Deciphering cell-cell interactions and communication from gene expression

Generation of Patterned Cocultures in 2D and 3D: State of the Art

Cells inside the body are embedded into a highly structured microenvironment that consists of cells that lie in direct or close contact with other cell types that regulate the overall tissue function. Therefore, coculture models are versatile tools that can generate tissue engineering constructs with improved mimicking of in vivo conditions. While there are many reviews that have focused on pattering a single cell type, very few reviews have been focused on techniques for coculturing multiple cell types on a single substrate with precise control. In this regard, this Review covers various technologies that have been utilized for the development of these patterned coculture models while mentioning the limitations associated with each of them. Further, the application of these models to various tissue engineering applications has been discussed.

An Aptamer-Functionalized DNA Circuit to Establish an Artificial Interaction between T Cells and Cancer Cells

Nongenetic strategies that enable control over the cell-cell interaction network would be highly desired, particularly in T cell-based cancer immunotherapy. In this work, we developed an aptamer-functionalized DNA circuit to modulate the interaction between T cells and cancer cells. This DNA circuit was composed of recognition-then-triggering and aggregation-then-activation modules. Upon recognizing target cancer cells, the triggering strand was released to induce aggregation of immune receptors on the T cell surface, leading to an enhancement of T cell activity for effective cancer eradication. Our results demonstrated the feasibility of this DNA circuit for promoting target cancer cell-directed stimulation of T cells, which, consequently, enhanced their killing effect on cancer cells. This DNA circuit, as a modular strategy to modulate intercellular interactions, could lead to a new paradigm for the development of nongenetic T cell-based immunotherapy.

Deactivation of ligand-receptor interactions enhancing lymphocyte infiltration drives melanoma resistance to Immune Checkpoint Blockade

Immune checkpoint blockade (ICB) is a promising cancer therapy; however, resistance often develops. To learn more about ICB resistance mechanisms, we developed IRIS (Immunotherapy Resistance cell-cell Interaction Scanner), a machine learning model aimed at identifying candidate ligand-receptor interactions (LRI) that are likely to mediate ICB resistance in the tumor microenvironment (TME). We developed and applied IRIS to identify resistance-mediating cell-type-specific ligand-receptor interactions by analyzing deconvolved transcriptomics data of the five largest melanoma ICB therapy cohorts. This analysis identifies a set of specific ligand-receptor pairs that are deactivated as tumors develop resistance, which we refer to as resistance deactivated interactions (RDI). Quite strikingly, the activity of these RDIs in pre-treatment samples offers a markedly stronger predictive signal for ICB therapy response compared to those that are activated as tumors develop resistance. Their predictive accuracy surpasses the state-of-the-art published transcriptomics biomarker signatures across an array of melanoma ICB datasets. Many of these RDIs are involved in chemokine signaling. Indeed, we further validate on an independent large melanoma patient cohort that their activity is associated with CD8+ T cell infiltration and enriched in hot/brisk tumors. Taken together, this study presents a new strongly predictive ICB response biomarker signature, showing that following ICB treatment resistant tumors turn inhibit lymphocyte infiltration by deactivating specific key ligand-receptor interactions.

Decoding functional cell-cell communication events by multi-view graph learning on spatial transcriptomics

Cell-cell communication events (CEs) are mediated by multiple ligand-receptor (LR) pairs. Usually only a particular subset of CEs directly works for a specific downstream response in a particular microenvironment. We name them as functional communication events (FCEs) of the target responses. Decoding FCE-target gene relations is: important for understanding the mechanisms of many biological processes, but has been intractable due to the mixing of multiple factors and the lack of direct observations. We developed a method HoloNet for decoding FCEs using spatial transcriptomic data by integrating LR pairs, cell-type spatial distribution and downstream gene expression into a deep learning model. We modeled CEs as a multi-view network, developed an attention-based graph learning method to train the model for generating target gene expression with the CE networks, and decoded the FCEs for specific downstream genes by interpreting trained models. We applied HoloNet on three Visium datasets of breast cancer and liver cancer. The results detangled the multiple factors of FCEs by revealing how LR signals and cell types affect specific biological processes, and specified FCE-induced effects in each single cell. We conducted simulation experiments and showed that HoloNet is more reliable on LR prioritization in comparison with existing methods. HoloNet is a powerful tool to illustrate cell-cell communication landscapes and reveal vital FCEs that shape cellular phenotypes. HoloNet is available as a Python package at https://github.com/lhc17/HoloNet.

Defining and identifying cell sub-crosstalk pairs for characterizing cell-cell communication patterns

Current cell-cell communication analysis focuses on quantifying intercellular interactions at cell type level. In the tissue microenvironment, one type of cells could be divided into multiple cell subgroups that function differently and communicate with other cell types or subgroups via different ligand-receptor-mediated signaling pathways. Given two cell types, we define a cell sub-crosstalk pair (CSCP) as a combination of two cell subgroups with strong and similar intercellular crosstalk signals and identify CSCPs based on coupled non-negative matrix factorization. Using single-cell spatial transcriptomics data of mouse olfactory bulb and visual cortex, we find that cells of different types within CSCPs are significantly spatially closer with each other than those in the whole single-cell spatial map. To demonstrate the utility of CSCPs, we apply 13 cell-cell communication analysis methods to sampled single-cell transcriptomics datasets at CSCP level and reveal ligand-receptor interactions masked at cell type level. Furthermore, by analyzing single-cell transcriptomics data from 29 breast cancer patients with different immunotherapy responses, we find that CSCPs are useful predictive features to discriminate patients responding to anti-PD-1 therapy from non-responders. Taken together, partitioning a cell type pair into CSCPs enables fine-grained characterization of cell-cell communication in tissue and tumor microenvironments.

Identifying spatial domains of spatially resolved transcriptomics via multi-view graph convolutional networks

MOTIVATION

Recent advances in spatially resolved transcriptomics (ST) technologies enable the measurement of gene expression profiles while preserving cellular spatial context. Linking gene expression of cells with their spatial distribution is essential for better understanding of tissue microenvironment and biological progress. However, effectively combining gene expression data with spatial information to identify spatial domains remains challenging.

RESULTS

To deal with the above issue, in this paper, we propose a novel unsupervised learning framework named STMGCN for identifying spatial domains using multi-view graph convolution networks (MGCNs). Specifically, to fully exploit spatial information, we first construct multiple neighbor graphs (views) with different similarity measures based on the spatial coordinates. Then, STMGCN learns multiple view-specific embeddings by combining gene expressions with each neighbor graph through graph convolution networks. Finally, to capture the importance of different graphs, we further introduce an attention mechanism to adaptively fuse view-specific embeddings and thus derive the final spot embedding. STMGCN allows for the effective utilization of spatial context to enhance the expressive power of the latent embeddings with multiple graph convolutions. We apply STMGCN on two simulation datasets and five real spatial transcriptomics datasets with different resolutions across distinct platforms. The experimental results demonstrate that STMGCN obtains competitive results in spatial domain identification compared with five state-of-the-art methods, including spatial and non-spatial alternatives. Besides, STMGCN can detect spatially variable genes with enriched expression patterns in the identified domains. Overall, STMGCN is a powerful and efficient computational framework for identifying spatial domains in spatial transcriptomics data.

CCPlotR: an R package for the visualization of cell-cell interactions

Summary

We present CCPlotR-an R package that generates visualizations of cell-cell interactions. CCPlotR is designed to work with the output of tools that predict cell-cell interactions from single-cell gene expression data and requires only a table of predicted interactions as input. The package can generate a comprehensive set of publication-ready figures such as heatmaps, dotplots, circos plots and network diagrams, providing a useful resource for researchers working on cell-cell interactions.

Availability and implementation

CCPlotR is available to download and install from GitHub (https://github.com/Sarah145/CCPlotR) and comes with a toy dataset to demonstrate the different functions. Support for users will be provided via the GitHub issues tracker (https://github.com/Sarah145/CCPlotR/issues).

scMultiSim: simulation of single cell multi-omics and spatial data guided by gene regulatory networks and cell-cell interactions

Simulated single-cell data is essential for designing and evaluating computational methods in the absence of experimental ground truth. Existing simulators typically focus on modeling one or two specific biological factors or mechanisms that affect the output data, which limits their capacity to simulate the complexity and multi-modality in real data. Here, we present scMultiSim, an in silico simulator that generates multi-modal single-cell data, including gene expression, chromatin accessibility, RNA velocity, and spatial cell locations while accounting for the relationships between modalities. scMultiSim jointly models various biological factors that affect the output data, including cell identity, within-cell gene regulatory networks (GRNs), cell-cell interactions (CCIs), and chromatin accessibility, hile also incorporating technical noises. Moreover, it allows users to adjust each factor's effect easily. We validated scMultiSim's simulated biological effects and demonstrated its applications by benchmarking a wide range of computational tasks, including multi-modal and multi-batch data integration, RNA velocity estimation, GRN inference and CCI inference using spatially resolved gene expression data, many of them were not benchmarked before due to the lack of proper tools. Compared to existing simulators, scMultiSim can benchmark a much broader range of existing computational problems and even new potential tasks.

Ferroptosis-related factors in the substantia nigra are associated with Parkinson's disease

Ferroptosis is an iron-dependent, lipid peroxidation-driven cell death pathway, while Parkinson's disease (PD) patients exhibit iron deposition and lipid peroxidation in the brain. Thus, the features of ferroptosis highly overlap with the pathophysiological features of PD. Despite this superficial connection, the possible role(s) of ferroptosis-related (Fr) proteins in dopaminergic neurons and/or glial cells in the substantia nigra (SN) in PD have not been examined in depth. To explore the correlations between the different SN cell types and ferroptosis at the single-cell level in PD patients, and to explore genes that may affect the sensitivity of dopaminergic neurons to ferroptosis, we performed in silico analysis of a single cell RNA sequence (RNA-seq) set (GSE178265) from the Gene Expression Omnibus (GEO) database. We identified differentially expressed genes (DEGs) in the different cell types in the human SN, and proceeded to perform enrichment analysis, constructing a protein-protein interaction network from the DEGs of dopaminergic neurons with the Metascape database. We examined the intersection of Fr genes present in the FerrDb database with DEGs from the GSE178265 set to identify Fr-DEGs in the different brain cells. Further, we identified Fr-DEGs encoding secreted proteins to implicate cell-cell interactions in the potential stimulation of ferroptosis in PD. The Fr-DEGs we identified were verified using the bulk RNA-seq sets (GSE49036 and GSE20164). The number of dopaminergic neurons decreased in the SN of PD patients. Interestingly, non-dopaminergic neurons possessed the fewest DEGs. Enrichment analysis of dopaminergic neurons' DEGs revealed changes in transmission across chemical synapses and ATP metabolic process in PD. The secreted Fr-DEGs identified were ceruloplasmin (CP), high mobility group box 1 (HMGB1) and transferrin (TF). The bulk RNA-seq set from the GEO database demonstrates that CP expression is increased in the PD brain. In conclusion, our results identify CP as a potential therapeutic target to protect dopaminergic neurons by reducing neurons' sensitivity to ferroptosis.

Effect of lipid oxidation on the channel properties of Cx26 hemichannels: A molecular dynamics study

Intercellular communication plays a crucial role in cancer, as well as other diseases, such as inflammation, tissue degeneration, and neurological disorders. One of the proteins responsible for this, are connexins (Cxs), which come together to form a hemichannel. When two hemichannels of opposite cells interact with each other, they form a gap junction (GJ) channel, connecting the intracellular space of these cells. They allow the passage of ions, reactive oxygen and nitrogen species (RONS), and signaling molecules from the interior of one cell to another cell, thus playing an essential role in cell growth, differentiation, and homeostasis. The importance of GJs for disease induction and therapy development is becoming more appreciated, especially in the context of oncology. Studies have shown that one of the mechanisms to control the formation and disruption of GJs is mediated by lipid oxidation pathways, but the underlying mechanisms are not well understood. In this study, we performed atomistic molecular dynamics simulations to evaluate how lipid oxidation influences the channel properties of Cx26 hemichannels, such as channel gating and permeability. Our results demonstrate that the Cx26 hemichannel is more compact in the presence of oxidized lipids, decreasing its pore diameter at the extracellular side and increasing it at the amino terminus domains, respectively. The permeability of the Cx26 hemichannel for water and RONS molecules is higher in the presence of oxidized lipids. The latter may facilitate the intracellular accumulation of RONS, possibly increasing oxidative stress in cells. A better understanding of this process will help to enhance the efficacy of oxidative stress-based cancer treatments.

Dynamic atlas of immune cells reveals multiple functional features of macrophages associated with progression of pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease with a high mortality rate and unclarified aetiology. Immune response is elaborately regulated during the progression of IPF, but immune cells subsets are complicated which has not been detailed described during IPF progression. Therefore, in the current study, we sought to investigate the role of immune regulation by elaborately characterize the heterogeneous of immune cells during the progression of IPF. To this end, we performed single-cell profiling of lung immune cells isolated from four stages of bleomycin-induced pulmonary fibrosis-a classical mouse model that mimics human IPF. The results revealed distinct components of immune cells in different phases of pulmonary fibrosis and close communication between macrophages and other immune cells along with pulmonary fibrosis progression. Enriched signals of SPP1, CCL5 and CXCL2 were found between macrophages and other immune cells. The more detailed definition of the subpopulations of macrophages defined alveolar macrophages (AMs) and monocyte-derived macrophages (mo-Macs)-the two major types of primary lung macrophages-exhibited the highest heterogeneity and dynamic changes in expression of profibrotic genes during disease progression. Our analysis suggested that Gpnmb and Trem2 were both upregulated in macrophages and may play important roles in pulmonary fibrosis progression. Additionally, the metabolic status of AMs and mo-Macs varied with disease progression. In line with the published data on human IPF, macrophages in the mouse model shared some features regarding gene expression and metabolic status with that of macrophages in IPF patients. Our study provides new insights into the pathological features of profibrotic macrophages in the lung that will facilitate the identification of new targets for disease intervention and treatment of IPF.

Unraveling the intercellular communication disruption and key pathways in Alzheimer's disease: An integrative study of single-nucleus transcriptomes and genetic association

Background

Recently, single-nucleus RNA-seq (snRNA-seq) analyses have revealed important cellular and functional features of Alzheimer's disease (AD), a prevalent neurodegenerative disease. However, our knowledge regarding intercellular communication mediated by dysregulated ligand-receptor (LR) interactions remains very limited in AD brains.

Methods

We systematically assessed the intercellular communication networks by using a discovery snRNA-seq dataset comprising 69,499 nuclei from 48 human postmortem prefrontal cortex (PFC) samples. We replicated the findings using an independent snRNA-seq dataset of 56,440 nuclei from 18 PFC samples. By integrating genetic signals from AD genome-wide association studies (GWAS) summary statistics and whole genome sequencing (WGS) data, we prioritized AD-associated Gene Ontology (GO) terms containing dysregulated LR interactions. We further explored drug repurposing for the prioritized LR pairs using the Therapeutic Targets Database.

Results

We identified 316 dysregulated LR interactions across six major cell types in AD PFC, of which 210 pairs were replicated. Among the replicated LR signals, we found globally downregulated communications in astrocytes-to-neurons signaling axis, characterized, for instance, by the downregulation of APOE-related and Calmodulin (CALM)-related LR interactions and their potential regulatory connections to target genes. Pathway analyses revealed 60 GO terms significantly linked to AD, highlighting Biological Processes such as 'amyloid precursor protein processing' and 'ion transmembrane transport', among others. We prioritized several drug repurposing candidates, such as cromoglicate, targeting the identified dysregulated LR pairs.

Conclusions

Our integrative analysis identified key dysregulated LR interactions in a cell type-specific manner and the associated GO terms in AD, offering novel insights into potential therapeutic targets involved in disrupted cell-cell communication in AD.

TimeTalk uses single-cell RNA-seq datasets to decipher cell-cell communication during early embryo development

Early embryonic development is a dynamic process that relies on proper cell-cell communication to form a correctly patterned embryo. Early embryo development-related ligand-receptor pairs (eLRs) have been shown to guide cell fate decisions and morphogenesis. However, the scope of eLRs and their influence on early embryo development remain elusive. Here, we developed a computational framework named TimeTalk from integrated public time-course mouse scRNA-seq datasets to decipher the secret of eLRs. Extensive validations and analyses were performed to ensure the involvement of identified eLRs in early embryo development. Process analysis identified that eLRs could be divided into six temporal windows corresponding to sequential events in the early embryo development process. With the interpolation strategy, TimeTalk is powerful in revealing paracrine settings and studying cell-cell communication during early embryo development. Furthermore, by using TimeTalk in the blastocyst and blastoid models, we found that the blastoid models share the core communication pathways with the epiblast and primitive endoderm lineages in the blastocysts. This result suggests that TimeTalk has transferability to other bio-dynamic processes. We also curated eLRs recognized by TimeTalk, which may provide valuable clues for understanding early embryo development and relevant disorders.

[Interactions between vascular endothelium and immune cells: A key control point of radiation-induced digestive lesions]

Radiation-induced toxicity of the digestive tract is a major clinical concern as many cancer survivors have received radiotherapy for tumours of the abdominopelvic area. The coordination and orchestration of a tissue's response to stress depend not only on the phenotype of the cells that make up the tissue but also on cell-cell interactions. The digestive system, i.e., the intestine/colon/rectum, is made up of a range of different cell populations: epithelial cells, stromal cells, i.e. endothelial cells and mesenchymal lineages, immune cells and nerve cells. Moreover, each of these populations is heterogeneous and presents very significant plasticity and differentiation states. The pathogenesis of radiation-induced digestive lesions is an integrated process that involves multiple cellular compartments interacting in a complex sequence of events. Understanding all the cellular events and communication networks that contribute to the tissue's response to stress is therefore a major conceptual and methodological scientific challenge. The study of heterogeneous populations of cells in a tissue is now possible thanks to "single cell' RNA sequencing and spatial transcriptomics techniques, which enable a comprehensive study of the transcriptomic profiles of individual cells in an integrated system. In addition, the mathematical and bioinformatics tools that are now available for the large-scale analysis of data allow the inference of cell-cell communication networks. Such approaches have become possible through advances in bioinformatics algorithms for the analysis and deciphering of interaction networks. Interactions influence the tissue regeneration process through expression of various molecules, including metabolites, integrins, junction proteins, ligands, receptors and proteins secreted into the extracellular space. The vascular network is viewed as a key player in the progression of digestive lesions, which are characterised by infiltration of a range of immune cells. A better characterisation of endothelium/immune cell interactions in suitable preclinical models, as well as in humans, may help to identify some promising therapeutic targets for the prediction, prevention or treatment of digestive toxicity after radiotherapy.

Deciphering ligand-receptor-mediated intercellular communication based on ensemble deep learning and the joint scoring strategy from single-cell transcriptomic data

BACKGROUND

Cell-cell communication in a tumor microenvironment is vital to tumorigenesis, tumor progression and therapy. Intercellular communication inference helps understand molecular mechanisms of tumor growth, progression and metastasis.

METHODS

Focusing on ligand-receptor co-expressions, in this study, we developed an ensemble deep learning framework, CellComNet, to decipher ligand-receptor-mediated cell-cell communication from single-cell transcriptomic data. First, credible LRIs are captured by integrating data arrangement, feature extraction, dimension reduction, and LRI classification based on an ensemble of heterogeneous Newton boosting machine and deep neural network. Next, known and identified LRIs are screened based on single-cell RNA sequencing (scRNA-seq) data in certain tissues. Finally, cell-cell communication is inferred by incorporating scRNA-seq data, the screened LRIs, a joint scoring strategy that combines expression thresholding and expression product of ligands and receptors.

RESULTS

The proposed CellComNet framework was compared with four competing protein-protein interaction prediction models (PIPR, XGBoost, DNNXGB, and OR-RCNN) and obtained the best AUCs and AUPRs on four LRI datasets, elucidating the optimal LRI classification ability. CellComNet was further applied to analyze intercellular communication in human melanoma and head and neck squamous cell carcinoma (HNSCC) tissues. The results demonstrate that cancer-associated fibroblasts highly communicate with melanoma cells and endothelial cells strong communicate with HNSCC cells.

CONCLUSIONS

The proposed CellComNet framework efficiently identified credible LRIs and significantly improved cell-cell communication inference performance. We anticipate that CellComNet can contribute to anticancer drug design and tumor-targeted therapy.

A Roadmap for the Human Gut Cell Atlas

The number of studies investigating the human gastrointestinal tract using various single-cell profiling methods has increased substantially in the past few years. Although this increase provides a unique opportunity for the generation of the first comprehensive Human Gut Cell Atlas (HGCA), there remains a range of major challenges ahead. Above all, the ultimate success will largely depend on a structured and coordinated approach that aligns global efforts undertaken by a large number of research groups. In this Roadmap, we discuss a comprehensive forward-thinking direction for the generation of the HGCA on behalf of the Gut Biological Network of the Human Cell Atlas. Based on the consensus opinion of experts from across the globe, we outline the main requirements for the first complete HGCA by summarizing existing data sets and highlighting anatomical regions and/or tissues with limited coverage. We provide recommendations for future studies and discuss key methodologies and the importance of integrating the healthy gut atlas with related diseases and gut organoids. Importantly, we critically overview the computational tools available and provide recommendations to overcome key challenges.

Reconstructing the cell-cell interaction network among mouse immune cells

Intercellular interactions and cell-cell communication are critical to regulating cell functions, especially in normal immune cells and immunotherapies. Ligand-receptor pairs mediating these cell-cell interactions can be identified using diverse experimental and computational approaches. Here, we reconstructed the intercellular interaction network between Mus musculus immune cells using publicly available receptor-ligand interaction databases and gene expression data from the immunological genome project. This reconstructed network accounts for 50,317 unique interactions between 16 cell types between 731 receptor-ligand pairs. Analysis of this network shows that cells of hematopoietic lineages use fewer communication pathways for interacting with each other, while nonhematopoietic stromal cells use the most network communications. We further observe that the WNT, BMP, and LAMININ pathways are the most significant contributors to the overall number of cell-cell interactions among the various pathways in the reconstructed communication network. This resource will enable the systematic analysis of normal and pathologic immune cell interactions, along with the study of emerging immunotherapies.

Single-cell RNA sequencing deciphers the mechanism of sepsis-induced liver injury and the therapeutic effects of artesunate

Liver, as an immune and detoxification organ, represents an important line of defense against bacteria and infection and a vulnerable organ that is easily injured during sepsis. Artesunate (ART) is an anti-malaria agent, that also exhibits broad pharmacological activities including anti-inflammatory, immune-regulation and liver protection. In this study, we investigated the cellular responses in liver to sepsis infection and ART hepatic-protective mechanisms against sepsis. Cecal ligation and puncture (CLP)-induced sepsis model was established in mice. The mice were administered ART (10 mg/kg, i.p.) at 4 h, and sacrificed at 12 h after the surgery. Liver samples were collected for preparing single-cell RNA transcriptome sequencing (scRNA-seq). The scRNA-seq analysis revealed that sepsis-induced a dramatic reduction of hepatic endothelial cells, especially the subtypes characterized with proliferation and differentiation. Macrophages were recruited during sepsis and released inflammatory cytokines (Tnf, Il1b, Il6), chemokines (Ccl6, Cd14), and transcription factor (Nfkb1), resulting in liver inflammatory responses. Massive apoptosis of lymphocytes and abnormal recruitment of neutrophils caused immune dysfunction. ART treatment significantly improved the survival of CLP mice within 96 h, and partially relieved or reversed the above-mentioned pathological features, mitigating the impact of sepsis on liver injury, inflammation, and dysfunction. This study provides comprehensive fundamental proof for the liver protective efficacy of ART against sepsis infection, which would potentially contribute to its clinical translation for sepsis therapy. Single cell transcriptome reveals the changes of various hepatocyte subtypes of CLP-induced liver injury and the potential pharmacological effects of artesunate on sepsis.

SGNet: Sequence-Based Convolution and Ligand Graph Network for Protein Binding Affinity Prediction

Protein-ligand binding can play an important role in many fields. It is of great importance to accurately predict the binding affinity between molecules by computational methods. Most computational binding affinity methods require molecular structures. However, there are still a large number of protein molecules with known amino acid sequences whose structures have not yet been solved. To address this issue, this paper proposes a sequence-based convolution and ligand graph network, called SGNet, to fuse the molecular graph information and the amino acid sequence information. This method integrates Conjoint Triad (CT) encoding of amino acid sequence and one-dimensional convolutional neural network module to extract protein molecules, develops graph attention network to extract molecular features of ligand, and then fuses the two feature sets to predict the binding affinity between molecules from the fully connected layer. As a result, SGNet achieves good prediction performance on both KIKD and IC50 data sets, with prediction error RMSEs of 1.287 and 1.58, and correlation Pearson Rs of 0.687 and 0.592, respectively. Comparative experimental results under the same conditions showed that SGNet outperformed Kdeep and GraphDTA in predicting binding affinities between protein-ligand molecules.

Single-cell analysis of human prepuce reveals dynamic changes in gene regulation and cellular communications

BACKGROUND

The cellular and molecular dynamics of human prepuce are crucial for understanding its biological and physiological functions, as well as the prevention of related genital diseases. However, the cellular compositions and heterogeneity of human prepuce at single-cell resolution are still largely unknown. Here we systematically dissected the prepuce of children and adults based on the single-cell RNA-seq data of 90,770 qualified cells.

RESULTS

We identified 15 prepuce cell subtypes, including fibroblast, smooth muscle cells, T/natural killer cells, macrophages, vascular endothelial cells, and dendritic cells. The proportions of these cell types varied among different individuals as well as between children and adults. Moreover, we detected cell-type-specific gene regulatory networks (GRNs), which could contribute to the unique functions of related cell types. The GRNs were also highly dynamic between the prepuce cells of children and adults. Our cell-cell communication network analysis among different cell types revealed a set of child-specific (e.g., CD96, EPO, IFN-1, and WNT signaling pathways) and adult-specific (e.g., BMP10, NEGR, ncWNT, and NPR1 signaling pathways) signaling pathways. The variations of GRNs and cellular communications could be closely associated with prepuce development in children and prepuce maintenance in adults.

CONCLUSIONS

Collectively, we systematically analyzed the cellular variations and molecular changes of the human prepuce at single-cell resolution. Our results gained insights into the heterogeneity of prepuce cells and shed light on the underlying molecular mechanisms of prepuce development and maintenance.

Magnetophoretic circuits: A review of device designs and implementation for precise single-cell manipulation

Lab-on-a-chip tools have played a pivotal role in advancing modern biology and medicine. A key goal in this field is to precisely transport single particles and cells to specific locations on a chip for quantitative analysis. To address this large and growing need, magnetophoretic circuits have been developed in the last decade to manipulate a large number of single bioparticles in a parallel and highly controlled manner. Inspired by electrical circuits, magnetophoretic circuits are composed of passive and active circuit elements to offer commensurate levels of control and automation for transporting individual bioparticles. These specifications make them unique compared to other technologies in addressing crucial bioanalytical applications and answering fundamental questions buried in highly heterogeneous cell populations. In this comprehensive review, we describe key theoretical considerations for manufacturing and simulating magnetophoretic circuits. We provide a detailed tutorial for operating magnetophoretic devices containing different circuit elements (e.g., conductors, diodes, capacitors, and transistors). Finally, we provide a critical comparison of the utility of these devices to other microchip-based platforms for cellular manipulation, and discuss how they may address unmet needs in single-cell biology and medicine.

A Roadmap for a Consensus Human Skin Cell Atlas and Single-Cell Data Standardization

Single-cell technologies have become essential to driving discovery in both basic and translational investigative dermatology. Despite the multitude of available datasets, a central reference atlas of normal human skin, which can serve as a reference resource for skin cell types, cell states, and their molecular signatures, is still lacking. For any such atlas to receive broad acceptance, participation by many investigators during atlas construction is an essential prerequisite. As part of the Human Cell Atlas project, we have assembled a Skin Biological Network to build a consensus Human Skin Cell Atlas and outline a roadmap toward that goal. We define the drivers of skin diversity to be considered when selecting sequencing datasets for the atlas and list practical hurdles during skin sampling that can result in data gaps and impede comprehensive representation and technical considerations for tissue processing and computational analysis, the accounting for which should minimize biases in cell type enrichments and exclusions and decrease batch effects. By outlining our goals for Atlas 1.0, we discuss how it will uncover new aspects of skin biology.

Decoding the Complexity of Immune-Cancer Cell Interactions: Empowering the Future of Cancer Immunotherapy

The tumor and tumor microenvironment (TME) consist of a complex network of cells, including malignant, immune, fibroblast, and vascular cells, which communicate with each other. Disruptions in cell-cell communication within the TME, caused by a multitude of extrinsic and intrinsic factors, can contribute to tumorigenesis, hinder the host immune system, and enable tumor evasion. Understanding and addressing intercellular miscommunications in the TME are vital for combating these processes. The effectiveness of immunotherapy and the heterogeneous response observed among patients can be attributed to the intricate cellular communication between immune cells and cancer cells. To unravel these interactions, various experimental, statistical, and computational techniques have been developed. These include ligand-receptor analysis, intercellular proximity labeling approaches, and imaging-based methods, which provide insights into the distorted cell-cell interactions within the TME. By characterizing these interactions, we can enhance the design of cancer immunotherapy strategies. In this review, we present recent advancements in the field of mapping intercellular communication, with a particular focus on immune-tumor cellular interactions. By modeling these interactions, we can identify critical factors and develop strategies to improve immunotherapy response and overcome treatment resistance.

Intercellular communication atlas reveals Oprm1 as a neuroprotective factor for retinal ganglion cells

The progressive death of mature neurons often results in neurodegenerative diseases. While the previous studies have mostly focused on identifying intrinsic mechanisms controlling neuronal survival, the extracellular environment also plays a critical role in regulating cell viability. Here we explore how intercellular communication contributes to the survival of retinal ganglion cells (RGCs) following the optic nerve crush (ONC). Although the direct effect of the ONC is restricted to the RGCs, we observed transcriptomic responses in other retinal cells to the injury based on the single-cell RNA-seq, with astrocytes and Müller glia having the most interactions with RGCs. By comparing the RGC subclasses showing distinct resilience to ONC-induced cell death, we found that the high-survival RGCs tend to have more ligand-receptor interactions with other retinal cells, suggesting that these RGCs are intrinsically programmed to foster more communication with their surroundings. Furthermore, we identified top 47 interactions that are stronger in the high-survival RGCs, likely representing neuroprotective interactions. We performed functional assays on one of the receptors, μ opioid receptor (Oprm1), a receptor known to play roles in regulating pain, reward, and addictive behavior. Although Oprm1 is preferentially expressed in intrinsically photosensitive retinal ganglion cells (ipRGCs), its neuroprotective effect could be transferred to multiple RGC subclasses by specific overexpressing Oprm1 in pan-RGCs in ONC, excitotoxicity, and glaucoma models. Lastly, manipulating Oprm1 activity improved visual functions and altered pupillary light response in mice. Our study provides an atlas of cell-cell interactions in both intact and post-ONC retina and an effective strategy to predict molecular mechanisms in neuroprotection, underlying the principal role played by extracellular environment in supporting neuron survival.

Spatial Transcriptomic Analysis of Focal and Normal Areas of Myocyte Disarray in Human Hypertrophic Cardiomyopathy

Hypertrophic Cardiomyopathy (HCM) is a common inherited disorder that can lead to heart failure and sudden cardiac death, characterized at the histological level by focal areas of myocyte disarray, hypertrophy and fibrosis, and only a few disease-targeted therapies exist. To identify the focal and spatially restricted alterations in the transcriptional pathways and reveal novel therapeutic targets, we performed a spatial transcriptomic analysis of the areas of focal myocyte disarray compared to areas of normal tissue using a commercially available platform (GeoMx, nanoString). We analyzed surgical myectomy tissue from four patients with HCM and the control interventricular septum tissue from two unused organ donor hearts that were free of cardiovascular disease. Histological sections were reviewed by an expert pathologist, and 72 focal areas with varying degrees of myocyte disarray (normal, mild, moderate, severe) were chosen for analysis. Areas of interest were interrogated with the Human Cancer Transcriptome Atlas designed to profile 1800 transcripts. Differential expression analysis revealed significant changes in gene expression between HCM and the control tissue, and functional enrichment analysis indicated that these genes were primarily involved in interferon production and mitochondrial energetics. Within the HCM tissue, differentially expressed genes between areas of normal and severe disarray were enriched for genes related to mitochondrial energetics and the extracellular matrix in severe disarray. An analysis of the gene expression of the ligand-receptor pair revealed that the HCM tissue exhibited downregulation of platelet-derived growth factor (PDGF), NOTCH, junctional adhesion molecule, and CD46 signaling while showing upregulation of fibronectin, CD99, cadherin, and amyloid precursor protein signaling. A deconvolution analysis utilizing the matched single nuclei RNA-sequencing (snRNA-seq) data to determine cell type composition in areas of interest revealed significant differences in fibroblast and vascular cell composition in areas of severe disarray when compared to normal areas in HCM samples. Cell composition in the normal areas of the control tissue was also divergent from the normal areas in HCM samples, which was consistent with the differential expression results. Overall, our data identify novel and potential disease-modifying targets for therapy in HCM.

Computational approaches in rheumatic diseases - Deciphering complex spatio-temporal cell interactions

Inflammatory arthritis, including rheumatoid (RA), and psoriatic (PsA) arthritis, are clinically and immunologically heterogeneous diseases with no identified cure. Chronic inflammation of the synovial tissue ushers loss of function of the joint that severely impacts the patient's quality of life, eventually leading to disability and life-threatening comorbidities. The pathogenesis of synovial inflammation is the consequence of compounded immune and stromal cell interactions influenced by genetic and environmental factors. Deciphering the complexity of the synovial cellular landscape has accelerated primarily due to the utilisation of bulk and single cell RNA sequencing. Particularly the capacity to generate cell-cell interaction networks could reveal evidence of previously unappreciated processes leading to disease. However, there is currently a lack of universal nomenclature as a result of varied experimental and technological approaches that discombobulates the study of synovial inflammation. While spatial transcriptomic analysis that combines anatomical information with transcriptomic data of synovial tissue biopsies promises to provide more insights into disease pathogenesis, in vitro functional assays with single-cell resolution will be required to validate current bioinformatic applications. In order to provide a comprehensive approach and translate experimental data to clinical practice, a combination of clinical and molecular data with machine learning has the potential to enhance patient stratification and identify individuals at risk of arthritis that would benefit from early therapeutic intervention. This review aims to provide a comprehensive understanding of the effect of computational approaches in deciphering synovial inflammation pathogenesis and discuss the impact that further experimental and novel computational tools may have on therapeutic target identification and drug development.

A Comparison of Cell-Cell Interaction Prediction Tools Based on scRNA-seq Data

Computational prediction of cell-cell interactions (CCIs) is becoming increasingly important for understanding disease development and progression. We present a benchmark study of available CCI prediction tools based on single-cell RNA sequencing (scRNA-seq) data. By comparing prediction outputs with a manually curated gold standard for idiopathic pulmonary fibrosis (IPF), we evaluated prediction performance and processing time of several CCI prediction tools, including CCInx, CellChat, CellPhoneDB, iTALK, NATMI, scMLnet, SingleCellSignalR, and an ensemble of tools. According to our results, CellPhoneDB and NATMI are the best performer CCI prediction tools, among the ones analyzed, when we define a CCI as a source-target-ligand-receptor tetrad. In addition, we recommend specific tools according to different types of research projects and discuss the possible future paths in the field.

STREAK: A supervised cell surface receptor abundance estimation strategy for single cell RNA-sequencing data using feature selection and thresholded gene set scoring

The accurate estimation of cell surface receptor abundance for single cell transcriptomics data is important for the tasks of cell type and phenotype categorization and cell-cell interaction quantification. We previously developed an unsupervised receptor abundance estimation technique named SPECK (Surface Protein abundance Estimation using CKmeans-based clustered thresholding) to address the challenges associated with accurate abundance estimation. In that paper, we concluded that SPECK results in improved concordance with Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data relative to comparative unsupervised abundance estimation techniques using only single-cell RNA-sequencing (scRNA-seq) data. In this paper, we outline a new supervised receptor abundance estimation method called STREAK (gene Set Testing-based Receptor abundance Estimation using Adjusted distances and cKmeans thresholding) that leverages associations learned from joint scRNA-seq/CITE-seq training data and a thresholded gene set scoring mechanism to estimate receptor abundance for scRNA-seq target data. We evaluate STREAK relative to both unsupervised and supervised receptor abundance estimation techniques using two evaluation approaches on six joint scRNA-seq/CITE-seq datasets that represent four human and mouse tissue types. We conclude that STREAK outperforms other abundance estimation strategies and provides a more biologically interpretable and transparent statistical model.

Phenotypic heterogeneity in human genetic diseases: ultrasensitivity-mediated threshold effects as a unifying molecular mechanism

Phenotypic heterogeneity is very common in genetic systems and in human diseases and has important consequences for disease diagnosis and treatment. In addition to the many genetic and non-genetic (e.g., epigenetic, environmental) factors reported to account for part of the heterogeneity, we stress the importance of stochastic fluctuation and regulatory network topology in contributing to phenotypic heterogeneity. We argue that a threshold effect is a unifying principle to explain the phenomenon; that ultrasensitivity is the molecular mechanism for this threshold effect; and discuss the three conditions for phenotypic heterogeneity to occur. We suggest that threshold effects occur not only at the cellular level, but also at the organ level. We stress the importance of context-dependence and its relationship to pleiotropy and edgetic mutations. Based on this model, we provide practical strategies to study human genetic diseases. By understanding the network mechanism for ultrasensitivity and identifying the critical factor, we may manipulate the weak spot to gently nudge the system from an ultrasensitive state to a stable non-disease state. Our analysis provides a new insight into the prevention and treatment of genetic diseases.

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.

Engineering Tissue-Scale Properties with Synthetic Cells: Forging One from Many

In metazoans, living cells achieve capabilities beyond individual cell functionality by assembling into multicellular tissue structures. These higher-order structures represent dynamic, heterogeneous, and responsive systems that have evolved to regenerate and coordinate their actions over large distances. Recent advances in constructing micrometer-sized vesicles, or synthetic cells, now point to a future where construction of synthetic tissue can be pursued, a boon to pressing material needs in biomedical implants, drug delivery systems, adhesives, filters, and storage devices, among others. To fully realize the potential of synthetic tissue, inspiration has been and will continue to be drawn from new molecular findings on its natural counterpart. In this review, we describe advances in introducing tissue-scale features into synthetic cell assemblies. Beyond mere complexation, synthetic cells have been fashioned with a variety of natural and engineered molecular components that serve as initial steps toward morphological control and patterning, intercellular communication, replication, and responsiveness in synthetic tissue. Particular attention has been paid to the dynamics, spatial constraints, and mechanical strengths of interactions that drive the synthesis of this next-generation material, describing how multiple synthetic cells can act as one.

RobustCCC: a robustness evaluation tool for cell-cell communication methods

Cell-cell communication (CCC) inference has become a routine task in single-cell data analysis. Many computational tools are developed for this purpose. However, the robustness of existing CCC methods remains underexplored. We develop a user-friendly tool, RobustCCC, to facilitate the robustness evaluation of CCC methods with respect to three perspectives, including replicated data, transcriptomic data noise and prior knowledge noise. RobustCCC currently integrates 14 state-of-the-art CCC methods and 6 simulated single-cell transcriptomics datasets to generate robustness evaluation reports in tabular form for easy interpretation. We find that these methods exhibit substantially different robustness performances using different simulation datasets, implying a strong impact of the input data on resulting CCC patterns. In summary, RobustCCC represents a scalable tool that can easily integrate more CCC methods, more single-cell datasets from different species (e.g., mouse and human) to provide guidance in selecting methods for identification of consistent and stable CCC patterns in tissue microenvironments. RobustCCC is freely available at https://github.com/GaoLabXDU/RobustCCC.

Comprehensive analysis of intercellular communication in the thermogenic adipose niche

Brown adipose tissue (BAT) is responsible for regulating body temperature through adaptive thermogenesis. The ability of thermogenic adipocytes to dissipate chemical energy as heat counteracts weight gain and has gained considerable attention as a strategy against obesity. BAT undergoes major remodeling in a cold environment. This remodeling results from changes in the number and function of brown adipocytes, expanding the network of blood vessels and sympathetic nerves, and changes in the composition and function of immune cells. Such synergistic adaptation requires extensive crosstalk between individual cells in the tissue to coordinate their responses. To understand the mechanisms of intercellular communication in BAT, we apply the CellChat algorithm to single-cell transcriptomic data of mouse BAT. We construct an integrative network of the ligand-receptor interactome in BAT and identify the major signaling inputs and outputs of each cell type. By comparing the ligand-receptor interactions in BAT of mice housed at different environmental temperatures, we show that cold exposure enhances the intercellular interactions among the major cell types in BAT, including adipocytes, adipocyte progenitors, lymphatic and vascular endothelial cells, myelinated and non-myelinated Schwann cells, and immune cells. These interactions are predicted to regulate the remodeling of the extracellular matrix, the inflammatory response, angiogenesis, and neurite growth. Together, our integrative analysis of intercellular communications in BAT and their dynamic regulation in response to housing temperatures provides a new understanding of the mechanisms underlying BAT thermogenesis. The resources presented in this study offer a valuable platform for future investigations of BAT development and thermogenesis.

TimiGP: Inferring cell-cell interactions and prognostic associations in the tumor immune microenvironment through gene pairs

Determining the prognostic association of different immune cell types in the tumor microenvironment is critical for understanding cancer biology and developing new therapeutic strategies. However, this is challenging in certain cancer types, where the abundance of different immune subsets is highly correlated. In this study, we develop a computational method named TimiGP to overcome this challenge. Based on bulk gene expression and survival data, TimiGP infers cell-cell interactions that reveal the association between immune cell relative abundance and prognosis. As demonstrated in metastatic melanoma, TimiGP prioritizes immune cells critical in prognosis based on the identified cell-cell interactions. Highly consistent results are obtained by TimiGP when applied to seven independent melanoma datasets and when different cell-type marker sets are used as inputs. Additionally, TimiGP can leverage single-cell RNA sequencing data to delineate the tumor immune microenvironment at high resolutions across a wide range of cancer types.

Intercellular communication atlas reveals Oprm1 as a neuroprotective factor for retinal ganglion cells

The progressive death of mature neurons often results in neurodegenerative diseases. While the previous studies have mostly focused on identifying intrinsic mechanisms controlling neuronal survival, the extracellular environment also plays a critical role in regulating cell viability. Here we explore how intercellular communication contributes to the survival of retinal ganglion cells (RGCs) following the optic nerve crush (ONC). Although the direct effect of the ONC is restricted to the RGCs, we observed transcriptomic responses in other retinal cells to the injury based on the single-cell RNA-seq, with astrocytes and Müller glia having the most interactions with RGCs. By comparing the RGC subclasses with distinct resilience to ONC-induced cell death, we found that the high-survival RGCs tend to have more ligand-receptor interactions with other retinal cells, suggesting that these RGCs are intrinsically programmed to foster more communication with their surroundings. Furthermore, we identified the top 47 interactions that are stronger in the high-survival RGCs, likely representing neuroprotective interactions. We performed functional assays on one of the receptors, μ-opioid receptor (Oprm1), a receptor known to play roles in regulating pain, reward, and addictive behavior. Although Oprm1 is preferentially expressed in intrinsically photosensitive retinal ganglion cells (ipRGC), its neuroprotective effect could be transferred to multiple RGC subclasses by selectively overexpressing Oprm1 in pan-RGCs in ONC, excitotoxicity, and glaucoma models. Lastly, manipulating Oprm1 activity improved visual functions or altered pupillary light response in mice. Our study provides an atlas of cell-cell interactions in intact and post-ONC retina, and a strategy to predict molecular mechanisms controlling neuroprotection, underlying the principal role played by extracellular environment in supporting neuron survival.

TCDD dysregulation of lncRNA expression, liver zonation and intercellular communication across the liver lobule

The persistent environmental aryl hydrocarbon receptor agonist and hepatotoxin TCDD (2,3,7,8-tetrachlorodibenzo-p-dioxin) induces hepatic lipid accumulation (steatosis), inflammation (steatohepatitis) and fibrosis. Thousands of liver-expressed, nuclear-localized lncRNAs with regulatory potential have been identified; however, their roles in TCDD-induced hepatoxicity and liver disease are unknown. We analyzed single nucleus (sn)RNA-seq data from control and subchronic (4 wk) TCDD-exposed mouse liver to determine liver cell-type specificity, zonation and differential expression profiles for thousands of lncRNAs. TCDD dysregulated >4000 of these lncRNAs in one or more liver cell types, including 684 lncRNAs specifically dysregulated in liver non-parenchymal cells. Trajectory inference analysis revealed major disruption by TCDD of hepatocyte zonation, affecting >800 genes, including 121 lncRNAs, with strong enrichment for lipid metabolism genes. TCDD also dysregulated expression of >200 transcription factors, including 19 Nuclear Receptors, most notably in hepatocytes and Kupffer cells. TCDD-induced changes in cell-cell communication patterns included marked decreases in EGF signaling from hepatocytes to non-parenchymal cells and increases in extracellular matrix-receptor interactions central to liver fibrosis. Gene regulatory networks constructed from the snRNA-seq data identified TCDD-exposed liver network-essential lncRNA regulators linked to functions such as fatty acid metabolic process, peroxisome and xenobiotic metabolism. Networks were validated by the striking enrichments that predicted regulatory lncRNAs showed for specific biological pathways. These findings highlight the power of snRNA-seq to discover functional roles for many xenobiotic-responsive lncRNAs in both hepatocytes and liver non-parenchymal cells and to elucidate novel aspects of foreign chemical-induced hepatotoxicity and liver disease, including dysregulation of intercellular communication within the liver lobule.

Deciphering intercellular signaling complexes by interaction-guided chemical proteomics

Indirect cell-cell interactions mediated by secreted proteins and their plasma membrane receptors play essential roles for regulating intercellular signaling. However, systematic profiling of the interactions between living cell surface receptors and secretome from neighboring cells remains challenging. Here we develop a chemical proteomics approach, termed interaction-guided crosslinking (IGC), to identify ligand-receptor interactions in situ. By introducing glycan-based ligation and click chemistry, the IGC approach via glycan-to-glycan crosslinking successfully captures receptors from as few as 0.1 million living cells using only 10 ng of secreted ligand. The unparalleled sensitivity and selectivity allow systematic crosslinking and identification of ligand-receptor complexes formed between cell secretome and surfaceome in an unbiased and all-to-all manner, leading to the discovery of a ligand-receptor interaction between pancreatic cancer cell-secreted urokinase (PLAU) and neuropilin 1 (NRP1) on pancreatic cancer-associated fibroblasts. This approach is thus useful for systematic exploring new ligand-receptor pairs and discovering critical intercellular signaling events.

Liver in infections: a single-cell and spatial transcriptomics perspective

The liver is an immune organ that plays a vital role in the detection, capture, and clearance of pathogens and foreign antigens that invade the human body. During acute and chronic infections, the liver transforms from a tolerant to an active immune state. The defence mechanism of the liver mainly depends on a complicated network of intrahepatic and translocated immune cells and non-immune cells. Therefore, a comprehensive liver cell atlas in both healthy and diseased states is needed for new therapeutic target development and disease intervention improvement. With the development of high-throughput single-cell technology, we can now decipher heterogeneity, differentiation, and intercellular communication at the single-cell level in sophisticated organs and complicated diseases. In this concise review, we aimed to summarise the advancement of emerging high-throughput single-cell technologies and re-define our understanding of liver function towards infections, including hepatitis B virus, hepatitis C virus, Plasmodium, schistosomiasis, endotoxemia, and corona virus disease 2019 (COVID-19). We also unravel previously unknown pathogenic pathways and disease mechanisms for the development of new therapeutic targets. As high-throughput single-cell technologies mature, their integration into spatial transcriptomics, multiomics, and clinical data analysis will aid in patient stratification and in developing effective treatment plans for patients with or without liver injury due to infectious diseases.

Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity in health and disease. However, the lack of physical relationships among dissociated cells has limited its applications. To address this issue, we present CeLEry (Cell Location recovEry), a supervised deep learning algorithm that leverages gene expression and spatial location relationships learned from spatial transcriptomics to recover the spatial origins of cells in scRNA-seq. CeLEry has an optional data augmentation procedure via a variational autoencoder, which improves the method's robustness and allows it to overcome noise in scRNA-seq data. We show that CeLEry can infer the spatial origins of cells in scRNA-seq at multiple levels, including 2D location and spatial domain of a cell, while also providing uncertainty estimates for the recovered locations. Our comprehensive benchmarking evaluations on multiple datasets generated from brain and cancer tissues using Visium, MERSCOPE, MERFISH, and Xenium demonstrate that CeLEry can reliably recover the spatial location information for cells using scRNA-seq data.

In Synergy: Optimizing CAR T Development and Personalizing Patient Care Using Single-Cell Technologies

Chimeric antigen receptor (CAR) T therapies hold immense promise to revolutionize cancer treatment. Nevertheless, key challenges, primarily in solid tumor settings, continue to hinder the application of this technology. Understanding CAR T-cell mechanism of action, in vivo activity, and clinical implications is essential for harnessing its full therapeutic potential. Single-cell genomics and cell engineering tools are becoming increasingly effective for the comprehensive research of complex biological systems. The convergence of these two technologies can accelerate CAR T-cell development. Here, we examine the potential of applying single-cell multiomics for the development of next-generation CAR T-cell therapies.

SIGNIFICANCE

Although CAR T-cell therapies have demonstrated remarkable clinical results in treating cancer, their effectiveness in most patients and tumor types remains limited. Single-cell technologies, which are transforming our understanding of molecular biology, provide new opportunities to overcome the challenges of CAR T-cell therapies. Given the potential of CAR T-cell therapy to tip the balance in the fight against cancer, it is important to understand how single-cell multiomic approaches can be leveraged to develop the next generations of more effective and less toxic CAR T-cell products and to provide powerful decision-making tools for clinicians to optimize treatment and improve patient outcomes.

SpatialDM for rapid identification of spatially co-expressed ligand-receptor and revealing cell-cell communication patterns

Cell-cell communication is a key aspect of dissecting the complex cellular microenvironment. Existing single-cell and spatial transcriptomics-based methods primarily focus on identifying cell-type pairs for a specific interaction, while less attention has been paid to the prioritisation of interaction features or the identification of interaction spots in the spatial context. Here, we introduce SpatialDM, a statistical model and toolbox leveraging a bivariant Moran's statistic to detect spatially co-expressed ligand and receptor pairs, their local interacting spots (single-spot resolution), and communication patterns. By deriving an analytical null distribution, this method is scalable to millions of spots and shows accurate and robust performance in various simulations. On multiple datasets including melanoma, Ventricular-Subventricular Zone, and intestine, SpatialDM reveals promising communication patterns and identifies differential interactions between conditions, hence enabling the discovery of context-specific cell cooperation and signalling.

A role for fibroblast-derived SASP factors in the activation of pyroptotic cell death in mammary epithelial cells

In normal tissue homeostasis, bidirectional communication between different cell types can shape numerous biological outcomes. Many studies have documented instances of reciprocal communication between fibroblasts and cancer cells that functionally change cancer cell behavior. However, less is known about how these heterotypic interactions shape epithelial cell function in the absence of oncogenic transformation. Furthermore, fibroblasts are prone to undergo senescence, which is typified by an irreversible cell cycle arrest. Senescent fibroblasts are also known to secrete various cytokines into the extracellular space; a phenomenon that is termed the senescence-associated secretory phenotype (SASP). While the role of fibroblast-derived SASP factors on cancer cells has been well studied, the impact of these factors on normal epithelial cells remains poorly understood. We discovered that treatment of normal mammary epithelial cells with conditioned media from senescent fibroblasts (SASP CM) results in a caspase-dependent cell death. This capacity of SASP CM to cause cell death is maintained across multiple senescence-inducing stimuli. However, the activation of oncogenic signaling in mammary epithelial cells mitigates the ability of SASP CM to induce cell death. Despite the reliance of this cell death on caspase activation, we discovered that SASP CM does not cause cell death by the extrinsic or intrinsic apoptotic pathway. Instead, these cells die by an NLRP3, caspase-1, and gasdermin D-dependent induction of pyroptosis. Taken together, our findings reveal that senescent fibroblasts can cause pyroptosis in neighboring mammary epithelial cells, which has implications for therapeutic strategies that perturb the behavior of senescent cells.

The Utility of Spatial Transcriptomics for Solid Organ Transplantation

Spatial transcriptomics (ST) measures and maps transcripts within intact tissue sections, allowing the visualization of gene activity within the spatial organization of complex biological systems. This review outlines advances in genomic sequencing technologies focusing on in situ sequencing-based ST, including applications in transplant and relevant nontransplant settings. We describe the experimental and analytical pipelines that underpin the current generation of spatial technologies. This context is important for understanding the potential role ST may play in expanding our knowledge, including in organ transplantation, and the important caveats/limitations when interpreting the vast data output generated by such methodological platforms.

CLARIFY: cell-cell interaction and gene regulatory network refinement from spatially resolved transcriptomics

MOTIVATION

Gene regulatory networks (GRNs) in a cell provide the tight feedback needed to synchronize cell actions. However, genes in a cell also take input from, and provide signals to other neighboring cells. These cell-cell interactions (CCIs) and the GRNs deeply influence each other. Many computational methods have been developed for GRN inference in cells. More recently, methods were proposed to infer CCIs using single cell gene expression data with or without cell spatial location information. However, in reality, the two processes do not exist in isolation and are subject to spatial constraints. Despite this rationale, no methods currently exist to infer GRNs and CCIs using the same model.

RESULTS

We propose CLARIFY, a tool that takes GRNs as input, uses them and spatially resolved gene expression data to infer CCIs, while simultaneously outputting refined cell-specific GRNs. CLARIFY uses a novel multi-level graph autoencoder, which mimics cellular networks at a higher level and cell-specific GRNs at a deeper level. We applied CLARIFY to two real spatial transcriptomic datasets, one using seqFISH and the other using MERFISH, and also tested on simulated datasets from scMultiSim. We compared the quality of predicted GRNs and CCIs with state-of-the-art baseline methods that inferred either only GRNs or only CCIs. The results show that CLARIFY consistently outperforms the baseline in terms of commonly used evaluation metrics. Our results point to the importance of co-inference of CCIs and GRNs and to the use of layered graph neural networks as an inference tool for biological networks.

AVAILABILITY AND IMPLEMENTATION

The source code and data is available at https://github.com/MihirBafna/CLARIFY.

Inferring Cell-Cell Communications from Spatially Resolved Transcriptomics Data Using a Bayesian Tweedie Model

Cellular communication through biochemical signaling is fundamental to every biological activity. Investigating cell signaling diffusions across cell types can further help understand biological mechanisms. In recent years, this has become an important research topic as single-cell sequencing technologies have matured. However, cell signaling activities are spatially constrained, and single-cell data cannot provide spatial information for each cell. This issue may cause a high false discovery rate, and using spatially resolved transcriptomics data is necessary. On the other hand, as far as we know, most existing methods focus on providing an ad hoc measurement to estimate intercellular communication instead of relying on a statistical model. It is undeniable that descriptive statistics are straightforward and accessible, but a suitable statistical model can provide more accurate and reliable inference. In this way, we propose a generalized linear regression model to infer cellular communications from spatially resolved transcriptomics data, especially spot-based data. Our BAyesian Tweedie modeling of COMmunications (BATCOM) method estimates the communication scores between cell types with the consideration of their corresponding distances. Due to the properties of the regression model, BATCOM naturally provides the direction of the communication between cell types and the interaction of ligands and receptors that other approaches cannot offer. We conduct simulation studies to assess the performance under different scenarios. We also employ BATCOM in a real-data application and compare it with other existing algorithms. In summary, our innovative model can fill gaps in the inference of cell-cell communication and provide a robust and straightforward result.

Single-cell A/B testing for cell-cell communication

A new method developed by Francisco Quintana's group, systematic perturbation of encapsulated associated cells followed by sequencing (SPEAC-seq), applies a CRISPR screen to co-cultured interacting cells to identify the ligands mediating cell-cell communication. Using this approach, the authors discover the molecular basis of a microglia-astrocyte feedback loop that suppresses neuroinflammatory disease.

Deciphering plant cell-cell communications using single-cell omics data

Plants have various cell types that respond to different environmental factors, and cell-cell communication is the fundamental process that controls these plant responses. The emergence of single-cell techniques provides opportunities to explore features unique to each cell type and construct a comprehensive cell-cell communication (CCC) network. Although the most current successes of CCC inference were achieved in animal research, computational methods can also be directly applied to plants. This review describes the current major models for cell-cell communication inference and summarizes the computational tools based on single-cell omics datasets. In addition, we discuss the limitations of plant cell-cell communication research and propose new directions to expand the field in meaningful ways.

Transcriptomic Analysis Reveals Candidate Ligand-Receptor Pairs and Signaling Networks Mediating Intercellular Communication between Hair Matrix Cells and Dermal Papilla Cells from Cashmere Goats

Hair fiber growth is determined by the spatiotemporally controlled proliferation, differentiation, and apoptosis of hair matrix cells (HMCs) inside the hair follicle (HF); however, dermal papilla cells (DPCs), the cell population surrounded by HMCs, manipulate the above processes via intercellular crosstalk with HMCs. Therefore, exploring how the mutual commutations between the cells are molecularly achieved is vital to understanding the mechanisms underlying hair growth. Here, based on our previous successes in cultivating HMCs and DPCs from cashmere goats, we combined a series of techniques, including in vitro cell coculture, transcriptome sequencing, and bioinformatic analysis, to uncover ligand-receptor pairs and signaling networks mediating intercellular crosstalk. Firstly, we found that direct cellular interaction significantly alters cell cycle distribution patterns and changes the gene expression profiles of both cells at the global level. Next, we constructed the networks of ligand-receptor pairs mediating intercellular autocrine or paracrine crosstalk between the cells. A few pairs, such as LEP-LEPR, IL6-EGFR, RSPO1-LRP6, and ADM-CALCRL, are found to have known or potential roles in hair growth by acting as bridges linking cells. Further, we inferred the signaling axis connecting the cells from transcriptomic data with the advantage of CCCExplorer. Certain pathways, including INHBA-ACVR2A/ACVR2B-ACVR1/ACVR1B-SMAD3, were predicted as the axis mediating the promotive effect of INHBA on hair growth via paracrine crosstalk between DPCs and HMCs. Finally, we verified that LEP-LEPR and IL1A-IL1R1 are pivotal ligand-receptor pairs involved in autocrine and paracrine communication of DPCs and HMCs to DPCs, respectively. Our study provides a comprehensive landscape of intercellular crosstalk between key cell types inside HF at the molecular level, which is helpful for an in-depth understanding of the mechanisms related to hair growth.

exFINDER: identify external communication signals using single-cell transcriptomics data

Cells make decisions through their communication with other cells and receiving signals from their environment. Using single-cell transcriptomics, computational tools have been developed to infer cell-cell communication through ligands and receptors. However, the existing methods only deal with signals sent by the measured cells in the data, the received signals from the external system are missing in the inference. Here, we present exFINDER, a method that identifies such external signals received by the cells in the single-cell transcriptomics datasets by utilizing the prior knowledge of signaling pathways. In particular, exFINDER can uncover external signals that activate the given target genes, infer the external signal-target signaling network (exSigNet), and perform quantitative analysis on exSigNets. The applications of exFINDER to scRNA-seq datasets from different species demonstrate the accuracy and robustness of identifying external signals, revealing critical transition-related signaling activities, inferring critical external signals and targets, clustering signal-target paths, and evaluating relevant biological events. Overall, exFINDER can be applied to scRNA-seq data to reveal the external signal-associated activities and maybe novel cells that send such signals.