Single-cell Network Research Articles

Blockchain-enabled cooperative spectrum sensing in 5G and B5G cognitive radio via massive multiple-input multiple-output nonorthogonal multiple access

One of the greatest ways to guarantee that networks designed for fifth generation (5G) and beyond reach the required levels of spectrum efficiency (SE) is through nonorthogonal multiple access (NOMA). This work presents two new blockchain-based techniques that take advantage of massive multiple input multiple output in a single-cell network to improve performance. NOMA power domain is used in the 5G cooperative cognitive radio network (CCRN) to improve the SE of the downlink. This study investigates a novel cooperative NOMA-based CCRN for underlay spectrum sharing. A cooperative NOMA technique is proposed by considering the access modes of relays and secondary users (SUs) on the secondary network. The proposed system's performance is assessed with respect to random channel characteristics, frequency-selective Rayleigh fading and perfect successive interference cancellation (SIC). The first signal decoded by SU identifies the relay states with the best channel quality between users and the destination users to offset the bit error rate. The throughput dropped during the period because of the perfect SIC. The precise closed-form expressions for the system throughput of the secondary network are derived under the interference constraint of the primary network to evaluate the efficacy of the suggested cooperative strategy. The suggested approaches are assessed under various conditions using the MATLAB application by considering varying transmit power levels, power location coefficients and lengths. In every case, four users are assumed to be using a 90 MHz bandwidth and M-ary Quadrature Amplitude Modulation technology.

Inference of single-cell network using mutual information for scRNA-seq data analysis

BackgroundWith the advance in single-cell RNA sequencing (scRNA-seq) technology, deriving inherent biological system information from expression profiles at a single-cell resolution has become possible. It has been known that network modeling by estimating the associations between genes could better reveal dynamic changes in biological systems. However, accurately constructing a single-cell network (SCN) to capture the network architecture of each cell and further explore cell-to-cell heterogeneity remains challenging.ResultsWe introduce SINUM, a method for constructing the SIngle-cell Network Using Mutual information, which estimates mutual information between any two genes from scRNA-seq data to determine whether they are dependent or independent in a specific cell. Experiments on various scRNA-seq datasets with different cell numbers based on eight performance indexes (e.g., adjusted rand index and F-measure index) validated the accuracy and robustness of SINUM in cell type identification, superior to the state-of-the-art SCN inference method. Additionally, the SINUM SCNs exhibit high overlap with the human interactome and possess the scale-free property.ConclusionsSINUM presents a view of biological systems at the network level to detect cell-type marker genes/gene pairs and investigate time-dependent changes in gene associations during embryo development. Codes for SINUM are freely available at https://github.com/SysMednet/SINUM.

3D chromatin architecture, BRD4, and Mediator have distinct roles in regulating genome-wide transcriptional bursting and gene network.

Discontinuous transcription is evolutionarily conserved and a fundamental feature of gene regulation; yet, the exact mechanisms underlying transcriptional bursting are unresolved. Analyses of bursting transcriptome-wide have focused on the role of cis-regulatory elements, but other factors that regulate this process remain elusive. We applied mathematical modeling to single-cell RNA sequencing data to infer bursting dynamics transcriptome-wide under multiple conditions to identify possible molecular mechanisms. We found that Mediator complex subunit 26 (MED26) primarily regulates frequency, MYC regulates burst size, while cohesin and Bromodomain-containing protein 4 (BRD4) can modulate both. Despite comparable effects on RNA levels among these perturbations, acute depletion of MED26 had the most profound impact on the entire gene regulatory network, acting downstream of chromatin spatial architecture and without affecting TATA box-binding protein (TBP) recruitment. These results indicate that later steps in the initiation of transcriptional bursts are primary nodes for integrating gene networks in single cells.

Abstract PO-001: GPR65-low tumor clones reprogram macrophage via VEGF signaling and confer antigen-independent CAR-T resistance

Abstract Chimeric antigen receptor (CAR)-T cell therapies can be curative for CD19+ hematological malignancies, though are limited by frequent patient relapse and response failures. Here, we identify GPR65 as a tumor-specific determinant of responsiveness to CAR-T cell therapy. In patients and an immune competent mouse model of B cell acute lymphoblastic leukemia (B-ALL) response to CAR-T cell therapy, high GPR65 is associated with a complete tumor response. In mice, GPR65 knockout (GPR65 KO) tumors are resistant to CAR-T cell therapy in vivo, without antigen loss. Single-cell network analyses reveal that GPR65 deficiency remodels tumor interactions with host macrophages, partially through increasing tumor VEGFA. This leads to increased macrophage numbers and preferential M2 macrophage polarization. Either depletion of host macrophages or deletion of VEGFA from GPR65 KO tumors restores responsiveness to CAR-T cell treatment. Anti-VEGFA therapy in combination with CAR-T cell administration also prolongs the survival of GPR65 KO tumor-bearing mice. Our results indicate that tumor GPR65 expression is important for B-ALL responses to CAR-T cell therapy through its impact on the tumor microenvironment and supports new approaches to identify and overcome CAR-T cell resistance. Citation Format: Jayadev Mavuluri. GPR65-low tumor clones reprogram macrophage via VEGF signaling and confer antigen-independent CAR-T resistance [abstract]. In: Proceedings of the Fourth AACR International Meeting on Advances in Malignant Lymphoma: Maximizing the Basic-Translational Interface for Clinical Application; 2024 Jun 19-22; Philadelphia, PA. Philadelphia (PA): AACR; Blood Cancer Discov 2024;5(3_Suppl):Abstract nr PO-001.

Single-cell network analysis reveals gene expression programs for Arabidopsis root development and metabolism

Single-cell RNA-sequencing datasets of Arabidopsis roots have been generated, but related comprehensive gene co-expression network analyses are lacking. We conducted a single-cell gene co-expression network analysis with publicly available scRNA-seq datasets of Arabidopsis roots using a SingleCellGGM algorithm. The analysis identified 149 gene co-expression modules, which we considered to be gene expression programs (GEPs). By examining their spatiotemporal expression, we identified GEPs specifically expressed in major root cell types along their developmental trajectories. These GEPs define gene programs regulating root cell development at different stages and are enriched with relevant developmental regulators. As examples, a GEP specific for the quiescent center (QC) contains 20 genes regulating QC and stem cell niche homeostasis, and four GEPs are expressed in sieve elements (SEs) from early to late developmental stages, with the early-stage GEP containing 17 known SE developmental regulators. We also identified GEPs for metabolic pathways with cell-type-specific expression, suggesting the existence of cell-type-specific metabolism in roots. Using the GEPs, we discovered and verified a columella-specific gene, NRL27, as a regulator of the auxin-related root gravitropism response. Our analysis thus systematically reveals GEPs that regulate Arabidopsis root development and metabolism and provides ample resources for root biology studies.

Network Activity Evaluation Reveals Significant Gene Regulatory Architectures during SARS-CoV-2 Viral Infection from Dynamic scRNA-seq Data.

The key to understand COVID-19 caused by SARS-CoV-2, which has caused massive deaths worldwide, is to reveal the gene activities at molecular level. Single-cell RNA-sequencing (scRNA-seq) technology allows us to capture gene expression at high resolution, thereby delineating cell-specific gene regulatory network (GRN). Network activity refers to the degree of consistency between GRN architectures and gene expression profiles in a specific condition or cellular microenvironment. Currently, numerous experimentally determined molecular interactions, including regulatory relationships closely related to SARS-CoV-2 infection, are documented in knowledge-bases. However, GRN activity is closely related to the cell dynamic environment and the heterogeneity of cell clusters. Therefore, to evaluate the consistency of GRN with gene expression profiles, we propose a single-cell Network Activity Evaluation framework, called scNAE. First, scNAE performs ODE modeling of time-course gene expression data. Then, the loss function with regularization penalty terms is constructed for formulating GRN inference rules from transcriptomic data. Furthermore, we have devised a rapid-convergence alternating direction method of multipliers to solve the regularized and constrained programs. Finally, an empirical P-value is derived based on a permutation statistical testing procedure to quantify the likelihood significance of the network matching with the data. The efficiency and advantage of scNAE have also been demonstrated by extensive numerical experiments, which can clearly depict the dynamic responses underlying GRN architectures triggered by the infection of SARS-CoV-2 in cells. The code and data of scNAE are available at https://github.com/zpliulab/scNAE.

RIS Aided NR-U and Wi-Fi Coexistence in Single Cell and Multiple Cell Networks on Unlicensed Bands

To address the scarcity of spectrum resource incurred by explosively increasing traffic load in existing fifth generation (5G) networks, we consider exploiting Reconfigurable intelligent surface (RIS) to harmonize the coexistence of cellular networks and WiFi system on the unlicensed bands. In particular, RIS is capable of not only improving the performance of new radio on unlicensed bands (NR-U) by increasing the signal to interference and noise ratio (SINR) of cellular users, but also decreasing the interference to the existing WiFi system caused by NR-U. Thus, we investigate the performance of RIS aided coexisting networks both in single cell and multiple cell scenarios. For the single cell scenario, we aim to improve the worst transmission rate of UEs in NR-U network. The problem is solved by a proposed alternating optimization-based solution. For the multiple cell scenario, cell cooperation is exploited jointly with multiple RISs to maximize the sum-rate of all UEs. Although the problem is more challenging than that of single cell case, we propose a multi-agent reinforcement learning (MARL) algorithm to solve it. Numeral results verify the effectiveness of our proposed schemes and show that deploying RISs is a promising solution for the enhancement of NR-U networks.

P-CSN: single-cell RNA sequencing data analysis by partial cell-specific network.

Although many single-cell computational methods proposed use gene expression as input, recent studies show that replacing 'unstable' gene expression with 'stable' gene-gene associations can greatly improve the performance of downstream analysis. To obtain accurate gene-gene associations, conditional cell-specific network method (c-CSN) filters out the indirect associations of cell-specific network method (CSN) based on the conditional independence of statistics. However, when there are strong connections in networks, the c-CSN suffers from false negative problem in network construction. To overcome this problem, a new partial cell-specific network method (p-CSN) based on the partial independence of statistics is proposed in this paper, which eliminates the singularity of the c-CSN by implicitly including direct associations among estimated variables. Based on the p-CSN, single-cell network entropy (scNEntropy) is further proposed to quantify cell state. The superiorities of our method are verified on several datasets. (i) Compared with traditional gene regulatory network construction methods, the p-CSN constructs partial cell-specific networks, namely, one cell to one network. (ii) When there are strong connections in networks, the p-CSN reduces the false negative probability of the c-CSN. (iii) The input of more accurate gene-gene associations further optimizes the performance of downstream analyses. (iv) The scNEntropy effectively quantifies cell state and reconstructs cell pseudo-time.

Subcellular spatially resolved gene neighborhood networks in single cells

Image-based spatial omics methods such as fluorescence in situ hybridization (FISH) generate molecular profiles of single cells at single-molecule resolution. Current spatial transcriptomics methods focus on the distribution of single genes. However, the spatial proximity of RNA transcripts can play an important role in cellular function. We demonstrate a spatially resolved gene neighborhood network (spaGNN) pipeline for the analysis of subcellular gene proximity relationships. In spaGNN, machine-learning-based clustering of subcellular spatial transcriptomics data yields subcellular density classes of multiplexed transcript features. The nearest-neighbor analysis produces heterogeneous gene proximity maps in distinct subcellular regions. We illustrate the cell-type-distinguishing capability of spaGNN using multiplexed error-robust FISH data of fibroblast and U2-OS cells and sequential FISH data of mesenchymal stem cells (MSCs), revealing tissue-source-specific MSC transcriptomics and spatial distribution characteristics. Overall, the spaGNN approach expands the spatial features that can be used for cell-type classification tasks.

Spatial subcellular organelle networks in single cells

Organelles play important roles in human health and disease, such as maintaining homeostasis, regulating growth and aging, and generating energy. Organelle diversity in cells not only exists between cell types but also between individual cells. Therefore, studying the distribution of organelles at the single-cell level is important to understand cellular function. Mesenchymal stem cells are multipotent cells that have been explored as a therapeutic method for treating a variety of diseases. Studying how organelles are structured in these cells can answer questions about their characteristics and potential. Herein, rapid multiplexed immunofluorescence (RapMIF) was performed to understand the spatial organization of 10 organelle proteins and the interactions between them in the bone marrow (BM) and umbilical cord (UC) mesenchymal stem cells (MSCs). Spatial correlations, colocalization, clustering, statistical tests, texture, and morphological analyses were conducted at the single cell level, shedding light onto the interrelations between the organelles and comparisons of the two MSC subtypes. Such analytics toolsets indicated that UC MSCs exhibited higher organelle expression and spatially spread distribution of mitochondria accompanied by several other organelles compared to BM MSCs. This data-driven single-cell approach provided by rapid subcellular proteomic imaging enables personalized stem cell therapeutics.

Oscillatory ERK Signaling and Morphology Determine Heterogeneity of Breast Cancer Cell Chemotaxis via MEK-ERK and p38-MAPK Signaling Pathways.

Chemotaxis, regulated by oscillatory signals, drives critical processes in cancer metastasis. Crucial chemoattractant molecules in breast cancer, CXCL12 and EGF, drive the activation of ERK and Akt. Regulated by feedback and crosstalk mechanisms, oscillatory signals in ERK and Akt control resultant changes in cell morphology and chemotaxis. While commonly studied at the population scale, metastasis arises from small numbers of cells that successfully disseminate, underscoring the need to analyze processes that cancer cells use to connect oscillatory signaling to chemotaxis at single-cell resolution. Furthermore, little is known about how to successfully target fast-migrating cells to block metastasis. We investigated to what extent oscillatory networks in single cells associate with heterogeneous chemotactic responses and how targeted inhibitors block signaling processes in chemotaxis. We integrated live, single-cell imaging with time-dependent data processing to discover oscillatory signal processes defining heterogeneous chemotactic responses. We identified that short ERK and Akt waves, regulated by MEK-ERK and p38-MAPK signaling pathways, determine the heterogeneous random migration of cancer cells. By comparison, long ERK waves and the morphological changes regulated by MEK-ERK signaling, determine heterogeneous directed motion. This study indicates that treatments against chemotaxis in consider must interrupt oscillatory signaling.

Subcellular spatially resolved gene neighborhood networks in single cells

Subcellular spatially resolved gene neighborhood networks in single cells

Single-cell gene regulatory network prediction by explainable AI.

The molecular heterogeneity of cancer cells contributes to the often partial response to targeted therapies and relapse of disease due to the escape of resistant cell populations. While single-cell sequencing has started to improve our understanding of this heterogeneity, it offers a mostly descriptive view on cellular types and states. To obtain more functional insights, we propose scGeneRAI, an explainable deep learning approach that uses layer-wise relevance propagation (LRP) to infer gene regulatory networks from static single-cell RNA sequencing data for individual cells. We benchmark our method with synthetic data and apply it to single-cell RNA sequencing data of a cohort of human lung cancers. From the predicted single-cell networks our approach reveals characteristic network patterns for tumor cells and normal epithelial cells and identifies subnetworks that are observed only in (subgroups of) tumor cells of certain patients. While current state-of-the-art methods are limited by their ability to only predict average networks for cell populations, our approach facilitates the reconstruction of networks down to the level of single cells which can be utilized to characterize the heterogeneity of gene regulation within and across tumors.

Imputation of single-cell transcriptome data enables the reconstruction of networks predictive of breast cancer metastasis

Single-cell transcriptome data provide a unique opportunity to explore the gene networks of a particular cell type. However, insufficient capture rate and high dimensionality of single-cell RNA sequencing (scRNA-seq) data challenge cell-type-specific gene network (CGN) reconstruction. Here, we demonstrated that the imputation of scRNA-seq data enables reconstruction of CGNs by effective retrieval of gene functional associations. We reconstructed CGNs for seven primary and nine metastatic breast cancer cell lines using scRNA-seq data with imputation. Key genes for primary or metastatic cell lines were prioritized based on network centrality measures and CGN hub genes that were presumed to be the major determinant of cell type characteristics. To identify novel genes in breast cancer metastasis, we used the average rank difference of centrality between the primary and metastatic cell lines. Genes predicted using CGN centrality analysis were more enriched for known breast cancer metastatic genes than those predicted using differential expression. The molecular chaperone CCT2 was identified as a novel gene for breast metastasis during knockdown assays of several candidate genes. Overall, our study demonstrated an effective CGN reconstruction technique with imputation of scRNA-seq data and the feasibility of identifying key genes for particular cell subsets using single-cell network analysis.

Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets.

There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead to the discovery of biomarkers and therapeutic targets. In this review, multi-omics data harmonization methods were introduced, and common approaches to molecular network inference were summarized. Our Prediction Logic Boolean Implication Networks (PLBINs) have advantages over other methods in constructing genome-scale multi-omics networks in bulk tumors and single cells in terms of computational efficiency, scalability, and accuracy. Based on the constructed multi-modal regulatory networks, graph theory network centrality metrics can be used in the prioritization of candidates for discovering biomarkers and therapeutic targets. Our approach to integrating multi-omics profiles in a patient cohort with large-scale patient EMRs such as the SEER-Medicare cancer registry combined with extensive external validation can identify potential biomarkers applicable in large patient populations. These methodologies form a conceptually innovative framework to analyze various available information from research laboratories and healthcare systems, accelerating the discovery of biomarkers and therapeutic targets to ultimately improve cancer patient survival outcomes.

ScHumanNet: a single-cell network analysis platform for the study of cell-type specificity of disease genes.

A major challenge in single-cell biology is identifying cell-type-specific gene functions, which may substantially improve precision medicine. Differential expression analysis of genes is a popular, yet insufficient approach, and complementary methods that associate function with cell type are required. Here, we describe scHumanNet (https://github.com/netbiolab/scHumanNet), a single-cell network analysis platform for resolving cellular heterogeneity across gene functions in humans. Based on cell-type-specific gene networks (CGNs) constructed under the guidance of the HumanNet reference interactome, scHumanNet displayed higher functional relevance to the cellular context than CGNs built by other methods on single-cell transcriptome data. Cellular deconvolution of gene signatures based on network compactness across cell types revealed breast cancer prognostic markers associated with T cells. scHumanNet could also prioritize genes associated with particular cell types using CGN centrality and identified the differential hubness of CGNs between disease and healthy conditions. We demonstrated the usefulness of scHumanNet by uncovering T-cell-specific functional effects of GITR, a prognostic gene for breast cancer, and functional defects in autism spectrum disorder genes specific for inhibitory neurons. These results suggest that scHumanNet will advance our understanding of cell-type specificity across human disease genes.

MINI-EX: Integrative inference of single-cell gene regulatory networks in plants

Multicellular organisms, such as plants, are characterized by highly specialized and tightly regulated cell populations, establishing specific morphological structures and executing distinct functions. Gene regulatory networks (GRNs) describe condition-specific interactions of transcription factors (TFs) regulating the expression of target genes, underpinning these specific functions. As efficient and validated methods to identify cell-type-specific GRNs from single-cell data in plants are lacking, limiting our understanding of the organization of specific cell types in both model species and crops, we developed MINI-EX (Motif-Informed Network Inference based on single-cell EXpression data), an integrative approach to infer cell-type-specific networks in plants. MINI-EX uses single-cell transcriptomic data to define expression-based networks and integrates TF motif information to filter the inferred regulons, resulting in networks with increased accuracy. Next, regulons are assigned to different cell types, leveraging cell-specific expression, and candidate regulators are prioritized using network centrality measures, functional annotations, and expression specificity. This embedded prioritization strategy offers a unique and efficient means to unravel signaling cascades in specific cell types controlling a biological process of interest. We demonstrate the stability of MINI-EX toward input data sets with low number of cells and its robustness toward missing data, and show that it infers state-of-the-art networks with a better performance compared with other related single-cell network tools. MINI-EX successfully identifies key regulators controlling root development in Arabidopsis and rice, leaf development in Arabidopsis, and ear development in maize, enhancing our understanding of cell-type-specific regulation and unraveling the roles of different regulators controlling the development of specific cell types in plants.

Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease.

Dysregulation of gene expression in Alzheimer's disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, AD-induced regulatory changes across brain cell types remains uncharted. To address this, we integrated single-cell multi-omics datasets to predict the gene regulatory networks of four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes, in control and AD brains. Importantly, we analyzed and compared the structural and topological features of networks across cell types and examined changes in AD. Our analysis shows that hub TFs are largely common across cell types and AD-related changes are relatively more prominent in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are also highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes. The further disease-module-drug association analysis suggests cell-type candidate drugs and their potential target genes. Finally, our network-based machine learning analysis systematically prioritized cell type risk genes likely involved in AD. Our strategy is validated using an independent dataset which showed that top ranked genes can predict clinical phenotypes (e.g., cognitive impairment) of AD with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals cell-type gene dysregulation in AD.

Cellular Network Power Allocation Algorithm Based on Deep Reinforcement Learning and Artificial Intelligence.

In the shortest path planning problem, the old algorithm usually has many defects, such as the robot's cognition being contrary to reality, the lack of practical operation feasibility, or the limitation of problem processing. Nowadays, with deep learning, artificial intelligence algorithms tend to be mature; it has become a mainstream trend to adopt end-to-end learning system instead of traditional old algorithms. In recent years, with the rise of the Internet of things emerging technology industry and the explosive surge of network data traffic, the drawback is the increasingly severe shortage of wireless spectrum resources. In order to effectively reduce the cochannel interference of D2D communication technology in the system and enhance the useable range of the cellular network, it is necessary to distribute the useful and efficient cellular resources of the system. In this article, we will study the D2D users and the selection scheme of D2D users' transmission power control mode and allocate the spectrum resources in the uplink of the cellular users in the communication network. In order to reduce the cochannel interference in a cellular network and improve the spectrum utilization of the system, the research direction of this article is to solve the problem of user communication resource allocation in a single-cell hybrid cellular network.

Single Cell Meta-Analysis of Endothelial to Mesenchymal Transition (EndMT) in Glucose Metabolism of the Digestive Diseases.

Background: Endothelial-to-mesenchymal transition (EndMT) is poorly understood in digestive diseases, and the function of metabolism in EndMT is uncertain. Objective: The goal of this study is to elucidate the role of EndMT in digestive diseases and to describe its metabolic state. Method: The GEO database was used to extract single-cell data in order to discover EndMT subpopulations in digestive organs such as premalignant lesions and cancer of the stomach, intestine, and pancreas. Results: By single-cell RNA sequencing in digestive diseases, we generated a single-cell atlas from tissues of patients spanning a cascade of premalignant lesions and cancer. We next established a single-cell network elucidating the cellular and molecular characteristics of endothelial cells (ECs) across many lesions and identified key genes linked with EndMT in premalignant lesions and cancer lesions. The EndMT activation of a wide variety of metabolic signaling pathways was discovered in ECs, and further study of premalignant lesions and cancer tissue indicated that glucose metabolism increased in premalignant lesions and reached a maximum in cancer tissue. Finally, it was shown that INSR and LDHA might be used as prognostic markers for developing premalignant lesions to cancer involving glucose metabolism in digestive diseases. Conclusion: For the first time, we discovered EndMT’s role in digestive diseases and described its metabolism, underscoring its crucial role in glucose metabolism in the disease. We found several targets via gene screening that are beneficial for predicting premalignant lesions that progress to cancer.