Deciphering cell-cell interactions and communication from gene expression

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.

Deciphering Cell-Cell Interactions with Integrative Single-Cell Secretion Profiling

Cell-cell interactions are the fundamental behaviors to regulate cellular activities. A comprehensive evaluation of intercellular interactions requires direct profiling of various signaling behaviors simultaneously at the single-cell level, which remains lacking. Herein, an integrative single-cell secretion analysis platform is presented to profile different secreted factors (four proteins, three extracellular vesicles (EV) phenotypes), spatial distances, and migration information (distances and direction) simultaneously from high-throughput paired single cells using an antibody-barcode microchip. Applying the platform to analyze the tumor-stromal and tumor-immune interactions with the human oral squamous cell carcinoma (OSCC) cell lines and primary OSCC cells reveals that the initial distances between cells would determine their migratory distances and direction to approach stable organization. The cell-cell in close proximity enhances protein secretions while attenuating EV secretions. Migration has a more profound correlation with protein secretions than EV secretions, in which absolute migration distance affects protein secretions significantly but not the direction. These findings highlight the significance of spatial organization in regulating cell signaling behaviors and demonstrate that the integrative single-cell secretion profiling platform is well-suited for a comprehensive dissection of intercellular communication and interactions, providing new avenues for understanding cell-cell interaction biology and how different signaling behaviors coordinate within the tumor microenvironment.

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.

Single-cell RNA sequencing in skeletal muscle developmental biology

Skeletal muscle is the most extensive tissue in mammals, and they perform several functions; it is derived from paraxial mesodermal somites and undergoes hyperplasia and hypertrophy to form multinucleated, contractile, and functional muscle fibers. Skeletal muscle is a complex heterogeneous tissue composed of various cell types that establish communication strategies to exchange biological information; therefore, characterizing the cellular heterogeneity and transcriptional signatures of skeletal muscle is central to understanding its ontogeny's details. Studies of skeletal myogenesis have focused primarily on myogenic cells' proliferation, differentiation, migration, and fusion and ignored the intricate network of cells with specific biological functions. The rapid development of single-cell sequencing technology has recently enabled the exploration of skeletal muscle cell types and molecular events during development. This review summarizes the progress in single-cell RNA sequencing and its applications in skeletal myogenesis, which will provide insights into skeletal muscle pathophysiology.

Emergence of division of labor in tissues through cell interactions and spatial cues

Most cell types in multicellular organisms can perform multiple functions. However, not all functions can be optimally performed simultaneously by the same cells. Functions incompatible at the level of individual cells can be performed at the cell population level, where cells divide labor and specialize in different functions. Division of labor can arise due to instruction by tissue environment or through self-organization. Here, we develop a computational framework to investigate the contribution of these mechanisms to division of labor within a cell-type population. By optimizing collective cellular task performance under trade-offs, we find that distinguishable expression patterns can emerge from cell-cell interactions versus instructive signals. We propose a method to construct ligand-receptor networks between specialist cells and use it to infer division-of-labor mechanisms from single-cell RNA sequencing (RNA-seq) and spatial transcriptomics data of stromal, epithelial, and immune cells. Our framework can be used to characterize the complexity of cell interactions within tissues.

Cellular-scale proximity labeling for recording cell spatial organization in mouse tissues

Proximity labeling has emerged as a powerful strategy for interrogating cell-cell interactions. However, the nanometer-scale labeling radius impedes the use of current methods for indirect cell communications and makes recording cell spatial organization in tissue samples difficult. Here, we develop quinone methide-assisted identification of cell spatial organization (QMID), a chemical strategy with the labeling radius matching the cell dimension. The activating enzyme is installed on the surface of bait cells, which produces QM electrophiles that can diffuse across micrometers and label proximal prey cells independent of cell-cell contacts. In cell coculture, QMID reveals gene expression of macrophages that are regulated by spatial proximity to tumor cells. Furthermore, QMID enables labeling and isolation of proximal cells of CD4⁺ and CD8⁺ T cells in the mouse spleen, and subsequent single-cell RNA sequencing uncovers distinctive cell populations and gene expression patterns within the immune niches of specific T cell subtypes. QMID should facilitate dissecting cell spatial organization in various tissues.

Cardiovascular complications of diabetes: role of non-coding RNAs in the crosstalk between immune and cardiovascular systems

Diabetes mellitus, a group of metabolic disorders characterized by high levels of blood glucose caused by insulin defect or impairment, is a major risk factor for cardiovascular diseases and related mortality. Patients with diabetes experience a state of chronic or intermittent hyperglycemia resulting in damage to the vasculature, leading to micro- and macro-vascular diseases. These conditions are associated with low-grade chronic inflammation and accelerated atherosclerosis. Several classes of leukocytes have been implicated in diabetic cardiovascular impairment. Although the molecular pathways through which diabetes elicits an inflammatory response have attracted significant attention, how they contribute to altering cardiovascular homeostasis is still incompletely understood. In this respect, non-coding RNAs (ncRNAs) are a still largely under-investigated class of transcripts that may play a fundamental role. This review article gathers the current knowledge on the function of ncRNAs in the crosstalk between immune and cardiovascular cells in the context of diabetic complications, highlighting the influence of biological sex in such mechanisms and exploring the potential role of ncRNAs as biomarkers and targets for treatments. The discussion closes by offering an overview of the ncRNAs involved in the increased cardiovascular risk suffered by patients with diabetes facing Sars-CoV-2 infection.

Deciphering the single-cell transcriptome network in keloids with intra-lesional injection of triamcinolone acetonide combined with 5-fluorouracil

Objectives

Keloid is a highly aggressive fibrotic disease resulting from excessive extracellular matrix deposition after dermal injury. Intra-lesional injection of triamcinolone acetonide (TAC) in combination with 5-fluorouracil (5-FU) is a commonly used pharmacological regimen and long-term repeated injections can achieve sustained inhibition of keloid proliferation. However, the molecular mechanisms underlying the inhibitory effect on keloids remain insufficiently investigated.

Methods and materials

This study performed single-cell RNA sequencing analysis of keloids treated with TAC+5-FU injections, keloids, and skins to explore patterns of gene expression regulation and cellular reprogramming.

Results

The results revealed that TAC+5-FU interrupted the differentiation trajectory of fibroblasts toward pro-fibrotic subtypes and induced keloid atrophy possibly by inhibiting the FGF signaling pathway in intercellular communication. It also stimulated partial fibroblasts to develop the potential for self-replication and multidirectional differentiation, which may be a possible cellular source of keloid recurrence. T cell dynamics demonstrated elevated expression of secretory globulin family members, which may be possible immunotherapeutic targets. Schwann cell populations achieved functional changes by increasing the proportion of apoptotic or senescence-associated cell populations and reducing cell clusters that promote epidermal development and fibroblast proliferation.

Conclusions

Our findings elucidated the molecular and cellular reprogramming of keloids by intra-lesional injection of TAC+5-FU, which will provide new insights to understand the mechanism of action and therapeutic targets.

Roles of extracellular vesicles in ageing-related chronic kidney disease: demon or angel

Ageing is a universal and unavoidable phenomenon that significantly increases the risk of developing chronic kidney disease (CKD). It has been reported that ageing is associated with functional disruption and structural damage to the kidneys. Extracellular vesicles (EVs), which are nanoscale membranous vesicles containing lipids, proteins, and nucleic acids, are secreted by cells into the extracellular spaces. They have diverse functions such as repairing and regenerating different forms of ageing-related CKD and playing a crucial role in intercellular communication. This paper reviews the etiology of ageing in CKD, with particular attention paid to the roles of EVs as carriers of ageing signals and anti-ageing therapeutic strategies in CKD. In this regard, the double-edged role of EVs in ageing-related CKD is examined, along with the potential for their application in clinical settings.

Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples

In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. While multiple cell-cell communication tools exist, results are specific to the tool of choice, due to the diverse assumptions made across computational frameworks. Moreover, tools are often limited to analyzing single samples or to performing pairwise comparisons. As experimental design complexity and sample numbers continue to increase in single-cell datasets, so does the need for generalizable methods to decipher cell-cell communication in such scenarios. Here, we integrate two tools, LIANA and Tensor-cell2cell, which combined can deploy multiple existing methods and resources, to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this protocol, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step-by-step in both Python and R, and we provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This protocol typically takes ~1.5h to complete from installation to downstream visualizations on a GPU-enabled computer, for a dataset of ~63k cells, 10 cell types, and 12 samples.

Optical Manipulation Heats up: Present and Future of Optothermal Manipulation

Optothermal manipulation is a versatile technique that combines optical and thermal forces to control synthetic micro-/nanoparticles and biological entities. This emerging technique overcomes the limitations of traditional optical tweezers, including high laser power, photon and thermal damage to fragile objects, and the requirement of refractive-index contrast between target objects and the surrounding solvents. In this perspective, we discuss how the rich opto-thermo-fluidic multiphysics leads to a variety of working mechanisms and modes of optothermal manipulation in both liquid and solid media, underpinning a broad range of applications in biology, nanotechnology, and robotics. Moreover, we highlight current experimental and modeling challenges in the pursuit of optothermal manipulation and propose future directions and solutions to the challenges.

Benchmarking of analytical combinations for COVID-19 outcome prediction using single-cell RNA sequencing data

The advances of single-cell transcriptomic technologies have led to increasing use of single-cell RNA sequencing (scRNA-seq) data in large-scale patient cohort studies. The resulting high-dimensional data can be summarized and incorporated into patient outcome prediction models in several ways; however, there is a pressing need to understand the impact of analytical decisions on such model quality. In this study, we evaluate the impact of analytical choices on model choices, ensemble learning strategies and integrate approaches on patient outcome prediction using five scRNA-seq COVID-19 datasets. First, we examine the difference in performance between using single-view feature space versus multi-view feature space. Next, we survey multiple learning platforms from classical machine learning to modern deep learning methods. Lastly, we compare different integration approaches when combining datasets is necessary. Through benchmarking such analytical combinations, our study highlights the power of ensemble learning, consistency among different learning methods and robustness to dataset normalization when using multiple datasets as the model input.

scTenifoldXct: A semi-supervised method for predicting cell-cell interactions and mapping cellular communication graphs

We present scTenifoldXct, a semi-supervised computational tool for detecting ligand-receptor (LR)-mediated cell-cell interactions and mapping cellular communication graphs. Our method is based on manifold alignment, using LR pairs as inter-data correspondences to embed ligand and receptor genes expressed in interacting cells into a unified latent space. Neural networks are employed to minimize the distance between corresponding genes while preserving the structure of gene regression networks. We apply scTenifoldXct to real datasets for testing and demonstrate that our method detects interactions with high consistency compared with other methods. More importantly, scTenifoldXct uncovers weak but biologically relevant interactions overlooked by other methods. We also demonstrate how scTenifoldXct can be used to compare different samples, such as healthy vs. diseased and wild type vs. knockout, to identify differential interactions, thereby revealing functional implications associated with changes in cellular communication status.

MLSpatial: A machine-learning method to reconstruct the spatial distribution of cells from scRNA-seq by extracting spatial features

MOTIVATION

Single-cell RNA sequencing (scRNA-seq) technologies allow us to interrogate the state of an individual cell within its microenvironment. However, prior to sequencing, cells should be dissociated first, making it difficult to obtain their spatial information. Since the spatial distribution of cells is critical in a few circumstances such as cancer immunotherapy, we present MLSpatial, a novel computational method to learn the relationship between gene expression patterns and spatial locations of cells, and then predict cell-to-cell distance distribution based on scRNA-seq data alone.

RESULTS

We collected the drosophila embryo dataset, which contains both the fluorescence in situ hybridization (FISH) data and single cell RNA-seq (scRNA-seq) data of drosophila embryo. The FISH data provided the spatial position of 3039 cells and the expression of 84 genes for each cell. The scRNA-seq data contains the expressions of 8924 genes in 1297 high-quality cells with cell location unknown. For a comparison, we also collected the MERFISH data of 645 osteosarcoma cells with cell location and the expression status of 10,050 genes known. For each data, the cells were randomly divided into a training set and a test set, in the ratio of 7:3. The cell-to-cell distances our model extracted had a higher correspondence (i.e., correlation coefficient 0.99) with those of the real situation than those of existing methods in the FISH data of drosophila embryo. However, in the osteosarcoma data, our model captured the spatial relationship between cells, with a correlation of 0.514 to that of the real situation. We also applied the model trained using the FISH data of drosophila embryo into the single cell data of drosophila embryo, for which the real location of cells are unknown. The reconstructed pseudo drosophila embryo and the real embryo (as shown by the FISH data) had a high similarity in the spatial distribution of gene expression.

CONCLUSION

MLSpatial can accurately restore the relative position of cells from scRNA-seq data; however, the performance depends on the type of cells. The trained model might be useful in reconstructing the spatial distributions of single cells with only scRNA-seq data, provided that the scRNA-seq data and the FISH data are under similar background (i.e., the same tissue with similar disease background).

Universal recording of cell-cell contacts in vivo for interaction-based transcriptomics

Cellular interactions are essential for tissue organization and functionality. In particular, immune cells rely on direct and usually transient interactions with other immune and non-immune populations to specify and regulate their function. To study these "kiss-and-run" interactions directly in vivo, we previously developed LIPSTIC (Labeling Immune Partnerships by SorTagging Intercellular Contacts), an approach that uses enzymatic transfer of a labeled substrate between the molecular partners CD40L and CD40 to label interacting cells. Reliance on this pathway limited the use of LIPSTIC to measuring interactions between CD4+ helper T cells and antigen presenting cells, however. Here, we report the development of a universal version of LIPSTIC (uLIPSTIC), which can record physical interactions both among immune cells and between immune and non-immune populations irrespective of the receptors and ligands involved. We show that uLIPSTIC can be used, among other things, to monitor the priming of CD8+ T cells by dendritic cells, reveal the cellular partners of regulatory T cells in steady state, and identify germinal center (GC)-resident T follicular helper (Tfh) cells based on their ability to interact cognately with GC B cells. By coupling uLIPSTIC with single-cell transcriptomics, we build a catalog of the immune populations that physically interact with intestinal epithelial cells (IECs) and find evidence of stepwise acquisition of the ability to interact with IECs as CD4+ T cells adapt to residence in the intestinal tissue. Thus, uLIPSTIC provides a broadly useful technology for measuring and understanding cell-cell interactions across multiple biological systems.

Acute kidney injury and distant organ dysfunction-network system analysis

Acute kidney injury (AKI) occurs in about half of critically ill patients and associates with high in-hospital mortality, increased long-term mortality post-discharge and subsequent progression to chronic kidney disease. Numerous clinical studies have shown that AKI is often complicated by dysfunction of distant organs, which is a cause of the high mortality associated with AKI. Experimental studies have elucidated many mechanisms of AKI-induced distant organ injury, which include inflammatory cytokines, oxidative stress and immune responses. This review will provide an update on evidence of organ crosstalk and potential therapeutics for AKI-induced organ injuries, and present the new concept of a systemic organ network to balance homeostasis and inflammation that goes beyond kidney-crosstalk with a single distant organ.

Advancing CAR T cell therapy through the use of multidimensional omics data

Despite the notable success of chimeric antigen receptor (CAR) T cell therapies in the treatment of certain haematological malignancies, challenges remain in optimizing CAR designs and cell products, improving response rates, extending the durability of remissions, reducing toxicity and broadening the utility of this therapeutic modality to other cancer types. Data from multidimensional omics analyses, including genomics, epigenomics, transcriptomics, T cell receptor-repertoire profiling, proteomics, metabolomics and/or microbiomics, provide unique opportunities to dissect the complex and dynamic multifactorial phenotypes, processes and responses of CAR T cells as well as to discover novel tumour targets and pathways of resistance. In this Review, we summarize the multidimensional cellular and molecular profiling technologies that have been used to advance our mechanistic understanding of CAR T cell therapies. In addition, we discuss current applications and potential strategies leveraging multi-omics data to identify optimal target antigens and other molecular features that could be exploited to enhance the antitumour activity and minimize the toxicity of CAR T cell therapy. Indeed, fully utilizing multi-omics data will provide new insights into the biology of CAR T cell therapy, further accelerate the development of products with improved efficacy and safety profiles, and enable clinicians to better predict and monitor patient responses.

An Engineered Living Intestinal Muscle Patch Produces Macroscopic Contractions that can Mix and Break Down Artificial Intestinal Contents

The intestinal muscle layers execute various gut wall movements to achieve controlled propulsion and mixing of intestinal content. Engineering intestinal muscle layers with complex contractile function is critical for developing bioartificial intestinal tissue to treat patients with short bowel syndrome. Here, the first demonstration of a living intestinal muscle patch capable of generating three distinct motility patterns and displaying multiple digesta manipulations is reported. Assessment of contractility, cellular morphology, and transcriptome profile reveals that successful generation of the contracting muscle patch relies on both biological factors in a serum-free medium and environmental cues from an elastic electrospun gelatin scaffold. By comparing gene-expression patterns among samples, it is shown that biological factors from the medium strongly affect ion-transport activities, while the scaffold unexpectedly regulates cell-cell communication. Analysis of ligandreceptor interactome identifies scaffold-driven changes in intercellular communication, and 78% of the upregulated ligand-receptor interactions are involved in the development and function of enteric neurons. The discoveries highlight the importance of combining biomolecular and biomaterial approaches for tissue engineering. The living intestinal muscle patch represents a pivotal advancement for building functional replacement intestinal tissue. It offers a more physiological model for studying GI motility and for preclinical drug discovery.

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.

Gene panel selection for targeted spatial transcriptomics

Targeted spatial transcriptomics hold particular promise in analysis of complex tissues. Most such methods, however, measure only a limited panel of transcripts, which need to be selected in advance to inform on the cell types or processes being studied. A limitation of existing gene selection methods is that they rely on scRNA-seq data, ignoring platform effects between technologies. Here we describe gpsFISH, a computational method to perform gene selection through optimizing detection of known cell types. By modeling and adjusting for platform effects, gpsFISH outperforms other methods. Furthermore, gpsFISH can incorporate cell type hierarchies and custom gene preferences to accommodate diverse design requirements.

Single-cell profiling to explore pancreatic cancer heterogeneity, plasticity and response to therapy

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer entity characterized by a heterogeneous genetic landscape and an immunosuppressive tumor microenvironment. Recent advances in high-resolution single-cell sequencing and spatial transcriptomics technologies have enabled an in-depth characterization of both malignant and host cell types and increased our understanding of the heterogeneity and plasticity of PDAC in the steady state and under therapeutic perturbation. In this Review we outline single-cell analyses in PDAC, discuss their implications on our understanding of the disease and present future perspectives of multimodal approaches to elucidate its biology and response to therapy at the single-cell level.

Single-cell and long-read sequencing to enhance modelling of splicing and cell-fate determination

Single-cell sequencing technologies have revolutionised the life sciences and biomedical research. Single-cell sequencing provides high-resolution data on cell heterogeneity, allowing high-fidelity cell type identification, and lineage tracking. Computational algorithms and mathematical models have been developed to make sense of the data, compensate for errors and simulate the biological processes, which has led to breakthroughs in our understanding of cell differentiation, cell-fate determination and tissue cell composition. The development of long-read (a.k.a. third-generation) sequencing technologies has produced powerful tools for investigating alternative splicing, isoform expression (at the RNA level), genome assembly and the detection of complex structural variants (at the DNA level). In this review, we provide an overview of the recent advancements in single-cell and long-read sequencing technologies, with a particular focus on the computational algorithms that help in correcting, analysing, and interpreting the resulting data. Additionally, we review some mathematical models that use single-cell and long-read sequencing data to study cell-fate determination and alternative splicing, respectively. Moreover, we highlight the emerging opportunities in modelling cell-fate determination that result from the combination of single-cell and long-read sequencing technologies.

IBRAP: integrated benchmarking single-cell RNA-sequencing analytical pipeline

Single-cell ribonucleic acid (RNA)-sequencing (scRNA-seq) is a powerful tool to study cellular heterogeneity. The high dimensional data generated from this technology are complex and require specialized expertise for analysis and interpretation. The core of scRNA-seq data analysis contains several key analytical steps, which include pre-processing, quality control, normalization, dimensionality reduction, integration and clustering. Each step often has many algorithms developed with varied underlying assumptions and implications. With such a diverse choice of tools available, benchmarking analyses have compared their performances and demonstrated that tools operate differentially according to the data types and complexity. Here, we present Integrated Benchmarking scRNA-seq Analytical Pipeline (IBRAP), which contains a suite of analytical components that can be interchanged throughout the pipeline alongside multiple benchmarking metrics that enable users to compare results and determine the optimal pipeline combinations for their data. We apply IBRAP to single- and multi-sample integration analysis using primary pancreatic tissue, cancer cell line and simulated data accompanied with ground truth cell labels, demonstrating the interchangeable and benchmarking functionality of IBRAP. Our results confirm that the optimal pipelines are dependent on individual samples and studies, further supporting the rationale and necessity of our tool. We then compare reference-based cell annotation with unsupervised analysis, both included in IBRAP, and demonstrate the superiority of the reference-based method in identifying robust major and minor cell types. Thus, IBRAP presents a valuable tool to integrate multiple samples and studies to create reference maps of normal and diseased tissues, facilitating novel biological discovery using the vast volume of scRNA-seq data available.

Quantifying heterogeneity to drug response in cancer-stroma kinetics

A crucial challenge in medicine is choosing which drug (or combination) will be the most advantageous for a particular patient. Usually, drug response rates differ substantially, and the reasons for this response unpredictability remain ambiguous. Consequently, it is central to classify features that contribute to the observed drug response variability. Pancreatic cancer is one of the deadliest cancers with limited therapeutic achievements due to the massive presence of stroma that generates an environment that enables tumor growth, metastasis, and drug resistance. To understand the cancer-stroma cross talk within the tumor microenvironment and to develop personalized adjuvant therapies, there is a necessity for effective approaches that offer measurable data to monitor the effect of drugs at the single-cell level. Here, we develop a computational approach, based on cell imaging, that quantifies the cellular cross talk between pancreatic tumor cells (L3.6pl or AsPC1) and pancreatic stellate cells (PSCs), coordinating their kinetics in presence of the chemotherapeutic agent gemcitabine. We report significant heterogeneity in the organization of cellular interactions in response to the drug. For L3.6pl cells, gemcitabine sensibly decreases stroma-stroma interactions but increases stroma-cancer interactions, overall enhancing motility and crowding. In the AsPC1 case, gemcitabine promotes the interactions among tumor cells, but it does not affect stroma-cancer interplay, possibly suggesting a milder effect of the drug on cell dynamics.

The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth

Cancers represent complex ecosystems comprising tumor cells and a multitude of non-cancerous cells, embedded in an altered extracellular matrix. The tumor microenvironment (TME) includes diverse immune cell types, cancer-associated fibroblasts, endothelial cells, pericytes, and various additional tissue-resident cell types. These host cells were once considered bystanders of tumorigenesis but are now known to play critical roles in the pathogenesis of cancer. The cellular composition and functional state of the TME can differ extensively depending on the organ in which the tumor arises, the intrinsic features of cancer cells, the tumor stage, and patient characteristics. Here, we review the importance of the TME in each stage of cancer progression, from tumor initiation, progression, invasion, and intravasation to metastatic dissemination and outgrowth. Understanding the complex interplay between tumor cell-intrinsic, cell-extrinsic, and systemic mediators of disease progression is critical for the rational development of effective anti-cancer treatments.

A molecularly defined and spatially resolved cell atlas of the whole mouse brain

In mammalian brains, tens of millions to billions of cells form complex interaction networks to enable a wide range of functions. The enormous diversity and intricate organization of cells in the brain have so far hindered our understanding of the molecular and cellular basis of its functions. Recent advances in spatially resolved single-cell transcriptomics have allowed systematic mapping of the spatial organization of molecularly defined cell types in complex tissues1-3. However, these approaches have only been applied to a few brain regions1-11 and a comprehensive cell atlas of the whole brain is still missing. Here, we imaged a panel of >1,100 genes in ~8 million cells across the entire adult mouse brain using multiplexed error-robust fluorescence in situ hybridization (MERFISH)12 and performed spatially resolved, single-cell expression profiling at the whole-transcriptome scale by integrating MERFISH and single-cell RNA-sequencing (scRNA-seq) data. Using this approach, we generated a comprehensive cell atlas of >5,000 transcriptionally distinct cell clusters, belonging to ~300 major cell types, in the whole mouse brain with high molecular and spatial resolution. Registration of the MERFISH images to the common coordinate framework (CCF) of the mouse brain further allowed systematic quantifications of the cell composition and organization in individual brain regions defined in the CCF. We further identified spatial modules characterized by distinct cell-type compositions and spatial gradients featuring gradual changes in the gene-expression profiles of cells. Finally, this high-resolution spatial map of cells, with a transcriptome-wide expression profile associated with each cell, allowed us to infer cell-type-specific interactions between several hundred pairs of molecularly defined cell types and predict potential molecular (ligand-receptor) basis and functional implications of these cell-cell interactions. These results provide rich insights into the molecular and cellular architecture of the brain and a valuable resource for future functional investigations of neural circuits and their dysfunction in diseases.

Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST

Spatial transcriptomics technologies generate gene expression profiles with spatial context, requiring spatially informed analysis tools for three key tasks, spatial clustering, multisample integration, and cell-type deconvolution. We present GraphST, a graph self-supervised contrastive learning method that fully exploits spatial transcriptomics data to outperform existing methods. It combines graph neural networks with self-supervised contrastive learning to learn informative and discriminative spot representations by minimizing the embedding distance between spatially adjacent spots and vice versa. We demonstrated GraphST on multiple tissue types and technology platforms. GraphST achieved 10% higher clustering accuracy and better delineated fine-grained tissue structures in brain and embryo tissues. GraphST is also the only method that can jointly analyze multiple tissue slices in vertical or horizontal integration while correcting batch effects. Lastly, GraphST demonstrated superior cell-type deconvolution to capture spatial niches like lymph node germinal centers and exhausted tumor infiltrating T cells in breast tumor tissue.

Single-cell discovery of the scene and potential immunotherapeutic target in hypopharyngeal tumor environment

Hypopharyngeal carcinoma is a cancer with the worst prognosis. We constructed the first single-cell transcriptome map for hypopharyngeal carcinoma and explored its underlying mechanisms. We systematically studied single-cell transcriptome data of 17,599 cells from hypopharyngeal carcinoma and paracancerous tissues. We identified categories of cells by dimensionality reduction and performed further subgroup analysis. Focusing on the potential mechanism in the cellular communication of hypopharyngeal carcinoma, we predicted ligand-receptor interactions and verified them via immunohistochemical and cellular experiments. In total, seven cell types were identified, including epithelial and myeloid cells. Subsequently, subgroup analysis showed significant tumor heterogeneity. Based on the pathological type of squamous cell carcinoma, we focused on intercellular communication between epithelial cells and various cells. We predicted the crosstalk and inferred the regulatory effect of cellular active ligands on the surface receptor of epithelial cells. From the top potential pairs, we focused on the BMPR2 receptor for further research, as it showed significantly higher expression in epithelial cancer tissue than in adjacent tissue. Further bioinformatics analysis, immunohistochemical staining, and cell experiments also confirmed its cancer-promoting effects. Overall, the single-cell perspective revealed complex crosstalk in hypopharyngeal cancer, in which BMPR2 promotes its proliferation and migration, providing a rationale for further study and treatment of this carcinoma.

Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing

Spatial transcriptomics (ST) technologies are rapidly becoming the extension of single-cell RNA sequencing (scRNAseq), holding the potential of profiling gene expression at a single-cell resolution while maintaining cellular compositions within a tissue. Having both expression profiles and tissue organization enables researchers to better understand cellular interactions and heterogeneity, providing insight into complex biological processes that would not be possible with traditional sequencing technologies. Data generated by ST technologies are inherently noisy, high-dimensional, sparse, and multi-modal (including histological images, count matrices, etc.), thus requiring specialized computational tools for accurate and robust analysis. However, many ST studies currently utilize traditional scRNAseq tools, which are inadequate for analyzing complex ST datasets. On the other hand, many of the existing ST-specific methods are built upon traditional statistical or machine learning frameworks, which have shown to be sub-optimal in many applications due to the scale, multi-modality, and limitations of spatially resolved data (such as spatial resolution, sensitivity, and gene coverage). Given these intricacies, researchers have developed deep learning (DL)-based models to alleviate ST-specific challenges. These methods include new state-of-the-art models in alignment, spatial reconstruction, and spatial clustering, among others. However, DL models for ST analysis are nascent and remain largely underexplored. In this review, we provide an overview of existing state-of-the-art tools for analyzing spatially resolved transcriptomics while delving deeper into the DL-based approaches. We discuss the new frontiers and the open questions in this field and highlight domains in which we anticipate transformational DL applications.

Tissue interplay during morphogenesis

The process by which biological systems such as cells, tissues and organisms acquire shape has been named as morphogenesis and it is central to a plethora of biological contexts including embryo development, wound healing, or even cancer. Morphogenesis relies in both self-organising properties of the system and in environmental inputs (biochemical and biophysical). The classical view of morphogenesis is based on the study of external biochemical molecules, such as morphogens. However, recent studies are establishing that the mechanical environment is also used by cells to communicate within tissues, suggesting that this mechanical crosstalk is essential to synchronise morphogenetic transitions and self-organisation. In this article we discuss how tissue interaction drive robust morphogenesis, starting from a classical biochemical view, to finalise with more recent advances on how the biophysical properties of a tissue feedback with their surroundings to allow form acquisition. We also comment on how in silico models aid to integrate and predict changes in cell and tissue behaviour. Finally, considering recent advances from the developmental biomechanics field showing that mechanical inputs work as cues that promote morphogenesis, we invite to revisit the concept of morphogen.

Towards AI-driven longevity research: An overview

While in the past technology has mostly been utilized to store information about the structural configuration of proteins and molecules for research and medical purposes, Artificial Intelligence is nowadays able to learn from the existing data how to predict and model properties and interactions, revealing important knowledge about complex biological processes, such as aging. Modern technologies, moreover, can rely on a broader set of information, including those derived from the next-generation sequencing (e.g., proteomics, lipidomics, and other omics), to understand the interactions between human body and the external environment. This is especially relevant as external factors have been shown to have a key role in aging. As the field of computational systems biology keeps improving and new biomarkers of aging are being developed, artificial intelligence promises to become a major ally of aging research.