Single-cell Techniques Research Articles

Macrophage heterogeneity in cardiometabolic disease

Abstract Metabolic diseases such as obesity and atherosclerosis are characterised by chronic inflammation that leads to the accumulation of immune cells in affected organs. Macrophages in the obese adipose tissue and the atherosclerotic plaque show transcriptional, phenotypic and functional heterogeneity. The accumulation of CD11c+ macrophages in atherosclerosis and obesity suggests the presence of conserved macrophage phenotypes in metabolic tissues. We aimed to characterise shared and distinct features of monocytes and macrophages in the aorta and adipose tissue in the context of metabolic disease using single-cell techniques. Single-cell RNA sequencing of the blood, aorta and visceral adipose tissue during a time course of metabolic disease progression (steady state, 12w western diet, 18w western diet) and data integration with murine aortic and adipose tissue community datasets revealed conserved and tissue specific features of metabolic disease. Resident macrophages in the aorta and adipose tissue displayed tissue specific transcriptional programs, highlighting that cell maintenance and function of tissue resident macrophages differ between metabolic tissues. Further, macrophages in the aorta and adipose tissue undergo disease associated compositional changes. For example, we uncovered distinct CD11c+ macrophage subsets that are conserved in the aorta and adipose tissue across multiple metabolic disease models and share core transcriptional signatures between both metabolic tissues. However, CD11c+ macrophage subsets showed tissue specific dynamics during metabolic disease progression. Overall, our analysis underscores that tissue niche affects macrophage composition as well as activation and transcriptional regulation patterns. Understanding similarities and differences between the two tissues will be important to inform identification of new therapeutic targets for cardiometabolic diseases.

Role of glia in delirium: proposed mechanisms and translational implications.

Delirium is a common acute onset neurological syndrome characterised by transient fluctuations in cognition. It affects over 20% of medical inpatients and 50% of those critically ill. Delirium is associated with morbidity and mortality, causes distress to patients and carers, and has significant socioeconomic costs in ageing populations. Despite its clinical significance, the pathophysiology of delirium is understudied, and many underlying cellular mechanisms remain unknown. There are currently no effective pharmacological treatments which directly target underlying disease processes. Although many studies focus on neuronal dysfunction in delirium, glial cells, primarily astrocytes, microglia, and oligodendrocytes, and their associated systems, are increasingly implicated in delirium pathophysiology. In this review, we discuss current evidence which implicates glial cells in delirium, including biomarker studies, post-mortem tissue analyses and pre-clinical models. In particular, we focus on how astrocyte pathology, including aberrant brain energy metabolism and glymphatic dysfunction, reactive microglia, blood-brain barrier impairment, and white matter changes may contribute to the pathogenesis of delirium. We also outline limitations in this body of work and the unique challenges faced in identifying causative mechanisms in delirium. Finally, we discuss how established neuroimaging and single-cell techniques may provide further mechanistic insight at pre-clinical and clinical levels.

Epstein-Barr virus reactivation induces divergent abortive, reprogrammed, and host shutoff states by lytic progression.

Viral infection leads to heterogeneous cellular outcomes ranging from refractory to abortive and fully productive states. Single cell transcriptomics enables a high resolution view of these distinct post-infection states. Here, we have interrogated the host-pathogen dynamics following reactivation of Epstein-Barr virus (EBV). While benign in most people, EBV is responsible for infectious mononucleosis, up to 2% of human cancers, and is a trigger for the development of multiple sclerosis. Following latency establishment in B cells, EBV reactivates and is shed in saliva to enable infection of new hosts. Beyond its importance for transmission, the lytic cycle is also implicated in EBV-associated oncogenesis. Conversely, induction of lytic reactivation in latent EBV-positive tumors presents a novel therapeutic opportunity. Therefore, defining the dynamics and heterogeneity of EBV lytic reactivation is a high priority to better understand pathogenesis and therapeutic potential. In this study, we applied single-cell techniques to analyze diverse fate trajectories during lytic reactivation in three B cell models. Consistent with prior work, we find that cell cycle and MYC expression correlate with cells refractory to lytic reactivation. We further found that lytic induction yields a continuum from abortive to complete reactivation. Abortive lytic cells upregulate NFκB and IRF3 pathway target genes, while cells that proceed through the full lytic cycle exhibit unexpected expression of genes associated with cellular reprogramming. Distinct subpopulations of lytic cells further displayed variable profiles for transcripts known to escape virus-mediated host shutoff. These data reveal previously unknown and promiscuous outcomes of lytic reactivation with broad implications for viral replication and EBV-associated oncogenesis.

Single-cell technology for drug discovery and development

The success rate of drug development today remains low, with long development cycles and high costs, especially in areas such as oncology, neurology, immunology, and infectious diseases. Single-cell omics, encompassing transcriptomics, genomics, epigenomics, proteomics, and metabolomics enable the analysis of gene expression profiles and cellular heterogeneity from the perspective of individual cells, offering a high-resolution view of their functional diversity. These technologies can help reveal disease mechanisms, drug target identification and validation, selection of preclinical models and candidate drugs, and clinical decision-making based on disease response to drugs, all at the single-cell level. The development of deep learning technology has provided a powerful tool for research in drug discovery based on single-cell techniques, which has evolved with the advent of large-scale public databases to predict drug responses and targets. In addition, traditional Chinese medicine (TCMs) research has also entered the era of single-cell technology. Single-cell omics technologies offer an alternative way in deciphering the mechanisms of TCMs in disease treatment, revealing drug targets, screening new drugs, and designing combinations of TCMs. This review aims to explore the application of single-cell omics technologies in drug screening and development comprehensively, highlighting how they accelerate the drug development process and facilitate personalized medicine by precisely identifying therapeutic targets, predicting drug responsiveness, deciphering mechanisms of action. It is also concluded that drug development process and therapeutic efficacy of drugs can be improved by combining single-cell omics and artificial intelligence techniques.

Exploiting β-Lactams-Induced Lysis and DNA Fragmentation for Rapid Molecular Antimicrobial Susceptibility Testing of Neisseria Gonorrhoeae via Dual-Digital PCR.

The evolution of antimicrobial resistance (AMR) presents substantial challenges to global medical health systems. Neisseria gonorrhoeae (N. gonorrhoeae), in particular, has developed resistance to all currently available antimicrobials. Addressing this issue necessitates not only discovering new antimicrobials but also deepening the understanding of bacterial responses to these agents, which can lead to new markers for rapid antimicrobial susceptibility testing (AST). Such advancements can enhance treatment outcomes and promote antimicrobial stewardship. In this study, single-cell techniques, including live-cell imaging, flow cytometry, and digital polymerase chain reaction (PCR) are utilized, to investigate the lysis dynamics and molecular features of N. gonorrhoeae upon exposure to β-lactam antimicrobials. Distinct patterns of bacterial lysis and DNA fragmentation are uncovered in susceptible strains. Leveraging these discoveries, A microfluidic dual-digital PCR approach that combines single-cell and single-molecule analyses, facilitating rapid and efficient phenotypic molecular AST for N. gonorrhoeae against β-lactams is developed. This proof-of-concept validation demonstrates the effectiveness of the method in accessing antimicrobial susceptibility across a range of bacterial strains, contributing valuable insights for advancing the battle against AMR.

Investigating TCR-pMHC interactions for TCRs without identified epitopes by constructing a computational pipeline

The molecular mechanisms underlying epitope recognition by T cell receptors (TCRs) are critical for activating T cell immune responses and rationally designing TCR-based therapeutics. Single-cell sequencing techniques vastly boost the accumulation of TCR sequences, while the limitation of available TCR-pMHC structures hampers further investigations. In this study, we proposed a computational pipeline that incorporates structural information and single-cell sequencing data to investigate the epitope-recognition mechanisms for TCRs without identified epitopes. By antigen specificity clustering, we mapped the epitope sequences between epitope-known and epitope-unknown TCRs from COVID-19 patients. One reported SARS-CoV-2 epitope, NQKLIANQF (S919–927), was identified for a TCR expressed by 614 T cells (TCR-614). Epitope screening also identified a potential cross-reactive epitope, KLKTLVATA (NSP31790–1798), for a TCR expressed by 204 T cells (TCR-204). By molecular dynamics (MD) simulations, we revealed the detailed epitope-recognition mechanisms for both TCRs. The structural motifs responsible for epitope recognition revealed by the MD simulations are consistent with the sequential features recognized by the sequence-based clustering method. We hope that this strategy could facilitate the discovery and optimization of TCR-based therapeutics.

Single-cell heterogeneity in ribosome content and the consequences for the growth laws.

Across species and environments, the ribosome content of cell populations correlates with population growth rate. The robustness and universality of this correlation have led to its classification as a "growth law." This law has fueled theories about how evolution selects for microbial organisms that maximize their growth rate based on nutrient availability, and it has informed models about how individual cells regulate their growth rates and ribosomal content. However, due to methodological limitations, this growth law has rarely been studied at the level of individual cells. While populations of fast-growing cells tend to have more ribosomes than populations of slow-growing cells, it is unclear whether individual cells tightly regulate their ribosome content to match their environment. Here, we employ recent groundbreaking single-cell RNA sequencing techniques to study this growth law at the single-cell level in two different microbes, S. cerevisiae (a single-celled yeast and eukaryote) and B. subtilis (a bacterium and prokaryote). In both species, we observe significant variation in the ribosomal content of single cells that is not predictive of growth rate. Fast-growing populations include cells exhibiting transcriptional signatures of slow growth and stress, as do cells with the highest ribosome content we survey. Broadening our focus to non-ribosomal transcripts reveals subpopulations of cells in unique transcriptional states suggestive that they have evolved to do things other than maximize their rate of growth. Overall, these results indicate that single-cell ribosome levels are not finely tuned to match population growth rates or nutrient availability and cannot be predicted by a Gaussian process model that assumes measurements are sampled from a normal distribution centered on the population average. This work encourages the expansion of growth law and other models that predict how growth rates are regulated or how they evolve to consider single-cell heterogeneity. To this end, we provide extensive data and analysis of ribosomal and transcriptomic variation across thousands of single cells from multiple conditions, replicates, and species.

Langerhans cells and skin immune diseases.

Langerhans cells (LCs) are the key antigen-presenting cells in the epidermis in normal conditions and respond differentially to environmental and/or endogenous stimuli, exerting either proinflammatory or anti-inflammatory effects. Current knowledge about LCs mainly originates from studies utilizing mouse models, whereas with the development of single-cell techniques, there has been significant progress for human LCs, which has updated our understanding of the phenotype, ontogeny, differentiation regulation, and function of LCs. In this review, we delineated the progress of human LCs and summarized LCs' function in inflammatory skin diseases, providing new ideas for precise regulation of LC function in the prevention and treatment of skin diseases.

Single-Cell RNA Sequencing and Combinatorial Approaches for Understanding Heart Biology and Disease.

By directly measuring multiple molecular features in hundreds to millions of single cells, single-cell techniques allow for comprehensive characterization of the diversity of cells in the heart. These single-cell transcriptome and multi-omic studies are transforming our understanding of heart development and disease. Compared with single-dimensional inspections, the combination of transcriptomes with spatial dimensions and other omics can provide a comprehensive understanding of single-cell functions, microenvironment, dynamic processes, and their interrelationships. In this review, we will introduce the latest advances in cardiac health and disease at single-cell resolution; single-cell detection methods that can be used for transcriptome, genome, epigenome, and proteome analysis; single-cell multi-omics; as well as their future application prospects.

Multi-modal transcriptomic analysis reveals metabolic dysregulation and immune responses in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD), a progressive inflammatory condition of the airways, emerges from the complex interplay between genetic predisposition and environmental factors. Notably, its incidence is on the rise, particularly among the elderly demographic. Current research increasingly highlights cellular senescence as a key driver in chronic lung pathologies. Despite this, the detailed mechanisms linking COPD with senescent genomic alterations remain elusive. To address this gap, there is a pressing need for comprehensive bioinformatics methodologies that can elucidate the molecular intricacies of this link. This approach is crucial for advancing our understanding of COPD and its association with cellular aging processes. Utilizing a spectrum of advanced bioinformatics techniques, this research delved into the potential mechanisms linking COPD with aging-related genes, identifying four key genes (EP300, MTOR, NFE2L1, TXN) through machine learning and weighted gene co-expression network analysis (WGCNA) analyses. Subsequently, a precise diagnostic model leveraging an artificial neural network was developed. The study further employed single-cell analysis and molecular docking to investigate senescence-related cell types in COPD tissues, particularly focusing on the interactions between COPD and NFE2L1, thereby enhancing the understanding of COPD's molecular underpinnings. Leveraging artificial neural networks, we developed a robust classification model centered on four genes—EP300, MTOR, NFE2L1, TXN—exhibiting significant predictive capability for COPD and offering novel avenues for its early diagnosis. Furthermore, employing various single-cell analysis techniques, the study intricately unraveled the characteristics of senescence-related cell types in COPD tissues, enriching our understanding of the disease's cellular landscape. This research anticipates offering novel biomarkers and therapeutic targets for early COPD intervention, potentially alleviating the disease's impact on individuals and healthcare systems, and contributing to a reduction in global COPD-related mortality. These findings carry significant clinical and public health ramifications, bolstering the foundation for future research and clinical strategies in managing and understanding COPD.

Microbial single-cell applications under anoxic conditions.

The field of microbiology traditionally focuses on studying microorganisms at the population level. Nevertheless, the application of single-cell level methods, including microfluidics and imaging techniques, has revealed heterogeneity within populations, making these methods essential to understand cellular activities and interactions at a higher resolution. Moreover, single-cell sorting has opened new avenues for isolating cells of interest from microbial populations or complex microbial communities. These isolated cells can be further interrogated in downstream single-cell "omics" analyses, providing physiological and functional information. However, applying these methods to study anaerobic microorganisms under in situ conditions remains challenging due to their sensitivity to oxygen. Here, we review the existing methodologies for the analysis of viable anaerobic microorganisms at the single-cell level, including live-imaging, cell sorting, and microfluidics (lab-on-chip) applications, and we address the challenges involved in their anoxic operation. Additionally, we discuss the development of non-destructive imaging techniques tailored for anaerobes, such as oxygen-independent fluorescent probes and alternative approaches.

A cross-reactive imaging matrix of membrane protein profiling for single-cell analysis

Membrane proteins play a critical role in cellular processes such as chemical transport, signal transduction, and enzymatic catalysis. A comprehensive understanding of membrane protein types and accurately measuring their expression levels are essential for advancing cell biology. However, current methods often fail to characterize the types and amounts of multiple membrane proteins in specific cell types due to inherent measurement limitations. Here, we have developed a single-cell image analysis technique based on a cross-reactive imaging matrix using membrane protein tags. This technique allows quantitative and qualitative analysis of multiple membrane protein types and their expression levels within single cells. As a proof of concept, we used four specific DNA aptamers to tag four different membrane proteins in seven different cell types. The four proteins are MUC1, EGFR, HeR2, and TLR4. The seven cell lines are 3t3, 4T1, A549, CT26, HeLa, Hepa1–6, Raw, respectively. We captured a comprehensive image matrix using wide-field fluorescence imaging, allowing precise quantification of protein expression at the single-cell level. Our approach introduces a multi-labeling analysis method for membrane proteins and reveals the influence of membrane protein diversity on cell heterogeneity analysis. We found that the accuracy of cell heterogeneity recognition can be affected by adjusting the number of membrane proteins. As the diversity of membrane protein species increases, the accuracy of cell type recognition increases. Our approach facilitates the evaluation of heterogeneity in diverse cells, providing new insights into cellular diversity and a powerful tool for studying single-cell biology.

Single-Cell Analysis of Bone-Marrow-Disseminated Tumour Cells

Metastasis frequently targets bones, where cancer cells from the primary tumour migrate to the bone marrow, initiating new tumour growth. Not only is bone the most common site for metastasis, but it also often marks the first site of metastatic recurrence. Despite causing over 90% of cancer-related deaths, effective treatments for bone metastasis are lacking, with current approaches mainly focusing on palliative care. Circulating tumour cells (CTCs) are pivotal in metastasis, originating from primary tumours and circulating in the bloodstream. They facilitate metastasis through molecular interactions with the bone marrow environment, involving direct cell-to-cell contacts and signalling molecules. CTCs infiltrate the bone marrow, transforming into disseminated tumour cells (DTCs). While some DTCs remain dormant, others become activated, leading to metastatic growth. The presence of DTCs in the bone marrow strongly correlates with future bone and visceral metastases. Research on CTCs in peripheral blood has shed light on their release mechanisms, yet investigations into bone marrow DTCs have been limited. Challenges include the invasiveness of bone marrow aspiration and the rarity of DTCs, complicating their isolation. However, advancements in single-cell analysis have facilitated insights into these elusive cells. This review will summarize recent advancements in understanding bone marrow DTCs using single-cell analysis techniques.

MultiSC: a deep learning pipeline for analyzing multiomics single-cell data.

Single-cell technologies enable researchers to investigate cell functions at an individual cell level and study cellular processes with higher resolution. Several multi-omics single-cell sequencing techniques have been developed to explore various aspects of cellular behavior. Using NEAT-seq as an example, this method simultaneously obtains three kinds of omics data for each cell: gene expression, chromatin accessibility, and protein expression of transcription factors (TFs). Consequently, NEAT-seq offers a more comprehensive understanding of cellular activities in multiple modalities. However, there is a lack of tools available for effectively integrating the three types of omics data. To address this gap, we propose a novel pipeline called MultiSC for the analysis of MULTIomic Single-Cell data. Our pipeline leverages a multimodal constraint autoencoder (single-cell hierarchical constraint autoencoder) to integrate the multi-omics data during the clustering process and a matrix factorization-based model (scMF) to predict target genes regulated by a TF. Moreover, we utilize multivariate linear regression models to predict gene regulatory networks from the multi-omics data. Additional functionalities, including differential expression, mediation analysis, and causal inference, are also incorporated into the MultiSC pipeline. Extensive experiments were conducted to evaluate the performance of MultiSC. The results demonstrate that our pipeline enables researchers to gain a comprehensive view of cell activities and gene regulatory networks by fully leveraging the potential of multiomics single-cell data. By employing MultiSC, researchers can effectively integrate and analyze diverse omics data types, enhancing their understanding of cellular processes.

Genomic profiling of Antarctic geothermal microbiomes using long-read, Hi-C, and single-cell techniques

Geothermal features in Antarctica provide favorable conditions for diverse microorganisms, yet their genomic diversity remains poorly understood. Here, we present an integrated dataset comprising PacBio HiFi and Hi-C metagenomic sequencing, along with single-cell amplified genomes (SAGs) from two high-altitude geothermal sites, Mount Melbourne and Mount Rittmann, in Antarctica. The long-read HiFi sequencing, coupled with Hi-C, enhances the understanding of microbiome diversity and functionality in this unique ecosystem by providing more complete and accurate genomic information. SAGs complement this by recovering rare microbial taxa and offering a strain-resolved perspective. This dataset aims to deepen our understanding of microbial evolution and ecology in Antarctic geothermal environments, and facilitate cross-comparison with other geothermal environments globally.

Vegetable phylloplane microbiomes harbour class 1 integrons in novel bacterial hosts and drive the spread of chlorite resistance

Bacterial hosts in vegetable phylloplanes carry mobile genetic elements, such as plasmids and transposons that are associated with integrons. These mobile genetic elements and their cargo genes can enter human microbiomes via consumption of fresh agricultural produce, including uncooked vegetables. This presents a risk of acquiring antimicrobial resistance genes from uncooked vegetables. To better understand horizontal gene transfer of class 1 integrons in these compartments, we applied epicPCR, a single-cell fusion-PCR surveillance technique, to link the class 1 integron integrase (intI1) gene with phylogenetic markers of their bacterial hosts. Ready-to-eat salads carried class 1 integrons from the phyla Bacteroidota and Pseudomonadota, including four novel genera that were previously not known to be associated with intI1. We whole-genome sequenced Pseudomonas and Erwinia hosts of pre-clinical class 1 integrons that are embedded in Tn402-like transposons. The proximal gene cassette in these integrons was identified as a chlorite dismutase gene cassette, which we showed experimentally to confer chlorite resistance. Chlorine-derived compounds such as acidified sodium chlorite and chloride dioxide are used to disinfectant raw vegetables in food processing facilities, suggesting selection for chlorite resistance in phylloplane integrons. The spread of integrons conferring chlorite resistance has the potential to exacerbate integron-mediated antimicrobial resistance (AMR) via co-selection of chlorite resistance and AMR, thus highlighting the importance of monitoring chlorite residues in agricultural produce. These results demonstrate the strength of combining epicPCR and culture-based isolation approaches for identifying hosts and dissecting the molecular ecology of class 1 integrons.

Venezuelan equine encephalitis virus non-structural protein 3 dictates superinfection exclusion in mammalian cells

Superinfection exclusion (SIE) prevents secondary infections of already infected cells. Arthritogenic alphaviruses induce SIE via early proteolytical cleavage of replicase precursor by non-structural protein 2 (nsP2). Here, we explore the SIE mechanism of the encephalitic Venezuelan equine encephalitis virus (VEEV). Using single-cell imaging techniques and VEEV replicons encoding green or red fluorescent proteins, we observed full SIE capacity in three hours. Transient expression of VEEV nsP3, but not nsP2, reduced alphavirus replication, suggesting a key role for VEEV nsP3 in the SIE mechanism. In particular, the VEEV nsP3 C-terminal hypervariable domain (HVD) was found to be required and sufficient for the SIE of VEEV and the more distantly related Sindbis virus. As the nsP3 HVD is known to bind multiple host proteins to form RNA replication complexes and modulate the cellular stress response, we propose that sequestering essential host protein(s) by VEEV nsP3 interferes with RNA replication of the superinfecting alphavirus.

Deciphering the impact of aggregated autophagy-related genes TUBA1B and HSP90AA1 on colorectal cancer evolution: a single-cell sequencing study of the tumor microenvironment

BackgroundColorectal cancer (CRC) is the third most prevalent cancer worldwide, with the tumor microenvironment (TME) playing a crucial role in its progression. Aggregated autophagy (AA) has been recognized as a factor that exacerbates CRC progression. This study aims to study the relationship between aggregated autophagy and CRC using single-cell sequencing techniques. Our goal is to explain the heterogeneity of the TME and to explore the potential for targeted personalized therapies.ObjectiveTo study the role of AA in CRC, we employed single-cell sequencing to discern distinct subpopulations within the TME. These subpopulations were characterized by their autophagy levels and further analyzed to identify specific biological processes and marker genes.ResultsOur study revealed significant correlations between immune factors and both clinical and biological characteristics of the tumor microenvironment (TME), particularly in cells expressing TUBA1B and HSP90AA1. These immune factors were associated with T cell depletion, a reduction in protective factors, diminished efficacy of immune checkpoint blockade (ICB), and enhanced migration of cancer-associated fibroblasts (CAFs), resulting in pronounced inflammation. In vitro experiments showd that silencing TUBA1B and HSP90AA1 using siRNA (Si-TUBA1B and Si-HSP90AA1) significantly reduced the expression of IL-6, IL-7, CXCL1, and CXCL2 and inhibition of tumor cell growth in Caco-2 and Colo-205 cell lines. This reduction led to a substantial alleviation of chronic inflammation and highlighted the heterogeneous nature of the TME.ConclusionThis study marks an initial foray into understanding how AA-associated processes may potentiate the TME and weaken immune function. Our findings provide insights into the complex dynamics of the TME and highlight potential targets for therapeutic intervention, suggesting a key role for AA in the advancement of colorectal cancer.

Single Cell Droplet-Based Efficacy and Transcriptomic Analysis of a Novel Anti-KLRG1 Antibody for Elimination of Autoreactive T Cells.

Progress in developing improvements in the treatment of autoimmune disease has been gradual, due to challenges presented by the nature of these conditions. Namely, the need to suppress a patient's immune response while maintaining the essential activity of the immune system in controlling disease. Targeted treatments to eliminate the autoreactive immune cells driving disease symptoms present a promising new option for major improvements in treatment efficacy and side effect management. Monoclonal antibody therapies can be applied to target autoreactive immune cells if the cells possess unique surface marker expression patterns. Killer cell lectin like receptor G1 (KLRG1) expression on autoreactive T cells presents an optimal target for this type of cell depleting antibody therapy. In this study, we apply a variety of in vitro screening methods to determine the efficacy of a novel anti-KLRG1 antibody at mediating specific natural killer (NK) cell mediated antibody-dependent cellular cytotoxicity (ADCC). The methods include single-cell droplet microfluidic techniques, allowing timelapse imaging and sorting based on cellular interactions. Included in this study is the development of a novel method of sorting cells using a droplet-sorting platform and a fluorescent calcium dye to separate cells based on CD16 recognition of cell-bound antibody. We applied this novel sorting method to visualize transcriptomic variation between NK cells that are or are not activated by binding the anti-KLRG1 antibody using RNA sequencing. The data in this study reveals a reliable and target-specific cytotoxicity of the cell depleting anti-KLRG1 antibody, and supports our droplet-sorting calcium assay as a novel method of sorting cells based on receptor activation.

Scoping Review: Methods and Applications of Spatial Transcriptomics in Tumor Research.

Spatial transcriptomics (ST) examines gene expression within its spatial context on tissue, linking morphology and function. Advances in ST resolution and throughput have led to an increase in scientific interest, notably in cancer research. This scoping study reviews the challenges and practical applications of ST, summarizing current methods, trends, and data analysis techniques for ST in neoplasm research. We analyzed 41 articles published by the end of 2023 alongside public data repositories. The findings indicate cancer biology is an important focus of ST research, with a rising number of studies each year. Visium (10x Genomics, Pleasanton, CA, USA) is the leading ST platform, and SCTransform from Seurat R library is the preferred method for data normalization and integration. Many studies incorporate additional data types like single-cell sequencing and immunohistochemistry. Common ST applications include discovering the composition and function of tumor tissues in the context of their heterogeneity, characterizing the tumor microenvironment, or identifying interactions between cells, including spatial patterns of expression and co-occurrence. However, nearly half of the studies lacked comprehensive data processing protocols, hindering their reproducibility. By recommending greater transparency in sharing analysis methods and adapting single-cell analysis techniques with caution, this review aims to improve the reproducibility and reliability of future studies in cancer research.