synthetic scaffold matrices, hydrogels, might provide solutions to these issues and deepen our understanding on how intestinal cells sense and respond to key biophysical properties of the surrounding matrices. This review provides an overview of developing intestinal models and biomaterials, thereby leading to better understanding of current intestinal organoid systems for both biologists and materials scientists.

Organoids and Colorectal Cancer

Organoids were first established as a three-dimensional cell culture system from mouse small intestine. Subsequent development has made organoids a key system to study many human physiological and pathological processes that affect a variety of tissues and organs. In particular, organoids are becoming very useful tools to dissect colorectal cancer (CRC) by allowing the circumvention of classical problems and limitations, such as the impossibility of long-term culture of normal intestinal epithelial cells and the lack of good animal models for CRC. In this review, we describe the features and current knowledge of intestinal organoids and how they are largely contributing to our better understanding of intestinal cell biology and CRC genetics. Moreover, recent data show that organoids are appropriate systems for antitumoral drug testing and for the personalized treatment of CRC patients.