Single-cell Level Research Articles

Leveraging explainable deep learning methodologies to elucidate the biological underpinnings of Huntington’s disease using single-cell RNA sequencing data

BackgroundHuntington’s disease (HD) is a hereditary neurological disorder caused by mutations in HTT, leading to neuronal degeneration. Traditionally, HD is associated with the misfolding and aggregation of mutant huntingtin due to an extended polyglutamine domain encoded by an expanded CAG tract. However, recent research has also highlighted the role of global transcriptional dysregulation in HD pathology. However, understanding the intricate relationship between mRNA expression and HD at the cellular level remains challenging. Our study aimed to elucidate the underlying mechanisms of HD pathology using single-cell sequencing data.ResultsWe used single-cell RNA sequencing analysis to determine differential gene expression patterns between healthy and HD cells. HD cells were effectively modeled using a residual neural network (ResNet), which outperformed traditional and convolutional neural networks. Despite the efficacy of our approach, the F1 score for the test set was 96.53%. Using the SHapley Additive exPlanations (SHAP) algorithm, we identified genes influencing HD prediction and revealed their roles in HD pathobiology, such as in the regulation of cellular iron metabolism and mitochondrial function. SHAP analysis also revealed low-abundance genes that were overlooked by traditional differential expression analysis, emphasizing its effectiveness in identifying biologically relevant genes for distinguishing between healthy and HD cells. Overall, the integration of single-cell RNA sequencing data and deep learning models provides valuable insights into HD pathology.ConclusionWe developed the model capable of analyzing HD at single-cell transcriptomic level.

Microfluidic platform for microbial spore germination studies in multiple growth conditions

BackgroundSpores are highly resistant dormant cells, adapted for survival and dispersal, that can withstand unfavourable environmental conditions for extended periods of time and later reactivate. Understanding the germination process of microbial spores is important in numerous areas including agriculture, food safety and health, and other sectors of biotechnology. Microfluidics combined with high-resolution microscopy allows to study spore germination at the single-cell level, revealing behaviours that would be hidden in standard population-level studies.MethodsHere, we present a microfluidic platform – the so-called four-conditions microfluidic chemostat (4CMC) – for germination studies where spores are confined to monolayers inside microchambers, allowing the testing of four growth conditions in parallel. This platform can be used with multiple species, including non-model organisms, and is compatible with existing image analysis software.ResultsIn this study, we focused on three soil dwellers, two bacteria and one fungus, and revealed new insights into their germination. We studied endospores of the model bacterium Bacillus subtilis and demonstrated a correlation between spore density and germination in rich media. We then investigated the germination of the obligate-oxalotrophic environmental bacterium Ammoniphilus oxalaticus in a concentration gradient of potassium oxalate, showing that lower concentrations result in more spores germinating compared to higher concentrations. We also used this microfluidic platform to study the soil beneficial filamentous fungus Trichoderma rossicum, showing for the first time that the size of the spores and hyphae increase in response to increased nutrient availability, while germination times remain the same.DiscussionOur platform allows to better understand microbial behaviour at the single-cell level, under a variety of controlled conditions. While we used it to decipher the responsiveness of soil dwellers’ spores, it would also be suitable for other spores from bacteria or filamentous fungi, but also vegetative cells and yeast, and even microbial communities.

A random survival forest-based pathomics signature classifies immunotherapy prognosis and profiles TIME and genomics in ES-SCLC patients

BackgroundSmall cell lung cancer (SCLC) is a highly aggressive neuroendocrine tumor with high mortality, and only a limited subset of extensive-stage SCLC (ES-SCLC) patients demonstrate prolonged survival under chemoimmunotherapy, which warrants the exploration of reliable biomarkers. Herein, we built a machine learning-based model using pathomics features extracted from hematoxylin and eosin (H&E)-stained images to classify prognosis and explore its potential association with genomics and TIME.MethodsWe retrospectively recruited ES-SCLC patients receiving first-line chemoimmunotherapy at Nanjing Jinling Hospital between April 2020 and August 2023. Digital H&E-stained whole-slide images were acquired, and targeted next-generation sequencing, programmed death ligand-1 staining, and multiplex immunohistochemical staining for immune cells were performed on a subset of patients. A random survival forest (RSF) model encompassing clinical and pathomics features was established to predict overall survival. The function of putative genes was assessed via single-cell RNA sequencing.Results and conclusionDuring the median follow-up period of 12.12 months, 118 ES-SCLC patients receiving first-line immunotherapy were recruited. The RSF model utilizing three pathomics features and liver metastases, bone metastases, smoking status, and lactate dehydrogenase, could predict the survival of first-line chemoimmunotherapy in patients with ES-SCLC with favorable discrimination and calibration. Underlyingly, the higher RSF-Score potentially indicated more infiltration of CD8+ T cells in the stroma as well as a greater probability of MCL-1 amplification and EP300 mutation. At the single-cell level, MCL-1 was associated with TNFA-NFKB signaling and apoptosis-related processes. Hopefully, this noninvasive model could act as a biomarker for immunotherapy, potentially facilitating precision medicine in the management of ES-SCLC.

PD-1 blockade does not improve efficacy of EpCAM-directed CAR T-cell in lung cancer brain metastasis

BackgroundLung cancer brain metastasis has a devastating prognosis, necessitating innovative treatment strategies. While chimeric antigen receptor (CAR) T-cell show promise in hematologic malignancies, their efficacy in solid tumors, including brain metastasis, is limited by the immunosuppressive tumor environment. The PD-L1/PD-1 pathway inhibits CAR T-cell activity in the tumor microenvironment, presenting a potential target to enhance therapeutic efficacy. This study aims to evaluate the impact of anti-PD-1 antibodies on CAR T-cell in treating lung cancer brain metastasis.MethodsWe utilized a murine immunocompetent, syngeneic orthotopic cerebral metastasis model for repetitive intracerebral two-photon laser scanning microscopy, enabling in vivo characterization of red fluorescent tumor cells and CAR T-cell at a single-cell level over time. Red fluorescent EpCAM-transduced Lewis lung carcinoma cells (EpCAM/tdtLL/2 cells) were implanted intracranially. Following the formation of brain metastasis, EpCAM-directed CAR T-cell were injected into adjacent brain tissue, and animals received either anti-PD-1 or an isotype control.ResultsCompared to controls receiving T-cell lacking a CAR, mice receiving EpCAM-directed CAR T-cell showed higher intratumoral CAR T-cell densities in the beginning after intraparenchymal injection. This finding was accompanied with reduced tumor growth and translated into a survival benefit. Additional anti-PD-1 treatment, however, did not affect intratumoral CAR T-cell persistence nor tumor growth and thereby did not provide an additional therapeutic effect.ConclusionCAR T-cell therapy for brain malignancies appears promising. However, additional anti-PD-1 treatment did not enhance intratumoral CAR T-cell persistence or effector function, highlighting the need for novel strategies to improve CAR T-cell therapy in solid tumors.

In Situ Isotope Tracing at Single-Cell Resolution Using Mass Spectrometry Imaging.

Mass spectrometry imaging (MSI) allows for label-free spatial molecular interrogation of tissues. With advances in the field over recent years, the spatial resolution at which MSI data can be recorded has reached the single-cell level. This makes MSI complementary to other single-cell omics technologies. As metabolism is a highly dynamic process, capturing the metabolic turnover adds a valuable layer of information. Here, we describe how to set up in situ stable isotope tracing followed by MSI-enabled spatial metabolomics to perform dynamic metabolomics at the single-cell level.

Visualizing Macrophage Polarization through Fluorescent mRNA Profiling.

Macrophages, known for their phenotypic plasticity, play a critical role in maintaining homeostasis and inflammation-related pathogenesis. Although identifying diverse macrophage phenotypes holds promise for enhancing diagnoses and treatments of diseases mediated by macrophages, existing methodologies for differentiating macrophages often lack precision. They are limited by the cumbersome procedures that require large-scale equipment, such as flow cytometry and transcriptomic analysis. In this context, we have engineered fluorescent polyadenine (polyA)-mediated sticky flares that enable practical visualization of macrophages. This technology facilitates the highly sensitive detection of macrophage phenotypes through the specific recognition of intracellular mRNAs, permitting in situ imaging. Our approach demonstrates the potential for determining macrophage polarization status at the single-cell level within dynamic immune microenvironments, thereby providing crucial diagnostic and prognostic information that could guide the development of tailored treatments for macrophage-related diseases in personalized medicine.

Mechanisms of Berberine in anti-pancreatic ductal adenocarcinoma revealed by integrated multi-omics profiling

This study integrates pharmacology databases with bulk RNA-seq and scRNA-seq to reveal the latent anti-PDAC capacities of BBR. Target genes of BBR were sifted through TargetNet, CTD, SwissTargetPrediction, and Binding Database. Based on the GSE183795 dataset, DEG analysis, GSEA, and WGCNA were sequentially run to build a disease network. Through sub-network filtration acquired PDAC-related hub genes. A PPI network was established using the shared genes. Degree algorithm from cytoHubba screened the key cluster in the network. Analysis of differential mRNA expression and ROC curves gauged the diagnostic performance of clustered genes. CYBERSORT uncovered the potential role of the key cluster on PDAC immunomodulation. ScRNA-seq analysis evaluated the distribution and expression profile of the key cluster at the single-cell level, assessing enrichment within annotated cell subpopulations to delineate the target distribution of BBR in PDAC. We identified 425 drug target genes and 771 disease target genes, using 57 intersecting genes to construct the PPI network. CytoHubba anchored the top 10 highest contributing genes to be the key cluster. mRNA expression levels and ROC curves confirmed that these genes showed good robustness for PDAC. CYBERSORT revealed that the key cluster influenced immune pathways predominantly associated with Macrophages M0, CD8 T cells, and naïve B cells. ScRNA-seq analysis clarified that BBR mainly acted on epithelial cells and macrophages in PDAC tissues. BBR potentially targets CDK1, CCNB1, CTNNB1, CDK2, TOP2A, MCM2, RUNX2, MYC, PLK1, and AURKA to exert therapeutic effects on PDAC. The mechanisms of action appear to significantly involve macrophage polarization-related immunological responses.

Detailed cellular and spatial characterization of chronic lung allograft dysfunction using imaging mass cytometry

Long-term survival after lung transplantation remains limited by chronic lung allograft dysfunction (CLAD), with two main phenotypes: bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS). We aimed to assess CLAD lung allografts using imaging mass cytometry (IMC), a high dimensional tissue imaging system allowing a multiparametric in situ exploration at a single cell level. 4 BOS, 4 RAS, and 4 control lung samples were stained with 35 heavy metal-tagged antibodies selected to assess structural and immune proteins of interest. We identified 50 immune and non-immune cell clusters. CLAD lungs had significantly reduced club cells. A Ki67-high basal cell population was mostly present in RAS and in proximity to memory T cells. Memory CD8+ T cells were more frequent in CLAD lungs, regulatory T cells more prominent in RAS. IMC is a powerful technology for detailed cellular analysis within intact organ structures that may shed further light on CLAD mechanisms.

Single same-cell multiome for dissecting key plant traits

Understanding molecular dynamics at the single cell level is crucial to understand plant traits. Recently, Liu et al. and Cui et al. reported multiome analysis in the same cell/nucleus to dissect the key plant traits (osmotic stress response and pod development). Their results provide novel insights into pathways and regulatory networks at a single cell resolution.

Investigation of Signaling in Primary Populations of Human Senescent T Cells.

Mitogen-activated protein kinases, a family of three stress-related kinases, the Erks and Jnks and p38s, are activated by three-layer transphosphorylation cascades and are important for the activation, differentiation, and effector functions of lymphocytes. Recent studies on the aged immune systems from both humans and mice have uncovered a different mode of MAPK signaling that is independent of canonical activation cascades and instead occurs through simultaneous self-phosphorylation reactions within the sestrin-MAPK activation complex (sMAC), an immune-inhibitory complex not previously observed. In this chapter, we discuss methodologies to study these pathways at the population and single cell level, which allows rejuvenating immune cell differentiation and fate.

The effects of small plastic particles on antibiotic resistance gene transfer revealed by single cell and community level analysis

Small plastic particles with sizes comparable to bacterial cells, widely exist in environment. However, their effects on antibiotic resistance gene (ARG) dissemination remain unclear. Using polystyrene (PS) particles (0.2µm, 2µm, 5µm, 10µm, 15µm, 20µm) as models, conjugative transfer of ARGs between the donor E. coli and different recipients (E. coli or sludge bacterial community) was investigated. Compared to the pure strain, the sludge bacterial community exposed to PS particles showed higher transfer frequencies (1.67 to 14.31 times the blank control). The transfer frequencies first decreased and then increased with particle size, and plastics similar in size to bacteria (e.g., 2µm) appear to be a transitional zone with minimal impact on ARG transmission. Furthermore, using microfluidics, in-situ observation at single cell level found that 2µm plastics can act as barriers between donor and recipient bacteria inhibiting growth, but conjugation events mostly occurred around them. Conversely, nanoplastics (e.g., 0.2µm) and larger microplastics (e.g., 20µm) significantly promote conjugation, mainly due to increased reactive oxygen species production and cell membrane permeability, or facilitating bacterial adhesion and biofilm formation, respectively. This study aids in assessing environmental risks of small plastic particles on ARG dissemination.

VSPACE: exploring virtual spatial representation of articular chondrocytes at the single-cell level.

vSPACE is a web-based application presenting a spatial representation of scRNAseq data obtained from human articular cartilage by emulating the concept of spatial transcriptomics technology, but virtually. This virtual 2D plot presentation of human articular cartage cells generates several zonal distribution patterns, for one or multiple genes at a time, revealing patterns that scientists can appreciate as imputed spatial distribution patterns along the zonal axis. vSPACE is implemented in Python Dash as a web-based toolbox designed for data visualization of zonal gene expression patterns in articular cartilage chondrocytes. This tool is freely accessible at: https://vspace.cse.uconn.edu/The source code and extra materials for this service can be downloaded from: https://github.com/zhacheny/vSPACE.

Construction and validation of a machine learning-based immune-related prognostic model for glioma.

Glioma stands as the most prevalent primary brain tumor found within the central nervous system, characterized by high invasiveness and treatment resistance. Although immunotherapy has shown potential in various tumors, it still faces challenges in gliomas. This study seeks to develop and validate a prognostic model for glioma based on immune-related genes, to provide new tools for precision medicine. Glioma samples were obtained from a database that includes the ImmPort database. Additionally, we incorporated ten machine learning algorithms to assess the model's performance using evaluation metrics like the Harrell concordance index (C-index). The model genes were further studied using GSCA, TISCH2, and HPA databases to understand their role in glioma pathology at the genomic, molecular, and single-cell levels, and validate the biological function of IKBKE in vitro experiments. In this study, a total of 199 genes associated with prognosis were identified using univariate Cox analysis. Subsequently, a consensus prognostic model was developed through the application of machine learning algorithms. In which the Lasso + plsRcox algorithm demonstrated the best predictive performance. The model showed a good ability to distinguish two groups in both the training and test sets. Additionally, the model genes were closely related to immunity (oligodendrocytes and macrophages), and mutation burden.The results of in vitro experiments showed that the expression level of the IKBKE gene had a significant effect on the apoptosis and migration of GL261 glioma cells. Western blot analysis showed that down-regulation of IKBKE resulted in increased expression of pro-apoptotic protein Bax and decreased expression of anti-apoptotic protein Bcl-2, which was consistent with increased apoptosis rate. On the contrary, IKBKE overexpression caused a decrease in Bax expression an increase in Bcl-2 expression, and a decrease in apoptosis rate. Tunel results further confirmed that down-regulation of IKBKE promoted apoptosis, while overexpression of IKBKE reduced apoptosis. In addition, cells with down-regulated IKBKE had reduced migration in scratch experiments, while cells with overexpression of IKBKE had increased migration. This study successfully constructed a glioma prognosis model based on immune-related genes. These findings provide new perspectives for glioma prognosis assessment and immunotherapy.

Single-cell transcriptome analysis reveals evolving tumour microenvironment induced by immunochemotherapy in nasopharyngeal carcinoma.

Combinatory therapeutic strategy containing immunochemotherapy as part of induction therapy components is one of the current trends in the treatment of high-risk metastatic locally advanced nasopharyngeal carcinoma (NPC). However, the mechanism underlying the heterogeneity of response at the single-cell level has not been underexplored. 18 bulks and 11 single-cell RNA sequencing from paired before-treatment and on-treatment samples in patients with treatment-naive high-risk metastatic locally advanced NPCs were obtained. Following quality control, a total of 87191 cells were included in the subsequence bioinformatics analysis. Immunochemotherapy was associated with on-treatment tumour microenvironment (TME) remodelling, including upregulation of anti-TMEs signatures, downregulation of pro-TMEs signatures, reversing CD8+ T exhaustion, and repolarizing proinflammatory TAMs. For the patients achieving a complete response, the cytotoxic activity of CD8+ T cells was stimulated and more interferon-gamma was provided, which would be the key for TAMs proinflammatory repolarization and eventually promote the CD8+ T cells maturation in turn. Among patients who did not reach complete response, differentiation and hypoxia signatures for endothelial cells were elevated after therapy. These patients exhibited higher levels of immune checkpoint genes in malignant cells at the baseline (before treatment), and decreased tumour antigen presentation activity, which may underlie the resistance mechanism to therapy. This study pictures a map of TME modulation following immunochemotherapy-based combination induction therapy and provides potential future approaches. Immunochemotherapy remodeled T cell phenotypes. For the patients achieving complete response, more interferon gamma was provided by CD8+ T cells after therapy, which would be the key for TAMs pro-inflammatory repolarization and eventually promote the CD8+ T cells maturation in turns. Among patients who did not reach complete response, malignant cells exhibited higher level of immune checkpoint genes before therapy, and decreased tumor antigen presentation activity, which may underlie the resistance mechanism to therapy.

ToxProfiler: A novel human-based reporter assay for in vitro chemical safety assessment

In vitro chemical safety assessment often relies on simple and general cytotoxicity endpoint measurements and fails to adequately predict human toxicity. To improve the in vitro chemical safety assessment, it is important to understand the underlying mechanisms of toxicity. Here we introduce ToxProfiler, a novel human-based reporter assay that quantifies the chemical-induced stress responses at a single-cell level and reveals the toxicological mode-of-action (MoA) of novel drugs and chemicals. The assay accurately measures the activation of seven major cellular stress response pathways (oxidative stress, cell cycle stress, endoplasmic reticulum stress, ion stress, protein stress, autophagy and inflammation) that play a role in the adaptive responses prior to cellular toxicity. To assess the applicability of the assay in predicting the toxicity MoA of chemicals, we tested a set of 100 chemicals with well-known in vitro and in vivo toxicological profiles. Concentration response modeling and point-of-departure estimation for each reporter protein allowed for chemical potency ranking and revealed the primary toxicological MoA of chemicals. Furthermore, the assay could effectively group chemicals based on their shared toxicity signatures and link them to specific toxicological targets, e.g. mitochondrial toxicity and genotoxicity, and different human pathologies, including liver toxicity and cardiotoxicity. Overall, ToxProfiler is a quantitative in vitro reporter assay that can accurately provide insight into the toxicological MoA of compounds, thereby assisting in the future mechanism-based safety assessment of chemicals.

Single-cell transcriptional profiling reveals cell type-specific responses to duck reovirus infection in the Bursa of Fabricius of Cairna moschata

Duck reovirus (DRV) is a universal waterfowl virus that causes significant economic losses in the duck industry. However, the role of the host innate immune response of the Bursa of Fabricius to DRV infection is largely unknown. In the present study, we constructed a single-cell resolution transcriptomic atlas of the Bursa of Fabricius of Cairna moschata after infection with HN10 (a novel DRV). Ten cell-type marker genes were used to annotate the cell type, indicating a high degree of cell heterogeneity in the Bursa of Fabricius. Most of the innate and adaptive immune system-related genes were highly expressed in T cells, B cells, neutrophils, macrophages, and DCs. In the Bursa of Fabricius, the proportions of DCs and macrophages were largely increased by HN10 infection at 14 d, suggesting that DCs and macrophages play important roles in the long-term viral response. Notably, a number of innate and adaptive immune system-related genes were highly expressed at 24 h after HN10 infection, indicating that the Bursa of Fabricius has a very strong immune function even in the early developmental stage. In the immune system, the NOD-like receptor signaling pathway and RIG-I-like receptor signaling pathway were significantly activated at the early stage of HN10 infection, while the Toll-like receptor signaling pathway was significantly activated at the late stage. Enrichment analysis suggested that different immune signaling pathways play roles in specific developmental stages. Our data provide an opportunity to reveal the immune response to DRV infection at the single-cell level.

Targeting Mitochondria: Influence of Metabolites on Mitochondrial Heterogeneity.

Mitochondria are vital organelles that provide energy for the metabolic processes of cells. These include regulating cellular metabolism, autophagy, apoptosis, calcium ions, and signaling processes. Despite their varying functions, mitochondria are considered semi-independent organelles that possess their own genome, known as mtDNA, which encodes 13 proteins crucial for oxidative phosphorylation. However, their diversity reflects an organism's adaptation to physiological conditions and plays a complex function in cellular metabolism. Mitochondrial heterogeneity exists at the single-cell and tissue levels, impacting cell shape, size, membrane potential, and function. This heterogeneity can contribute to the progression of diseases such as neurodegenerative diseases, metabolic diseases, and cancer. Mitochondrial dynamics enhance the stability of cells and sufficient energy requirement, but these activities are not universal and can lead to uneven mitochondria, resulting in heterogeneity. Factors such as genetics, environmental compounds, and signaling pathways are found to affect these cellular processes and heterogeneity. Additionally, the varying roles of metabolites such as NADH and ATP affect glycolysis's speed and efficiency. An imbalance in metabolites can impair ATP production and redox potential in the mitochondria. Therefore, this review will explore the influence of metabolites in shaping mitochondrial morphology, how these changes contribute to age-related diseases and the therapeutic targets for regulating mitochondrial heterogeneity.

HER4 is a high-affinity dimerization partner for all EGFR/HER/ErbB family proteins.

Human epidermal growth factor receptors (HER)-also known as EGFR or ErbB receptors-are a subfamily of receptor tyrosine kinases (RTKs) that play crucial roles in cell growth, division, and differentiation. HER4 (ErbB4) is the least studied member of this family, partly because its expression is lower in later stages of development. Recent work has suggested that HER4 can play a role in metastasis by regulating cell migration and invasiveness; however, unlike EGFR and HER2, the precise role that HER4 plays in tumorigenesis is still unresolved. Early work on HER family proteins suggested that there are direct interactions between the four members, but to date, there has been no single study of all four receptors in the same cell line with the same biophysical method. Here, we quantitatively measure the degree of association between HER4 and the other HER family proteins in live cells with a time-resolved fluorescence technique called pulsed interleaved excitation fluorescence cross-correlation spectroscopy (PIE-FCCS). PIE-FCCS is sensitive to the oligomerization state of membrane proteins in live cells, while simultaneously measuring single-cell protein expression levels and diffusion coefficients. Our PIE-FCCS results demonstrate that HER4 interacts directly with all HER family members in the cell plasma membrane. The interaction between HER4 and other HER family members intensified in the presence of a HER4-specific ligand. Our work suggests that HER4 is a preferred dimerization partner for all HER family proteins, even in the absence of ligands.

Heterogeneity in Dental Tissue–Derived MSCs Revealed by Single-Cell RNA-seq

Mesenchymal stromal cells (MSCs) are multipotent, progenitor cells that reside in tissues across the human body, including the periodontal ligament (PDL) and gingiva. They are a promising therapeutic tool for various degenerative and inflammatory diseases. However, different heterogeneity levels caused by tissue-to-tissue and donor-to-donor variability, and even intercellular differences within a given MSCs population, restrict their therapeutic potential. There are considerable efforts to decipher these heterogeneity levels using different “omics” approaches, including single-cell transcriptomics. Previous studies applied this approach to compare MSCs isolated from various tissues of different individuals, but distinguishing between donor-to-donor and tissue-to-tissue variability is still challenging. In this study, MSCs were isolated from the PDL and gingiva of 5 periodontally healthy individuals and cultured in vitro. A total of 3,844 transcriptomes were generated using single-cell mRNA sequencing. Clustering across the 2 different tissues per donor identified PDL- and gingiva-specific and tissue-spanning MSCs subpopulations with unique upregulated gene sets. Gene/pathway enrichment and protein-protein interaction (PPI) network analysis revealed differences restricted to several cellular processes between tissue-specific subpopulations, indicating a limited tissue-of-origin variability in MSCs. Gene expression, pathway enrichment, and PPI network analysis across all donors’ PDL- or gingiva-specific subpopulations showed significant but limited donor-to-donor differences. In conclusion, this study demonstrates tissue- and donor-specific variabilities in the transcriptome level of PDL- and gingiva-derived MSCs, which seem restricted to specific cellular processes. Identifying tissue-specific and tissue-spanning subpopulations highlights the intercellular differences in dental tissue–derived MSCs. It could be reasonable to control MSCs at a single-cell level to ensure their properties before transplantation.

Regulation of plasma soluble receptors of TNF and IL-1 in patients with COVID-19 differs from that observed in sepsis

IL-1α/β and TNF are closely linked to the pathology of severe COVID-19 and sepsis. The soluble forms of their receptors, functioning as decoy receptors, exhibit inhibitory effects. However, little is known about their regulation in severe bacterial and viral infections, which we aimed to investigate in this study. The circulating soluble receptors of TNF (sTNFR1 and sTNFR2) and IL-1α/β (sIL-1R1, sIL-1R2) were evaluated in the plasma of patients with COVID-19, severe bacterial infections, and sepsis and compared with healthy controls. Additionally, IL1R1, IL1R2, TNFRSF1A, and TNFRSF1B expression was evaluated at the single cell level in PBMCs derived from COVID-19 or sepsis patients. Plasma concentrations of sIL-1R1, sTNFR1, and sTNFR2 were significantly higher in COVID-19 patients compared to healthy subjects. Notably, sIL-1R1 levels were particularly elevated in ICU COVID-19 patients, and transcriptome analysis indicated heightened IL1R1 expression in PBMCs from severe COVID-19 patients. In severe bacterial infections, only sTNFR1 and sTNFR2 exhibited increased levels compared to healthy controls. Sepsis patients had decreased sIL-1R1 plasma concentrations but elevated sIL-1R2, sTNFR1, and sTNFR2 levels compared to healthy individuals, reflecting the heightened expression due to the increased numbers of monocytes present in sepsis. Finally, elevated concentrations of sIL-1R2, sTNFR1, and sTNFR2 were moderately associated with reduced 28-day survival in sepsis patients. Our study reveals distinct regulation of plasma concentrations of soluble IL-1 receptors in COVID-19 and sepsis. Moreover, soluble TNF receptors 1 and 2 consistently rise in all conditions and show a positive correlation with disease severity in sepsis.