Cell-cell Communication Networks Research Articles

Single-cell transcriptome atlas of testes from mice with high-fat diets

Obesity is accompanied by multiple known health risks and increased morbidity, and obese men display reduced reproductive health. However, the impact of obesity on the testes at the molecular levels remain inadequately explored. This is partially attributed to the lack of monitoring tools for tracking alterations within cell clusters in testes associated with obesity. Here, we utilized single-cell RNA sequencing to analyze over 70,000 cells from testes of obese and lean mice, and to study changes related to obesity in non-spermatogenic cells and spermatogenesis. The Testicular Library encompasses all non-spermatogenic cells and spermatogenic cells spanning from spermatogonia to spermatozoa, which will significantly aid in characterizing alterations in cellular niches and the testicular microenvironment during high-fat diet (HFD)-induced obesity. This comprehensive dataset is indispensable for studying how HFD disrupts cell-cell communication networks within the testis and impacts alterations in the testicular microenvironment that regulate spermatogenesis. Being the inaugural dataset of single-cell RNA-seq in the testes of diet-induced obese (DIO) mice, this holds the potential to offer innovative insights and directions in the realm of single-cell transcriptomics concerning male reproductive injury associated with HFD.

DNA Reaction Circuits to Establish Designated Biological Functions in Multicellular Community.

In multicellular organisms, individual cells are coordinated through complex communication networks to accomplish various physiological tasks. Aiming to establish new biological functions in the multicellular community, we used DNA as the building block to develop a cascade of nongenetic reaction circuits to establish a dynamic cell-cell communication network. Utilizing membrane-anchored amphiphilic DNA tetrahedra (TDN) as the nanoscaffold, reaction circuits were incorporated into three unrelated cells in order to uniquely regulate their sense-and-response behaviors. As a proof-of-concept, this step enabled these cells to simulate significant biological events involved in T cell-mediated anticancer immunity. Such events included cancer-associated antigen recognition and the presentation of antigen-presenting cells (APCs), APC-facilitated T cell activation and dissociation, and T cell-mediated cancer targeting and killing. By combining the excellent programmability and molecular recognition ability of DNA, our cell-surface reaction circuits hold promise for mimicking and manipulating many biological processes.

Single-cell RNA-sequencing reveals the transcriptional landscape of lacrimal gland in GVHD mouse model

PurposeTo investigate the global transcriptional landscape of lacrimal gland cell populations in the GVHD mouse model. MethodsSingle-cell RNA sequencing and further bioinformatic analysis of dissociated lacrimal gland (LG) cells from the mouse model were performed. Parts of transcriptional results were confirmed by immunofluorescence staining. ResultsWe identified 23 cell populations belonging to 11 cell types. In GVHD LG, the proportion of acinar cells, myoepithelial cells, and endothelial cells was remarkably decreased, while T cells and macrophages were significantly expanded. Gene expression analysis indicated decreased secretion function, extracellular matrix (ECM) synthesis, and increased chemokines of myoepithelial cells. A newly described epithelial population named Lrg1high epithelial cells, expressing distinct gene signatures, was exclusively identified in GVHD LG. The fibroblasts exhibited an inflammation gene pattern. The gene pattern of endothelial cells suggested an increased ability to recruit immune cells and damaged cell-cell junctions. T cells were mainly comprised of Th2 cells and effective memory CD8+ T cells. GVHD macrophages exhibited a Th2 cell-linked pattern. ConclusionsThis single-cell atlas uncovered alterations of proportion and gene expression patterns of cell populations and constructed cell-cell communication networks of GVHD LG. These data may provide some new insight into understanding the development of ocular GVHD.

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.

SEGCECO: Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication.

Recent advances in single-cell RNA sequencing technology have eased analyses of signaling networks of cells. Recently, cell-cell interaction has been studied based on various link prediction approaches on graph-structured data. These approaches have assumptions about the likelihood of node interaction, thus showing high performance for only some specific networks. Subgraph-based methods have solved this problem and outperformed other approaches by extracting local subgraphs from a given network. In this work, we present a novel method, called Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication (SEGCECO), which uses an attributed graph convolutional neural network to predict cell-cell communication from single-cell RNA-seq data. SEGCECO captures the latent and explicit attributes of undirected, attributed graphs constructed from the gene expression profile of individual cells. High-dimensional and sparse single-cell RNA-seq data make converting the data into a graphical format a daunting task. We successfully overcome this limitation by applying SoptSC, a similarity-based optimization method in which the cell-cell communication network is built using a cell-cell similarity matrix which is learned from gene expression data. We performed experiments on six datasets extracted from the human and mouse pancreas tissue. Our comparative analysis shows that SEGCECO outperforms latent feature-based approaches, and the state-of-the-art method for link prediction, WLNM, with 0.99 ROC and 99% prediction accuracy. The datasets can be found at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84133 and the code is publicly available at Github https://github.com/sheenahora/SEGCECO and Code Ocean https://codeocean.com/capsule/8244724/tree.

CPPLS-MLP: a method for constructing cell-cell communication networks and identifying related highly variable genes based on single-cell sequencing and spatial transcriptomics data.

In the growth and development of multicellular organisms, the immune processes of the immune system and the maintenance of the organism's internal environment, cell communication plays a crucial role. It exerts a significant influence on regulating internal cellular states such as gene expression and cell functionality. Currently, the mainstream methods for studying intercellular communication are focused on exploring the ligand-receptor-transcription factor and ligand-receptor-subunit scales. However, there is relatively limited research on the association between intercellular communication and highly variable genes (HVGs). As some HVGs are closely related to cell communication, accurately identifying these HVGs can enhance the accuracy of constructing cell communication networks. The rapid development of single-cell sequencing (scRNA-seq) and spatial transcriptomics technologies provides a data foundation for exploring the relationship between intercellular communication and HVGs. Therefore, we propose CPPLS-MLP, which can identify HVGs closely related to intercellular communication and further analyze the impact of Multiple Input Multiple Output cellular communication on the differential expression of these HVGs. By comparing with the commonly used method CCPLS for constructing intercellular communication networks, we validated the superior performance of our method in identifying cell-type-specific HVGs and effectively analyzing the influence of neighboring cell types on HVG expression regulation. Source codes for the CPPLS_MLP R, python packages and the related scripts are available at 'CPPLS_MLP Github [https://github.com/wuzhenao/CPPLS-MLP]'.

Single-cell profiling transcriptomic reveals cellular heterogeneity and cellular crosstalk in choroidal neovascularization model

Choroidal neovascularization (CNV) is a hallmark of neovascular age-related macular degeneration (nAMD) and a major contributor to vision loss in nAMD cases. However, the identification of specific cell types associated with nAMD remains challenging. Herein, we performed single-cell sequencing to comprehensively explore the cellular diversity and understand the foundational components of the retinal pigment epithelium (RPE)/choroid complex. We unveiled 10 distinct cell types within the RPE/choroid complex. Notably, we observed significant heterogeneity within endothelial cells (ECs), fibroblasts, and macrophages, underscoring the intricate nature of the cellular composition in the RPE/choroid complex. Within the EC category, four distinct clusters were identified and EC cluster 0 was tightly associated with choroidal neovascularization. We identified five clusters of fibroblasts actively involved in the pathogenesis of nAMD, influencing fibrotic responses, angiogenic effects, and photoreceptor function. Additionally, three clusters of macrophages were identified, suggesting their potential roles in regulating the progression of nAMD through immunomodulation and inflammation regulation. Through CellChat analysis, we constructed a complex cell-cell communication network, revealing the role of EC clusters in interacting with fibroblasts and macrophages in the context of nAMD. These interactions were found to govern angiogenic effects, fibrotic responses, and inflammatory processes. In summary, this study reveals noteworthy cellular heterogeneity in the RPE/choroid complex and provides valuable insights into the pathogenesis of CNV. These findings will open up potential avenues for deep understanding and targeted therapeutic interventions in nAMD.

Abstract 1587: Multimodal spatial transcriptomics uncover distinct tumor microenvironment states and cell-cell communication networks in molecular pancreatic cancer subtypes

Abstract The tumor microenvironment (TME) presents a complex ecosystem comprising a diversity of immune and stromal cell populations that influence tumor progression, drug delivery and therapy outcome. High levels of inter- and intra-tumor heterogeneity in TME state are driven by molecular tumor phenotypes. Pancreatic ductal adenocarcinoma (PDAC), one of the most lethal cancer types, displays a high genetic heterogeneity along with an immunosuppressive TME. However, it remains largely elusive how distinct TME states are mechanistically influenced, and which molecular processes dictate the emergence of different modes of immunosuppression within the TME of molecular PDAC subtypes.Here, we performed a systematic analysis of functional associations between the major molecular PDAC subtypes, the classical and mesenchymal subtype, and their TME states. We describe the TME composition and complex cell-cell communication networks within PDAC subtypes using a multimodal integration of spatial transcriptomics (ST) with complementary approaches, such as scRNA-seq, MS-based Secretomics and multiplexed histocytometry. We generated ST data sets (Visium) of a tumor cohort derived from a Kras-driven PDAC mouse model. To analyze TME composition as well as spatial niches and cell type communities, we computationally enhanced the spot-resolution of ST datasets, followed by cell type deconvolution and cell-cell communication analysis to identify subtype-specific TME communities and communication networks.To this end, we generated a large resource of PDAC mouse models which represent molecular subtypes of the disease and mimic the heterogeneity of TME states found in human PDAC cohorts. This analysis revealed that a set of subtype-specific secreted factors shape the immunosuppressive PDAC TME via direct and indirect communication networks with immunosuppressive myeloid and T cells in molecular PDAC subtypes. Multimodal ST analysis delineated spatial subtype-specific TME communities as well as spatial communication patterns. We functionally investigate the tumor cell intrinsic regulation of the identified subtype-specific secreted factors, providing potential therapeutic vulnerabilities for more effective combinatorial subtype-specific therapies including immunotherapeutic approaches. Citation Format: Stefanie Baerthel, Chiara Falcomatà, Vanessa Goelling, Daniele Lucarelli, Rushin Gindra, Constantin Schmitt, Jonathan Swietlik, Felix Meissner, Marc Schmidt-Supprian, Dieter Saur. Multimodal spatial transcriptomics uncover distinct tumor microenvironment states and cell-cell communication networks in molecular pancreatic cancer subtypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1587.

Abstract 1152: Pan-cancer single-cell and subcellular characterization of lymphoid aggregates and tertiary lymphoid structures using high-plex spatial molecular imaging

Abstract Tertiary lymphoid structures (TLSs), clusters of immune cells that form in non-lymphoid tissues, are frequently identified in a wide range of solid tumors. They function as immunological “hubs”, driving anti-tumor responses. Previous studies have demonstrated that the presence of TLSs is often associated with improved responses to immunotherapy and superior clinical outcomes. Despite these insights, our understanding of their phenotypic diversity, spatial organization, intercellular communication, and interactions with cancer cells and the tumor microenvironment remains incomplete. Existing methods lack the resolution, depth, and scale required for a thorough understanding of these complex structures. We have obtained in-house and public spatial multi-omics data generated on various tissues across 9 different cancer types using the cutting-edge single-cell spatial transcriptomics and high-plex spatial molecular imaging platforms. We have developed innovative computational approaches for the detailed characterization of TLSs at single-cell and subcellular resolution and carried out an extensive analysis of lymphoid aggregates (LAs) and TLSs in tissue sections across these different solid cancer types. We defined the phenotypic state of each individual cell within TLSs including B/plasma cells, follicular helper/regulatory/stressed/exhausted T cells, follicular dendritic cells, fibroblasts and high endothelial venules. Then, we performed unbiased phenotyping and classification of TLSs based on their cellular states, compositions and organization patterns across various types of tissues. Additionally, we performed spatial neighborhood analysis, characterized the neighboring cells of each TLS and analyzed their cell-cell communication networks. Our approach allowed an unprecedented examination of the spatial phenotypes of TLSs in large, complex tissues at super resolution, in ways that were not previously described. We comprehensively characterized TLSs across different solid tumor types. Our analysis revealed a high degree of spatial and phenotypic heterogeneity in LAs and TLSs. We created a comprehensive spatial atlas of LAs and TLSs across solid tumor types, profiled TLSs at various maturation states with distinct cellular composition and organization patterns, and discovered new TLS states. Notably, we observed distinct communication networks associated with TLS phenotypic states. Our innovative methods and analytical approaches offer a more comprehensive way to study LAs and TLSs, enhancing detection capabilities and providing deeper insights into their spatial interaction and functional phenotypes. This research holds the potential to significantly advance our understanding of TLSs and their roles in immunobiology and immunotherapy responses across a broad range of cancers and diseases. Citation Format: Yunhe Liu, Yuanyuan Zhang, Guangchun Han, Guangsheng Pei, Kyung Serk Cho, Enyu Dai, Yanshuo Chu, Jiahui Jiang, Ansam Sinjab, Luisa M. Soto, Alejandra Serrano, Jianjun Gao, Humam Kadara, Catherine Fridman, Wolf Herve Fridman, Linghua Wang. Pan-cancer single-cell and subcellular characterization of lymphoid aggregates and tertiary lymphoid structures using high-plex spatial molecular imaging [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1152.

ICELLNET v2: a versatile method for cell-cell communication analysis from human transcriptomic data.

Several methods have been developed in the past years to infer cell-cell communication networks from transcriptomic data based on ligand and receptor expression. Among them, ICELLNET is one of the few approaches to consider the multiple subunits of ligands and receptors complexes to infer and quantify cell communication. In here, we present a major update of ICELLNET. As compared to its original implementation, we (i) drastically expanded the ICELLNET ligand-receptor database from 380 to 1669 biologically curated interactions, (ii) integrated important families of communication molecules involved in immune crosstalk, cell adhesion, and Wnt pathway, (iii) optimized ICELLNET framework for single-cell RNA sequencing data analyses, (iv) provided new visualizations of cell-cell communication results to facilitate prioritization and biological interpretation. This update will broaden the use of ICELLNET by the scientific community in different biological fields. ICELLNET package is implemented in R. Source code, documentation and tutorials are available on GitHub (https://github.com/soumelis-lab/ICELLNET).

Single-cell transcriptomics identifies senescence-associated secretory phenotype (SASP) features of testicular aging in human.

The male reproductive system experiences degradation with age, predominantly impacting the testes. Testicular aging can result in failure to produce physiological testosterone levels, normal sperm concentrations, or both. However, we cannot predict the onset of testicular aging in advance. Using single-cell RNA sequencing (scRNA-seq) from Gene Expression Omnibus (GEO) database, we conducted cell-cell communication network of human testis between older and young group, indicating Leydig cells' potential role in spermatogenesis microenvironment of aging testis. And we depicted the senescence-Associated Secretory Phenotype (SASP) features of aging testis by identifying differentially expressed senescence-associated secretory phenotype (SASP)-related genes between two group. Notably, IGFBP7 mainly expressed in Leydig cells of those differentially expressed SASP-related genes in aging testis. Furthermore, IGFBP7 protein located in the interstitial compartment of older mice confirmed by immunofluorescence and highly expressed in both human seminal plasma and mouse testis in the older group confirmed through Western blot. Together, our findings suggest that IGFBP7 may be a new biomarker of testicular aging.

Cell-cell communication network-based interpretable machine learning predicts cancer patient response to immune checkpoint inhibitors.

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment. However, only some patients respond to ICIs, and current biomarkers for ICI efficacy have limited performance. Here, we devised an interpretable machine learning (ML) model trained using patient-specific cell-cell communication networks (CCNs) decoded from the patient's bulk tumor transcriptome. The model could (i) predict ICI efficacy for patients across four cancer types (median AUROC: 0.79) and (ii) identify key communication pathways with crucial players responsible for patient response or resistance to ICIs by analyzing more than 700 ICI-treated patient samples from 11 cohorts. The model prioritized chemotaxis communication of immune-related cells and growth factor communication of structural cells as the key biological processes underlying response and resistance to ICIs, respectively. We confirmed the key communication pathways and players at the single-cell level in patients with melanoma. Our network-based ML approach can be used to expand ICIs' clinical benefits in cancer patients.

Characterization of CD4+ and CD8+ T cells responses in the mixed lymphocyte reaction by flow cytometry and single cell RNA sequencing.

The Mixed Lymphocyte Reaction (MLR) consists in the allogeneic co-culture of monocytes derived dendritic cells (MoDCs) with T cells from another donor. This in vitro assay is largely used for the assessment of immunotherapy compounds. Nevertheless, the phenotypic changes associated with lymphocyte responsiveness under MLR have never been thoroughly evaluated. Here, we used multiplex cytokine and chemokine assays, multiparametric flow cytometry and single cell RNA sequencing to deeply characterize T cells activation and function in the context of CD4+- and CD8+-specific MLR kinetics. We showed that CD4+ and CD8+ T cells in MLR share common classical markers of response such as polyfunctionality, increased proliferation and CD25 expression but differ in their kinetics and amplitude of activation as well as their patterns of cytokines secretion and immune checkpoints expression. The analysis of immunoreactive Ki-67+CD25+ T cells identified PBK, LRR1 and MYO1G as new potential markers of MLR response. Using cell-cell communication network inference and pathway analysis on single cell RNA sequencing data, we also highlighted key components of the immunological synapse occurring between T cells and the stimulatory MoDCs together with downstream signaling pathways involved in CD4+ and CD8+ T cells activation. These results provide a deep understanding of the kinetics of the MLR assay for CD4+ or CD8+ T cells and may allow to better characterize compounds impacting MLR and eventually identify new strategies for immunotherapy in cancer.

Bioengineered hydrogels enhance ex vivo preservation of patient-derived tumor explants for drug evaluation

Ex vivo patient-derived tumor slices (PDTS) are currently limited by short-term viability in culture. Here, we show how bioengineered hydrogels enable the identification of key matrix parameters that significantly enhance PDTS viability compared to conventional culture systems. As demonstrated using single-cell RNA sequencing and high-dimensional flow cytometry, hydrogel-embedded PDTS tightly preserved cancer, cancer-associated fibroblast, and various immune cell populations and subpopulations in the corresponding original tumor. Cell-cell communication networks within the tumor microenvironment, including immune checkpoint ligand-receptor interactions, were also maintained. Remarkably, our results from a co-clinical trial suggest hydrogel-embedded PDTS may predict sensitivity to immune checkpoint inhibitors (ICIs) in head and neck cancer patients. Further, we show how these longer term-cultured tumor explants uniquely enable the sampling and detection of temporal evolution in molecular readouts when treated with ICIs. By preserving the compositional heterogeneity and complexity of patient tumors, hydrogel-embedded PDTS provide a valuable tool to facilitate experiments targeting the tumor microenvironment.

An advanced comprehensive muti-cell-type-specific model for predicting anti-PD-1 therapeutic effect in melanoma.

Rationale: Immune checkpoint inhibitors targeting the programmed cell death (PD)-1/PD-L1 pathway have promise in patients with advanced melanoma. However, drug resistance usually results in limited patient benefits. Recent single-cell RNA sequencing studies have elucidated that MM patients display distinctive transcriptional features of tumor cells, immune cells and interstitial cells, including loss of antigen presentation function of tumor cells, exhaustion of CD8+T and extracellular matrix secreted by fibroblasts to prevents immune infiltration, which leads to a poor response to immune checkpoint inhibitors (ICIs). However, cell subgroups beneficial to anti-tumor immunity and the model developed by them remain to be further identified. Methods: In this clinical study of neoadjuvant therapy with anti-PD-1 in advanced melanoma, tumor tissues were collected before and after treatment for single-nucleus sequencing, and the results were verified using multicolor immunofluorescence staining and public datasets. Results: This study describes four cell subgroups which are closely associated with the effectiveness of anti-PD-1 treatment. It also describes a cell-cell communication network, in which the interaction of the four cell subgroups contributes to anti-tumor immunity. Furthermore, we discuss a newly developed predictive model based on these four subgroups that holds significant potential for assessing the efficacy of anti-PD-1 treatment. Conclusions: These findings elucidate the primary mechanism of anti-PD-1 resistance and offer guidance for clinical drug administration for melanoma.

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.

Early Intervention and Favorable Biologic Effects of Personalized Neoantigen Vaccines on the Tumor Immune Microenvironment in Smoldering Waldenstrom Macroglobulinemia

Lymphoplasmacytic lymphoma (LPL) is an incurable low-grade B-cell lymphoma, characterized by the presence of clonal tumor cells infiltrating the bone marrow. Despite a cumulative risk of progression, there is no approved therapy for patients in the asymptomatic phase. We conducted a first-in-human clinical trial of a novel individualized therapeutic DNA idiotype neoantigen vaccine as an early intervention in nine patients with untreated asymptomatic LPL. The vaccine platform encoded the autologous LPL-derived Ig single chain variable fragment fused to human chemokine CCL20 (macrophage inflammatory protein-3, MIP-3a), which was designed to trigger T-cell immunity by targeting delivery of the expressed fusion protein to antigen presenting cells. The vaccine was well tolerated with no dose limiting toxicities. One patient achieved a minor response, and all remaining patients experienced stable disease, with median time to disease progression of 61+ months, which compares favorably to median time to progression of 3.9 years in published series. Direct interrogation of the tumor microenvironment by single-cell transcriptome analysis revealed explanations for lack of more robust objective clinical responses. Comparing paired pre- and post- vaccine bone marrow samples, we observed a striking dichotomous pattern of significantly reduced numbers of clonal tumor cells, identified by their unique BCR sequences, and downregulation of genes involved in signaling pathways critical for B-cell survival among the mature B cells subpopulation post-vaccine in the majority of patients, but no change in clonally related plasma cell-like clusters of any patient (Figure 1). Downregulation of HLA molecule expression suggested intrinsic resistance by tumor plasma cell subpopulations and cell-cell interaction analyses predicted paradoxical upregulation of IGF signaling post vaccine by plasma cell, but not mature B-cell subpopulations, suggesting a potential mechanism of acquired resistance. Vaccine therapy induced dynamic changes in bone marrow T-cells, including upregulation of signaling pathways involved in T-cell activation, expansion of T-cell clonotypes (Figure 2), increased T-cell clonal diversity, and functional tumor idiotype-specific cytokine production, with little change in co-inhibitory pathways or Treg. Vaccine therapy also globally altered cell-cell communication networks across various bone marrow cell types. CellChat analysis inferred significant downregulation of signaling pathways post-vaccine that likely directly promote growth of LPL cells, such as APRIL and IL-6 which are known to promote B- or plasma cell survival. Other pathways were reduced post-vaccine, such as RESISTIN, which has a known role in supporting proliferation of solid cancers. A role for RESISTIN in supporting LPL has not been previously inferred, but it has been reported to induce multidrug resistance in multiple myeloma. Bioinformatic analysis also identified a predominant role for myeloid cells in the tumor microenvironment as a source of vaccine-induced, downregulated pro-tumoral signaling to LPL cells. The pathways affected were primarily associated with monocytic, rather than granulocytic or dendritic cell subpopulations, particularly non-classical CD14 -CD16 + monocytes, and to a lesser extent classical CD14 +CD16 - monocytes . Recent reports indicate that an increased pro-inflammatory myeloid signature is an early step in the development of LPL and in monoclonal gammopathy of undetermined significance. Taken together, these results suggest that this prototype neoantigen vaccine favorably perturbed the tumor immune microenvironment, resulting in reduction of clonal tumor mature B-cell, but not plasma cell subpopulations. Future functional studies of the pathways affected are needed to confirm mechanisms of resistance elucidated and to design combination strategies to circumvent them. Such strategies could include adding IFN gamma or epigenetic drugs, designed to increase HLA molecule expression on plasma cell-like LPL subpopulations and combining neoantigen vaccines with agents that specifically target plasma cells or pathways known to promote their growth, such as IGF-1 receptor inhibitors. Finally, our data suggest that combinations of these vaccines with myeloid cell checkpoint blockade may be worthwhile.

Galectin-1 Fuels Monocyte-Driven Hyperinflammation and Represents a Novel Therapeutic Target in Myeloproliferative Neoplasms

JDP and STO co-corresponding authors Previous studies by our group and others have demonstrated that monocytes play a key role in driving hyperinflammation in myeloproliferative neoplasms (MPNs), and that aberrant inflammatory cytokine signaling contributes to disease progression and poor prognosis in MPNs. To interrogate relationships between cellular sources and targets of inflammatory cytokines, we performed single cell RNA-seq (scRNA-seq) in PBMCs and progenitor cells from MPN patients and healthy controls. Monocytes from MPN patients displayed significant enrichment of inflammation-relevant genes as expected. Cell-cell communication networks inferred from expression of ligands and receptors predicted monocytes as a pivotal mediator of cell interactions. Further analysis revealed galectin signaling as one of the most robust input/output pathways for monocytes and significant enrichment of LGALS1 (galectin-1) expression in MPN monocytes. Galectins are a class of proteins that bind specifically to β-galactoside carbohydrates, such as N-linked or O-linked glycosylated proteins. Abnormal expression of galectins in various cancers has been found to mediate broad biological functions, including cell proliferation, apoptosis, adhesion, and inflammation. However, the mechanisms by which galectins contribute to MPN pathogenesis remain incompletely understood. To corroborate our sRNA-seq findings, we performed flow cytometry analysis which demonstrated elevated expression of galectin-1 in MPN patient CD14+ monocytes. Increased plasma levels of galectin-1 were also identified in MPN patients compared to healthy individuals. Higher galectin-1 protein levels in both Kit+ progenitor cells and CD11b+ myeloid cells from Jak2 V617F knock-in mice were also observed when compared to wild type mice. Additionally, galectin-1 expression was induced by MPL W515L and JAK2 V617F and inhibited by ruxolitinib in Ba/F3 cells. Thus, our results show enrichment of galectin-1 in both MPN patients and mouse models, with evidence of direct contribution from specific MPN driver mutations. Our scRNA-seq dataset analysis showed significant correlations of galectin-1 expression with inflammatory and oxidative phosphorylation (OXPHOS) genes in MPN patient monocytes. To investigate the effects of galectin-1 in monocyte activation, we incubated CD14+ monocytes from MPN patients with recombinant galectin-1 (rGal-1) and observed markedly stimulated secretion of inflammatory cytokines, such as IL-1α, IL-1β, IL-6, IL-8 and TNF-α. Furthermore, OTX008, a galectin-1 inhibitor, inhibited the transcription and secretion of inflammatory cytokines in MPN patient CD14+ monocytes and monocytic cell lines. Notably, both genetic and pharmacologic inhibition of galectin-1 reduced cellular ATP level and oxygen consumption rate, suggesting a metabolic reprogramming in MPNs. Although galectin-1 mediates broad biological functions, the molecular mechanisms by which galectin-1 regulates signaling pathways and transcription in MPN cells had not been delineated. Our scRNA-seq heterogeneity analysis suggested that genes in the PI3K-AKT-mTOR pathway were differentially expressed between galectin-1 high- and low-expressing monocytes. We therefore incubated both monocytic cell lines and monocytes from MPN patients with rGal-1 and observed stimulation of the PI3K-AKT-mTOR pathway, represented by increased levels of phosphorylated mTOR, AKT and S6. Consistently, genetic and pharmacologic inhibition of galectin-1 inhibited activation of the PI3K-AKT-mTOR pathway. In summary, our results demonstrate upregulation of galectin-1 in MPN monocytes, potentially due to activation of JAK2-driven signaling. We further demonstrate that galectin-1 fuels inflammation potentially via metabolic reprogramming of monocytes and activation of PI3K-AKT-mTOR signaling. Our data also suggest galectin-1 as a putative therapeutic target in MPNs.

Single-nucleus RNA sequencing reveals heterogenous microenvironments and specific drug response between cervical squamous cell carcinoma and adenocarcinoma

Cervical squamous cell carcinoma (CSCC) and adenocarcinoma (CAde) are two major pathological types of cervical cancer (CC), but their high-resolution heterogeneity of tumor and immune microenvironment remains elusive. Here, we performed single-nucleus RNA sequencing (snRNA-seq) from five CSCC and three CAde samples, and systematically outlined their specific transcriptome atlas. We found CD8+ T cells in CSCC were more cytotoxic but lower exhausted compared to those in CAde, and phagocytic MRC1+ macrophages were specifically enriched in CSCC. Interestingly, we discovered that pro-tumoral cancer-associated myofibroblasts (myoCAFs) and cancer-associated vascular-fibroblasts (vCAFs) were more abundant in CSCC, and further verified their pro-metastatic roles invitro. Furthermore, we also identified some specific chemotherapy drugs for CSCC (Dasatinib and Doramapimod) and CAde (Pyrimethamine and Lapatinib) by revealing their heterogeneity in transcriptomic profiles of malignant epithelial cells, and further verified their specific sensitivity in cell lines and constructed CC-derived organoids. Cell-cell communication networks revealed that the pathways of NRG1-ERBB2, and FN1-ITAG3 were specific for CAde and CSCC, respectively, which may partly explain the specificities of identified chemotherapy drugs. Our study described the immune heterogeneity and specific cellular interactions between CSCC and CAde, which could provide insights for uncovering pathogenesis and designing personalized treatment. National Key R&D Program of China (2021YFC2701201), National Natural Science Foundation of China (82072895, 82141106, 82103134, 81903114).

Single-cell RNA-seq public data reveal the gene regulatory network landscape of respiratory epithelial and peripheral immune cells in COVID-19 patients.

Infection with SARS-CoV-2 leads to coronavirus disease 2019 (COVID-19), which can result in acute respiratory distress syndrome and multiple organ failure. However, its comprehensive influence on pathological immune responses in the respiratory epithelium and peripheral immune cells is not yet fully understood. In this study, we analyzed multiple public scRNA-seq datasets of nasopharyngeal swabs and peripheral blood to investigate the gene regulatory networks (GRNs) of healthy individuals and COVID-19 patients with mild/moderate and severe disease, respectively. Cell-cell communication networks among cell types were also inferred. Finally, validations were conducted using bulk RNA-seq and proteome data. Similar and dissimilar regulons were identified within or between epithelial and immune cells during COVID-19 severity progression. The relative transcription factors (TFs) and their targets were used to construct GRNs among different infection sites and conditions. Between respiratory epithelial and peripheral immune cells, different TFs tended to be used to regulate the activity of a cell between healthy individuals and COVID-19 patients, although they had some TFs in common. For example, XBP1, FOS, STAT1, and STAT2 were activated in both the epithelial and immune cells of virus-infected individuals. In contrast, severe COVID-19 cases exhibited activation of CEBPD in peripheral immune cells, while CEBPB was exclusively activated in respiratory epithelial cells. Moreover, in patients with severe COVID-19, although some inflammatory genes, such as S100A8/A9, were found to be upregulated in both respiratory epithelial and peripheral immune cells, their relative regulators can differ in terms of cell types. The cell-cell communication analysis suggested that epidermal growth factor receptor signaling among epithelia contributes to mild/moderate disease, and chemokine signaling among immune cells contributes to severe disease. This study identified cell type- and condition-specific regulons in a wide range of cell types from the initial infection site to the peripheral blood, and clarified the diverse mechanisms of maladaptive responses to SARS-CoV-2 infection.