A draft network of ligand-receptor-mediated multicellular signalling in human

Mapping spatial organization and genetic cell-state regulators to target immune evasion in ovarian cancer

The drivers of immune evasion are not entirely clear, limiting the success of cancer immunotherapies. Here we applied single-cell spatial and perturbational transcriptomics to delineate immune evasion in high-grade serous tubo-ovarian cancer. To this end, we first mapped the spatial organization of high-grade serous tubo-ovarian cancer by profiling more than 2.5 million cells in situ in 130 tumors from 94 patients. This revealed a malignant cell state that reflects tumor genetics and is predictive of T cell and natural killer cell infiltration levels and response to immune checkpoint blockade. We then performed Perturb-seq screens and identified genetic perturbations-including knockout of PTPN1 and ACTR8-that trigger this malignant cell state. Finally, we show that these perturbations, as well as a PTPN1/PTPN2 inhibitor, sensitize ovarian cancer cells to T cell and natural killer cell cytotoxicity, as predicted. This study thus identifies ways to study and target immune evasion by linking genetic variation, cell-state regulators and spatial biology.

A spatial expression atlas of the adult human proximal small intestine

The mouse small intestine shows profound variability in gene expression along the crypt-villus axis1,2. Whether similar spatial heterogeneity exists in the adult human gut remains unclear. Here we use spatial transcriptomics, spatial proteomics and single-molecule fluorescence in situ hybridization to reconstruct a comprehensive spatial expression atlas of the adult human proximal small intestine. We describe zonated expression and cell type representation for epithelial, mesenchymal and immune cell types. We find that migrating enterocytes switch from lipid droplet assembly and iron uptake at the villus bottom to chylomicron biosynthesis and iron release at the tip. Villus tip cells are pro-immunogenic, recruiting γδ T cells and macrophages to the tip, in contrast to their immunosuppressive roles in mouse. We also show that the human small intestine contains abundant serrated and branched villi that are enriched at the tops of circular folds. Our study presents a detailed resource for understanding the biology of the adult human small intestine.

Transcriptome-Derived Ligand-Receptor Interactome of Major PitNET Subgroups

Introduction  Pituitary neuroendocrine tumors (PitNETs) are rare skull base tumors which can impart significant disability owing to their locally invasive potential. To date, the gamut of PitNET subtypes remains ill-understood at the ligand-receptor (LR) interactome level, potentially limiting therapeutic options. Here, we present findings from in silico analysis of LR complexes formed by PitNETs with clinical presentations of acromegaly, Cushing's disease, high prolactin production, and without symptoms of hormone hypersecretion. Methods  Previously published PitNET gene expression data was acquired from ArrayExpress. These data represented all secretion types. LR interactions were analyzed via a crosstalk score approach. Results  Cortisol (CORT) ligand was significantly involved in tumor-to-tumor signaling across all PitNET subtypes but prolactinomas, which evidenced active CORT depletion. Likewise, CCL25 ligand was implicated in 20% of the top LR complex interactions along the tumor-to-stroma signaling axis, but silent PitNETs reported unique depletion of the CCL25 ligand. Along the stroma-to-tumor signaling axis, all clinical PitNET subtypes enriched stromal vasoactive intestinal polypeptide ligand interactions with tumor secretin receptor. All clinical PitNET subtypes enriched stromal DEFB103B (human β-defensin 103B) ligand interactions with stromal chemokine receptors along the stroma-to-stroma signaling axis. In PitNETs causing Cushing's disease, immune checkpoint ligand CD274 reported high stromal expression, and prolactinomas reported low stromal expression. Moreover, prolactinomas evidenced distinctly high stromal expression of immune-exhausted T cell response marker IL10RA compared with other clinical subtypes. Conclusion  Relative crosstalk score analysis revealed a great diversity of LR complex interactions across clinical PitNET subtypes and between solid tumor compartments. More data are needed to validate these findings and exact clinical importance.

Dynamic intrauterine crosstalk promotes porcine embryo implantation during early pregnancy

Dynamic crosstalk between the embryo and mother is crucial during implantation. Here, we comprehensively profile the single-cell transcriptome of pig peri-implantation embryos and corresponding maternal endometrium, identifying 4 different lineages in embryos and 13 cell types in the endometrium. Cell-specific gene expression characterizes 4 distinct trophectoderm subpopulations, showing development from undifferentiated trophectoderm to polar and mural trophectoderm. Dynamic expression of genes in different types of endometrial cells illustrates their molecular response to embryos during implantation. Then, we developed a novel tool, ExtraCellTalk, generating an overall dynamic map of maternal-foetal crosstalk using uterine luminal proteins as bridges. Through cross-species comparisons, we identified a conserved RBP4/STRA6 pathway in which embryonic-derived RBP4 could target the STRA6 receptor on stromal cells to regulate the interaction with other endometrial cells. These results provide insight into the maternal-foetal crosstalk during embryo implantation and represent a valuable resource for further studies to improve embryo implantation.

Single-cell transcriptome analysis deciphers the CD74-mediated immune evasion and tumour growth in lung squamous cell carcinoma with chronic obstructive pulmonary disease

BACKGROUND

Chronic obstructive pulmonary disease (COPD) contributes to the incidence and prognosis of lung cancer. The presence of COPD significantly increases the risk of lung squamous cell carcinoma (LSCC). COPD may promote an immunosuppressive microenvironment in LSCC by regulating the expression of immune-inhibitory factors in T cells, although the mechanisms remain unclear. In this study, we aimed to decipher the tumour microenvironment signature for LSCC with COPD at a single-cell level.

METHODS

We performed single-cell RNA sequencing on tumour tissues from LSCC with or without COPD, then investigated the features of the immune and tumour cells. We employed multiple techniques, including multispectral imaging, flow cytometry, tissue microarray analysis, survival analysis, co-culture systems and in vitro and in vivo treatment experiments, to validate the findings obtained from single-cell analyses.

RESULTS

LSCC with COPD showed increased proportions of tumour-associated macrophages (TAMs) and higher levels of CD8+ T cell exhaustion molecules, which contributed to an immunosuppressive microenvironment. Further analysis revealed a critical cluster of CD74+ tumour cells that expressed both epithelial and immune cell signatures, exhibited a stronger capacity for tumorigenesis and predicted worse overall survival. Notably, migration inhibitory factor (MIF) secreted by TAMs from LSCC with COPD may promote the activation of CD74. MIF-CD74 may interact with CD8+ T cells and impair their anti-tumour activity by regulating the PI3K-STAT3-programmed cell death-1 ligand 1 signalling pathway, facilitating tumour proliferation and immune evasion.

CONCLUSIONS

Our comprehensive picture of the tumour ecosystem in LSCC with COPD provides deeper insights into relevant immune evasion mechanisms and potential targets for immunotherapy.

HIGHLIGHT

Our results demonstrated higher proportions of tumour-associated macrophages (TAMs) and higher levels of exhaustion molecules in CD8+ T cells in the microenvironment of LSCC with COPD. CD74+tumour cells were associated with poor disease prognosis. Migration inhibitory factor (MIF)-CD74 may interact with CD8+ T cells and impair their anti-tumour activity by regulating the PI3K-STAT3-PD-L1 signalling pathway, facilitating immune evasion.

Mapping the Human Cell Surface Interactome: A Key to Decode Cell-to-Cell Communication

Proteins on the surfaces of cells serve as physical connection points to bridge one cell with another, enabling direct communication between cells and cohesive structure. As biomedical research makes the leap from characterizing individual cells toward understanding the multicellular organization of the human body, the binding interactions between molecules on the surfaces of cells are foundational both for computational models and for clinical efforts to exploit these influential receptor pathways. To achieve this grander vision, we must assemble the full interactome of ways surface proteins can link together. This review investigates how close we are to knowing the human cell surface protein interactome. We summarize the current state of databases and systematic technologies to assemble surface protein interactomes, while highlighting substantial gaps that remain. We aim for this to serve as a road map for eventually building a more robust picture of the human cell surface protein interactome.

TME-NET: an interpretable deep neural network for predicting pan-cancer immune checkpoint inhibitor responses

Immunotherapy with immune checkpoint inhibitors (ICIs) is increasingly used to treat various tumor types. Determining patient responses to ICIs presents a significant clinical challenge. Although components of the tumor microenvironment (TME) are used to predict patient outcomes, comprehensive assessments of the TME are frequently overlooked. Using a top-down approach, the TME was divided into five layers-outcome, immune role, cell, cellular component, and gene. Using this structure, a neural network called TME-NET was developed to predict responses to ICIs. Model parameter weights and cell ablation studies were used to investigate the influence of TME components. The model was developed and evaluated using a pan-cancer cohort of 948 patients across four cancer types, with Area Under the Curve (AUC) and accuracy as performance metrics. Results show that TME-NET surpasses established models such as support vector machine and k-nearest neighbors in AUC and accuracy. Visualization of model parameter weights showed that at the cellular layer, Th1 cells enhance immune responses, whereas myeloid-derived suppressor cells and M2 macrophages show strong immunosuppressive effects. Cell ablation studies further confirmed the impact of these cells. At the gene layer, the transcription factors STAT4 in Th1 cells and IRF4 in M2 macrophages significantly affect TME dynamics. Additionally, the cytokine-encoding genes IFNG from Th1 cells and ARG1 from M2 macrophages are crucial for modulating immune responses within the TME. Survival data from immunotherapy cohorts confirmed the prognostic ability of these markers, with p-values <0.01. In summary, TME-NET performs well in predicting immunotherapy responses and offers interpretable insights into the immunotherapy process. It can be customized at https://immbal.shinyapps.io/TME-NET.

Lipid-associated macrophages reshape BAT cell identity in obesity

Obesity and type 2 diabetes cause a loss in brown adipose tissue (BAT) activity, but the molecular mechanisms that drive BAT cell remodeling remain largely unexplored. Using a multilayered approach, we comprehensively mapped a reorganization in BAT cells. We uncovered a subset of macrophages as lipid-associated macrophages (LAMs), which were massively increased in genetic and dietary model of BAT expansion. LAMs participate in this scenario by capturing extracellular vesicles carrying damaged lipids and mitochondria released from metabolically stressed brown adipocytes. CD36 scavenger receptor drove LAM phenotype, and CD36-deficient LAMs were able to increase brown fat genes in adipocytes. LAMs released transforming growth factor β1 (TGF-β1), which promoted the loss of brown adipocyte identity through aldehyde dehydrogenase 1 family member A1 (Aldh1a1) induction. These findings unfold cell dynamic changes in BAT during obesity and identify LAMs as key responders to tissue metabolic stress and drivers of loss of brown adipocyte identity.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Deciphering the role of SMARCA4 in cardiac disorders: Insights from single-cell studies on dilated cardiomyopathy and coronary heart disease

BACKGROUND

Dilated cardiomyopathy (DCM) and coronary heart disease (CHD) stand as two of the foremost causes of mortality. However, the comprehensive comprehension of the regulatory mechanisms governing DCM and CHD remains limited, particularly from the vantage point of single-cell transcriptional analysis.

METHOD

We used the GSE121893 dataset from the GEO database, analyzing single-cell expressions with tools like DropletUtils, Seurat, and Monocle. We also utilized the GSVA package for comparing gene roles in DCM and CHD, Finally, we conducted qRT-PCR and Western blot analyses to measure the expression levels of SMARCA4, Col1A1, Col3A1 and α-SMA, and the role of SMARCA4 on fibroblasts were explored by EdU and Transwell assay.

RESULTS

Our analysis identified six cell types in heart tissue, with fibroblasts showing the most interaction with other cells. DEGs in fibroblasts were linked to muscle development and morphogenesis. Pseudotime analysis revealed the dynamics of fibroblast changes in both the normal and disease groups and many transcription factors (TFs) potentially involved in this process. Among these TFs, SMARCA4 which was translated into protein BRG1, showed the most significantly difference. In vivo experiments have demonstrated that SMARCA4 indeed promoted fibroblasts proliferation and migration.

CONCLUSION

This study provides a clearer understanding of cell-type dynamics in heart diseases, emphasizing the role of fibroblasts and the significance of SMARCA4 in their function. Our results offer insights into the cellular mechanisms underlying DCM and CHD, potentially guiding future therapeutic strategies.

Developing a dormancy-associated ECM signature in TNBC that is linked to immunosuppressive tumor microenvironment and selective sensitivity to MAPK inhibitors

Aims

Cellular dormancy is a state of quiescence subpopulation of tumor cells, characterized by low differentiation and lack of mitotic activity. They could evade chemotherapy and targeted therapy, leading to drug resistance and disease recurrence. Recent studies have shown a correlation between dormant cancer cells and unique extracellular matrix (ECM) composition, which is critical in regulating cell behavior. However, their interacting roles in TNBC patients remains to be characterized.

Main methods

Dormant cancer cells in MDA-MB-231 cell line with highest PKH26 dye-retaining were FACS-sorted and gene expression was then analyzed. Dormant associated ECM (DA-ECM) signature was characterized by pathway analysis. Unsupervised hierarchical clustering was used to define distinct ECM features for TNBC patients. ECM-specific tumor biology was defined by integration of bulk RNA-seq with single-cell RNA-seq data, analysis of ligand-receptor interactions and enriched biological pathways, and in silico drug screening. We validated the sensitivity of dormant cancer cells to MAPK inhibitors by flow cytometry in vitro.

Key findings

We observed that dormant TNBC cells preferentially expressed ∼10 % DA-ECM genes. The DA-ECM High subtype defined by unsupervised hierarchical clustering analysis was associated with immunosuppressive tumor microenvironment. Moreover, ligand-receptor interaction and pathway analysis revealed that the DA-ECM High subtype may likely help maintain tumor cell dormancy through MAPK, Hedgehog and Notch signaling pathways. Finally, in silico drug screening against the DA-ECM signature and in vitro assay showed dormant cancer cells were relatively sensitive to the MAPK pathway inhibitors, which may represent a potential therapeutic strategy for treating TNBC.

Significance

Collectively, our research revealed that dormancy-associated ECM characterized tumor cells possess significant ECM remodeling capacity, and treatment strategies towards these cells could improve TNBC patient outcome.

STPDA: Leveraging spatial-temporal patterns for downstream analysis in spatial transcriptomic data

Spatial transcriptomics, a groundbreaking field in cellular biology, faces the challenge of effectively deciphering complex spatial-temporal gene expression patterns. Traditional data analysis methods often fail to capture the intricate nuances of this data, limiting the depth of understanding in spatial distribution and gene interactions. In response, we present Spatial-Temporal Patterns for Downstream Analysis (STPDA), a sophisticated computational framework tailored for spatial transcriptomic data analysis. STPDA leverages high-resolution mapping to bridge the gap between genomics and histopathology, offering a comprehensive perspective on the spatial dynamics of gene expression within tissues. This approach enables a view of cellular function and organization, marking a paradigm shift in our comprehension of biological systems. By employing Autoregressive Moving Average (ARMA) and Long Short-Term Memory (LSTM) models, STPDA effectively deciphers both global and local spatio-temporal dynamics in cellular environments. This integration of spatial-temporal patterns for downstream analysis offers a transformative approach to spatial transcriptomics data analysis. STPDA excels in various single-cell analytical tasks, including the identification of ligand-receptor interactions and cell type classification. Its ability to harness spatial-temporal patterns not only matches but frequently surpasses the performance of existing state-of-the-art methods. To ensure widespread usability and impact, we have encapsulated STPDA in a scalable and accessible Python package, addressing single-cell tasks through advanced spatial-temporal pattern analysis. This development promises to enhance our understanding of cellular biology, offering novel insights and therapeutic strategies, and represents a substantial advancement in the field of spatial transcriptomics.

Secretome of Cancer-Associated Fibroblasts (CAFs) Influences Drug Sensitivity in Cancer Cells

Resistance is a major problem with effective cancer treatment and the stroma forms a significant portion of the tumor mass but traditional drug screens involve cancer cells alone. Cancer-associated fibroblasts (CAFs) are a major tumor stroma component and its secreted proteins may influence the function of cancer cells. The majority of secretome studies compare different cancer or CAF cell lines exclusively. Here, we present the direct characterization of the secreted protein profiles between CAFs and KRAS mutant-cancer cell lines from colorectal, lung, and pancreatic tissues using multiplexed mass spectrometry. 2573 secreted proteins were annotated, and differential analysis highlighted understudied CAF-enriched secreted proteins, including Wnt family member 5B (WNT5B), in addition to established CAF markers, such as collagens. The functional role of CAF secreted proteins was explored by assessing its effect on the response to 97 anticancer drugs since stromal cells may cause a differing cancer drug response, which may be missed on routine drug screening using cancer cells alone. CAF secreted proteins caused specific effects on each of the cancer cell lines, which highlights the complexity and challenges in cancer treatment and so the importance to consider stromal elements.

Epithelial tubule interconnection driven by HGF-Met signaling in the kidney

The formation of functional epithelial tubules is a central feature of many organ systems. Although the process of tubule formation by epithelial cells is well-studied, the way in which tubules connect with each other (i.e. anastomose) to form functional networks both in vivo and in vitro is not well understood. A key, unanswered question in the kidney is how the renal vesicles of the embryonic kidney connect with the nascent collecting ducts to form a continuous urinary system. We performed a ligand-receptor pair analysis on single cell RNA-seq data from embryonic mouse kidney tubules undergoing anastomosis to select candidates that might mediate this process in vivo. This analysis identified hepatocyte growth factor (HGF), which has known roles in cell proliferation, migration, and tubulogenesis, as one of several possible candidates. To test this possibility, we designed a novel assay to quantitatively examine epithelial tubule anastomosis in vitro using epithelial spheroids with fluorescently-tagged apical surfaces to enable direct visualization of anastomosis. This revealed that HGF is a potent inducer of tubule anastomosis. Tubule anastomosis occurs through a proliferation-independent mechanism that acts through the MAPK signaling cascade and matrix metalloproteinases (MMPs), the latter suggestive of a role in extracellular matrix turnover. Accordingly, treatment of explanted embryonic mouse kidneys with HGF and collagenase was sufficient to induce kidney tubule anastomosis. These results lay the groundwork for investigating how to promote functional interconnections between tubular epithelia, which have important clinical implications for utilizing in vitro grown kidney tissue in transplant medicine.

Spatially resolved transcriptomic profiling of placental development in dairy cow

The placenta plays a crucial role in successful mammalian reproduction. Ruminant animals possess a semi-invasive placenta characterized by a highly vascularized structure formed by maternal endometrial caruncles and fetal placental cotyledons, essential for full-term fetal development. The cow placenta harbors at least two trophoblast cell populations: uninucleate (UNC) and binucleate (BNC) cells. However, the limited capacity to elucidate the transcriptomic dynamics of the placental natural environment has resulted in a poor understanding of both the molecular and cellular interactions between trophoblast cells and niches, and the molecular mechanisms governing trophoblast differentiation and functionalization. To fill this knowledge gap, we employed Stereo-seq to map spatial gene expression patterns at near single-cell resolution in the cow placenta at 90 and 130 days of gestation, attaining high-resolution, spatially resolved gene expression profiles. Based on clustering and cell marker gene expression analyses, key transcription factors, including YBX1 and NPAS2, were shown to regulate the heterogeneity of trophoblast cell subpopulations. Cell communication and trajectory analysis provided a framework for understanding cell-cell interactions and the differentiation of trophoblasts into BNCs in the placental microenvironment. Differential analysis of cell trajectories identified a set of genes involved in regulation of trophoblast differentiation. Additionally, spatial modules and co-variant genes that help shape specific tissue structures were identified. Together, these findings provide foundational insights into important biological pathways critical to the placental development and function in cows.

Mitigation of acute lung injury by human bronchial epithelial cell-derived extracellular vesicles via ANXA1-mediated FPR signaling

Acute lung injury (ALI) is characterized by respiratory failure resulting from the disruption of the epithelial and endothelial barriers as well as immune system. In this study, we evaluated the therapeutic potential of airway epithelial cell-derived extracellular vesicles (EVs) in maintaining lung homeostasis. We isolated human bronchial epithelial cell-derived EVs (HBEC-EVs), which endogenously express various immune-related surface markers and investigated their immunomodulatory potential in ALI. In ALI cellular models, HBEC-EVs demonstrated immunosuppressive effects by reducing the secretion of proinflammatory cytokines in both THP-1 macrophages and HBECs. Mechanistically, these effects were partially ascribed to nine of the top 10 miRNAs enriched in HBEC-EVs, governing toll-like receptor-NF-κB signaling pathways. Proteomic analysis revealed the presence of proteins in HBEC-EVs involved in WNT and NF-κB signaling pathways, pivotal in inflammation regulation. ANXA1, a constituent of HBEC-EVs, interacts with formyl peptide receptor (FPR)2, eliciting anti-inflammatory responses by suppressing NF-κB signaling in inflamed epithelium, including type II alveolar epithelial cells. In a mouse model of ALI, intratracheal administration of HBEC-EVs reduced lung injury, inflammatory cell infiltration, and cytokine levels. Collectively, these findings suggest the therapeutic potential of HBEC-EVs, through their miRNAs and ANXA1 cargo, in mitigating lung injury and inflammation in ALI patients.

Distinct molecular profiles drive multifaceted characteristics of colorectal cancer metastatic seeds

Metastasis of primary tumors remains a challenge for early diagnosis and prevention. The cellular properties and molecular drivers of metastatically competent clones within primary tumors remain unclear. Here, we generated 10-16 single cell-derived lines from each of three colorectal cancer (CRC) tumors to identify and characterize metastatic seeds. We found that intrinsic factors conferred clones with distinct metastatic potential and cellular communication capabilities, determining organ-specific metastasis. Poorly differentiated or highly metastatic clones, rather than drug-resistant clones, exhibited poor clinical prognostic impact. Personalized genetic alterations, instead of mutation burden, determined the occurrence of metastatic potential during clonal evolution. Additionally, we developed a gene signature for capturing metastatic potential of primary CRC tumors and demonstrated a strategy for identifying metastatic drivers using isogenic clones with distinct metastatic potential in primary tumors. This study provides insight into the origin and mechanisms of metastasis and will help develop potential anti-metastatic therapeutic targets for CRC patients.

Single-cell RNA sequencing to explore cancer-associated fibroblasts heterogeneity: "Single" vision for "heterogeneous" environment

Cancer-associated fibroblasts (CAFs), a phenotypically and functionally heterogeneous stromal cell, are one of the most important components of the tumour microenvironment. Previous studies have consolidated it as a promising target against cancer. However, variable therapeutic efficacy-both protumor and antitumor effects have been observed not least owing to the strong heterogeneity of CAFs. Over the past 10 years, advances in single-cell RNA sequencing (scRNA-seq) technologies had a dramatic effect on biomedical research, enabling the analysis of single cell transcriptomes with unprecedented resolution and throughput. Specifically, scRNA-seq facilitates our understanding of the complexity and heterogeneity of diverse CAF subtypes. In this review, we discuss the up-to-date knowledge about CAF heterogeneity with a focus on scRNA-seq perspective to investigate the emerging strategies for integrating multimodal single-cell platforms. Furthermore, we summarized the clinical application of scRNA-seq on CAF research. We believe that the comprehensive understanding of the heterogeneity of CAFs form different visions will generate innovative solutions to cancer therapy and achieve clinical applications.

Spatial and single-cell colocalisation analysis reveals MDK-mediated immunosuppressive environment with regulatory T cells in colorectal carcinogenesis

BACKGROUND

Cell-cell interaction factors that facilitate the progression of adenoma to sporadic colorectal cancer (CRC) remain unclear, thereby hindering patient survival.

METHODS

We performed spatial transcriptomics on five early CRC cases, which included adenoma and carcinoma, and one advanced CRC. To elucidate cell-cell interactions within the tumour microenvironment (TME), we investigated the colocalisation network at single-cell resolution using a deep generative model for colocalisation analysis, combined with a single-cell transcriptome, and assessed the clinical significance in CRC patients.

FINDINGS

CRC cells colocalised with regulatory T cells (Tregs) at the adenoma-carcinoma interface. At early-stage carcinogenesis, cell-cell interaction inference between colocalised adenoma and cancer epithelial cells and Tregs based on the spatial distribution of single cells highlighted midkine (MDK) as a prominent signalling molecule sent from tumour epithelial cells to Tregs. Interaction between MDK-high CRC cells and SPP1+ macrophages and stromal cells proved to be the mechanism underlying immunosuppression in the TME. Additionally, we identified syndecan4 (SDC4) as a receptor for MDK associated with Treg colocalisation. Finally, clinical analysis using CRC datasets indicated that increased MDK/SDC4 levels correlated with poor overall survival in CRC patients.

INTERPRETATION

MDK is involved in the immune tolerance shown by Tregs to tumour growth. MDK-mediated formation of the TME could be a potential target for early diagnosis and treatment of CRC.

FUNDING

Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Science Research; OITA Cancer Research Foundation; AMED under Grant Number; Japan Science and Technology Agency (JST); Takeda Science Foundation; The Princess Takamatsu Cancer Research Fund.

SEnSCA: Identifying possible ligand-receptor interactions and its application in cell-cell communication inference

Multicellular organisms have dense affinity with the coordination of cellular activities, which severely depend on communication across diverse cell types. Cell-cell communication (CCC) is often mediated via ligand-receptor interactions (LRIs). Existing CCC inference methods are limited to known LRIs. To address this problem, we developed a comprehensive CCC analysis tool SEnSCA by integrating single cell RNA sequencing and proteome data. SEnSCA mainly contains potential LRI acquisition and CCC strength evaluation. For acquiring potential LRIs, it first extracts LRI features and reduces the feature dimension, subsequently constructs negative LRI samples through K-means clustering, finally acquires potential LRIs based on Stacking ensemble comprising support vector machine, 1D-convolutional neural networks and multi-head attention mechanism. During CCC strength evaluation, SEnSCA conducts LRI filtering and then infers CCC by combining the three-point estimation approach and single cell RNA sequencing data. SEnSCA computed better precision, recall, accuracy, F1 score, AUC and AUPR under most of conditions when predicting possible LRIs. To better illustrate the inferred CCC network, SEnSCA provided three visualization options: heatmap, bubble diagram and network diagram. Its application on human melanoma tissue demonstrated its reliability in CCC detection. In summary, SEnSCA offers a useful CCC inference tool and is freely available at https://github.com/plhhnu/SEnSCA.

Interleukin-1β polarization in M1 macrophage mediates myocardial fibrosis in diabetes

BACKGROUND

Diabetes is a global health problem whose common complication is diabetic cardiomyopathy, characterized by chronic inflammation of the heart muscle. Macrophages are the main white blood cells found in the resting heart. Therefore, we investigated the underling mechanism of macrophage on myocardial fibrosis in diabetes.

METHODS

Here, echocardiography was utilized to evaluate cardiac function, and the degree of myocardial fibrosis was assessed using Masson's trichrome staining, followed by single-cell RNA sequencing (scRNA-seq) to analyze the phenotype, function, developmental trajectory, and interactions between immune cells, endothelial cells (ECs), and fibroblasts (FBs) in the hearts of db/db mice at different stages of diabetes. Macrophages and cardiac fibroblasts were also co-cultured in order to study the signaling between macrophages and fibroblasts.

RESULTS

We found that with the development of diabetes mellitus, myocardial hypertrophy and fibrosis occurred that was accompanied by cardiac dysfunction. A significant proportion of immune cells, endothelial cells, and fibroblasts were identified by RNA sequencing. The most significant changes observed were in macrophages, which undergo M1 polarization and are critical for oxidative stress and extracellular matrix (ECM) formation. We further found that M1 macrophages secreted interleukin-1β (IL-1β), which interacted with the receptor on the surface of fibroblasts, to cause myocardial fibrosis. In addition, crosstalk between M1 macrophages and endothelial cells also plays a key role in fibrosis and immune response regulation through IL-1β and corresponding receptors.

CONCLUSIONS

M1 macrophages mediate diabetic myocardial fibrosis through interleukin-1β interaction with fibroblasts.

A map of signaling responses in the human airway epithelium

Receptor-mediated signaling plays a central role in tissue regeneration, and it is dysregulated in disease. Here, we build a signaling-response map for a model regenerative human tissue: the airway epithelium. We analyzed the effect of 17 receptor-mediated signaling pathways on organotypic cultures to determine changes in abundance and phenotype of epithelial cell types. This map recapitulates the gamut of known airway epithelial signaling responses to these pathways. It defines convergent states induced by multiple ligands and diverse, ligand-specific responses in basal cell and secretory cell metaplasia. We show that loss of canonical differentiation induced by multiple pathways is associated with cell-cycle arrest, but that arrest is not sufficient to block differentiation. Using the signaling-response map, we show that a TGFB1-mediated response underlies specific aberrant cells found in multiple lung diseases and identify interferon responses in COVID-19 patient samples. Thus, we offer a framework enabling systematic evaluation of tissue signaling responses. A record of this paper's transparent peer review process is included in the supplemental information.

Synovial fibroblast gene expression is associated with sensory nerve growth and pain in rheumatoid arthritis

It has been presumed that rheumatoid arthritis (RA) joint pain is related to inflammation in the synovium; however, recent studies reveal that pain scores in patients do not correlate with synovial inflammation. We developed a machine-learning approach (graph-based gene expression module identification or GbGMI) to identify an 815-gene expression module associated with pain in synovial biopsy samples from patients with established RA who had limited synovial inflammation at arthroplasty. We then validated this finding in an independent cohort of synovial biopsy samples from patients who had early untreated RA with little inflammation. Single-cell RNA sequencing analyses indicated that most of these 815 genes were most robustly expressed by lining layer synovial fibroblasts. Receptor-ligand interaction analysis predicted cross-talk between human lining layer fibroblasts and human dorsal root ganglion neurons expressing calcitonin gene-related peptide (CGRP+). Both RA synovial fibroblast culture supernatant and netrin-4, which is abundantly expressed by lining fibroblasts and was within the GbGMI-identified pain-associated gene module, increased the branching of pain-sensitive murine CGRP+ dorsal root ganglion neurons in vitro. Imaging of solvent-cleared synovial tissue with little inflammation from humans with RA revealed CGRP+ pain-sensing neurons encasing blood vessels growing into synovial hypertrophic papilla. Together, these findings support a model whereby synovial lining fibroblasts express genes associated with pain that enhance the growth of pain-sensing neurons into regions of synovial hypertrophy in RA.

PTPN7 mediates macrophage-polarization and determines immunotherapy in gliomas: A single-cell sequencing analysis

BACKGROUND

Protein tyrosine phosphatase non-receptor type 7 (PTPN7) is a signaling molecule that regulates a multitude of cellular processes, spanning cell proliferation, cellular differentiation, the mitotic cycle, and oncogenic metamorphosis. However, the characteristic of PTPN7 in the glioma microenvironment has yet to be elucidated.

METHODS

The prognostic value, genomic features, immune characteristics, chemotherapy prediction, and immunotherapy prediction of PTPN7 were systematically explored at the bulk sequencing level. The cell evolution trajectory, cell communication pattern, and cell metabolic activity related to PTPN7 were systematically explored at the single-cell sequencing level. HMC3 and M0 cells were cocultured with U251 and T98G cells, and flow cytometry was carried out to investigate the polarization of HMC3 and M0. Transwell assay and CCK-8 assay were performed to explore the migration and proliferation activity of U251 and T98G.

RESULTS

The expression level of PTPN7 is significantly elevated in glioma and indicates malignant features. PTPN7 expression predicts worse prognosis of glioma patients. PTPN7 is associated with genome alteration and immune infiltration. Besides, PTPN7 plays a crucial role in modulating metabolic and immunogenic processes, particularly by influencing the activity of microglia and macrophages through multiple signaling pathways involved in cellular communication. Specifically, PTPN7 actively mediates inflammation-resolving-polarization of macrophages and microglia and protects glioma from immune attack. PTPN7 could also predict the response of immunotherapy.

CONCLUSIONS

PTPN7 is critically involved in inflammation-resolving-polarization mediated by macrophage and microglia and promotes the immune escape of glioma cells.

The Origin and Evolution of Sex Peptide and Sex Peptide Receptor Interactions

Post-mating responses play a vital role in successful reproduction across diverse species. In fruit flies, sex peptide binds to the sex peptide receptor, triggering a series of post-mating responses. However, the origin of sex peptide receptor predates the emergence of sex peptide. The evolutionary origins of the interactions between sex peptide and sex peptide receptor and the mechanisms by which they interact remain enigmatic. In this study, we used ancestral sequence reconstruction, AlphaFold2 predictions, and molecular dynamics simulations to study sex peptide-sex peptide receptor interactions and their origination. Using AlphaFold2 and long-time molecular dynamics simulations, we predicted the structure and dynamics of sex peptide-sex peptide receptor interactions. We show that sex peptide potentially binds to the ancestral states of Diptera sex peptide receptor. Notably, we found that only a few amino acid changes in sex peptide receptor are sufficient for the formation of sex peptide-sex peptide receptor interactions. Ancestral sequence reconstruction and molecular dynamics simulations further reveal that sex peptide receptor interacts with sex peptide through residues that are mostly involved in the interaction interface of an ancestral ligand, myoinhibitory peptides. We propose a potential mechanism whereby sex peptide-sex peptide receptor interactions arise from the preexisting myoinhibitory peptides-sex peptide receptor interface as well as early chance events both inside and outside the preexisting interface that created novel sex peptide-specific sex peptide-sex peptide receptor interactions. Our findings provide new insights into the origin and evolution of sex peptide-sex peptide receptor interactions and their relationship with myoinhibitory peptides-sex peptide receptor interactions.

Sarcoma microenvironment cell states and ecosystems are associated with prognosis and predict response to immunotherapy

Characterization of the diverse malignant and stromal cell states that make up soft tissue sarcomas and their correlation with patient outcomes has proven difficult using fixed clinical specimens. Here, we employed EcoTyper, a machine-learning framework, to identify the fundamental cell states and cellular ecosystems that make up sarcomas on a large scale using bulk transcriptomes with clinical annotations. We identified and validated 23 sarcoma-specific, transcriptionally defined cell states, many of which were highly prognostic of patient outcomes across independent datasets. We discovered three conserved cellular communities or ecotypes associated with underlying genomic alterations and distinct clinical outcomes. We show that one ecotype defined by tumor-associated macrophages and epithelial-like malignant cells predicts response to immune-checkpoint inhibition but not chemotherapy and validate our findings in an independent cohort. Our results may enable identification of patients with soft tissue sarcomas who could benefit from immunotherapy and help develop new therapeutic strategies.

Cellular heterogeneity and dynamics of the human uterus in healthy premenopausal women

The human uterus is a complex and dynamic organ whose lining grows, remodels, and regenerates in every menstrual cycle or upon tissue damage. Here we applied single-cell RNA sequencing to profile more the 50,000 uterine cells from both the endometrium and myometrium of 5 healthy premenopausal individuals, and jointly analyzed the data with a previously published dataset from 15 subjects. The resulting normal uterus cell atlas contains more than 167K cells representing the lymphatic endothelium, blood endothelium, stromal, ciliated epithelium, unciliated epithelium, and immune cell populations. Focused analyses within each major cell type and comparisons with subtype labels from prior studies allowed us to document supporting evidence, resolve naming conflicts, and to propose a consensus annotation system of 39 subtypes. We release their gene expression centroids, differentially expressed genes, and mRNA patterns of literature-based markers as a shared community resource. We find many subtypes show dynamic changes over different phases of the cycle and identify multiple potential progenitor cells: compartment-wide progenitors for each major cell type, transitional cells that are upstream of other subtypes, and potential cross-lineage multipotent stromal progenitors that may be capable of replenishing the epithelial, stromal, and endothelial compartments. When compared to the healthy premenopausal samples, a postpartum and a postmenopausal uterus sample revealed substantially altered tissue composition, involving the rise or fall of stromal, endothelial, and immune cells. The cell taxonomy and molecular markers we report here are expected to inform studies of both basic biology of uterine function and its disorders.

SIGNIFICANCE

We present single-cell RNA sequencing data from seven individuals (five healthy pre-menopausal women, one post-menopausal woman, and one postpartum) and perform an integrated analysis of this data alongside 15 previously published scRNA-seq datasets. We identified 39 distinct cell subtypes across four major cell types in the uterus. By using RNA velocity analysis and centroid-centroid comparisons we identify multiple computationally predicted progenitor populations for each of the major cell compartments, as well as potential cross-compartment, multi-potent progenitors. While the function and interactions of these cell populations remain to be validated through future experiments, the markers and their "dual characteristics" that we describe will serve as a rich resource to the scientific community. Importantly, we address a significant challenge in the field: reconciling multiple uterine cell taxonomies being proposed. To achieve this, we focused on integrating historical and contemporary knowledge across multiple studies. By providing detailed evidence used for cell classification we lay the groundwork for establishing a stable, consensus cell atlas of the human uterus.

Identifying potential ligand-receptor interactions based on gradient boosted neural network and interpretable boosting machine for intercellular communication analysis

Cell-cell communication is essential to many key biological processes. Intercellular communication is generally mediated by ligand-receptor interactions (LRIs). Thus, building a comprehensive and high-quality LRI resource can significantly improve intercellular communication analysis. Meantime, due to lack of a "gold standard" dataset, it remains a challenge to evaluate LRI-mediated intercellular communication results. Here, we introduce CellGiQ, a high-confident LRI prediction framework for intercellular communication analysis. Highly confident LRIs are first inferred by LRI feature extraction with BioTriangle, LRI selection using LightGBM, and LRI classification based on ensemble of gradient boosted neural network and interpretable boosting machine. Subsequently, known and identified high-confident LRIs are filtered by combining single-cell RNA sequencing (scRNA-seq) data and further applied to intercellular communication inference through a quartile scoring strategy. To validation the predictions, CellGiQ exploited several evaluation strategies: using AUC and AUPR, it surpassed six competing LRI prediction models on four LRI datasets; through Venn diagrams and molecular docking, its predicted LRIs were validated by five other popular intercellular communication inference methods; based on the overlapping LRIs, it computed high Jaccard index with six other state-of-the-art intercellular communication prediction tools within human HNSCC tissues; by comparing with classical models and literature retrieve, its inferred HNSCC-related intercellular communication results was further validated. The novelty of this study is to identify high-confident LRIs based on machine learning as well as design several LRI validation ways, providing reference for computational LRI prediction. CellGiQ provides an open-source and useful tool to decompose LRI-mediated intercellular communication at single cell resolution. CellGiQ is freely available at https://github.com/plhhnu/CellGiQ.

Comparative analysis of cell-cell communication at single-cell resolution

Inference of cell-cell communication from single-cell RNA sequencing data is a powerful technique to uncover intercellular communication pathways, yet existing methods perform this analysis at the level of the cell type or cluster, discarding single-cell-level information. Here we present Scriabin, a flexible and scalable framework for comparative analysis of cell-cell communication at single-cell resolution that is performed without cell aggregation or downsampling. We use multiple published atlas-scale datasets, genetic perturbation screens and direct experimental validation to show that Scriabin accurately recovers expected cell-cell communication edges and identifies communication networks that can be obscured by agglomerative methods. Additionally, we use spatial transcriptomic data to show that Scriabin can uncover spatial features of interaction from dissociated data alone. Finally, we demonstrate applications to longitudinal datasets to follow communication pathways operating between timepoints. Our approach represents a broadly applicable strategy to reveal the full structure of niche-phenotype relationships in health and disease.

The Origin and Evolution of Sex Peptide and Sex Peptide Receptor Interactions

Post-mating responses play a vital role in successful reproduction across diverse species. In fruit flies, sex peptide (SP) binds to the sex peptide receptor (SPR), triggering a series of post-mating responses. However, the origin of SPR predates the emergence of SP. The evolutionary origins of the interactions between SP and SPR and the mechanisms by which they interact remain enigmatic. In this study, we used ancestral sequence reconstruction, AlphaFold2 predictions, and molecular dynamics simulations to study SP-SPR interactions and their origination. Using AlphaFold2 and long-time molecular dynamics (MD) simulations, we predicted the structure and dynamics of SP-SPR interactions. We show that SP potentially binds to the ancestral states of Diptera SPR. Notably, we found that only a few amino acid changes in SPR are sufficient for the formation of SP-SPR interactions. Ancestral sequence reconstruction and MD simulations further reveal that SPR interacts with SP through residues that are mostly involved in the interaction interface of an ancestral ligand, myoinhibitory peptides (MIPs). We propose a potential mechanism whereby SP-SPR interactions arise from the pre-existing MIP-SPR interface as well as early chance events both inside and outside the pre-existing interface that created novel SP-specific SP-SPR interactions. Our findings provide new insights into the origin and evolution of SP-SPR interactions and their relationship with MIP-SPR interactions.

NeuroPep 2.0: An Updated Database Dedicated to Neuropeptide and Its Receptor Annotations

Neuropeptides not only work through nervous system but some of them also work peripherally to regulate numerous physiological processes. They are important in regulation of numerous physiological processes including growth, reproduction, social behavior, inflammation, fluid homeostasis, cardiovascular function, and energy homeostasis. The various roles of neuropeptides make them promising candidates for prospective therapeutics of different diseases. Currently, NeuroPep has been updated to version 2.0, it now holds 11,417 unique neuropeptide entries, which is nearly double of the first version of NeuroPep. When available, we collected information about the receptor for each neuropeptide entry and predicted the 3D structures of those neuropeptides without known experimental structure using AlphaFold2 or APPTEST according to the peptide sequence length. In addition, DeepNeuropePred and NeuroPred-PLM, two neuropeptide prediction tools developed by us recently, were also integrated into NeuroPep 2.0 to help to facilitate the identification of new neuropeptides. NeuroPep 2.0 is freely accessible at https://isyslab.info/NeuroPepV2/.

MDIC3: Matrix decomposition to infer cell-cell communication

Crosstalk among cells is vital for maintaining the biological function and intactness of systems. Most existing methods for investigating cell-cell communications are based on ligand-receptor (L-R) expression, and they focus on the study between two cells. Thus, the final communication inference results are particularly sensitive to the completeness and accuracy of the prior biological knowledge. Because existing L-R research focuses mainly on humans, most existing methods can only examine cell-cell communication for humans. As far as we know, there is currently no effective method to overcome this species limitation. Here, we propose MDIC3 (matrix decomposition to infer cell-cell communication), an unsupervised tool to investigate cell-cell communication in any species, and the results are not limited by specific L-R pairs or signaling pathways. By comparing it with existing methods for the inference of cell-cell communication, MDIC3 obtained better performance in both humans and mice.

WITHDRAWN: Oncogenic KRAS G12D extrinsically induces an immunosuppressive microenvironment in lung adenocarcinoma

This manuscript has been withdrawn by the authors due to a dispute over co-first authorship that is currently being arbitrated by the medical school at our institution. Therefore, the authors do not wish this work to be cited as reference for the project. Upon completion of the arbitration process, we will take steps to revert the current withdrawn status. If you have any questions, please contact the corresponding author.

A nasal cell atlas reveals heterogeneity of tuft cells and their role in directing olfactory stem cell proliferation

The olfactory neuroepithelium serves as a sensory organ for odors and forms part of the nasal mucosal barrier. Olfactory sensory neurons are surrounded and supported by epithelial cells. Among them, microvillous cells (MVCs) are strategically positioned at the apical surface, but their specific functions are enigmatic, and their relationship to the other specialized epithelial cells is unclear. Here, we establish that the family of MVCs comprises tuft cells and ionocytes in both mice and humans. Integrating analysis of the respiratory and olfactory epithelia, we define the distinct receptor expression of TRPM5+ tuft-MVCs compared with Gɑ-gustducinhigh respiratory tuft cells and characterize a previously undescribed population of glandular DCLK1+ tuft cells. To establish how allergen sensing by tuft-MVCs might direct olfactory mucosal responses, we used an integrated single-cell transcriptional and protein analysis. Inhalation of Alternaria induced mucosal epithelial effector molecules including Chil4 and a distinct pathway leading to proliferation of the quiescent olfactory horizontal basal stem cell (HBC) pool, both triggered in the absence of olfactory apoptosis. Alternaria- and ATP-elicited HBC proliferation was dependent on TRPM5+ tuft-MVCs, identifying these specialized epithelial cells as regulators of olfactory stem cell responses. Together, our data provide high-resolution characterization of nasal tuft cell heterogeneity and identify a function of TRPM5+ tuft-MVCs in directing the olfactory mucosal response to allergens.

Cellular Ligand-Receptor Perturbations Unravel MEIS2 as a Key Factor for the Aggressive Progression and Prognosis in Stage II/III Colorectal Cancer

Approximately 10-15% of stage II and 25-30% of stage III colorectal cancer (CRC) patients experience recurrence within 5 years after surgery, and existing taxonomies are insufficient to meet the needs of clinical precision treatment. Thus, robust biomarkers and precise management were urgently required to stratify stage II and III CRC and identify potential patients who will benefit from postoperative adjuvant therapy. Alongside, interactions of ligand-receptor pairs point to an emerging direction in tumor signaling with far-reaching implications for CRC, while their impact on tumor subtyping has not been elucidated. Herein, based on multiple large-sample multicenter cohorts and perturbations of the ligand-receptor interaction network, four well-characterized ligand-receptor-driven subtypes (LRDS) were established and further validated. These molecular taxonomies perform with unique heterogeneity in terms of molecular characteristics, immune and mutational landscapes, and clinical features. Specifically, MEIS2, a key LRDS4 factor, performs significant associations with proliferation, invasion, migration, and dismal prognosis of stage II/III CRC, revealing promising directions for prognostic assessment and individualized treatment of CRC patients. Overall, our study sheds novel insights into the implications of intercellular communication on stage II/III CRC from a ligand-receptor interactome perspective and revealed MEIS2 as a key factor in the aggressive progression and prognosis for stage II/III CRC.

Computational cell-cell interaction technologies drive mechanistic and biomarker discovery in the tumor microenvironment

Complex networks of cell-cell interactions (CCIs) within the tumor microenvironment (TME) play a crucial role in cancer persistence. These communication axes represent prime targets for therapeutic intervention, but our incomplete understanding of the cellular heterogeneity and interacting partners within the TME remains a stubborn barrier to complete drug responses. This review outlines recent advances in the study of CCIs that leverage single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) technologies that can clarify TME dynamics. We anticipate that these strategies will promote discovery of CCIs critical to the tumor-immune interface and will, by extension, expand the repertoire of druggable tumor biomarkers.

Spatial Transcriptomics Suggests That Alterations Occur in the Preneoplastic Breast Microenvironment of BRCA1/2 Mutation Carriers

Breast cancer is the most common cancer in females, affecting one in every eight women and accounting for the majority of cancer-related deaths in women worldwide. Germline mutations in the BRCA1 and BRCA2 genes are significant risk factors for specific subtypes of breast cancer. BRCA1 mutations are associated with basal-like breast cancers, whereas BRCA2 mutations are associated with luminal-like disease. Defects in mammary epithelial cell differentiation have been previously recognized in germline BRCA1/2 mutation carriers even before cancer incidence. However, the underlying mechanism is largely unknown. Here, we employ spatial transcriptomics to investigate defects in mammary epithelial cell differentiation accompanied by distinct microenvironmental alterations in preneoplastic breast tissues from BRCA1/2 mutation carriers and normal breast tissues from noncarrier controls. We uncovered spatially defined receptor-ligand interactions in these tissues for the investigation of autocrine and paracrine signaling. We discovered that β1-integrin-mediated autocrine signaling in BRCA2-deficient mammary epithelial cells may differ from BRCA1-deficient mammary epithelial cells. In addition, we found that the epithelial-to-stromal paracrine signaling in the breast tissues of BRCA1/2 mutation carriers is greater than in control tissues. More integrin-ligand pairs were differentially correlated in BRCA1/2-mutant breast tissues than noncarrier breast tissues with more integrin receptor-expressing stromal cells.

IMPLICATIONS

These results suggest alterations in the communication between mammary epithelial cells and the microenvironment in BRCA1 and BRCA2 mutation carriers, laying the foundation for designing innovative breast cancer chemo-prevention strategies for high-risk patients.

Single-Cell Analysis Reveals Cxcl14+ Fibroblast Accumulation in Regenerating Diabetic Wounds Treated by Hydrogel-Delivering Carbon Monoxide

Nonhealing skin wounds are a problematic complication associated with diabetes. Therapeutic gases delivered by biomaterials have demonstrated powerful wound healing capabilities. However, the cellular responses and heterogeneity in the skin regeneration process after gas therapy remain elusive. Here, we display the benefit of the carbon monoxide (CO)-releasing hyaluronan hydrogel (CO@HAG) in promoting diabetic wound healing and investigate the cellular responses through single-cell transcriptomic analysis. The presented CO@HAG demonstrates wound microenvironment responsive gas releasing properties and accelerates the diabetic wound healing process in vivo. It is found that a new cluster of Cxcl14+ fibroblasts with progenitor property is accumulated in the CO@HAG-treated wound. This cluster of Cxcl14+ fibroblasts is yet unreported in the skin regeneration process. CO@HAG-treated wound macrophages feature a decrease in pro-inflammatory property, while their anti-inflammatory property increases. Moreover, the TGF-β signal between the pro-inflammatory (M1) macrophage and the Cxcl14+ fibroblast in the CO@HAG-treated wound is attenuated based on cell-cell interaction analysis. Our study provides a useful hydrogel-mediated gas therapy method for diabetic wounds and new insights into cellular events in the skin regeneration process after gas-releasing biomaterials therapy.

Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.

Lymph node and tumor-associated PD-L1+ macrophages antagonize dendritic cell vaccines by suppressing CD8+ T cells

Current immunotherapies provide limited benefits against T cell-depleted tumors, calling for therapeutic innovation. Using multi-omics integration of cancer patient data, we predict a type I interferon (IFN) responseHIGH state of dendritic cell (DC) vaccines, with efficacious clinical impact. However, preclinical DC vaccines recapitulating this state by combining immunogenic cancer cell death with induction of type I IFN responses fail to regress mouse tumors lacking T cell infiltrates. Here, in lymph nodes (LNs), instead of activating CD4+/CD8+ T cells, DCs stimulate immunosuppressive programmed death-ligand 1-positive (PD-L1+) LN-associated macrophages (LAMs). Moreover, DC vaccines also stimulate PD-L1+ tumor-associated macrophages (TAMs). This creates two anatomically distinct niches of PD-L1+ macrophages that suppress CD8+ T cells. Accordingly, a combination of PD-L1 blockade with DC vaccines achieves significant tumor regression by depleting PD-L1+ macrophages, suppressing myeloid inflammation, and de-inhibiting effector/stem-like memory T cells. Importantly, clinical DC vaccines also potentiate T cell-suppressive PD-L1+ TAMs in glioblastoma patients. We propose that a multimodal immunotherapy and vaccination regimen is mandatory to overcome T cell-depleted tumors.

Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions

Existing methods for analysis of spatial transcriptomic data focus on delineating the global gene expression variations of cell types across the tissue, rather than local gene expression changes driven by cell-cell interactions. We propose a new statistical procedure called niche-differential expression (niche-DE) analysis that identifies cell-type-specific niche-associated genes, which are differentially expressed within a specific cell type in the context of specific spatial niches. We further develop niche-LR, a method to reveal ligand-receptor signaling mechanisms that underlie niche-differential gene expression patterns. Niche-DE and niche-LR are applicable to low-resolution spot-based spatial transcriptomics data and data that is single-cell or subcellular in resolution.

SORC: an integrated spatial omics resource in cancer

The interactions between tumor cells and the microenvironment play pivotal roles in the initiation, progression and metastasis of cancer. The advent of spatial transcriptomics data offers an opportunity to unravel the intricate dynamics of cellular states and cell-cell interactions in cancer. Herein, we have developed an integrated spatial omics resource in cancer (SORC, http://bio-bigdata.hrbmu.edu.cn/SORC), which interactively visualizes and analyzes the spatial transcriptomics data in cancer. We manually curated currently available spatial transcriptomics datasets for 17 types of cancer, comprising 722 899 spots across 269 slices. Furthermore, we matched reference single-cell RNA sequencing data in the majority of spatial transcriptomics datasets, involving 334 379 cells and 46 distinct cell types. SORC offers five major analytical modules that address the primary requirements of spatial transcriptomics analysis, including slice annotation, identification of spatially variable genes, co-occurrence of immune cells and tumor cells, functional analysis and cell-cell communications. All these spatial transcriptomics data and in-depth analyses have been integrated into easy-to-browse and explore pages, visualized through intuitive tables and various image formats. In summary, SORC serves as a valuable resource for providing an unprecedented spatially resolved cellular map of cancer and identifying specific genes and functional pathways to enhance our understanding of the tumor microenvironment.

CellCommuNet: an atlas of cell-cell communication networks from single-cell RNA sequencing of human and mouse tissues in normal and disease states

Cell-cell communication, as a basic feature of multicellular organisms, is crucial for maintaining the biological functions and microenvironmental homeostasis of cells, organs, and whole organisms. Alterations in cell-cell communication contribute to many diseases, including cancers. Single-cell RNA sequencing (scRNA-seq) provides a powerful method for studying cell-cell communication by enabling the analysis of ligand-receptor interactions. Here, we introduce CellCommuNet (http://www.inbirg.com/cellcommunet/), a comprehensive data resource for exploring cell-cell communication networks in scRNA-seq data from human and mouse tissues in normal and disease states. CellCommuNet currently includes 376 single datasets from multiple sources, and 118 comparison datasets between disease and normal samples originating from the same study. CellCommuNet provides information on the strength of communication between cells and related signalling pathways and facilitates the exploration of differences in cell-cell communication between healthy and disease states. Users can also search for specific signalling pathways, ligand-receptor pairs, and cell types of interest. CellCommuNet provides interactive graphics illustrating cell-cell communication in different states, enabling differential analysis of communication strength between disease and control samples. This comprehensive database aims to be a valuable resource for biologists studying cell-cell communication networks.

Hallmarks of stemness in mammalian tissues

All adult tissues experience wear and tear. Most tissues can compensate for cell loss through the activity of resident stem cells. Although the cellular maintenance strategies vary greatly between different adult (read: postnatal) tissues, the function of stem cells is best defined by their capacity to replace lost tissue through division. We discuss a set of six complementary hallmarks that are key enabling features of this basic function. These include longevity and self-renewal, multipotency, transplantability, plasticity, dependence on niche signals, and maintenance of genome integrity. We discuss these hallmarks in the context of some of the best-understood adult stem cell niches.

Dictionary of immune responses to cytokines at single-cell resolution

Cytokines mediate cell-cell communication in the immune system and represent important therapeutic targets1-3. A myriad of studies have highlighted their central role in immune function4-13, yet we lack a global view of the cellular responses of each immune cell type to each cytokine. To address this gap, we created the Immune Dictionary, a compendium of single-cell transcriptomic profiles of more than 17 immune cell types in response to each of 86 cytokines (>1,400 cytokine-cell type combinations) in mouse lymph nodes in vivo. A cytokine-centric view of the dictionary revealed that most cytokines induce highly cell-type-specific responses. For example, the inflammatory cytokine interleukin-1β induces distinct gene programmes in almost every cell type. A cell-type-centric view of the dictionary identified more than 66 cytokine-driven cellular polarization states across immune cell types, including previously uncharacterized states such as an interleukin-18-induced polyfunctional natural killer cell state. Based on this dictionary, we developed companion software, Immune Response Enrichment Analysis, for assessing cytokine activities and immune cell polarization from gene expression data, and applied it to reveal cytokine networks in tumours following immune checkpoint blockade therapy. Our dictionary generates new hypotheses for cytokine functions, illuminates pleiotropic effects of cytokines, expands our knowledge of activation states of each immune cell type, and provides a framework to deduce the roles of specific cytokines and cell-cell communication networks in any immune response.

Reciprocal interactions between malignant cells and macrophages enhance cancer stemness and M2 polarization in HBV-associated hepatocellular carcinoma

Background: The tumor microenvironment of cancers has emerged as a crucial component in regulating cancer stemness and plays a pivotal role in cell-cell communication. However, the specific mechanisms underlying these phenomena remain poorly understood. Methods: We performed the single-cell RNA sequencing (scRNA-seq) on nine HBV-associated hepatocellular carcinoma (HCC) patients. The heterogeneity of the malignant cells in pathway functions, transcription factors (TFs) regulation, overall survival, stemness, as well as ligand-receptor-based intercellular communication with macrophages were characterized. The aggressive and stemness feature for the target tumor subclone was validated by the conduction of in vitro assays including sphere formation, proliferation, Annexin V apoptosis, flow cytometry, siRNA library screening assays, and multiple in vivo preclinical mouse models including mouse hepatoma cell and human HCC cell xenograft models with subcutaneous or orthotopic injection. Results: Our analysis yielded a comprehensive atlas of 31,664 cells, revealing a diverse array of malignant cell subpopulations. Notably, we identified a stemness-related subclone of HCC cells with concurrent upregulation of CD24, CD47, and ICAM1 expression that correlated with poorer overall survival. Functional characterization both in vitro and in vivo validated S100A11 as one of the top downstream mediators for tumor initiation and stemness maintenance of this subclone. Further investigation of cell-cell communication within the tumor microenvironment revealed a propensity for bi-directional crosstalk between this stemness-related subclone and tumor-associated macrophages (TAMs). Co-culture study showed that this interaction resulted in the maintenance of the expression of cancer stem cell markers and driving M2-like TAM polarization towards a pro-tumorigenic niche. We also consolidated an inverse relationship between the proportions of TAMs and tumor-infiltrating T cells. Conclusions: Our study highlighted the critical role of stemness-related cancer cell populations in driving an immunosuppressive tumor microenvironment and identified the S100A11 gene as a key mediator for stemness maintenance in HCC. Moreover, our study provides support that the maintenance of cancer stemness is more attributed to M2 polarization than the recruitment of the TAMs.

Role of paralogs in the sex-bias transcriptional and metabolic regulation of the brain-placental axis in mice

INTRODUCTION

Duplicated genes or paralogs play important roles in the adaptive function of eukaryotic genomes. Animal studies have shown evidence for the functional role of paralogs in pregnancy, but our knowledge about the role of paralogs in the fetoplacental regulation remains limited. In particular, if fetoplacental metabolic regulation is modulated by differential expression of paralogs remains unexamined.

METHODS

In this study, gene expression profiles of day-15 placenta and fetal brain were compared to identify families or groups of paralogous genes expressed in the placenta and brain of male versus female fetuses in mice. A Bayesian modeling was applied to infer directional relationship of transcriptional variation of the paralogs relative to the phylogenetic variation of the genes in each family. Gas chromatography-mass spectrometry (GC-MS) was used to perform untargeted metabolomics analysis of day-15 placenta and fetal brain of both sexes.

RESULTS

We identified paralog groups that were expressed in a sex and/or tissue biased manner between the placenta and fetal brain. Bayesian modeling showed evidence for directional relationship between expression and phylogeny of specific paralogs. These relationships were sex specific. GC-MS analysis identified metabolites that were expressed in a sex-bias manner between the placenta and fetal brain. By performing integrative analysis of the metabolomics and gene expression data, we showed that specific groups of metabolites and paralogous genes were expressed in a coordinated manner between the placenta and fetal brain.

DISCUSSION

The findings of this study collectively suggest that paralogs play an influential role in the regulation of the brain-placental axis in mice.

Lipidation and PEGylation Strategies to Prolong the in Vivo Half-Life of a Nanomolar EphA4 Receptor Antagonist

The EphA4 receptor tyrosine kinase plays a role in neurodegenerative diseases, inhibition of nerve regeneration, cancer progression and other diseases. Therefore, EphA4 inhibition has potential therapeutic value. Selective EphA4 kinase inhibitors are not available, but we identified peptide antagonists that inhibit ephrin ligand binding to EphA4 with high specificity. One of these peptides is the cyclic APY-d3 (βAPYCVYRβASWSC-NH2), which inhibits ephrin-A5 ligand binding to EphA4 with low nanomolar binding affinity and is highly protease resistant. Here we describe modifications of APY-d3 that yield two different key derivatives with greatly increased half-lives in the mouse circulation, the lipidated APY-d3-laur8 and the PEGylated APY-d3-PEG4. These two derivatives inhibit ligand induced EphA4 activation in cells with sub-micromolar potency. Since they retain high potency and specificity for EphA4, lipidated and PEGylated APY-d3 derivatives represent new tools for discriminating EphA4 activities in vivo and for preclinical testing of EphA4 inhibition in animal disease models.

Myeloid cells interact with a subset of thyrocytes to promote their migration and follicle formation through NF-κB

The pathogenesis of thyroid dysgenesis (TD) is not well understood. Here, using a combination of single-cell RNA and spatial transcriptome sequencing, we identify a subgroup of NF-κB-activated thyrocytes located at the center of thyroid tissues in postnatal mice, which maintained a partially mesenchymal phenotype. These cells actively protruded out of the thyroid primordium and generated new follicles in zebrafish embryos through continuous tracing. Suppressing NF-κB signaling affected thyrocyte migration and follicle formation, leading to a TD-like phenotype in both mice and zebrafish. Interestingly, during thyroid folliculogenesis, myeloid cells played a crucial role in promoting thyrocyte migration by maintaining close contact and secreting TNF-α. We found that cebpa mutant zebrafish, in which all myeloid cells were depleted, exhibited thyrocyte migration defects. Taken together, our results suggest that myeloid-derived TNF-α-induced NF-κB activation plays a critical role in promoting the migration of vertebrate thyrocytes for follicle generation.

An engineered glioblastoma model yields novel macrophage-secreted drivers of invasion

Glioblastomas (GBMs) are highly invasive brain tumors replete with brain- and blood-derived macrophages, collectively known as tumor-associated macrophages (TAMs). Targeting TAMs has been proposed as a therapeutic strategy but has thus far yielded limited clinical success in slowing GBM progression, due in part to an incomplete understanding of TAM function in GBM. Here, by using an engineered hyaluronic acid-based 3D invasion platform, patient-derived GBM cells, and multi-omics analysis of GBM tumor microenvironments, we show that M2-polarized macrophages stimulate GBM stem cell (GSC) mesenchymal transition and invasion. We identify TAM-derived transforming growth factor beta induced (TGFβI/BIGH3) as a pro-tumorigenic factor in the GBM microenvironment. In GBM patients, BIGH3 mRNA expression correlates with poor patient prognosis and is highest in the most aggressive GBM molecular subtype. Inhibiting TAM-derived BIGH3 signaling with a blocking antibody or small molecule inhibitor suppresses GSC invasion. Our work highlights the utility of 3D in vitro tumor microenvironment platforms to investigate TAM-cancer cell crosstalk and offers new insights into TAM function to guide novel TAM-targeting therapies.