Engineered cell entry links receptor biology with single-cell genomics.

Cells communicate with each other via receptor-ligand interactions. Here, we describe lentiviral-mediated cell entry by engineered receptor-ligand interaction (ENTER) to display ligand proteins, deliver payloads, and record receptor specificity. We optimize ENTER to decode interactions between T cell receptor (TCR)-MHC peptides, antibody-antigen, and other receptor-ligand pairs. A viral presentation strategy allows ENTER to capture interactions between B cell receptor and any antigen. We engineer ENTER to deliver genetic payloads to antigen-specific T or B cells to selectively modulate cellular behavior in mixed populations. Single-cell readout of ENTER by RNA sequencing (ENTER-seq) enables multiplexed enumeration of antigen specificities, TCR clonality, cell type, and states of individual T cells. ENTER-seq of CMV-seropositive patient blood samples reveals the viral epitopes that drive effector memory T cell differentiation and inter-clonal vs. intra-clonal phenotypic diversity targeting the same epitope. ENTER technology enables systematic discovery of receptor specificity, linkage to cell fates, and antigen-specific cargo delivery.

Functional Expression of the Ectodomain of Plant Receptor Kinases in Plant Suspension Culture.

Extracellular signals are usually perceived by membrane-localized receptors that transduce intercellular signals to activate various pathways. In plants, single transmembrane receptor kinases act as receptors for extracellular signals. Endogenous secreted peptide hormones have been recognized as novel signaling molecules, functioning through the formation of ligand-receptor pairs in plants. Recently, research on plant peptide hormone-receptor interactions based on the structural biology approach has greatly improved; however, the dissociation constant of recombinant receptor molecules expressed in insect cells using the baculovirus expression system is relatively low. We introduce here a method for creating a stable and functional homogeneous expression system for plant receptor kinases using tobacco BY-2 cells while maintaining conventional ligand-binding activity. This strategy will help improve our understanding of plant endogenous peptide ligand-receptor interactions.

Melanoma molecular subtyping and scoring model construction based on ligand-receptor pairs.

Melanoma is a malignancy of melanocytes, responsible for a high percentage of skin cancer mortality. Ligand-Receptor pairs, a type of cellular communication, are essential for tumor genesis, growth, metastasis, and prognosis. Yet, the role of Ligand-Receptor pairs in melanoma has not been fully elucidated. Our research focused on the function of Ligand-Receptor pairs in melanoma prognosis. We screened 131 melanoma prognosis corresponded ligand-receptor pairs by analyzing the TCGA data of melanoma and the 2293 LR pairs retrieved from the connectomeDB2020 database. And further developed subtypes of melanoma according to the expression of these ligand-receptor pairs by Consensus Clustering. Then we using lasso cox regression and stepwise multivariate regression analysis established a ligand-receptor pairs-based scoring model for the evaluation of melanoma prognosis. Our study demonstrated that the ligand-receptor pairs are vital to the molecular heterogeneity of melanoma, and characterized three different melanoma ligand-receptor pairs subtypes. Among them, the C3 subtype showed a better prognosis, while the C1 subtype exhibited a low prognosis state. And our analysis then found out that this could be related to the differed activation and inhabitation of the cell cycle and immune-related pathways. Using lasso cox regression and stepwise multivariate regression analysis, we further identified 9 key ligand-receptor pairs and established a scoring model that effectively correlated with the prognosis, immune pathways, and therapy of melanoma, showing that the LR.score model was a trustworthy and independent biomarker for melanoma prognosis evaluation. In sum, we found that ligand-receptor pairs are significantly associated with the prognosis and therapy of melanoma. And our ligand-receptor-based scoring model showed potential for the evaluation of melanoma prognosis and immune therapy outcome prediction, which is crucial to the survival for the patients.

Nasopharynx Battlefield: Cellular Immune Responses Mediated by Midkine in Nasopharyngeal Carcinoma and COVID-19.

Clinical evidence suggests that the severe respiratory illness coronavirus disease 2019 (COVID-19) is often associated with a cytokine storm that results in dysregulated immune responses. Prolonged COVID-19 positivity is thought to disproportionately affect cancer patients. With COVID-19 disrupting the delivery of cancer care, it is crucial to gain momentum and awareness of the mechanistic intersection between these two diseases. This review discusses the role of the cytokine midkine (MK) as an immunomodulator in patients with COVID-19 and nasopharyngeal carcinoma (NPC), both of which affect the nasal cavity. We conducted a review and analysis of immunocellular similarities and differences based on clinical studies, research articles, and published transcriptomic datasets. We specifically focused on ligand-receptor pairs that could be used to infer intercellular communication, as well as the current medications used for each disease, including NPC patients who have contracted COVID-19. Based on our findings, we recommend close monitoring of the MK axis to maintain the desirable effects of therapeutic regimens in fighting both NPC and COVID-19 infections.

Molecular subtyping for lung adenocarcinoma and a novel prognostic model based on ligand-receptor pairs.

PURPOSE: Lung adenocarcinoma (LUAD) is a leading cause of cancer death worldwide. Ligands and receptors play important roles in cell communication. This study aimed to demonstrate the importance of ligand-receptor (LR) pairs in LUAD development through constructing molecular subtypes and a prognostic model based on LR pairs. MATERIALS AND METHODS: A total of 1110 LUAD samples with clinical and expression data were obtained from public databases. Unsupervised consensus clustering was applied to construct molecular subtypes based on LR pairs. Least absolute shrinkage and selection operator (LASSO) Cox regression and stepwise Akaike information criterion (stepAIC) were conducted to build a prognostic model. RESULTS: Three molecular subtypes (C1, C2 and C3) were constructed based on 17 prognosis-related LR pairs. C1 subtype had the worst prognosis, while C3 subtype had the optimal prognosis. Oncogenic pathways such as epithelial-mesenchymal transition (EMT) were activated in C1 subtype. A prognostic model was built based on 8 LR pairs, and could classify samples into high- and low-LR score groups. Two groups had distinct overall survival and tumor microenvironment (TME). High-LR score group was more sensitive to chemotherapeutic drugs, while low-LR score group could benefit much from anti-PD-1/PD-L1 therapy. CONCLUSIONS: The study showed that LR pairs played critical roles in LUAD development. The prognostic model could predict prognosis and guide personalized therapy for LUAD patients.

The molecular subtypes of triple negative breast cancer were defined and a ligand-receptor pair score model was constructed by comprehensive analysis of ligand-receptor pairs.

Background: Intercellular communication mediated by ligand-receptor interactions in tumor microenvironment (TME) has a profound impact on tumor progression. This study aimed to explore the molecular subtypes mediated by ligand-receptor (LR) pairs in triple negative breast cancer (TNBC), identify the most important LR pairs to construct a prognostic risk model, and study their effect on TNBC immunotherapy. Methods: LR pairs subclasses of TNBC were categorized by consensus clustering based on LR Pairs in METABRIC dataset. Least absolute shrinkage and selection operator (LASSO) Cox regression and stepwise Akaike information criterion (stepAIC) were conducted to build a LR pairs score model. The relationship between LR pairs score and immune cell infiltration, stromal score and immune score associated with TME was analyzed, and the prediction of drug therapy and immunotherapy efficacy by LR pairs score was evaluated. Results: According to the expression pattern of 145 TNBC prognostic LR pairs, the samples were divided into three subclasses with different survival outcomes, copy number variation (CNV), TME immune cell infiltration, stromal score and immune score. The LR pairs score model constructed in the METABRIC dataset was composed of four LR pairs, and its predictive significance for TNBC prognosis was verified in GSE58812 and GSE21653 cohorts. In addition, LR pairs score was negatively correlated with several immune pathways regulating immunity and immune score, and related to the sensitivity of anti-neoplastic drugs and the effect of anti-PD-L1 therapy. Conclusion: Our study confirmed the impact of LR pairs on the molecular heterogeneity of TNBC, characterized three LR pairs subtypes with different survival outcomes and TME patterns, and proposed a LR pairs score system with predictive significance for TNBC prognosis and anti-PD-L1 therapeutic effect, which provides a potential evaluation scheme for TNBC management.

Cell-to-cell interaction analysis of prognostic ligand-receptor pairs in human pancreatic ductal adenocarcinoma.

Cell-to-cell interactions (CCIs) through ligand-receptor (LR) pairs in the tumor microenvironment underlie the poor prognosis of pancreatic ductal adenocarcinoma (PDAC). However, there is scant knowledge of the association of CCIs with PDAC prognosis, which is critical to the identification of potential therapeutic candidates. Here, we sought to identify the LR pairs associated with PDAC patient prognosis by integrating survival analysis and single-cell CCI prediction. Via survival analysis using gene expression from cancer cohorts, we found 199 prognostic LR pairs. CCI prediction based on single-cell RNA-seq data revealed the enriched LR pairs associated with poor prognosis. Notably, the CCIs involved epithelial tumor cells, cancer-associated fibroblasts, and tumor-associated macrophages through integrin-related and ANXA1-FPR pairs. Finally, we determined that CCIs involving 33 poor-prognostic LR pairs were associated with tumor grade. Although the clinical implication of the set of LR pairs must be determined, our results may provide potential therapeutic targets in PDAC.

Single-Cell Transcriptome Analysis Identifies Ligand-Receptor Pairs Associated With BCP-ALL Prognosis.

B cell precursor acute lymphoblastic leukemia (BCP-ALL) is a blood cancer that originates from the abnormal proliferation of B-lymphoid progenitors. Cell population components and cell-cell interaction in the bone marrow microenvironment are significant factors for progression, relapse, and therapy resistance of BCP-ALL. In this study, we identified specifically expressed genes in B cells and myeloid cells by analyzing single-cell RNA sequencing data for seven BCP-ALL samples and four healthy samples obtained from a public database. Integrating 1356 bulk RNA sequencing samples from a public database and our previous study, we found a total of 57 significant ligand-receptor pairs (24 upregulated and 33 downregulated) in the autocrine crosstalk network of B cells. Via assessment of the communication between B cells and myeloid cells, another 29 ligand-receptor pairs were discovered, some of which notably affected survival outcomes. A score-based model was constructed with least absolute shrinkage and selection operator (LASSO) using these ligand-receptor pairs. Patients with higher scores had poorer prognoses. This model can be applied to create predictions for both pediatric and adult BCP-ALL patients.

Identification of cell-to-cell interactions by ligand-receptor pairs in human fetal heart.

The heart is the first organ to form during embryogenesis and its development is a complex process. In this study, we identified 120 ligand-receptor pairs including 65 ligands and 58 receptors specifically expressed in one of the nine cell types. The correlation analysis of the cell proportions revealed that the cell-to-cell contact exhibited spatial patterns in human fetal heart. Specifically, the cardiomyocytes (CMs) proportion might have negative correlation with proportion of endothelial cell in left atrium and ventricle during the heart development. In contrast, fibroblast-like cells and macrophages were jointly increased with the gestation. Furthermore, the ligand in CM, NPPA (Natriuretic Peptide A), and receptor in endothelial cell (EC), NPR3 (Natriuretic Peptide Receptor 3), were specifically expressed in atrial CM and endocardial cells, respectively, indicating that the atrial CM might communicate with endocardial cells via NPPA-NRP3 interaction. Moreover, the interplay between fibroblast-like cell and macrophage was observed in both left and right atriums via the ligand-receptor interactions of COL1A1/COL1A2 (Collagen Type I Alpha 1/2 Chain)-CD36 and CTGF (connective tissue growth factor)-ITGB2 (Integrin Subunit Beta 2). Functional enrichment analysis revealed that the ligand-receptor interactions might be associated with the intracellular activation of cGMP-PKG signaling pathway in ECs, PDGF-beta signaling pathway in fibroblast-like cell, and Toll-like receptor signaling in macrophage, respectively. Collectively, the present study unveiled the potential cell-cell communication and underlying mechanism involved in cardiac development, which broadened our insights into developmental biology of heart.

Analysis of PLETHORA Gradient Formation by Secreted Peptides During Root Development.

The formation of longitudinal zonation patterns is important for normal root development and is regulated by the transcription factor PLETHORA (PLT). PLT proteins form a concentration gradient, and PLT protein levels determine root zonation. A peptide hormone root meristem growth factor (RGF) and its receptors (RGFRs) act as key regulators of root development by regulating the PLT protein expression pattern. Here, we describe a method for monitoring PLT protein gradient patterns in Arabidopsis with exogenous RGF treatment.