Single-cell Level Research Articles

NcPlantDB: a plant ncRNA database with potential ncPEP information and cell type-specific interaction.

The field of plant non-coding RNAs (ncRNAs) has seen significant advancements in recent years, with many ncRNAs recognized as important regulators of gene expression during plant development and stress responses. Moreover, the coding potential of these ncRNAs, giving rise to ncRNA-encoded peptides (ncPEPs), has emerged as an essential area of study. However, existing plant ncRNA databases lack comprehensive information on ncRNA-encoded peptides (ncPEPs) and cell type-specific interactions. To address this gap, we present ncPlantDB (https://bis.zju.edu.cn/ncPlantDB), a comprehensive database integrating ncRNA and ncPEP data across 43 plant species. ncPlantDB encompasses 353 140 ncRNAs, 3799 ncPEPs and 4 647 071 interactions, sourced from established databases and literature mining. The database offers unique features including translational potential data, cell-specific interaction networks derived from single-cell RNA sequencing and Ribo-seq analyses, and interactive visualization tools. ncPlantDB provides a user-friendly interface for exploring ncRNA expression patterns at the single-cell level, facilitating the discovery of tissue-specific ncRNAs and potential ncPEPs. By integrating diverse data types and offering advanced analytical tools, ncPlantDB serves as a valuable resource for researchers investigating plant ncRNA functions, interactions, and their potential coding capacity. This database significantly enhances our understanding of plant ncRNA biology and opens new avenues for exploring the complex regulatory networks in plant genomics.

DNMT3A CHIP-driver mutations ignite the bone marrow

Abstract Background Clonal hematopoiesis of indeterminate potential (CHIP) is an acquired, genetic cardiovascular (CV) risk factor affecting about 10% of the general population at the age of 70 years. Most CHIP-driver mutations affect the epigenetic regulator gene DNMT3A. Experimental studies suggest a causal link between CHIP and inflammation-driven atherosclerosis, but the underlying mechanisms of how few mutant clones provoke adverse clinical outcomes in patients remain obscure. Methods Single monocytes and bone marrow cells from DNMT3A-mutation carriers undergoing coronary angiography and open-heart surgery underwent RNA and genomic DNA sequencing in parallel on a single cell level. This approach enabled us to compare mutant and non-mutant cells directly within carriers, and to non-carrier cells. Atherosclerotic chimeric mice reconstituted with a mixture of Dnmt3a-deficient (CD45.2) and -competent (CD45.1) bone marrow served as an experimental model to compare mutant to non-mutant macrophages in atherosclerotic lesions. Results Surprisingly, RNA/gDNA parallel sequencing revealed that gene expression profiles of mutant monocytes resembled those of non-mutant monocytes in carriers of DNMT3A-CHIP mutations (n=4). Collectively, however, monocytes of DNMT3A mutation carriers were more inflammatory than those of matched non-carrier patients. Mutant hematopoietic stem and progenitor cells, analyzed by RNA/gDNA parallel sequencing in two carriers, expressed more activation and inflammatory markers compared to non-mutant cells within the same bone marrow, and intraindividual differences declined as cells differentiated into monocytes. Macrophages were isolated from atherosclerotic aortas of mixed bone marrow chimeric mice based on CD45.1 (non-mutant) or CD45.2 (mutant or non-mutant controls) expression and subjected to single cell RNA sequencing. Inflammation markers were higher expressed in non-mutant macrophages within carrier mice than in non-mutant macrophages of non-carrier chimeras, and showed relatively small differences compared to Dnmt3a-deficient macrophages. Conclusions Our experimental findings in humans and mice support a pathomechanistic model in which few DNMT3A-CHIP mutation-carrying hematopoietic stem cells stimulate non-mutant cells within the bone marrow, thereby augmenting inflammation downstream in all circulating monocytes and their progeny in atherosclerotic lesions.

Dissecting the cell microenvironment of ovarian endometrioma through single-cell RNA sequencing.

Ovarian endometrioma (OE), also known as "chocolate cysts," is a cystic mass that develops in the ovaries due to endometriosis and is a common gynecological condition characterized by the growth of endometrial tissue outside the uterus, leading to symptoms such as dysmenorrhea, pelvic pain, and infertility. However, the precise molecular and cellular mechanisms driving this pathophysiology remain largely unknown, posing challenges for diagnosis and treatment. Here, we employed integrated single-cell transcriptomic profiling of over 52,000 individual cells from endometrial tissues of OE patients and healthy donors and identified twelve major cell populations. We identified notable alterations in cell type-specific proportions and molecular signatures associated with OE. Notably, the activation of IGFBP5+ macrophages with pro-inflammatory properties, NK cell exhaustion, and aberrant proliferation of IQCG+ and KLF2+ epithelium are key features and may be the potential mechanisms underlying the pathogenesis of OE. Collectively, our data contribute to a better understanding of OE at the single cell level and may pave the way for the development of novel therapeutic strategies.

Prediction of cardiovascular outcomes by subtle changes in the peripheral immune cell landscape - insights from the LURIC single cell RNA-sequencing study (LuRNA)

Abstract Background and Aims Atherosclerosis and its clinical sequelae, myocardial infarction and stroke, represent the main causes of death worldwide. Although preclinical evidence has suggested the existence of an inflammatory response driving disease, the extend of cellular alterations in human atherosclerosis remains enigmatic. Here, we employ single cell RNA-sequencing (scRNA-seq) on peripheral blood mononucleated cells (PBMCs) in a well-defined cardiovascular risk cohort from the "Ludwigshafen Risk and Cardiovascular Health (LURIC) trial" to define changes in the immune cell landscape in atherosclerotic patients. Methods and Results Of 3317 patients enrolled in the LURIC trial between 1997 and 2000, we selected individuals with stable coronary-artery disease (CAD) (n=31) and healthy patients (no CAD) (n=16) by propensity score matching, adjusted for CRP, NTproBNP, Troponin T, LDL-C, and Cystatin-C, in a case-control design. scRNA-seq was performed by droplet sequencing on PBMCs stored in liquid nitrogen. Individual data sets were integrated and changes between healthy individuals and patients with CAD were evaluated on numeric and transcriptional level. Single cell transcriptomes were annotated using an established CITE-seq reference atlas. While bulk RNA-sequencing of PBMC preparations revealed only minor changes between both conditions, we identified several leukocyte populations on a single cell level with specific transcriptomes that were differentially regulated between both conditions. Interestingly, we found that several subsets of Natural Killer (NK) cells, T cells with an enrichment of proliferation-associated pathways, and a population resembling hematopoietic stem cells were more frequent in patients with CAD, while naïve phenotypes of B and T cells were overrepresented in the absence of CAD. Consistently, most T and B cell populations in patients with CAD were enriched in pathways associated with cell activation, immune receptor signalling, and differentiation. In addition, we detected restricted repertoires of T cell receptor sequences in several adaptive immune cell subtypes from patients with CAD, suggestive of increased cellular clonality and antigen-specificity. Finally, in a comparison of cellular frequencies with cardiovascular outcomes over more than 15 years, several immune cell subsets and cell-type specific transcriptional programs predicted cardiovascular death in this case-control scenario in multi-variate adjusted regression analysis. Notably, addition of scRNA-seq derived parameters was superior to traditional risk prediction with clinical characteristics and soluble biomarkers alone. Conclusion Employing scRNAseq, we demonstrate that atherosclerosis is associated with a profound change in the circulating immune cell landscape even in the absence of measurable differences in inflammatory biomarkers. These findings propose the usage of scRNAseq for the discovery of outcome-relevant cellular biomarkers in the future.

Quantitative Characterization of Gene Regulatory Circuits Associated With Fungal Secondary Metabolism to Discover Novel Natural Products.

Microbial genetic circuits are vital for regulating gene expression and synthesizing bioactive compounds. However, assessing their strength and timing, especially in multicellular fungi, remains challenging. Here, an advanced microfluidic platform is combined with a mathematical model enabling precise characterization of fungal gene regulatory circuits (GRCs) at the single-cell level. Utilizing this platform, the expression intensity and timing of 30 transcription factor-promoter combinations derived from two representative fungal GRCs, using the model fungus Aspergillus nidulans are determined. As a proof of concept, the selected GRC combination is utilized to successfully refactor the biosynthetic pathways of bioactive molecules, precisely control their production, and activate the expression of the silenced biosynthetic gene clusters (BGCs). This study provides insights into microbial gene regulation and highlights the potential of platform in fungal synthetic biology applications and the discovery of novel natural products.

Single cell multi-omics analysis unveils a distinct mechanism of exercise-mediated cardioprotection in ischemic cardiomyopathy

Abstract Background Exercise is pivotal in heart disease prevention, with proven cardioprotective effects in clinical trials. However, its precise molecular mechanisms remain elusive. Recent advancements in single-cell omics have revealed cellular heterogeneity within heart tissues, offering a deeper understanding of the pathophysiology of cardiac diseases. Purpose To investigate the exercise-induced physiological changes in heart tissue at the single-cell level and elucidate the molecular mechanisms underlying its cardioprotective effect on ischemic cardiomyopathy. Methods Murine myocardial infarction (MI) model was established by ligating the left ascending coronary artery, and a six-week voluntary wheel running exercise regimen was implemented to investigate the effect of aerobic exercise on ischemic cardiomyopathy. Integrated multi-omics analyses of single-cell DNA methylation profiling, single-nucleus assay for transposase-accessible chromatin sequencing (snATAC-seq) and single-nucleus RNA sequencing (snRNA-seq) were conducted to clarify the molecular mechanism of exercise-mediated cardioprotection at single-cell resolution. Overexpression and short hairpin RNA-mediated knockdown of gene X were conducted for murine MI model using cardiomyocyte-specific Myo-AAV2a vector. snRNA-seq of heart tissues from heart failure patients was performed to validate the clinical significance of the expression of gene X in cardiomyocytes. Results Exercise attenuates pathological cardiac remodeling, inducing physiological hypertrophy in non-infarcted areas while suppressing pathological remodeling of cardiomyocytes in infarct border zones. Single-cell epigenetic analysis unveiled intricate molecular mechanisms underlying exercise-induced transcriptome changes, providing insights into the gene regulatory networks modulated by exercise in the heart. In addition, integrated with snRNA-seq, we identified exercise-specific cardiomyocyte clusters, highlighting the substantial role of gene X in the molecular mechanism of exercise. Functional studies confirmed the essential role of gene X in exercise-induced cardioprotection, as its overexpression suppressed the adverse remodeling after MI and knockdown abolished the beneficial effects of exercise. Furthermore, snRNA-seq of heart failure patients revealed that higher expression of gene X was associated with ventricular reverse remodeling and favorable clinical outcomes, underscoring its significance in the pathophysiology of patients with heart failure. Conclusion Our study elucidates exercise-specific gene expression regulation networks in ischemic cardiomyopathy by unraveling the cellular heterogeneity and molecular changes associated with exercise. We identified gene X as a central player in exercise-mediated cardioprotection and further confirmed its significance in clinical settings, providing a potential therapeutic target for heart failure.

GEEES: inferring cell-specific gene-enhancer interactions from multi-modal single-cell data.

Gene-enhancer interactions are central to transcriptional regulation. Current multi-modal single-cell datasets that profile transcriptome and chromatin accessibility simultaneously in a single cell are yielding opportunities to infer gene-enhancer associations in a cell type specific manner. Computational efforts for such multi-modal single-cell datasets thus far focused on methods for identification and refinement of cell types and trajectory construction. While initial attempts for inferring gene-enhancer interactions have emerged, these have not been evaluated against benchmark datasets that materialized from bulk genomic experiments. Furthermore, existing approaches are limited to inferring gene-enhancer associations at the level of grouped cells as opposed to individual cells, thereby ignoring regulatory heterogeneity among the cells. We present a new approach, GEEES for "Gene EnhancEr IntEractions from Multi-modal Single Cell Data," for inferring gene-enhancer associations at the single-cell level using multi-modal single-cell transcriptome and chromatin accessibility data. We evaluated GEEES alongside several multivariate regression-based alternatives we devised and state-of-the-art methods using a large number of benchmark datasets, providing a comprehensive assessment of current approaches. This analysis revealed significant discrepancies between gold-standard interactions and gene-enhancer associations derived from multi-modal single-cell data. Notably, incorporating gene-enhancer distance into the analysis markedly improved performance across all methods, positioning GEEES as a leading approach in this domain. While the overall improvement in performance metrics by GEEES is modest, it provides enhanced cell representation learning which can be leveraged for more effective downstream analysis. Furthermore, our review of existing experimentally driven benchmark datasets uncovers their limited concordance, underscoring the necessity for new high-throughput experiments to validate gene-enhancer interactions inferred from single-cell data. https://github.com/keleslab/GEEES.

Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery

Understanding transcriptional responses to chemical perturbations is central to drug discovery, but exhaustive experimental screening of disease-compound combinations is unfeasible. To overcome this limitation, here we introduce PRnet, a perturbation-conditioned deep generative model that predicts transcriptional responses to novel chemical perturbations that have never experimentally perturbed at bulk and single-cell levels. Evaluations indicate that PRnet outperforms alternative methods in predicting responses across novel compounds, pathways, and cell lines. PRnet enables gene-level response interpretation and in-silico drug screening for diseases based on gene signatures. PRnet further identifies and experimentally validates novel compound candidates against small cell lung cancer and colorectal cancer. Lastly, PRnet generates a large-scale integration atlas of perturbation profiles, covering 88 cell lines, 52 tissues, and various compound libraries. PRnet provides a robust and scalable candidate recommendation workflow and successfully recommends drug candidates for 233 diseases. Overall, PRnet is an effective and valuable tool for gene-based therapeutics screening.

Profiling the transcriptomic age of single-cells in humans

Although aging clocks predicting the age of individual organisms have been extensively studied, the age of individual cells remained largely unexplored. Most recently single-cell omics clocks were developed for the mouse, however, extensive profiling the age of human cells is still lacking. To fill this gap, here we use available scRNA-seq data of 1,058,909 blood cells of 508 healthy, human donors (between 19 and 75 years), for developing single-cell transcriptomic clocks and predicting the age of human blood cells. By the application of the proposed cell-type-specific single-cell clocks, our main observations are that (i) transcriptomic age is associated with cellular senescence; (ii) the transcriptomic age of classical monocytes as well as naive B and T cells is decreased in moderate COVID-19 followed by an increase for some cell types in severe COVID-19; and (iii) the human embryo cells transcriptomically rejuvenated at the morulae and blastocyst stages. In summary, here we demonstrate that single-cell transcriptomic clocks are useful tools to investigate aging and rejuvenation at the single-cell level.

Profiling Breast Tumor Heterogeneity and Identifying Breast Cancer Subtypes Through Tumor-Associated Immune Cell Signatures and Immuno Nano Sensors.

Breast cancer is a complex and heterogeneous disease with varying cellular, genetic, epigenetic, and molecular expressions.The detection of intratumor heterogeneity in breast cancer poses significant challenges due to its complex multifaceted characteristics, yet its identification is crucial for guiding effective treatment decisions and understanding the diseaseprogression. Currently, there exists no method capable of capturing the full extent of breast tumor heterogeneity. In this study, the aim is to identify and characterize metabolic heterogeneity in breast tumors using immune cells and an ultrafast laser-fabricated Immuno Nano Sensor.Combining spectral markers from both Natural Killer (NK)and T cells, a machine-learning approach is implemented to distinguish cancer from healthy samples, identify primary versus metastatic tumors, and determine estrogen receptor (ER)/progesterone receptor (PR) status at the single-cell level. The platform successfully distinguished heterogeneous breast cancer samples fromhealthy individuals, achieving97.8%sensitivity and92.2%specificity, andaccurately identifiedprimary tumors from metastatic tumors. Characteristic spectral signatures allow for discrimination between ER/PR-positive andnegative tumors with 97.5% sensitivity.This study demonstrates the potential of immune cell-based metabolic profiling in providing a comprehensive assessment of breast tumor heterogeneity and paving the way for minimally invasive liquid biopsy approaches in breast cancer diagnosis and management.

Phenotypic heterogeneity in bacteria: the rise of antibiotic persistence, clinical implications, and therapeutic opportunities.

The rising incidence of antimicrobial resistance (AMR) and the diminishing antibiotics discovery pipeline have created an unprecedented scenario where minor infections could become untreatable. AMR phenomenon is genetically encoded, and various genetic determinants have been implicated in their emergence and spread. Nevertheless, several non-genetic phenomena are also involved in antibiotic treatment failure which requires a systematic investigation. It has been observed that in an isogenic population of bacteria, not all cells behave or respond the same way to an antibiotic, because of the inherent heterogeneity among them. This heterogeneity is not always heritable but rather phenotypic. Three distinct types of phenotypic heterogeneity, namely tolerance, persistence, and heteroresistance have been observed in bacteria having significant clinical implications influencing the treatment outcome. While tolerance is when a population can survive high doses of antibiotics without changing the minimum inhibitory concentration (MIC) of the drug, persistence occurs in a subpopulation of bacteria that can survive exposure to high antibiotic doses. In contrast, when a subpopulation shows a very high MIC in comparison to the rest of the population, the phenomenon is called heteroresistance. In this article, we have highlighted bacterial persistence with a focus on their emergence and the underlying molecular mechanisms. Moreover, we have tried to associate the genome-wide methylation status with that of the heterogeneity at a single-cell level that may explain the role of epigenetic mechanisms in the development of persistence.

CancerSCEM 2.0: an updated data resource of single-cell expression map across various human cancers.

The field of single-cell RNA sequencing (scRNA-seq) has advanced rapidly in the past decade, generating significant amounts of valuable data for researchers to study complex tumor profiles. This data is crucial for gaining innovative insights into cancer biology. CancerSCEM (https://ngdc.cncb.ac.cn/cancerscem) is a public resource that integrates, analyzesand visualizes scRNA-seq data related to cancer, and it provides invaluable support to numerous cancer-related studies. With CancerSCEM 2.0, scRNA-seq data have increased from 208 to 1466 datasets, covering tumor, matching-normaland peripheral blood samples across 127 research projects and 74 cancer types. The new version of this resource enhances transcriptome analysis by adding copy number variation evaluation, transcription factor enrichment, pseudotime trajectory construction, and diverse biological feature scoring. It also introduces a new cancer metabolic map at the single-cell level, providing an intuitive understanding of metabolic regulation across different cancer types. CancerSCEM 2.0 has a more interactive analysis platform, including four modules and 14 analytical functions, allowing researchers to perform cancer scRNA-seq data analyses in various dimensions. These enhancements are expected to expand the usability of CancerSCEM 2.0 to a broader range of cancer research and clinical applications, potentially revolutionizing our understanding of cancer mechanisms and treatments.

Human genital dendritic cell heterogeneity confers differential rapid response to HIV-1 exposure.

Dendritic cells (DCs) play critical roles in HIV pathogenesis and require further investigation in the female genital tract, a main portal of entry for HIV infection. Here we characterized genital DC populations at the single cell level and how DC subsets respond to HIV immediately following exposure. We found that the genital CD11c+HLA-DR+ myeloid population contains three DC subsets (CD1c+ DC2s, CD14+ monocyte-derived DCs and CD14+CD1c+ DC3s) and two monocyte/macrophage populations with distinct functional and phenotypic properties during homeostasis. Following HIV exposure, the antiviral response was dominated by DCs' rapid secretory response, activation of non-classical inflammatory pathways and host restriction factors. Further, we uncovered subset-specific differences in anti-HIV responses. CD14+ DCs were the main population activated by HIV and mediated the secretory antimicrobial response, while CD1c+ DC2s activated inflammasome pathways and IFN responses. Identification of subset-specific responses to HIV immediately after exposure could aid targeted strategies to prevent HIV infection.

Single-layer graphene oxide nanosheets induce proliferation and Osteogenesis of single-cell hBMSCs encapsulated in Alginate Microgels

Microfluidics cell encapsulation offers a way to mimic a 3D microenvironment that supports cell growth and proliferation, while also protecting cells from environmental stress. This technique has found extensive applications in tissue engineering and cell therapies. Several studies have demonstrated the advantages of graphene oxide (GO) as an osteogenic inducer; however, the significance of GO on stem cell fate in the single-cell state is still unclear. Here, a microfluidics-based approach is developed for continuous encapsulation of mesenchymal stem cells (MSCs) at the single-cell level using alginate microgels. So, single-layer graphene oxide (slGO) nanosheet is used to be encapsulated inside the alginate droplets. The results of AFM and SEM show that slGO can increase the roughness and reduce the stiffness of alginate hydrogels. The Young’s modulus of the alginate and alginate-slGO was obtained as 1414 kPa and 985.9 kPa, respectively. Live/dead assay and fluorescence microscopy images illustrate that slGO can maintain the viability and proliferation of microencapsulated hBMSCs. The obtained results show that slGO increases the mineralization of the encapsulated hBMSCs, so that microgels containing hBMSCs gradually became opaque during 21 days of culture. RT-qPCR results indicate that the expression of OCN, Runx2, and ALP in the alginate-slGO microgels is significantly higher than in the alginate microgels. The expression of OCN and Runx2 in the alginate-slGO microgels is 4.27 and 5.87-fold higher than in the alginate microgels, respectively. It can be concluded that low doses of slGO nanosheets have the potential to be utilized in the development of tissue engineering and bone regeneration. This finding offers a new method for creating injectable tissue transplants that are minimally invasive.

RCAN1.4 regulates tumor cell engraftment and invasion in a thyroid cancer to lung metastasis-on-a-chip microphysiological system

Progressive metastasis is the primary cause of cancer-related deaths. It has been recognized that many cancers are characterized by long periods of stability followed by subsequent progression. Genes termed metastasis progression suppressors (MPS) are functional gatekeepers of this process, and their loss leads to late-stage progression. Previously, we identified regulator of calcineurin 1, isoform 4 (RCAN1.4) as a functional MPS for several cancers, including thyroid cancer, a tumor type prone to metastatic dormancy. RCAN1.4 knockdown increases expression of the cancer-promoting transcription factor NFE2-like bZIP transcription factor (NFE2L3), and through this mechanism increases cancer cell proliferation and invasion inin vitroandin vivoand promotes metastatic potential to lungs in tail vein models. However, the mechanisms by which RCAN 1.4 regulates specific metastatic steps is incompletely characterized. Studies of the metastatic cascade are limited in mouse systems due to high cost and long duration. Here, we have shown the creation of a thyroid-to-lung metastasis-on-a-chip (MOC) model to address these limitations, allowing invasion analysis and quantification on a single cell level. We then deployed the platform to investigate RCAN1.4 knockdown in fluorescently tagged hTh74 and FTC236 thyroid cancer cell lines. Cells were circulated through microfluidic channels, running parallel to lung hydrogel constructs allowing tumor cell-lung tissue interactions. Similar to studies in mouse models, RCAN1.4 knockdown increased NFE2L3 expression, globally increased invasion distance into lung constructs and had cell line and clonally dependent variations on bulk metastatic burden. In line with previousin vivoobservations, RCAN1.4 knockdown had a greater impact on hTh74 metastatic propensity than FTC236. In summary, we have developed and validated a novel MOC system evaluate and quantify RCAN1.4-regulated thyroid cancer cell lung adherence and invasion. This system creates opportunities for more detailed and rapid mechanistic studies the metastatic cascade and creates opportunities for translational assay development.

Super Resolved Single-Cell Spatial Metabolomics from Multimodal Mass Spectrometry Imaging guided by Imaging Mass Cytometry.

Mass spectrometry imaging (MSI) is a powerful technique for spatially resolved analysis of metabolites and other biomolecules within biological tissues. However, the inherent low spatial resolution of MSI often limits its ability to provide detailed cellular-level information. To address this limitation, we propose a guided super-resolution (GSR) approach that leverages high-resolution Imaging Mass Cytometry (IMC) images to enhance the spatial resolution of low-resolution MSI data. By using these detailed IMC images as guides, we improve the resolution of MSI images, creating high-resolution metabolite maps. This enhancement facilitates more precise analysis of cellular structures and tissue architectures, providing deeper insights into super-resolved spatial metabolomics at the single-cell level.

Nanoplastics: Immune Impact, Detection, and Internalization after Human Blood Exposure by Single-Cell Mass Cytometry.

The increasing exposure to nanoplastics (NPs) raises significant concerns for human health, primarily due to their potential bioaccumulative properties. While NPs have recently been detected in human blood, their interactions with specific immune cell subtypes and their impact on immune regulation remain unclear. In this proof-of-concept study, model palladium-doped polystyrene NPs (PS-Pd NPs) are utilized to enable single-cell mass cytometry (CyTOF) detection. The size-dependent impact of carboxylate polystyrene NPs (50-200 nm) is investigated across 15 primary immune cell subpopulations using CyTOF. By taking advantage of Pd-doping for detecting PS-Pd NPs, this work evaluates their impact on human immune-cells at the single-cell level following blood exposure. This work traces PS-Pd NPs in 37 primary immune-cell subpopulations from human blood, quantifying the palladium atom count per cell by CyTOF while simultaneously assessing the impact of PS-Pd NPs on cell viability, functionality, and uptake. These results demonstrate that NPs can interact with, interfere with, and translocate into several immune cell subpopulations after exposure. In vivo distribution experiments in mice further confirmed their accumulation in immune cells within the liver, blood, and spleen, particularly in monocytes, macrophages, and dendritic cells. These findings provide valuable insights into the impact of NPs on human health.

Secretion correlation between matrix metalloproteinases and extracellular vesicles revealed by synchronized dynamic monitoring of single cells on a microfluidic chip

Despite growing interest in matrix metalloproteinases (MMPs) as promising therapeutic targets for cancer treatments, cellular trafficking and release of MMPs remain underexplored. To evaluate to what content the MMP secretion is associated with extracellular vesicles (EVs), in this study, we report a droplet microfluidic assay for simultaneous dynamic monitoring of the MMP and EV secretion at the single-cell level. Microdroplets containing single cells were generated in a flow-focusing microchannel and then guided to a microcolumn array for on-chip patterning, incubation and time-lapse microscopy. Utilizing a green fluorescent substrate for MMP activity and a red fluorescent protein reporter for EVs, temporal secretion dynamics of MMPs and EVs from individual cells were recorded respectively. With this dual-reporter system, a strong correlation between MMP and EV secretion was revealed. We further demonstrated that promotion or inhibition of EV release by altering intracellular calcium levels would induce a rise or fall in single-cell MMP secretion, which was masked by population-based experiments. More importantly, the activated release of this EV subpopulation (exosomes) also resulted in a higher correlation coefficient between MMP and EV secretion, and vice versa. Therefore, compared to other EV subpopulations, exosomes should be more closely connected with MMP secretion, and thus may represent a potential target for combination therapy. When in contrast with bulk experiments, single-cell analysis of the secretion correlation between MMPs and EVs not only reveals extensive cellular heterogeneity, but also exhibits a higher resolution.

Identification of hub genes through integrated single-cell and microarray transcriptome analysis in osteoarthritic meniscus

BackgroundOsteoarthritis (OA) is marked by the progressive degradation of joint cartilage and subchondral bone. The precise molecular mechanisms driving meniscus deterioration in OA, especially at the single-cell level, remain poorly understood.MethodWe analyzed two datasets from the GEO database, GSE220243 and GSE98918, focusing on meniscus tissue sequencing data from OA and non-OA patients. The standard Seurat procedure was employed to process single-cell data and identify differentially expressed genes (DEGs). Immune cell infiltration was assessed using the Microenvironment Cell Populations (MCP) counter and CIBERSORT algorithms. For the microarray data, DEGs were identified with the limma package, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using ClusterProfiler. The overlapping DEGs from both datasets were imported into Cytoscape to generate protein-protein interaction (PPI) networks and identify hub genes. Transcription factor (TF) and miRNA interaction networks were analyzed using NetworkAnalyst, and gene-related predictive drugs were enriched through the DSigDB platform.ResultAfter quality control, 34,763 cells from the OA patients and 34,145 cells from the healthy controls were analyzed. UMAP identified and SingleR annotated 14 cell clusters. The 10 largest cell clusters were selected for further analysis. The OA group exhibited a notable increase in macrophages and a reduction in cytotoxic lymphocytes and endothelial cells in the meniscus. In GSE98918, 220 DEGs were identified, and the MCODE plug-in in Cytoscape pinpointed a key module containing 12 candidate genes. The MCC methodfiltered the top 20 DEGs in each GSE220243 cluster. Overlapping DEGs from GSE220243 and GSE98918 identified COL1A1, COL3A1, COL5A2, COL6A3, LOX, and VEGFA as significantly decreased in OA, with COL3A1, COL5A2, LOX, and VEGFA upregulated in meniscal chondrocytes. The interaction network highlighted 3 key miRNAs and 13 shared TFs. Ten gene-related predictive drug molecules were identified.ConclusionThis research highlights crucial genes in the OA meniscus and uncovers their differing regulatory patterns between chondrocytes and non-chondrocytes. These findings enhance our understanding of the molecular mechanisms driving OA pathogenesis and aid in identifying potential drug targets.

Imaging mass cytometry-based characterisation of fibroblast subsets and their cellular niches in systemic sclerosis

ObjectivesTranscriptomic data demonstrated that fibroblasts are heterogeneous with functionally diverse subpopulations. Although fibroblasts are key effector cells of fibrotic diseases such as systemic sclerosis (SSc), they have not yet been...