Bioinformatic identification of potential autocrine signaling loops in cancers from gene expression profiles

Predicting intercellular communication based on metabolite-related ligand-receptor interactions with MRCLinkdb

BACKGROUND

Metabolite-associated cell communications play critical roles in maintaining human biological function. However, most existing tools and resources focus only on ligand-receptor interaction pairs where both partners are proteinaceous, neglecting other non-protein molecules. To address this gap, we introduce the MRCLinkdb database and algorithm, which aggregates and organizes data related to non-protein L-R interactions in cell-cell communication, providing a valuable resource for predicting intercellular communication based on metabolite-related ligand-receptor interactions.

RESULTS

Here, we manually curated the metabolite-ligand-receptor (ML-R) interactions from the literature and known databases, ultimately collecting over 790 human and 670 mouse ML-R interactions. Additionally, we compiled information on over 1900 enzymes and 260 transporter entries associated with these metabolites. We developed Metabolite-Receptor based Cell Link Database (MRCLinkdb) to store these ML-R interactions data. Meanwhile, the platform also offers extensive information for presenting ML-R interactions, including fundamental metabolite information and the overall expression landscape of metabolite-associated gene sets (such as receptor, enzymes, and transporter proteins) based on single-cell transcriptomics sequencing (covering 35 human and 26 mouse tissues, 52 human and 44 mouse cell types) and bulk RNA-seq/microarray data (encompassing 62 human and 39 mouse tissues). Furthermore, MRCLinkdb introduces a web server dedicated to the analysis of intercellular communication based on ML-R interactions. MRCLinkdb is freely available at https://www.cellknowledge.com.cn/mrclinkdb/ .

CONCLUSIONS

In addition to supplementing ligand-receptor databases, MRCLinkdb may provide new perspectives for decoding the intercellular communication and advancing related prediction tools based on ML-R interactions.

sc2MeNetDrug: A computational tool to uncover inter-cell signaling targets and identify relevant drugs based on single cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) is a powerful technology to investigate the transcriptional programs in stromal, immune, and disease cells, like tumor cells or neurons within the Alzheimer's Disease (AD) brain or tumor microenvironment (ME) or niche. Cell-cell communications within ME play important roles in disease progression and immunotherapy response and are novel and critical therapeutic targets. Though many tools of scRNA-seq analysis have been developed to investigate the heterogeneity and sub-populations of cells, few were designed for uncovering cell-cell communications of ME and predicting the potentially effective drugs to inhibit the communications. Moreover, the data analysis processes of discovering signaling communication networks and effective drugs using scRNA-seq data are complex and involve a set of critical analysis processes and external supportive data resources, which are difficult for researchers who have no strong computational background and training in scRNA-seq data analysis. To address these challenges, in this study, we developed a novel open-source computational tool, sc2MeNetDrug (https://fuhaililab.github.io/sc2MeNetDrug/). It was specifically designed using scRNA-seq data to identify cell types within disease MEs, uncover the dysfunctional signaling pathways within individual cell types and interactions among different cell types, and predict effective drugs that can potentially disrupt cell-cell signaling communications. sc2MeNetDrug provided a user-friendly graphical user interface to encapsulate the data analysis modules, which can facilitate the scRNA-seq data-based discovery of novel inter-cell signaling communications and novel therapeutic regimens.

Large-scale analysis of cell-cell communication reveals angiogenin-dependent tumor progression in clear cell renal cell carcinoma

Cellular crosstalk in the tumor microenvironment (TME) is still largely uncharacterized, while it plays an essential role in shaping immunosuppression or anti-tumor response. Large-scale analyses are needed to better decipher cell-cell communication in cancer. In this work, we used original and publicly available single-cell RNA sequencing (scRNAseq) data to characterize in-depth the communication networks in human clear cell renal cell carcinoma (ccRCC). We identified 50 putative communication channels specifically used by cancer cells to interact with other cells, including two novel angiogenin-mediated interactions. Expression of angiogenin and its receptors was validated at the protein level in primary ccRCC. Mechanistically, angiogenin enhanced ccRCC cell line proliferation and down-regulated secretion of IL-6, IL-8, and MCP-1 proinflammatory molecules. This study provides novel biological insights into molecular mechanisms of ccRCC, and suggests angiogenin and its receptors as potential therapeutic targets in clear cell renal cancer.

The bone marrow stroma in human myelodysplastic syndrome reveals alterations that regulate disease progression

Myelodysplastic syndromes (MDSs) are a heterogenous group of diseases affecting the hematopoietic stem cell that are curable only by stem cell transplantation. Both hematopoietic cell intrinsic changes and extrinsic signals from the bone marrow (BM) niche seem to ultimately lead to MDS. Animal models of MDS indicate that alterations in specific mesenchymal progenitor subsets in the BM microenvironment can induce or select for abnormal hematopoietic cells. Here, we identify a subset of human BM mesenchymal cells marked by the expression of CD271, CD146, and CD106. This subset of human mesenchymal cells is comparable with mouse mesenchymal cells that, when perturbed, result in an MDS-like syndrome. Its transcriptional analysis identified Osteopontin (SPP1) as the most overexpressed gene. Selective depletion of Spp1 in the microenvironment of the mouse MDS model, Vav-driven Nup98-HoxD13, resulted in an accelerated progression as demonstrated by increased chimerism, higher mutant myeloid cell burden, and a more pronounced anemia when compared with that in wild-type microenvironment controls. These data indicate that molecular perturbations can occur in specific BM mesenchymal subsets of patients with MDS. However, the niche adaptations to dysplastic clones include Spp1 overexpression that can constrain disease fitness and potentially progression. Therefore, niche changes with malignant disease can also serve to protect the host.

Sctensor detects many-to-many cell-cell interactions from single cell RNA-sequencing data

BACKGROUND

Complex biological systems are described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing studies focus on CCIs based on ligand-receptor (L-R) gene co-expression but the analytical methods are not appropriate to detect many-to-many CCIs.

RESULTS

In this work, we propose scTensor, a novel method for extracting representative triadic relationships (or hypergraphs), which include ligand-expression, receptor-expression, and related L-R pairs.

CONCLUSIONS

Through extensive studies with simulated and empirical datasets, we have shown that scTensor can detect some hypergraphs that cannot be detected using conventional CCI detection methods, especially when they include many-to-many relationships. scTensor is implemented as a freely available R/Bioconductor package.

Monolayer platform to generate and purify primordial germ-like cells in vitro provides insights into human germline specification

Generating primordial germ cell-like cells (PGCLCs) from human pluripotent stem cells (hPSCs) advances studies of human reproduction and development of infertility treatments, but often entails complex 3D aggregates. Here we develop a simplified, monolayer method to differentiate hPSCs into PGCs within 3.5 days. We use our simplified differentiation platform and single-cell RNA-sequencing to achieve further insights into PGCLC specification. Transient WNT activation for 12 h followed by WNT inhibition specified PGCLCs; by contrast, sustained WNT induced primitive streak. Thus, somatic cells (primitive streak) and PGCLCs are related-yet distinct-lineages segregated by temporally-dynamic signaling. Pluripotency factors including NANOG are continuously expressed during the transition from pluripotency to posterior epiblast to PGCs, thus bridging pluripotent and germline states. Finally, hPSC-derived PGCLCs can be easily purified by virtue of their CXCR4+PDGFRA-GARP- surface-marker profile and single-cell RNA-sequencing reveals that they harbor transcriptional similarities with fetal PGCs.

SiGra: single-cell spatial elucidation through an image-augmented graph transformer

Recent advances in high-throughput molecular imaging have pushed spatial transcriptomics technologies to subcellular resolution, which surpasses the limitations of both single-cell RNA-seq and array-based spatial profiling. The multichannel immunohistochemistry images in such data provide rich information on the cell types, functions, and morphologies of cellular compartments. In this work, we developed a method, single-cell spatial elucidation through image-augmented Graph transformer (SiGra), to leverage such imaging information for revealing spatial domains and enhancing substantially sparse and noisy transcriptomics data. SiGra applies hybrid graph transformers over a single-cell spatial graph. SiGra outperforms state-of-the-art methods on both single-cell and spot-level spatial transcriptomics data from complex tissues. The inclusion of immunohistochemistry images improves the model performance by 37% (95% CI: 27-50%). SiGra improves the characterization of intratumor heterogeneity and intercellular communication and recovers the known microscopic anatomy. Overall, SiGra effectively integrates different spatial modality data to gain deep insights into spatial cellular ecosystems.

Collective directional migration drives the formation of heteroclonal cancer cell clusters

Metastasisation occurs through the acquisition of invasive and survival capabilities that allow tumour cells to colonise distant sites. While the role of multicellular aggregates in cancer dissemination is acknowledged, the mechanisms that drive the formation of multiclonal cell aggregates are not fully elucidated. Here, we show that cancer cells of different tissue of origins can perform collective directional migration and can actively form heteroclonal aggregates in 3D, through a proliferation-independent mechanism. Coalescence of distant cell clusters is mediated by subcellular actin-rich protrusions and multicellular outgrowths that extend towards neighbouring aggregates. Coherently, perturbation of cytoskeletal dynamics impairs collective migration while myosin II activation is necessary for multicellular movements. We put forward the hypothesis that cluster attraction is mediated by secreted soluble factors. Such a hypothesis is consistent with the abrogation of aggregation by inhibition of PI3K/AKT/mTOR and MEK/ERK, the chemoattracting activity of conditioned culture media and with a wide screening of secreted proteins. Our results present a novel collective migration model and shed light on the mechanisms of formation of heteroclonal aggregates in cancer.

A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell-Cell Communication

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of cellular heterogeneity, identification of rare but significant cell types, and exploration of cell-cell communications and interactions. Its broad applications span both basic and clinical research domains. In this comprehensive review, we survey the current landscape of scRNA-seq analysis methods and tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, and the inference of cell-cell communication. We review the challenges encountered in scRNA-seq analysis, including issues of sparsity or low expression, reliability of cell annotation, and assumptions in data integration, and discuss the potential impact of suboptimal clustering and differential expression analysis tools on downstream analyses, particularly in identifying cell subpopulations. Finally, we discuss recent advancements and future directions for enhancing scRNA-seq analysis. Specifically, we highlight the development of novel tools for annotating single-cell data, integrating and interpreting multimodal datasets covering transcriptomics, epigenomics, and proteomics, and inferring cellular communication networks. By elucidating the latest progress and innovation, we provide a comprehensive overview of the rapidly advancing field of scRNA-seq analysis.

Genome-wide association analyses define pathogenic signaling pathways and prioritize drug targets for IgA nephropathy

IgA nephropathy (IgAN) is a progressive form of kidney disease defined by glomerular deposition of IgA. Here we performed a genome-wide association study of 10,146 kidney-biopsy-diagnosed IgAN cases and 28,751 controls across 17 international cohorts. We defined 30 genome-wide significant risk loci explaining 11% of disease risk. A total of 16 loci were new, including TNFSF4/TNFSF18, REL, CD28, PF4V1, LY86, LYN, ANXA3, TNFSF8/TNFSF15, REEP3, ZMIZ1, OVOL1/RELA, ETS1, IGH, IRF8, TNFRSF13B and FCAR. The risk loci were enriched in gene orthologs causing abnormal IgA levels when genetically manipulated in mice. We also observed a positive genetic correlation between IgAN and serum IgA levels. High polygenic score for IgAN was associated with earlier onset of kidney failure. In a comprehensive functional annotation analysis of candidate causal genes, we observed convergence of biological candidates on a common set of inflammatory signaling pathways and cytokine ligand-receptor pairs, prioritizing potential new drug targets.

Single-cell RNA sequencing and spatial transcriptomics reveal cancer-associated fibroblasts in glioblastoma with protumoral effects

Cancer-associated fibroblasts (CAFs) were presumed absent in glioblastoma given the lack of brain fibroblasts. Serial trypsinization of glioblastoma specimens yielded cells with CAF morphology and single-cell transcriptomic profiles based on their lack of copy number variations (CNVs) and elevated individual cell CAF probability scores derived from the expression of 9 CAF markers and absence of 5 markers from non-CAF stromal cells sharing features with CAFs. Cells without CNVs and with high CAF probability scores were identified in single-cell RNA-Seq of 12 patient glioblastomas. Pseudotime reconstruction revealed that immature CAFs evolved into subtypes, with mature CAFs expressing actin alpha 2, smooth muscle (ACTA2). Spatial transcriptomics from 16 patient glioblastomas confirmed CAF proximity to mesenchymal glioblastoma stem cells (GSCs), endothelial cells, and M2 macrophages. CAFs were chemotactically attracted to GSCs, and CAFs enriched GSCs. We created a resource of inferred crosstalk by mapping expression of receptors to their cognate ligands, identifying PDGF and TGF-β as mediators of GSC effects on CAFs and osteopontin and HGF as mediators of CAF-induced GSC enrichment. CAFs induced M2 macrophage polarization by producing the extra domain A (EDA) fibronectin variant that binds macrophage TLR4. Supplementing GSC-derived xenografts with CAFs enhanced in vivo tumor growth. These findings are among the first to identify glioblastoma CAFs and their GSC interactions, making them an intriguing target.

spaCI: deciphering spatial cellular communications through adaptive graph model

Cell-cell communications are vital for biological signalling and play important roles in complex diseases. Recent advances in single-cell spatial transcriptomics (SCST) technologies allow examining the spatial cell communication landscapes and hold the promise for disentangling the complex ligand-receptor (L-R) interactions across cells. However, due to frequent dropout events and noisy signals in SCST data, it is challenging and lack of effective and tailored methods to accurately infer cellular communications. Herein, to decipher the cell-to-cell communications from SCST profiles, we propose a novel adaptive graph model with attention mechanisms named spaCI. spaCI incorporates both spatial locations and gene expression profiles of cells to identify the active L-R signalling axis across neighbouring cells. Through benchmarking with currently available methods, spaCI shows superior performance on both simulation data and real SCST datasets. Furthermore, spaCI is able to identify the upstream transcriptional factors mediating the active L-R interactions. For biological insights, we have applied spaCI to the seqFISH+ data of mouse cortex and the NanoString CosMx Spatial Molecular Imager (SMI) data of non-small cell lung cancer samples. spaCI reveals the hidden L-R interactions from the sparse seqFISH+ data, meanwhile identifies the inconspicuous L-R interactions including THBS1-ITGB1 between fibroblast and tumours in NanoString CosMx SMI data. spaCI further reveals that SMAD3 plays an important role in regulating the crosstalk between fibroblasts and tumours, which contributes to the prognosis of lung cancer patients. Collectively, spaCI addresses the challenges in interrogating SCST data for gaining insights into the underlying cellular communications, thus facilitates the discoveries of disease mechanisms, effective biomarkers and therapeutic targets.

Integrated proteomic and transcriptomic landscape of macrophages in mouse tissues

Macrophages are involved in tissue homeostasis and are critical for innate immune responses, yet distinct macrophage populations in different tissues exhibit diverse gene expression patterns and biological processes. While tissue-specific macrophage epigenomic and transcriptomic profiles have been reported, proteomes of different macrophage populations remain poorly characterized. Here we use mass spectrometry and bulk RNA sequencing to assess the proteomic and transcriptomic patterns, respectively, of 10 primary macrophage populations from seven mouse tissues, bone marrow-derived macrophages and the cell line RAW264.7. The results show distinct proteomic landscape and protein copy numbers between tissue-resident and recruited macrophages. Construction of a hierarchical regulatory network finds cell-type-specific transcription factors of macrophages serving as hubs for denoting tissue and functional identity of individual macrophage subsets. Finally, Il18 is validated to be essential in distinguishing molecular signatures and cellular function features between tissue-resident and recruited macrophages in the lung and liver. In summary, these deposited datasets and our open proteome server ( http://macrophage.mouseprotein.cn ) integrating all information will provide a valuable resource for future functional and mechanistic studies of mouse macrophages.

Inferring a spatial code of cell-cell interactions across a whole animal body

Cell-cell interactions shape cellular function and ultimately organismal phenotype. Interacting cells can sense their mutual distance using combinations of ligand-receptor pairs, suggesting the existence of a spatial code, i.e., signals encoding spatial properties of cellular organization. However, this code driving and sustaining the spatial organization of cells remains to be elucidated. Here we present a computational framework to infer the spatial code underlying cell-cell interactions from the transcriptomes of the cell types across the whole body of a multicellular organism. As core of this framework, we introduce our tool cell2cell, which uses the coexpression of ligand-receptor pairs to compute the potential for intercellular interactions, and we test it across the Caenorhabditis elegans' body. Leveraging a 3D atlas of C. elegans' cells, we also implement a genetic algorithm to identify the ligand-receptor pairs most informative of the spatial organization of cells across the whole body. Validating the spatial code extracted with this strategy, the resulting intercellular distances are negatively correlated with the inferred cell-cell interactions. Furthermore, for selected cell-cell and ligand-receptor pairs, we experimentally confirm the communicatory behavior inferred with cell2cell and the genetic algorithm. Thus, our framework helps identify a code that predicts the spatial organization of cells across a whole-animal body.

Mapping and Validation of scRNA-Seq-Derived Cell-Cell Communication Networks in the Tumor Microenvironment

Recent advances in single-cell technologies, particularly single-cell RNA-sequencing (scRNA-seq), have permitted high throughput transcriptional profiling of a wide variety of biological systems. As scRNA-seq supports inference of cell-cell communication, this technology has and continues to anchor groundbreaking studies into the efficacy and mechanism of novel immunotherapies for cancer treatment. In this review, we will highlight methods developed to infer inter- and intracellular signaling from scRNA-seq and discuss how they have contributed to studies of immunotherapeutic intervention in the tumor microenvironment (TME). However, a central challenge remains in validating the hypothesized cell-cell interactions. Therefore, this review will also cover strategies for integration of these scRNA-seq-derived interaction networks with existing experimental and computational approaches. Integration of these networks with imaging, protein secretion measurements, and network analysis and mathematical modeling tools addresses challenges that remain with scRNA-seq to enhance studies of immunosuppressive and immunotherapy-altered signaling in the TME.

The Cancer Surfaceome Atlas integrates genomic, functional and drug response data to identify actionable targets

Cell-surface proteins (SPs) are a rich source of immune and targeted therapies. By systematically integrating single-cell and bulk genomics, functional studies and target actionability, in the present study we comprehensively identify and annotate genes encoding SPs (GESPs) pan-cancer. We characterize GESP expression patterns, recurrent genomic alterations, essentiality, receptor-ligand interactions and therapeutic potential. We also find that mRNA expression of GESPs is cancer-type specific and positively correlates with protein expression, and that certain GESP subgroups function as common or specific essential genes for tumor cell growth. We also predict receptor-ligand interactions substantially deregulated in cancer and, using systems biology approaches, we identify cancer-specific GESPs with therapeutic potential. We have made this resource available through the Cancer Surfaceome Atlas ( http://fcgportal.org/TCSA ) within the Functional Cancer Genome data portal.

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

Integrated computational analyses reveal novel insights into the stromal microenvironment of SHH-subtype medulloblastoma

Medulloblastoma is the most common malignant brain tumour of childhood. While our understanding of this disease has progressed substantially in recent years, the role of tumour microenvironment remains unclear. Given the increasing role of microenvironment-targeted therapeutics in other cancers, this study was aimed at further exploring its role in medulloblastoma. Multiple computational techniques were used to analyze open-source bulk and single cell RNA seq data from primary samples derived from all subgroups of medulloblastoma. Gene expression is used to infer stromal subpopulations, and network-based approaches are used to identify potential therapeutic targets. Bulk data was obtained from 763 medulloblastoma samples and single cell data from an additional 7241 cells from 23 tumours. Independent bulk (285 tumours) and single cell (32,868 cells from 29 tumours) validation cohorts were used to verify results. The SHH subgroup was found to be enriched in stromal activity, including the epithelial-to-mesenchymal transition, while group 3 is comparatively stroma-suppressed. Several receptor and ligand candidates underlying this difference are identified which we find to correlate with metastatic potential of SHH medulloblastoma. Additionally, a biologically active gradient is detected within SHH medulloblastoma, from "stroma-active" to "stroma-suppressed" cells which may have relevance to targeted therapy. This study serves to further elucidate the role of the stromal microenvironment in SHH-subgroup medulloblastoma and identify novel treatment possibilities for this challenging disease.

LR Hunting: A Random Forest Based Cell-Cell Interaction Discovery Method for Single-Cell Gene Expression Data

Cell–cell interactions (CCIs) and cell–cell communication (CCC) are critical for maintaining complex biological systems. The availability of single-cell RNA sequencing (scRNA-seq) data opens new avenues for deciphering CCIs and CCCs through identifying ligand-receptor (LR) gene interactions between cells. However, most methods were developed to examine the LR interactions of individual pairs of genes. Here, we propose a novel approach named LR hunting which first uses random forests (RFs)-based data imputation technique to link the data between different cell types. To guarantee the robustness of the data imputation procedure, we repeat the computation procedures multiple times to generate aggregated imputed minimal depth index (IMDI). Next, we identify significant LR interactions among all combinations of LR pairs simultaneously using unsupervised RFs. We demonstrated LR hunting can recover biological meaningful CCIs using a mouse cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) dataset and a triple-negative breast cancer scRNA-seq dataset.

Applications and analytical tools of cell communication based on ligand-receptor interactions at single cell level

BACKGROUND

Cellular communication is an essential feature of multicellular organisms. Binding of ligands to their homologous receptors, which activate specific cell signaling pathways, is a basic type of cellular communication and intimately linked to many degeneration processes leading to diseases.

MAIN BODY

This study reviewed the history of ligand-receptor and presents the databases which store ligand-receptor pairs. The recently applications and research tools of ligand-receptor interactions for cell communication at single cell level by using single cell RNA sequencing have been sorted out.

CONCLUSION

The summary of the advantages and disadvantages of analysis tools will greatly help researchers analyze cell communication at the single cell level. Learning cell communication based on ligand-receptor interactions by single cell RNA sequencing gives way to developing new target drugs and personalizing treatment.

Deciphering cell-cell interactions and communication from gene expression

Cell-cell interactions orchestrate organismal development, homeostasis and single-cell functions. When cells do not properly interact or improperly decode molecular messages, disease ensues. Thus, the identification and quantification of intercellular signalling pathways has become a common analysis performed across diverse disciplines. The expansion of protein-protein interaction databases and recent advances in RNA sequencing technologies have enabled routine analyses of intercellular signalling from gene expression measurements of bulk and single-cell data sets. In particular, ligand-receptor pairs can be used to infer intercellular communication from the coordinated expression of their cognate genes. In this Review, we highlight discoveries enabled by analyses of cell-cell interactions from transcriptomic data and review the methods and tools used in this context.

Cellinker: a platform of ligand-receptor interactions for intercellular communication analysis

MOTIVATION

Ligand-receptor (L-R) interactions mediate cell adhesion, recognition and communication and play essential roles in physiological and pathological signaling. With the rapid development of single-cell RNA sequencing (scRNA-seq) technologies, systematically decoding the intercellular communication network involving L-R interactions has become a focus of research. Therefore, construction of a comprehensive, high-confidence and well-organized resource to retrieve L-R interactions in order to study the functional effects of cell-cell communications would be of great value.

RESULTS

In this study, we developed Cellinker, a manually curated resource of literature-supported L-R interactions that play roles in cell-cell communication. We aimed to provide a useful platform for studies on cell-cell communication mediated by L-R interactions. The current version of Cellinker documents over 3,700 human and 3,200 mouse L-R protein-protein interactions (PPIs) and embeds a practical and convenient webserver with which researchers can decode intercellular communications based on scRNA-seq data. And over 400 endogenous small molecule (sMOL) related L-R interactions were collected as well. Moreover, to help with research on coronavirus (CoV) infection, Cellinker collects information on 16 L-R PPIs involved in CoV-human interactions (including 12 L-R PPIs involved in SARS-CoV-2 infection). In summary, Cellinker provides a user-friendly interface for querying, browsing and visualizing L-R interactions as well as a practical and convenient web tool for inferring intercellular communications based on scRNA-seq data. We believe this platform could promote intercellular communication research and accelerate the development of related algorithms for scRNA-seq studies.

AVAILABILITY

Cellinker is available at http://www.rna-society.org/cellinker/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Chromatin accessibility landscapes of skin cells in systemic sclerosis nominate dendritic cells in disease pathogenesis

Systemic sclerosis (SSc) is a disease at the intersection of autoimmunity and fibrosis. However, the epigenetic regulation and the contributions of diverse cell types to SSc remain unclear. Here we survey, using ATAC-seq, the active DNA regulatory elements of eight types of primary cells in normal skin from healthy controls, as well as clinically affected and unaffected skin from SSc patients. We find that accessible DNA elements in skin-resident dendritic cells (DCs) exhibit the highest enrichment of SSc-associated single-nucleotide polymorphisms (SNPs) and predict the degrees of skin fibrosis in patients. DCs also have the greatest disease-associated changes in chromatin accessibility and the strongest alteration of cell-cell interactions in SSc lesions. Lastly, data from an independent cohort of patients with SSc confirm a significant increase of DCs in lesioned skin. Thus, the DCs epigenome links inherited susceptibility and clinically apparent fibrosis in SSc skin, and can be an important driver of SSc pathogenesis.

A methodology for predicting tissue-specific metabolic roles of receptors applied to subcutaneous adipose

The human biological system uses 'inter-organ' communication to achieve a state of homeostasis. This communication occurs through the response of receptors, located on target organs, to the binding of secreted ligands from source organs. Albeit years of research, the roles these receptors play in tissues is only partially understood. This work presents a new methodology based on the enrichment analysis scores of co-expression networks fed into support vector machines (SVMs) and k-NN classifiers to predict the tissue-specific metabolic roles of receptors. The approach is primarily based on the detection of coordination patterns of receptors expression. These patterns and the enrichment analysis scores of their co-expression networks were used to analyse ~ 700 receptors and predict metabolic roles of receptors in subcutaneous adipose. To facilitate supervised learning, a list of known metabolic and non-metabolic receptors was constructed using a semi-supervised approach following literature-based verification. Our approach confirms that pathway enrichment scores are good signatures for correctly classifying the metabolic receptors in adipose. We also show that the k-NN method outperforms the SVM method in classifying metabolic receptors. Finally, we predict novel metabolic roles of receptors. These predictions can enhance biological understanding and the development of new receptor-targeting metabolic drugs.

CellTalkDB: a manually curated database of ligand-receptor interactions in humans and mice

Cell-cell communications in multicellular organisms generally involve secreted ligand-receptor (LR) interactions, which is vital for various biological phenomena. Recent advancements in single-cell RNA sequencing (scRNA-seq) have effectively resolved cellular phenotypic heterogeneity and the cell-type composition of complex tissues, facilitating the systematic investigation of cell-cell communications at single-cell resolution. However, assessment of chemical-signal-dependent cell-cell communication through scRNA-seq relies heavily on prior knowledge of LR interaction pairs. We constructed CellTalkDB (http://tcm.zju.edu.cn/celltalkdb), a manually curated comprehensive database of LR interaction pairs in humans and mice comprising 3398 human LR pairs and 2033 mouse LR pairs, through text mining and manual verification of known protein-protein interactions using the STRING database, with literature-supported evidence for each pair. Compared with SingleCellSignalR, the largest LR-pair resource, CellTalkDB includes not only 2033 mouse LR pairs but also 377 additional human LR pairs. In conclusion, the data on human and mouse LR pairs contained in CellTalkDB could help to further the inference and understanding of the LR-interaction-based cell-cell communications, which might provide new insights into the mechanism underlying biological processes.

Reading the heart at single-cell resolution

The burgeoning field of single-cell transcriptomics augments our ability to scrutinize organ systems at unprecedented resolutions. Single-cell RNA sequencing (scRNA-seq) and analytical techniques have shed light on the cellular heterogeneity, developmental trajectories, intercellular communications of the cardiac system, and thus contributed much to the understanding of cardiac development, homeostasis and disorders. Although generalized protocols are well established for scRNA-seq pipelines, customized sample preparation, quality control, and data interpretation are still needed in cardiac research. In this article, we highlight major steps that impact data quality in scRNA-seq experiments, with particular focus on sample and data processing of cardiomyocytes. We also summarize popular applications of scRNA-seq, outlining general tools, caveats and examples in cardiac research.

Glucocorticoid regulates mesenchymal cell differentiation required for perinatal lung morphogenesis and function

While antenatal glucocorticoids are widely used to enhance lung function in preterm infants, cellular and molecular mechanisms by which glucocorticoid receptor (GR) signaling influences lung maturation remain poorly understood. Deletion of the glucocorticoid receptor gene (Nr3c1) from fetal pulmonary mesenchymal cells phenocopied defects caused by global Nr3c1 deletion, while lung epithelial- or endothelial-specific Nr3c1 deletion did not impair lung function at birth. We integrated genome-wide gene expression profiling, ATAC-seq, and single cell RNA-seq data in mice in which GR was deleted or activated to identify the cellular and molecular mechanisms by which glucocorticoids control prenatal lung maturation. GR enhanced differentiation of a newly defined proliferative mesenchymal progenitor cell (PMP) into matrix fibroblasts (MFBs), in part by directly activating extracellular matrix-associated target genes, including Fn1, Col16a4, and Eln and by modulating VEGF, JAK-STAT, and WNT signaling. Loss of mesenchymal GR signaling blocked fibroblast progenitor differentiation into mature MFBs, which in turn increased proliferation of SOX9+ alveolar epithelial progenitor cells and inhibited differentiation of mature alveolar type II (AT₂) and AT₁ cells. GR signaling controls genes required for differentiation of a subset of proliferative mesenchymal progenitors into matrix fibroblasts, in turn, regulating signals controlling AT₂/AT₁ progenitor cell proliferation and differentiation and identifying cells and processes by which glucocorticoid signaling regulates fetal lung maturation.

Notch signalling drives synovial fibroblast identity and arthritis pathology

The synovium is a mesenchymal tissue composed mainly of fibroblasts with a lining and sublining that surrounds the joints. In rheumatoid arthritis (RA), the synovial tissue undergoes marked hyperplasia, becomes inflamed and invasive and destroys the joint^1,2. Recently, we and others found that a subset of fibroblasts located in the sublining undergoes major expansion in RA and is linked to disease activity^3,4,5. However, the molecular mechanism by which these fibroblasts differentiate and expand in RA remains unknown. Here, we identified a critical role for NOTCH3 signaling in the differentiation of perivascular and sublining CD90(THY1)+ fibroblasts. Using single cell RNA-sequencing and synovial tissue organoids, we found that NOTCH3 signaling drives both transcriptional and spatial gradients in fibroblasts emanating from vascular endothelial cells outward. In active RA, NOTCH3 and NOTCH target genes are markedly upregulated in synovial fibroblasts. Importantly, genetic deletion of Notch3 or monoclonal antibody-blockade of NOTCH3 signaling attenuates inflammation and prevents joint damage in inflammatory arthritis. Our results indicate that synovial fibroblasts exhibit positional identity regulated by endothelium-derived Notch signaling and that this stromal crosstalk pathway underlies inflammation and pathology in inflammatory arthritis.

Systematic Identification of Druggable Epithelial-Stromal Crosstalk Signaling Networks in Ovarian Cancer

Background

Bulk tumor tissue samples are used for generating gene expression profiles in most research studies, making it difficult to decipher the stroma–cancer crosstalk networks. In the present study, we describe the use of microdissected transcriptome profiles for the identification of cancer–stroma crosstalk networks with prognostic value, which presents a unique opportunity for developing new treatment strategies for ovarian cancer.

Methods

Transcriptome profiles from microdissected ovarian cancer–associated fibroblasts (CAFs) and ovarian cancer cells from patients with high-grade serous ovarian cancer (n = 70) were used as input data for the computational systems biology program CCCExplorer to uncover crosstalk networks between various cell types within the tumor microenvironment. The crosstalk analysis results were subsequently used for discovery of new indications for old drugs in ovarian cancer by computational ranking of candidate agents. Survival analysis was performed on ovarian tumor–bearing Dicer/Pten double-knockout mice treated with calcitriol, a US Food and Drug Administration–approved agent that suppresses the Smad signaling cascade, or vehicle control (9–11 mice per group). All statistical tests were two-sided.

Results

Activation of TGF-β-dependent and TGF-β-independent Smad signaling was identified in a particular subtype of CAFs and was associated with poor patient survival (patients with higher levels of Smad-regulated gene expression by CAFs: median overall survival = 15 months, 95% confidence interval [CI] = 12.7 to 17.3 months; vs patients with lower levels of Smad-regulated gene expression: median overall survival = 26 months, 95% CI = 15.9 to 36.1 months, P = .02). In addition, the activated Smad signaling identified in CAFs was found to be targeted by repositioning calcitriol. Calcitriol suppressed Smad signaling in CAFs, inhibited tumor progression in mice, and prolonged the median survival duration of ovarian cancer–bearing mice from 36 to 48 weeks (P = .04).

Conclusions

Our findings suggest the feasibility of using novel multicellular systems biology modeling to identify and repurpose known drugs targeting cancer–stroma crosstalk networks, potentially leading to faster and more effective cures for cancers.

Prospective isolation of nonhematopoietic cells of the niche and their differential molecular interactions with HSCs

A major limitation preventing in vivo modulation of hematopoietic stem cells (HSCs) is the incomplete understanding of the cellular and molecular support of the microenvironment in regulating HSC fate decisions. Consequently, murine HSCs cannot be generated, maintained, or expanded in culture over extended periods of time. A significantly improved understanding of the bone marrow niche environment and its molecular interactions with HSCs is pivotal to overcoming this challenge. We here prospectively isolated all major nonhematopoietic cellular niche components and cross-correlate them in detail with niche cells defined by lineage marking or tracing. Compiling an extensive database of soluble and membrane-bound ligand-receptor interactions, we developed a computational method to infer potential cell-to-cell interactions based on transcriptome data of sorter-purified niche cells and hematopoietic stem and progenitor cell subpopulations. Thus, we establish a compendium of the molecular communication between defined niche components and HSCs. Our analysis suggests an important role for cytokine antagonists in the regulation of HSC functions.

Gene mutation profile and risk stratification in AML1‑ETO‑positive acute myeloid leukemia based on next‑generation sequencing

Gene mutations play an important role in the development and progression of AML1-ETO-positive acute myeloid leukemia (AE-AML). Nevertheless, the gene mutation profile in this subtype of leukemia remains unclear. In addition, the clinical and prognostic effects of different mutant genes may be underestimated. In the present study, gene sequencing was conducted at diagnosis and relapse with next-generation sequencing (NGS) in 64 patients with newly diagnosed AE-AML, and 44/64 (68.8%) patients were found to present with a median of 2 (1–10) recurrent mutations at diagnosis and 6/11 (54.5%) cases were found to present with genetic alterations at relapse. c-KIT mutation was the most common in this cohort, with an incidence of 27/64 (42.2%) at diagnosis, followed by ASXL1 (n=10, 15.6%), MET (n=8, 12.5%), MLH1 (n=6, 9.4%), TET2 (n=5, 7.8%), and FBXW7, TP53 and DNMT3A (n=5, 7.8%). Survival analysis showed that c-KIT (exon 8, 17) but not exon 10 adversely affected survival. In addition, ASXL1 and TP53 were poor impact factors for recurrence-free survival (RFS) (P<0.05), and ASXL1, MET, FBXW7 and TP53 had a negative impact on overall survival (OS) (P<0.05). Multivariate analysis showed that c-KIT (exon 8, 17) [RFS: hazard ratio (HR) 3.36, 95% confidence interval (CI) 1.54–7.34, P=0.002; OS: HR 2.84, 95% CI 1.20–6.71, P=0.018] and ASXL1 mutations (RFS: HR 3.13, 95% CI 1.34–7.32, P=0.009; OS: HR 3.94, 95% CI 1.62–9.61, P=0.003) were independent adverse factors for survival. Further, co-mutation of these two genes showed even worse effect on disease outcome. Collectively, additional gene mutations play critical role in AE-AML. C-KIT and ASXL1 mutations are the two most common mutations in this subtype of leukemia. C-KIT (exon 8, 17) but not exon 10, and also the ASXL1 mutation poorly affect the disease outcome of this disease.

Cancer cell population growth kinetics at low densities deviate from the exponential growth model and suggest an Allee effect

Most models of cancer cell population expansion assume exponential growth kinetics at low cell densities, with deviations to account for observed slowing of growth rate only at higher densities due to limited resources such as space and nutrients. However, recent preclinical and clinical observations of tumor initiation or recurrence indicate the presence of tumor growth kinetics in which growth rates scale positively with cell numbers. These observations are analogous to the cooperative behavior of species in an ecosystem described by the ecological principle of the Allee effect. In preclinical and clinical models, however, tumor growth data are limited by the lower limit of detection (i.e., a measurable lesion) and confounding variables, such as tumor microenvironment, and immune responses may cause and mask deviations from exponential growth models. In this work, we present alternative growth models to investigate the presence of an Allee effect in cancer cells seeded at low cell densities in a controlled in vitro setting. We propose a stochastic modeling framework to disentangle expected deviations due to small population size stochastic effects from cooperative growth and use the moment approach for stochastic parameter estimation to calibrate the observed growth trajectories. We validate the framework on simulated data and apply this approach to longitudinal cell proliferation data of BT-474 luminal B breast cancer cells. We find that cell population growth kinetics are best described by a model structure that considers the Allee effect, in that the birth rate of tumor cells increases with cell number in the regime of small population size. This indicates a potentially critical role of cooperative behavior among tumor cells at low cell densities with relevance to early stage growth patterns of emerging and relapsed tumors.

Paired-cell sequencing enables spatial gene expression mapping of liver endothelial cells

Spatially resolved single-cell RNA sequencing (scRNAseq) is a powerful approach for inferring connections between a cell's identity and its position in a tissue. We recently combined scRNAseq with spatially mapped landmark genes to infer the expression zonation of hepatocytes. However, determining zonation of small cells with low mRNA content, or without highly expressed landmark genes, remains challenging. Here we used paired-cell sequencing, in which mRNA from pairs of attached mouse cells were sequenced and gene expression from one cell type was used to infer the pairs' tissue coordinates. We applied this method to pairs of hepatocytes and liver endothelial cells (LECs). Using the spatial information from hepatocytes, we reconstructed LEC zonation and extracted a landmark gene panel that we used to spatially map LEC scRNAseq data. Our approach revealed the expression of both Wnt ligands and the Dkk3 Wnt antagonist in distinct pericentral LEC sub-populations. This approach can be used to reconstruct spatial expression maps of non-parenchymal cells in other tissues.

Targeting TWIST1 through loss of function inhibits tumorigenicity of human glioblastoma

TWIST1 (TW) is a bHLH transcription factor (TF) and master regulator of the epithelial-to-mesenchymal transition (EMT). In vitro, TW promotes mesenchymal change, invasion, and self-renewal in glioblastoma (GBM) cells. However, the potential therapeutic relevance of TW has not been established through loss-of-function studies in human GBM cell xenograft models. The effects of TW loss of function (gene editing and knockdown) on inhibition of tumorigenicity of U87MG and GBM4 glioma stem cells were tested in orthotopic xenograft models and conditional knockdown in established flank xenograft tumors. RNAseq and the analysis of tumors investigated putative TW-associated mechanisms. Multiple bioinformatic tools revealed significant alteration of ECM, membrane receptors, signaling transduction kinases, and cytoskeleton dynamics leading to identification of PI3K/AKT signaling. We experimentally show alteration of AKT activity and periostin (POSTN) expression in vivo and/or in vitro. For the first time, we show that effect of TW knockout inhibits AKT activity in U87MG cells in vivo independent of PTEN mutation. The clinical relevance of TW and candidate mechanisms was established by analysis of the TCGA and ENCODE databases. TW expression was associated with decreased patient survival and LASSO regression analysis identified POSTN as one of top targets of TW in human GBM. While we previously demonstrated the role of TW in promoting EMT and invasion of glioma cells, these studies provide direct experimental evidence supporting protumorigenic role of TW independent of invasion in vivo and the therapeutic relevance of targeting TW in human GBM. Further, the role of TW driving POSTN expression and AKT signaling suggests actionable targets, which could be leveraged to mitigate the oncogenic effects of TW in GBM.

Extracting Intercellular Signaling Network of Cancer Tissues using Ligand-Receptor Expression Patterns from Whole-tumor and Single-cell Transcriptomes

Many behaviors of cancer, such as progression, metastasis and drug resistance etc., cannot be fully understood by genetic mutations or intracellular signaling alone. Instead, they are emergent properties of the cell community which forms a tumor. Studies of tumor heterogeneity reveal that many cancer behaviors critically depend on intercellular communication between cancer cells themselves and between cancer-stromal cells by secreted signaling molecules (ligands) and their cognate receptors. We analyzed public cancer transcriptome database for changes in cell-cell interactions as the characteristic of malignancy. We curated a list (>2,500 ligand-receptor pairs) and identified their joint enrichment in tumors from TCGA pan-cancer data. From single-cell RNA-Seq data for a case of melanoma and the specificity of the ligand-receptor interactions and their gene expression measured in individual cells, we constructed a map of a cell-cell communication network that indicates what signal is exchanged between which cell types in the tumor. Such networks establish a new formal phenotype of cancer which captures the cell-cell communication structure - it may open new opportunities for identifying molecular signatures of coordinated behaviors of cancer cells as a population - in turn may become a determinant of cancer progression potential and prognosis.

A Cell-type-resolved Liver Proteome

Parenchymatous organs consist of multiple cell types, primarily defined as parenchymal cells (PCs) and nonparenchymal cells (NPCs). The cellular characteristics of these organs are not well understood. Proteomic studies facilitate the resolution of the molecular details of different cell types in organs. These studies have significantly extended our knowledge about organogenesis and organ cellular composition. Here, we present an atlas of the cell-type-resolved liver proteome. In-depth proteomics identified 6000 to 8000 gene products (GPs) for each cell type and a total of 10,075 GPs for four cell types. This data set revealed features of the cellular composition of the liver: (1) hepatocytes (PCs) express the least GPs, have a unique but highly homogenous proteome pattern, and execute fundamental liver functions; (2) the division of labor among PCs and NPCs follows a model in which PCs make the main components of pathways, but NPCs trigger the pathways; and (3) crosstalk among NPCs and PCs maintains the PC phenotype. This study presents the liver proteome at cell resolution, serving as a research model for dissecting the cell type constitution and organ features at the molecular level.

Epithelial derived CTGF promotes breast tumor progression via inducing EMT and collagen I fibers deposition

Interactions among tumor cells, stromal cells, and extracellular matrix compositions are mediated through cytokines during tumor progression. Our analysis of 132 known cytokines and growth factors in published clinical breast cohorts and our 84 patient-derived xenograft models revealed that the elevated connective tissue growth factor (CTGF) in tumor epithelial cells significantly correlated with poor clinical prognosis and outcomes. CTGF was able to induce tumor cell epithelial-mesenchymal transition (EMT), and promote stroma deposition of collagen I fibers to stimulate tumor growth and metastasis. This process was mediated through CTGF-tumor necrosis factor receptor I (TNFR1)-IκB autocrine signaling. Drug treatments targeting CTGF, TNFR1, and IκB signaling each prohibited the EMT and tumor progression.

Integrated Bioinformatics Approach Reveals Crosstalk Between Tumor Stroma and Peripheral Blood Mononuclear Cells in Breast Cancer

Breast cancer is now the leading cause of cancer death in women worldwide. Cancer progression is driven not only by cancer cell intrinsic alterations and interactions with tumor microenvironment, but also by systemic effects. Integration of multiple profiling data may provide insights into the underlying molecular mechanisms of complex systemic processes. We performed a bioinformatic analysis of two public available microarray datasets for breast tumor stroma and peripheral blood mononuclear cells, featuring integrated transcriptomics data, protein-protein interactions (PPIs) and protein subcellular localization, to identify genes and biological pathways that contribute to dialogue between tumor stroma and the peripheral circulation. Genes of the integrin family as well as CXCR4 proved to be hub nodes of the crosstalk network and may play an important role in response to stroma-derived chemoattractants. This study pointed to potential for development of therapeutic strategies that target systemic signals travelling through the circulation and interdict tumor cell recruitment.

Three-dimensional chemotaxis-driven aggregation of tumor cells

One of the most important steps in tumor progression involves the transformation from a differentiated epithelial phenotype to an aggressive, highly motile phenotype, where tumor cells invade neighboring tissues. Invasion can occur either by isolated mesenchymal cells or by aggregates that migrate collectively and do not lose completely the epithelial phenotype. Here, we show that, in a three-dimensional cancer cell culture, collective migration of cells eventually leads to aggregation in large clusters. We present quantitative measurements of cluster velocity, coalescence rates, and proliferation rates. These results cannot be explained in terms of random aggregation. Instead, a model of chemotaxis-driven aggregation - mediated by a diffusible attractant - is able to capture several quantitative aspects of our results. Experimental assays of chemotaxis towards culture conditioned media confirm this hypothesis. Theoretical and numerical results further suggest an important role for chemotactic-driven aggregation in spreading and survival of tumor cells.

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

Cell-to-cell communication across multiple cell types and tissues strictly governs proper functioning of metazoans and extensively relies on interactions between secreted ligands and cell-surface receptors. Herein, we present the first large-scale map of cell-to-cell communication between 144 human primary cell types. We reveal that most cells express tens to hundreds of ligands and receptors to create a highly connected signalling network through multiple ligand-receptor paths. We also observe extensive autocrine signalling with approximately two-thirds of partners possibly interacting on the same cell type. We find that plasma membrane and secreted proteins have the highest cell-type specificity, they are evolutionarily younger than intracellular proteins, and that most receptors had evolved before their ligands. We provide an online tool to interactively query and visualize our networks and demonstrate how this tool can reveal novel cell-to-cell interactions with the prediction that mast cells signal to monoblastic lineages via the CSF1-CSF1R interacting pair.

Transcriptome analysis of individual stromal cell populations identifies stroma-tumor crosstalk in mouse lung cancer model

Emerging studies have begun to demonstrate that reprogrammed stromal cells play pivotal roles in tumor growth, metastasis, and resistance to therapy. However, the contribution of stromal cells to non-small-cell lung cancer (NSCLC) has remained underexplored. We used an orthotopic model of Kras-driven NSCLC to systematically dissect the contribution of specific hematopoietic stromal cells in lung cancer. RNA deep-sequencing analysis of individually sorted myeloid lineage and tumor epithelial cells revealed cell-type-specific differentially regulated genes, indicative of activated stroma. We developed a computational model for crosstalk signaling discovery based on ligand-receptor interactions and downstream signaling networks and identified known and novel tumor-stroma paracrine and tumor autocrine crosstalk-signaling pathways in NSCLC. We provide cellular and molecular insights into components of the lung cancer microenvironment that contribute to carcinogenesis. This study has the potential for development of therapeutic strategies that target tumor-stroma interactions and may complement conventional anti-cancer treatments.

A Progesterone-CXCR4 Axis Controls Mammary Progenitor Cell Fate in the Adult Gland

Progesterone drives mammary stem and progenitor cell dynamics through paracrine mechanisms that are currently not well understood. Here, we demonstrate that CXCR4, the receptor for stromal-derived factor 1 (SDF-1; CXC12), is a crucial instructor of hormone-induced mammary stem and progenitor cell function. Progesterone elicits specific changes in the transcriptome of basal and luminal mammary epithelial populations, where CXCL12 and CXCR4 represent a putative ligand-receptor pair. In situ, CXCL12 localizes to progesterone-receptor-positive luminal cells, whereas CXCR4 is induced in both basal and luminal compartments in a progesterone-dependent manner. Pharmacological inhibition of CXCR4 signaling abrogates progesterone-directed expansion of basal (CD24⁺CD49f^hi) and luminal (CD24⁺CD49f^lo) subsets. This is accompanied by a marked reduction in CD49b⁺SCA-1⁻ luminal progenitors, their functional capacity, and lobuloalveologenesis. These findings uncover CXCL12 and CXCR4 as novel paracrine effectors of hormone signaling in the adult mammary gland, and present a new avenue for potentially targeting progenitor cell growth and malignant transformation in breast cancer.

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Progesterone induces distinct molecular programs in mammary cell compartments

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CXCR4 induction occurs in lobuloalveoli and is progesterone dependent

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CXCR4 inhibition abrogates luminal progenitor expansion and mammopoiesis

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Targeting of the CXCL12-CXCR4 axis may limit mammary progenitor cell transformation

Inferring active and prognostic ligand-receptor pairs with interactions in survival regression models

Modeling signal transduction in cancer cells has implications for targeting new therapies and inferring the mechanisms that improve or threaten a patient’s treatment response. For transcriptome-wide studies, it has been proposed that simple correlation between a ligand and receptor pair implies a relationship to the disease process. Statistically, a differential correlation (DC) analysis across groups stratified by prognosis can link the pair to clinical outcomes. While the prognostic effect and the apparent change in correlation are both biological consequences of activation of the signaling mechanism, a correlation-driven analysis does not clearly capture this assumption and makes inefficient use of continuous survival phenotypes. To augment the correlation hypothesis, we propose that a regression framework assuming a patient-specific, latent level of signaling activation exists and generates both prognosis and correlation. Data from these systems can be inferred via interaction terms in survival regression models allowing signal transduction models beyond one pair at a time and adjusting for other factors. We illustrate the use of this model on ovarian cancer data from the Cancer Genome Atlas (TCGA) and discuss how the finding may be used to develop markers to guide targeted molecular therapies.

Protein-protein interaction databases

Years of meticulous curation of scientific literature and increasingly reliable computational predictions have resulted in creation of vast databases of protein interaction data. Over the years, these repositories have become a basic framework in which experiments are analyzed and new directions of research are explored. Here we present an overview of the most widely used protein-protein interaction databases and the methods they employ to gather, combine, and predict interactions. We also point out the trade-off between comprehensiveness and accuracy and the main pitfall scientists have to be aware before adopting protein interaction databases in any single-gene or genome-wide analysis.

Gene differential coexpression analysis based on biweight correlation and maximum clique

Differential coexpression analysis usually requires the definition of 'distance' or 'similarity' between measured datasets. Until now, the most common choice is Pearson correlation coefficient. However, Pearson correlation coefficient is sensitive to outliers. Biweight midcorrelation is considered to be a good alternative to Pearson correlation since it is more robust to outliers. In this paper, we introduce to use Biweight Midcorrelation to measure 'similarity' between gene expression profiles, and provide a new approach for gene differential coexpression analysis. Firstly, we calculate the biweight midcorrelation coefficients between all gene pairs. Then, we filter out non-informative correlation pairs using the 'half-thresholding' strategy and calculate the differential coexpression value of gene, The experimental results on simulated data show that the new approach performed better than three previously published differential coexpression analysis (DCEA) methods. Moreover, we use the maximum clique analysis to gene subset included genes identified by our approach and previously reported T2D-related genes, many additional discoveries can be found through our method.

Hyperactivation of mTORC1 and mTORC2 by multiple oncogenic events causes addiction to eIF4E-dependent mRNA translation in T-cell leukemia

High activation of the PI3K-AKT-mTOR pathway is characteristic for T-cell acute lymphoblastic leukemia (T-ALL). The activity of the master regulator of this pathway, PTEN, is often impaired in T-ALL. However, experimental evidence suggests that input from receptor tyrosine kinases (RTKs) is required for sustained mTOR activation, even in the absence of PTEN. We previously reported the expression of Neurotrophin receptor tyrosine kinases (TRKs) and their respective ligands in primary human leukemia samples. In the present study we aimed to dissect the downstream signaling cascades of TRK-induced T-ALL in a murine model and show that T-ALLs induced by deregulated receptor tyrosine kinase signaling acquire activating mutations in Notch1 and lose PTEN during clonal evolution. Some clones additionally lost one allele of the homeodomain transcription factor Cux1. All events independently led to a gradual hyperactivation of both mTORC1 and mTORC2 signaling. We dissected the role of the individual mTOR complexes by shRNA knockdown and found that the separate depletion of mTORC1 or mTORC2 reduced the growth of T-ALL blasts, but was not sufficient to induce apoptosis. In contrast, knockdown of the mTOR downstream effector eIF4E caused a striking cytotoxic effect, demonstrating a critical addiction to cap-dependent mRNA-translation. Although high mTORC2-AKT activation is commonly associated with drug-resistance, we demonstrate that T-ALL displaying a strong mTORC2-AKT activation were specifically susceptible to 4EGI-1, an inhibitor of the eIF4E-eIF4G interaction. To decipher the mechanism of 4EGI-1, we performed a genome-wide analysis of mRNAs that are translationally regulated by 4EGI-1 in T-ALL. 4EGI-1 effectively reduced the ribosomal occupancy of mRNAs that were strongly upregulated in T-ALL blasts compared with normal thymocytes including transcripts important for translation, mitochondria and cell cycle progression, such as cyclins and ribosomal proteins. These data suggest that disrupting the eIF4E-eIF4G interaction constitutes a promising therapy strategy in mTOR-deregulated T-cell leukemia.

Connecting prognostic ligand receptor signaling loops in advanced ovarian cancer

Understanding cancer cell signal transduction is a promising lead for uncovering therapeutic targets and building treatment-specific markers for epithelial ovarian cancer. To brodaly assay the many known transmembrane receptor systems, previous studies have employed gene expression data measured on high-throughput microarrays. Starting with the knowledge of validated ligand-receptor pairs (LRPs), these studies postulate that correlation of the two genes implies functional autocrine signaling. It is our goal to consider the additional weight of evidence that prognosis (progression-free survival) can bring to prioritize ovarian cancer specific signaling mechanism. We survey three large studies of epithelial ovarian cancers, with gene expression measurements and clinical information, by modeling survival times both categorically (long/short survival) and continuously. We use differential correlation and proportional hazards regression to identify sets of LRPs that are both prognostic and correlated. Of 475 candidate LRPs, 77 show reproducible evidence of correlation; 55 show differential correlation. Survival models identify 16 LRPs with reproduced, significant interactions. Only two pairs show both interactions and correlation (PDGFA[Formula: see text]PDGFRA and COL1A1[Formula: see text]CD44) suggesting that the majority of prognostically useful LRPs act without positive feedback. We further assess the connectivity of receptors using a Gaussian graphical model finding one large graph and a number of smaller disconnected networks. These LRPs can be organized into mutually exclusive signaling clusters suggesting different mechanisms apply to different patients. We conclude that a mix of autocrine and endocrine LRPs influence prognosis in ovarian cancer, there exists a heterogenous mix of signaling themes across patients, and we point to a number of novel applications of existing targeted therapies which may benefit ovarian cancer.

Platelet-derived growth factor alpha mediates the proliferation of peripheral T-cell lymphoma cells via an autocrine regulatory pathway

Peripheral T-cell lymphomas not otherwise specified (PTCL/NOS) are very aggressive tumors characterized by consistent aberrant expression of platelet-derived growth factor receptor alpha (PDGFRA). In this study, we aimed to identify the determinants of PDGFRA activity in PTCL/NOS and to elucidate the biological consequences of its activation. We observed overexpression of the PDGFRA gene by gene expression profiling in most of the tested PTCLs and confirmed the expression of PDGFRA and phospho-PDGFRA using immunohistochemistry. The integrity of the PDFGRA locus was demonstrated using several different approaches, including massive parallel sequencing and Sanger sequencing. PDGF-AA was found to be expressed and secreted by PTCL/NOS cells and to be necessary and sufficient for PDGFRA phosphorylation ex vivo by sustaining an autocrine stimulation. We documented consistently high PDGF-A expression in primary biopsies and patients' plasma and tracked PDGFRA signaling in primary tumors, achieving evidence of its activation. Indeed, we found that STAT1 and STAT5 are implicated in PDGFRA signaling transduction. Finally, we demonstrated that PDGFRA activation supported tumor cell proliferation and provided the first evidence of the anti-lymphoma activity of PDGRA inhibition in a PTCL/NOS patient. Altogether, our data demonstrated that PDGFRA activity fosters PTCL/NOS proliferation via an autocrine loop.