Comprehensive analysis of anoikis-related genes in diagnosis osteoarthritis: based on machine learning and single-cell RNA sequencing data

Osteoarthritis (OA) is a degenerative disease closely associated with Anoikis. The objective of this work was to discover novel transcriptome-based anoikis-related biomarkers and pathways for OA progression.The microarray datasets GSE114007 and GSE89408 were downloaded using the Gene Expression Omnibus (GEO) database. A collection of genes linked to anoikis has been collected from the GeneCards database. The intersection genes of the differential anoikis-related genes (DEARGs) were identified using a Venn diagram. Infiltration analyses were used to identify and study the differentially expressed genes (DEGs). Anoikis clustering was used to identify the DEGs. By using gene clustering, two OA subgroups were formed using the DEGs. GSE152805 was used to analyse OA cartilage on a single cell level. 10 DEARGs were identified by lasso analysis, and two Anoikis subtypes were constructed. MEgreen module was found in disease WGCNA analysis, and MEturquoise module was most significant in gene clusters WGCNA. The XGB, SVM, RF, and GLM models identified five hub genes (CDH2, SHCBP1, SCG2, C10orf10, P FKFB3), and the diagnostic model built using these five genes performed well in the training and validation cohorts. analysing single-cell RNA sequencing data from GSE152805, including 25,852 cells of 6 OA cartilage.

Space and genealogy determine inter-individual differences in siderophore gene expression in bacterial colonies

Heterogeneity in gene expression is common among clonal cells in bacteria, although the sources and functions of variation often remain unknown. Here, we track cellular heterogeneity in the bacterium Pseudomonas aeruginosa during colony growth by focusing on siderophore gene expression (pyoverdine versus pyochelin) important for iron nutrition. We find that the spatial position of cells within colonies and non-genetic yet heritable differences between cell lineages are significant sources of cellular heterogeneity, while cell pole age and lifespan have no effect. Regarding functions, our results indicate that cells adjust their siderophore investment strategies along a gradient from the colony center to its edge. Moreover, cell lineages with below-average siderophore investment benefit from lineages with above-average siderophore investment, presumably due to siderophore sharing. Our study highlights that single-cell experiments with dual gene expression reporters can identify sources of gene expression variation of interlinked traits and offer explanations for adaptive benefits in bacteria.

Gastric cancer immunosuppressive microenvironment heterogeneity: implications for therapy development

Although immunotherapy has revolutionized solid tumor treatment, durable responses in gastric cancer (GC) remain limited. The heterogeneous tumor microenvironment (TME) facilitates immune evasion, contributing to resistance to conventional and immune therapies. Recent studies have highlighted how specific TME components in GC acquire immune escape capabilities through cancer-specific factors. Understanding the underlying molecular mechanisms and targeting the immunosuppressive TME will enhance immunotherapy efficacy and patient outcomes. This review summarizes recent advances in GC TME research and explores the role of the immune-suppressive system as a context-specific determinant. We also provide insights into potential treatments beyond checkpoint inhibition.

The Role of Immune Cells in DKD: Mechanisms and Targeted Therapies

Diabetic kidney disease (DKD), is a common microvascular complication and a major cause of death in patients with diabetes. Disorders of immune cells and immune cytokines can accelerate DKD development of in a number of ways. As the kidney is composed of complex and highly differentiated cells, the interactions among different cell types and immune cells play important regulatory roles in disease development. Here, we summarize the latest research into the molecular mechanisms underlying the interactions among various immune and renal cells in DKD. In addition, we discuss the most recent studies related to single cell technology and bioinformatics analysis in the field of DKD. The aims of our review were to explore immune cells as potential therapeutic targets in DKD and provide some guidance for future clinical treatments.

High-Dimensional Single-Cell Multimodal Landscape of Human Carotid Atherosclerosis

BACKGROUND

Atherosclerotic plaques are complex tissues composed of a heterogeneous mixture of cells. However, our understanding of the comprehensive transcriptional and phenotypic landscape of the cells within these lesions is limited.

METHODS

To characterize the landscape of human carotid atherosclerosis in greater detail, we combined cellular indexing of transcriptomes and epitopes by sequencing and single-cell RNA sequencing to classify all cell types within lesions (n=21; 13 symptomatic) to achieve a comprehensive multimodal understanding of the cellular identities of atherosclerosis and their association with clinical pathophysiology.

RESULTS

We identified 25 cell populations, each with a unique multiomic signature, including macrophages, T cells, NK (natural killer) cells, mast cells, B cells, plasma cells, neutrophils, dendritic cells, endothelial cells, fibroblasts, and smooth muscle cells (SMCs). Among the macrophages, we identified 2 proinflammatory subsets enriched in IL-1B (interleukin-1B) or C1Q expression, 2 TREM2-positive foam cells (1 expressing inflammatory genes), and subpopulations with a proliferative gene signature and SMC-specific gene signature with fibrotic pathways upregulated. Further characterization revealed various subsets of SMCs and fibroblasts, including SMC-derived foam cells. These foamy SMCs were localized in the deep intima of coronary atherosclerotic lesions. Utilizing cellular indexing of transcriptomes and epitopes by sequencing data, we developed a flow cytometry panel, using cell surface proteins CD29, CD142, and CD90, to isolate SMC-derived cells from lesions. Lastly, we observed reduced proportions of efferocytotic macrophages, classically activated endothelial cells, and contractile and modulated SMC-derived cells, while inflammatory SMCs were enriched in plaques of clinically symptomatic versus asymptomatic patients.

CONCLUSIONS

Our multimodal atlas of cell populations within atherosclerosis provides novel insights into the diversity, phenotype, location, isolation, and clinical relevance of the unique cellular composition of human carotid atherosclerosis. These findings facilitate both the mapping of cardiovascular disease susceptibility loci to specific cell types and the identification of novel molecular and cellular therapeutic targets for the treatment of the disease.

scNovel: a scalable deep learning-based network for novel rare cell discovery in single-cell transcriptomics

Single-cell RNA sequencing has achieved massive success in biological research fields. Discovering novel cell types from single-cell transcriptomics has been demonstrated to be essential in the field of biomedicine, yet is time-consuming and needs prior knowledge. With the unprecedented boom in cell atlases, auto-annotation tools have become more prevalent due to their speed, accuracy and user-friendly features. However, existing tools have mostly focused on general cell-type annotation and have not adequately addressed the challenge of discovering novel rare cell types. In this work, we introduce scNovel, a powerful deep learning-based neural network that specifically focuses on novel rare cell discovery. By testing our model on diverse datasets with different scales, protocols and degrees of imbalance, we demonstrate that scNovel significantly outperforms previous state-of-the-art novel cell detection models, reaching the most AUROC performance(the only one method whose averaged AUROC results are above 94%, up to 16.26% more comparing to the second-best method). We validate scNovel's performance on a million-scale dataset to illustrate the scalability of scNovel further. Applying scNovel on a clinical COVID-19 dataset, three potential novel subtypes of Macrophages are identified, where the COVID-related differential genes are also detected to have consistent expression patterns through deeper analysis. We believe that our proposed pipeline will be an important tool for high-throughput clinical data in a wide range of applications.

Spatiotemporal analysis of mRNA-protein relationships enhances transcriptome-based developmental inference

Elucidating the complex relationships between mRNA and protein expression at high spatiotemporal resolution is critical for unraveling multilevel gene regulation and enhancing mRNA-based developmental analyses. In this study, we conduct a single-cell analysis of mRNA and protein expression of transcription factors throughout C. elegans embryogenesis. Initially, cellular co-presence of mRNA and protein is low, increasing to a medium-high level (73%) upon factoring in delayed protein synthesis and long-term protein persistence. These factors substantially affect mRNA-protein concordance, leading to potential inaccuracies in mRNA-reliant gene detection and specificity characterization. Building on the learned relationship, we infer protein presence from mRNA expression and demonstrate its utility in identifying tissue-specific genes and elucidating relationships between genes and cells. This approach facilitates identifying the role of sptf-1/SP7 in neuronal lineage development. Collectively, this study provides insights into gene expression dynamics during rapid embryogenesis and approaches for improving the efficacy of transcriptome-based developmental analyses.

Modular dual-color BiAD sensors for locus-specific readout of epigenome modifications in single cells

Dynamic changes in the epigenome at defined genomic loci play crucial roles during cellular differentiation and disease development. Here, we developed dual-color bimolecular anchor detector (BiAD) sensors for high-sensitivity readout of locus-specific epigenome modifications by fluorescence microscopy. Our BiAD sensors comprise an sgRNA/dCas9 complex as anchor and double chromatin reader domains as detector modules, both fused to complementary parts of a split IFP2.0 fluorophore, enabling its reconstitution upon binding of both parts in close proximity. In addition, a YPet fluorophore is recruited to the sgRNA to mark the genomic locus of interest. With these dual-color BiAD sensors, we detected H3K9me2/3 and DNA methylation and their dynamic changes upon RNAi or inhibitor treatment with high sensitivity at endogenous genomic regions. Furthermore, we showcased locus-specific H3K36me2/3 readout as well as H3K27me3 and H3K9me2/3 enrichment on the inactive X chromosome, highlighting the broad applicability of our dual-color BiAD sensors for single-cell epigenome studies.

Unraveling IFN-I response dynamics and TNF crosstalk in the pathophysiology of systemic lupus erythematosus

Introduction

The innate immune system serves the crucial first line of defense against a wide variety of potential threats, during which the production of pro-inflammatory cytokines IFN-I and TNFα are key. This astonishing power to fight invaders, however, comes at the cost of risking IFN-I-related pathologies, such as observed during autoimmune diseases, during which IFN-I and TNFα response dynamics are dysregulated. Therefore, these response dynamics must be tightly regulated, and precisely matched with the potential threat. This regulation is currently far from understood.

Methods

Using droplet-based microfluidics and ODE modeling, we studied the fundamentals of single-cell decision-making upon TLR signaling in human primary immune cells (n = 23). Next, using biologicals used for treating autoimmune diseases [i.e., anti-TNFα, and JAK inhibitors], we unraveled the crosstalk between IFN-I and TNFα signaling dynamics. Finally, we studied primary immune cells isolated from SLE patients (n = 8) to provide insights into SLE pathophysiology.

Results

single-cell IFN-I and TNFα response dynamics display remarkable differences, yet both being highly heterogeneous. Blocking TNFα signaling increases the percentage of IFN-I-producing cells, while blocking IFN-I signaling decreases the percentage of TNFα-producing cells. Single-cell decision-making in SLE patients is dysregulated, pointing towards a dysregulated crosstalk between IFN-I and TNFα response dynamics.

Discussion

We provide a solid droplet-based microfluidic platform to study inherent immune secretory behaviors, substantiated by ODE modeling, which can challenge the conceptualization within and between different immune signaling systems. These insights will build towards an improved fundamental understanding on single-cell decision-making in health and disease.

CTC-Race: Single-Cell Motility Assay of Circulating Tumor Cells from Metastatic Lung Cancer Patients

Distinctive subpopulations of circulating tumor cells (CTCs) with increased motility are considered to possess enhanced tumor-initiating potential and contribute to metastasis. Single-cell analysis of the migratory CTCs may increase our understanding of the metastatic process, yet most studies are limited by technical challenges associated with the isolation and characterization of these cells due to their extreme scarcity and heterogeneity. We report a microfluidic method based on CTCs' chemotactic motility, termed as CTC-Race assay, that can analyze migrating CTCs from metastatic non-small-cell lung cancer (NSCLC) patients with advanced tumor stages and enable concurrent biophysical and biochemical characterization of them with single-cell resolution. Analyses of motile CTCs in the CTC-Race assay, in synergy with other single cell characterization techniques, could provide insights into cancer metastasis.

High Dynamic Range Probing of Single-Molecule Mechanical Force Transitions at Cell-Matrix Adhesion Bonds by a Plasmonic Tension Nanosensor

Mechanical signals in animal tissues are complex and rapidly changed, and how the force transduction emerges from the single-cell adhesion bonds remains unclear. DNA-based molecular tension sensors (MTS), albeit successful in cellular force probing, were restricted by their detection range and temporal resolution. Here, we introduced a plasmonic tension nanosensor (PTNS) to make straight progress toward these shortcomings. Contrary to the fluorescence-based MTS that only has specific force response thresholds, PTNS enabled the continuous and reversible force measurement from 1.1 to 48 pN with millisecond temporal resolution. We used the PTNS to visualize the high dynamic range single-molecule force transitions at cell-matrix adhesions during adhesion formation and migration. Time-resolved force traces revealed that the lifetime and duration of stepwise force transitions of molecular clutches are strongly modulated by the traction force through filamentous actin. The force probing technique is sensitive, fast, and robust and constitutes a potential tool for single-molecule and single-cell biophysics.

Unveiling spatial complexity in solid tumor immune microenvironments through multiplexed imaging

Deciphering cellular components and the spatial interaction network of the tumor immune microenvironment (TIME) of solid tumors is pivotal for understanding biologically relevant cross-talks and, ultimately, advancing therapies. Multiplexed tissue imaging provides a powerful tool to elucidate spatial complexity in a holistic manner. We established and cross-validated a comprehensive immunophenotyping panel comprising over 121 markers for multiplexed tissue imaging using MACSima™ imaging cyclic staining (MICS) alongside an end-to-end analysis workflow. Applying this panel and workflow to primary cancer tissues, we characterized tumor heterogeneity, investigated potential therapeutical targets, conducted in-depth profiling of cell types and states, sub-phenotyped T cells within the TIME, and scrutinized cellular neighborhoods of diverse T cell subsets. Our findings highlight the advantage of spatial profiling, revealing immunosuppressive molecular signatures of tumor-associated myeloid cells interacting with neighboring exhausted, PD1high T cells in the TIME of hepatocellular carcinoma (HCC). This study establishes a robust framework for spatial exploration of TIMEs in solid tumors and underscores the potency of multiplexed tissue imaging and ultra-deep cell phenotyping in unraveling clinically relevant tumor components.

Droplet Microfluidics for Current Cancer Research: From Single-Cell Analysis to 3D Cell Culture

Cancer is the second leading cause of death worldwide. Differences in drug resistance and treatment response caused by the heterogeneity of cancer cells are the primary reasons for poor cancer therapy outcomes in patients. In addition, current in vitro anticancer drug-screening methods rely on two-dimensional monolayer-cultured cancer cells, which cannot accurately predict drug behavior in vivo. Therefore, a powerful tool to study the heterogeneity of cancer cells and produce effective in vitro tumor models is warranted to leverage cancer research. Droplet microfluidics has become a powerful platform for the single-cell analysis of cancer cells and three-dimensional cell culture of in vitro tumor spheroids. In this review, we discuss the use of droplet microfluidics in cancer research. Droplet microfluidic technologies, including single- or double-emulsion droplet generation and passive- or active-droplet manipulation, are concisely discussed. Recent advances in droplet microfluidics for single-cell analysis of cancer cells, circulating tumor cells, and scaffold-free/based 3D cell culture of tumor spheroids have been systematically introduced. Finally, the challenges that must be overcome for the further application of droplet microfluidics in cancer research are discussed.

Single-Cell Sequencing Reveals MYOF-Enriched Monocyte/Macrophage Subcluster as a Favorable Prognostic Factor in Sepsis

This research utilized single-cell RNA sequencing to map the immune cell landscape in sepsis, revealing 28 distinct cell clusters and categorizing them into nine major types. Delving into the monocyte/macrophage subclusters, 12 unique subclusters are identified and pathway enrichment analyses are conducted using KEGG and GO, discovering enriched pathways such as oxidative phosphorylation and antigen processing. Further GSVA and AUCell assessments show varied activation of interferon pathways, especially in subclusters 4 and 11. The clinical correlation analysis reveals genes significantly linked to survival outcomes. Additionally, cellular differentiation in these subclusters is explored. Building on these insights, the differential gene expression within these subclusters is specifically scrutinized, which reveal MYOF as a key gene with elevated expression levels in the survivor group. This finding is further supported by in-depth pathway enrichment analysis and the examination of cellular differentiation trajectories, where MYOF's role became evident in the context of immune response regulation and sepsis progression. Validating the role of the MYOF gene in sepsis, a dose-dependent response to LPS in THP-1 cells and C57 mice is observed. Finally, inter-cellular communications are analyzed, particularly focusing on the MYOF+Mono/Macro subcluster, which indicates a pivotal role in immune regulation and potential therapeutic targeting.

Precision unveiled: Synergistic genomic landscapes in breast cancer-Integrating single-cell analysis and decoding drug toxicity for elite prognostication and tailored therapeutics

BACKGROUND

Globally, breast cancer, with diverse subtypes and prognoses, necessitates tailored therapies for enhanced survival rates. A key focus is glutamine metabolism, governed by select genes. This study explored genes associated with T cells and linked them to glutamine metabolism to construct a prognostic staging index for breast cancer patients for more precise medical treatment.

METHODS

Two frameworks, T-cell related genes (TRG) and glutamine metabolism (GM), stratified breast cancer patients. TRG analysis identified key genes via hdWGCNA and machine learning. T-cell communication and spatial transcriptomics emphasized TRG's clinical value. GM was defined using Cox analyses and the Lasso algorithm. Scores categorized patients as TRG_high+GM_high (HH), TRG_high+GM_low (HL), TRG_low+GM_high (LH), or TRG_low+GM_low (LL). Similarities between HL and LH birthed a "Mixed" class and the TRG_GM classifier. This classifier illuminated gene variations, immune profiles, mutations, and drug responses.

RESULTS

Utilizing a composite of two distinct criteria, we devised a typification index termed TRG_GM classifier, which exhibited robust prognostic potential for breast cancer patients. Our analysis elucidated distinct immunological attributes across the classifiers. Moreover, by scrutinizing the genetic variations across groups, we illuminated their unique genetic profiles. Insights into drug sensitivity further underscored avenues for tailored therapeutic interventions.

CONCLUSION

Utilizing TRG and GM, a robust TRG_GM classifier was developed, integrating clinical indicators to create an accurate predictive diagnostic map. Analysis of enrichment disparities, immune responses, and mutation patterns across different subtypes yields crucial subtype-specific characteristics essential for prognostic assessment, clinical decision-making, and personalized therapies. Further exploration is warranted into multiple fusions between metrics to uncover prognostic presentations across various dimensions.

Single-cell analytical technologies: uncovering the mechanisms behind variations in immune responses

The immune landscape varies among individuals. It determines the immune response and results in surprisingly diverse symptoms, even in response to similar external stimuli. However, the detailed mechanisms underlying such diverse immune responses have remained mostly elusive. The utilization of recently developed single-cell multimodal analysis platforms has started to answer this question. Emerging studies have elucidated several molecular networks that may explain diversity with respect to age or other factors. An elaborate interplay between inherent physical conditions and environmental conditions has been demonstrated. Furthermore, the importance of modifications by the epigenome resulting in transcriptome variation among individuals is gradually being revealed. Accordingly, epigenomes and transcriptomes are direct indicators of the medical history and dynamic interactions with environmental factors. Coronavirus disease 2019 (COVID-19) has recently become one of the most remarkable examples of the necessity of in-depth analyses of diverse responses with respect to various factors to improve treatment in severe cases and to prevent viral transmission from asymptomatic carriers. In fact, determining why some patients develop serious symptoms is still a pressing issue. Here, we review the current "state of the art" in single-cell analytical technologies and their broad applications to healthy individuals and representative diseases, including COVID-19.

Recent Progress in Mass Spectrometry-Based Single-Cell Metabolic Analysis

Single-cell analysis enables the measurement of biomolecules at the level of individual cells, facilitating in-depth investigations into cellular heterogeneity and precise interpretation of the related biological mechanisms. Among these biomolecules, cellular metabolites exhibit remarkable sensitivity to environmental and biochemical changes, unveiling a hidden world underlying cellular heterogeneity and allowing for the determination of cell physiological states. However, the metabolic analysis of single cells is challenging due to the extremely low concentrations, substantial content variations, and rapid turnover rates of cellular metabolites. Mass spectrometry (MS), characterized by its high sensitivity, wide dynamic range, and excellent selectivity, is employed in single-cell metabolic analysis. This review focuses on recent advances and applications of MS-based single-cell metabolic analysis, encompassing three key steps of single-cell isolation, detection, and application. It is anticipated that MS will bring profound implications in biomedical practices, serving as advanced tools to depict the single-cell metabolic landscape.

Genomic and immune landscape in hepatocellular carcinoma: Implications for personalized therapeutics

Hepatocellular carcinoma (HCC) is a globally prevalent malignancy, marked by genetic heterogeneity and intricate tumor microenvironment interactions. In this study, we undertook a detailed single-cell analysis of six active HCC patients, highlighting strong correlations between gene expression levels and cellular characteristics. UMAP clustering revealed seven distinct cell categories with associated gene expressions. A divergence was observed in tumor cells into high and low cuproptosis groups, each associated with distinct pathways: oxidative stress for the high cuproptosis group and inflammatory and angiogenesis pathways for the low group. CellChat analysis on the TCGA-LIHC cohort displayed unique intercellular interactions among hepatocytes, T cells, and other cells, with pathways like COLLAGEN and VEGF being pivotal. Functional enrichment analyses exposed pathways enriched between cuproptosis groups, with KEGG emphasizing diseases like Parkinson's. COX survival analysis identified key prognostic genes, revealing distinct survival rates between risk groups in TCGA and GSE14520 cohorts. Mutation data highlighted missense mutations, with TTN, TP53, and CTNNB1 being the most mutated in HCC. Immune infiltration analysis via CIBERSORTx indicated differences between risk groups in NK cells, neutrophils, and other cells. Our drug sensitivity investigation showed significant correlations between model genes and drug responsiveness, emphasizing the importance of patient risk stratification for therapeutic approaches. Further, ATP6V1G1 was recognized in its role in apoptosis and migration in HCC cells. In conclusion, our findings illuminate the complexities of HCC progression, potential predictive genetic markers for drug response, and the pivotal role of ATP6V1G1, suggesting avenues for targeted therapeutic strategies in HCC.

Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms

Raman spectroscopy is an emerging method for the identification of bacteria. Nevertheless, a lot of different parameters need to be considered to establish a reliable database capable of identifying real-world samples such as medical or environmental probes. In this review, the establishment of such reliable databases with the proper design in microbiological Raman studies is demonstrated, shining a light into all the parts that require attention. Aspects such as the strain selection, sample preparation and isolation requirements, the phenotypic influence, measurement strategies, as well as the statistical approaches for discrimination of bacteria, are presented. Furthermore, the influence of these aspects on spectra quality, result accuracy, and read-out are discussed. The aim of this review is to serve as a guide for the design of microbiological Raman studies that can support the establishment of this method in different fields.

13C-SpaceM: Spatial single-cell isotope tracing reveals heterogeneity of de novo fatty acid synthesis in cancer

Metabolism has emerged as a key factor in homeostasis and disease including cancer. Yet, little is known about the heterogeneity of metabolic activity of cancer cells due to the lack of tools to directly probe it. Here, we present a novel method, 13C-SpaceM for spatial single-cell isotope tracing of glucose-dependent de novo lipogenesis. The method combines imaging mass spectrometry for spatially-resolved detection of 13C6-glucose-derived 13C label incorporated into esterified fatty acids with microscopy and computational methods for data integration and analysis. We validated 13C-SpaceM on a spatially-heterogeneous normoxia-hypoxia model of liver cancer cells. Investigating cultured cells, we revealed single-cell heterogeneity of lipogenic acetyl-CoA pool labelling degree upon ACLY knockdown that is hidden in the bulk analysis and its effect on synthesis of individual fatty acids. Next, we adapted 13C-SpaceM to analyze tissue sections of mice harboring isocitrate dehydrogenase (IDH)-mutant gliomas. We found a strong induction of de novo fatty acid synthesis in the tumor tissue compared to the surrounding brain. Comparison of fatty acid isotopologue patterns revealed elevated uptake of mono-unsaturated and essential fatty acids in the tumor. Furthermore, our analysis uncovered substantial spatial heterogeneity in the labelling of the lipogenic acetyl-CoA pool indicative of metabolic reprogramming during microenvironmental adaptation. Overall, 13C-SpaceM enables novel ways for spatial probing of metabolic activity at the single cell level. Additionally, this methodology provides unprecedented insight into fatty acid uptake, synthesis and modification in normal and cancerous tissues.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels the molecular feature of M2 macrophages of head and neck squamous cell carcinoma

The connection between head and neck squamous cell carcinoma (HNSC) and M2 tumour-associated macrophages is not yet fully understood. We gathered gene expression profiles and clinical data from HNSC patients in the TCGA database. Using Consensus Clustering, we categorized these patients into M2 macrophage-related clusters. We developed a M2 macrophage-related signature (MRS) through statistical analyses. Additionally, we assessed gene expression in HNSC cells using single-cell sequencing data (GSE139324). We identified three distinct M2 macrophage-related clusters in HNSC, each with different prognostic outcomes and immune characteristics. Patients with different MRS profiles exhibited variations in immune infiltration, genetic mutations and prognosis. FCGR2A may play a role in creating an immunosuppressive tumour microenvironment and could potentially serve as a therapeutic target for HNSC. Our study demonstrated that M2 macrophage-related genes significantly impact the development and progression of HNSC. The M2 macrophage-related model offered a more comprehensive assessment of HNSC patient prognosis, genetic mutations and immune features. FCGR2A was implicated in immunosuppressive microenvironments and may hold promise for the development of novel immunotherapeutic strategies for HNSC.

Single-cell analysis reveals one cancer-associated fibroblasts subtype linked to metastasis in breast cancer: MXRA5 as a potential novel marker for prognosis

Cancer-associated fibroblasts (CAFs) are prevalent in the tumor microenvironment of breast cancer, comprising a group of cell subpopulations with spatial, phenotypic, and functional heterogeneity. Due to the lack of specific markers for CAF subpopulations, their specific mechanisms in breast cancer remain unclear. We identified eight distinct CAF phenotypes in breast cancer using multiple single-cell RNA sequencing datasets and determined distinct transcription factors (TFs) of CAFs through SCENIC analysis. Our study highlights one CAF subtype in breast cancer, FN1+CAF2, associated with metastasis and macrophage polarization. We observed elevated FN1 expression in the stromal tissue of breast cancer patients. Furthermore, FN1 knockdown in CAFs reduced the migration ability of breast cancer cells. We identified a regulatory gene, MXRA5, in CAF2, which may play crucial roles in breast cancer. Our results indicated upregulated MXRA5 expression in breast cancer tissues and CAFs from patients with lymph node metastasis in the following experiment. Overall, our study reveals that the FN1+CAF2 subtype is associated with metastasis and suggests that MXRA5 may be a novel marker mediating the effects of CAF2 on breast cancer metastasis. This study enriches our understanding of CAF heterogeneity and offers new insights for treating breast cancer metastasis.

3D Amplified Single-Cell RNA and Protein Imaging Identifies Oncogenic Transcript Subtypes in B-Cell Acute Lymphoblastic Leukemia

Simultaneous in situ detection of transcript and protein markers at the single-cell level is essential for gaining a better understanding of tumor heterogeneity and for predicting and monitoring treatment responses. However, the limited accessibility to advanced 3D imaging techniques has hindered their rapid implementation. Here, we present a 3D single-cell imaging technique, termed 3D digital rolling circle amplification (4DRCA), capable of the multiplexed and amplified simultaneous digital quantification of single-cell RNAs and proteins using standard fluorescence microscopy and off-the-shelf reagents. We generated spectrally distinguishable DNA amplicons from molecular markers through an integrative protocol combining single-cell RNA and protein assays and directly enumerated the amplicons by leveraging an open-source algorithm for 3D deconvolution with a custom-built automatic gating algorithm. With 4DRCA, we were able to simultaneously quantify surface protein markers and cytokine transcripts in T-lymphocytes. We also show that 4DRCA can distinguish BCR-ABL1 fusion transcript positive B-cell acute lymphoblastic leukemia cells with or without CD19 protein expression. The accessibility and extensibility of 4DRCA render it broadly applicable to other cell-based diagnostic workflows, enabling sensitive and accurate single-cell RNA and protein profiling.

Integrated single-cell and bulk characterization of branched chain amino acid metabolism-related key gene BCAT1 and association with prognosis and immunogenicity of clear cell renal cell carcinoma

BACKGROUND

The relationship between clear cell renal cell carcinoma (ccRCC) and branched-chain amino acids (BCAA) metabolism has yet to be thoroughly explored.

METHODS

The BCAA metabolism-related clusters were constructed using non-negative matrix factorization (NMF). The features of BCAA metabolism in ccRCC were evaluated by building a prognostic model using least absolute shrinkage and selection operator (LASSO) regression algorithm. Real-time quantitative PCR (RT-qPCR) was employed to analyze differential expression of branched-chain amino acid transaminase 1 (BCAT1) between cancer and paracancer tissues and between different cell lines. Cell counting kit-8, wound healing and Transwell chamber assays were conducted to determine changes in proliferative and metastatic abilities of A498 and 786-O cells.

RESULTS

Two BCAA metabolism-related clusters with distinct prognostic and immune infiltration characteristics were identified in ccRCC. The BCAA metabolic signature (BMS) was capable of distinguishing immune features, tumor mutation burden, responses to immunotherapy, and drug sensitivity among ccRCC patients. RT-qPCR revealed overexpression of BCAT1 in ccRCC tissues and cell lines. Additionally, single-gene RNA sequencing analysis demonstrated significant enrichment of BCAT1 in macrophages and tumor cells. BCAT1 played tumor-promoting role in ccRCC and was closely associated with immunosuppressive cells and checkpoints. BCAT1 promoted ccRCC cell proliferation and metastasis.

CONCLUSIONS

The BMS played a crucial role in determining the prognosis, tumor mutation burden, responses to immunotherapy and drug sensitivity of ccRCC patients, as well as the immune cell infiltration features. BCAT1 was linked to immunosuppressive microenvironments and may offer new sights into ccRCC immunotherapeutic targets.

An unbiased approach of molecular characterization of the endometrium: toward defining endometrial-based infertility

Infertility is a complex condition affecting millions of couples worldwide. The current definition of infertility, based on clinical criteria, fails to account for the molecular and cellular changes that may occur during the development of infertility. Recent advancements in sequencing technology and single-cell analysis offer new opportunities to gain a deeper understanding of these changes. The endometrium has a potential role in infertility and has been extensively studied to identify gene expression profiles associated with (impaired) endometrial receptivity. However, limited overlap among studies hampers the identification of relevant downstream pathways that could play a role in the development of endometrial-related infertility. To address these challenges, we propose sequencing the endometrial transcriptome of healthy and infertile women at the single-cell level to consistently identify molecular signatures. Establishing consensus on physiological patterns in endometrial samples can aid in identifying deviations in infertile patients. A similar strategy has been used with great success in cancer research. However, large collaborative initiatives, international uniform protocols of sample collection and processing are crucial to ensure reliability and reproducibility. Overall, the proposed approach holds promise for an objective and accurate classification of endometrial-based infertility and has the potential to improve diagnosis and treatment outcomes.

Screening of Candidate Inflammatory Markers of Epithelial Cells in Hepatocellular Carcinoma Based on Integration Analysis of TCGA/ICGC Databases and Single-cell Sequencing

BACKGROUND

Hepatocellular Carcinoma (HCC) is closely linked to inflammatory reactions, with chronic liver diseases acting as major risk factors. In the inflammatory microenvironment, repeated damage and repair of liver cells lead to genetic mutations, abnormal proliferation, and tumorigenesis.

OBJECTIVE

This study aimed to investigate the expression profile of specific cell clusters under inflammatory stimulation in HCC and identify potential therapeutic drugs.

METHODS

Comprehensive analysis of HCC transcriptome data and single-cell sequencing data from TCGA, ICGC, and GEO databases was conducted to explore the specific molecular mechanisms of epithelial cells. Virtual screening of natural compounds in the ZINC database and in vitro cell experiments were performed to identify drugs that regulate the expression of inflammatory factors in epithelial cells.

RESULTS

Analysis of the single-cell dataset revealed cell clusters closely associated with HCC, notably Epithelial cells, Hepatocytes, MSC, and iPS cells, with Epithelial cells playing a pivotal role in HCC development. Further investigation of TCGA data unveiled 83 differentially expressed genes (DEGs) related to inflammatory responses in HCC. Intersection analysis of DEGs in epithelial cells and HCC DEGs identified 12 common DEGs, including ADRM1, ATP2B1, FZD5, GPC3, KIF1B, KLF6, LY6E, MET, NAMPT, SERPINE1, SPHK1, and SRI. Prognostic analysis revealed that CCL7, GPR132, ITGB8, PTAFR, SELL, and VIP were influential in the survival prognosis of HCC. A prognostic model based on the expression levels of these genes demonstrated an increased risk of HCC associated with higher differential expression of inflammatory response genes. Additionally, molecular dynamics simulations indicated that compounds NADH and Deferoxamine formed stable docking models with the inflammatory protein VIP, suggesting their potential as candidates for targeted therapy.

CONCLUSION

Inflammatory factors CCL7, GPR132, ITGB8, PTAFR, SELL, and VIP influence the inflammatory cascade response in HCC epithelial cells, and their expression correlates with the survival prognosis of HCC patients. Interfering with VIP expression effectively suppresses proliferation, migration, and invasion of HCC cells, as well as inhibiting the occurrence of inflammatory cascade reactions, thus slowing down the progression of hepatocellular carcinoma. Furthermore, compounds NADH and Deferoxamine have the potential to target and bind to the inflammatory protein VIP, highlighting their relevance in potential HCC treatment.

Digital PCR for Single-Cell Analysis

Single-cell analysis provides an overwhelming strategy for revealing cellular heterogeneity and new perspectives for understanding the biological function and disease mechanism. Moreover, it promotes the basic and clinical research in many fields at a single-cell resolution. A digital polymerase chain reaction (dPCR) is an absolute quantitative analysis technology with high sensitivity and precision for DNA/RNA or protein. With the development of microfluidic technology, digital PCR has been used to achieve absolute quantification of single-cell gene expression and single-cell proteins. For single-cell specific-gene or -protein detection, digital PCR has shown great advantages. So, this review will introduce the significance and process of single-cell analysis, including single-cell isolation, single-cell lysis, and single-cell detection methods, mainly focusing on the microfluidic single-cell digital PCR technology and its biological application at a single-cell level. The challenges and opportunities for the development of single-cell digital PCR are also discussed.

Pan-Cancer Analysis Reveals Disulfidoptosis-Associated Genes as Promising Immunotherapeutic Targets: Insights Gained from Bulk Omics and Single-Cell Sequencing Validation

Disulfidoptosis, a novel form of cell death, is distinct from other well-known cell death mechanisms. Consequently, a profound investigation into disulfidoptosis elucidates the fundamental mechanisms underlying tumorigenesis, presenting promising avenues for therapeutic intervention. Comprehensive analysis of disulfidoptosis-associated gene (DRG) expression in pan cancer utilized TCGA, GEO, and ICGC datasets, including survival and Cox-regression analyses for prognostic evaluation. We analyzed the association between DRG expression and both immune cell infiltration and immune-related gene expression using the ESTIMATE and TISDIB datasets. We obtained our single-cell RNA sequencing (scRNA-seq) data from the GEO repository. Subsequently, we assessed disulfidoptosis activity in various cell types. Evaluation of immune cell infiltration and biological functions was analyzed via single-sample gene set enrichment (ssGSEA) and gene set variation analysis (GSVA). For in vitro validation experiments, the results from real-time PCR (RT-qPCR) and Western blot were used to explore the expression of SLC7A11 in hepatocellular carcinoma (HCC) tissues and different cancer cell lines, while siRNA-mediated SLC7A11 knockdown effects on HCC cell proliferation and migration were examined. Expression levels of DRGs, especially SLC7A11, were significantly elevated in tumor samples compared to normal samples, which was associated with poorer outcomes. Except for SLC7A11, DRGs consistently exhibited high CNV and SNV rates, particularly in HCC. In various tumors, DRGs were negatively associated with DNA promoter methylation. TME analyses further illustrated a negative correlation of DRG expression with ImmuneScore and StromalScore and a positive correlation with tumor purity. Our analysis unveiled diverse cellular subgroups within HCC, particularly focusing on Treg cell populations, providing insights into the intricate interplay of immune activation and suppression within the tumor microenvironment (TME). These findings were further validated through RT-qPCR, Western blot analyses, and immunohistochemical analyses. Additionally, the knockdown of SLC7A11 induced a suppression of proliferation and migration in HCC cell lines. In conclusion, our comprehensive pan-cancer analysis research has demonstrated the significant prognostic and immunological role of disulfidoptosis across a spectrum of tumors, notably HCC, and identified SLC7A11 as a promising therapeutic target.

Microfluidic Single-Cell Study on Arabidopsis thaliana Protoplast Fusion-New Insights on Timescales and Reversibilities

Plant cells are omnipotent and breeding of new varieties can be achieved by protoplast fusion. Such fusions can be achieved by treatment with poly(ethylene glycol) or by applying an electric field. Microfluidic devices allow for controlled conditions and targeted manipulation of small batches of cells down to single-cell analysis. To provide controlled conditions for protoplast fusions and achieve high reproducibility, we developed and characterized a microfluidic device to reliably trap some Arabidopsis thaliana protoplasts and induced cell fusion by controlled addition of poly(ethylene glycol) (PEG, with a molecular weight of 6000). Experiments were conducted to determine the survival rate of isolated protoplasts in our microfluidic system. Afterward, PEG-induced fusion was studied. Our results indicate that the following fusion parameters had a significant impact on the fusion efficiency and duration: PEG concentration, osmolality of solution and flow velocity. A PEG concentration below 10% led to only partial fusion. The osmolality of the PEG fusion solution was found to strongly impact the fusion process; complete fusion of two source cells sufficiently took part in slightly hyper-osmotic solutions, whereas iso-osmotic solutions led to only partial fusion at a 20% PEG concentration. We observed accelerated fusion for higher fluid velocities. Until this study, it was common sense that fusion is one-directional, i.e., once two cells are fused into one cell, they stay fused. Here, we present for the first time the reversible fusion of protoplasts. Our microfluidic device paves the way to a deeper understanding of the kinetics and processes of cell fusion.

Cell-to-cell heterogeneity drives host-virus coexistence in a bloom-forming alga

Algal blooms drive global biogeochemical cycles of key nutrients and serve as hotspots for biological interactions in the ocean. The massive blooms of the cosmopolitan coccolithophore Emiliania huxleyi are often infected by the lytic E. huxleyi virus, which is a major mortality agent triggering bloom demise. This multi-annual "boom and bust" pattern of E. huxleyi blooms suggests that coexistence is essential for these host-virus dynamics. To investigate host-virus coexistence, we developed a new model system from an E. huxleyi culture that recovered from viral infection. The recovered population coexists with the virus, as host cells continue to divide in parallel to viral production. By applying single-molecule fluorescence in situ hybridization (smFISH) to quantify the fraction of infected cells, and assessing infection-specific lipid biomarkers, we identified a small subpopulation of cells that were infected and produced new virions, whereas most of the host population could resist infection. To further assess population heterogeneity, we generated clonal strain collections using single-cell sorting and subsequently phenotyped their susceptibility to E. huxleyi virus infection. This unraveled substantial cell-to-cell heterogeneity across a continuum of susceptibility to resistance, highlighting that infection outcome may vary depending on the individual cell. These results add a new dimension to our understanding of the complexity of host-virus interactions that are commonly assessed in bulk and described by binary definitions of resistance or susceptibility. We propose that phenotypic heterogeneity drives the host-virus coexistence and demonstrate how the coexistence with a lytic virus provides an ecological advantage for the host by killing competing strains.

Comprehensive analysis of senescence-associated genes in sepsis based on bulk and single-cell sequencing data

Background: Sepsis is a pathological state resulting from dysregulated immune response in host during severe infection, leading to persistent organ dysfunction and ultimately death. Senescence-associated genes (SAGs) have manifested their potential in controlling the proliferation and dissemination of a variety of diseases. Nevertheless, the correlation between sepsis and SAGs remains obscure and requires further investigation. Methods: Two RNA expression datasets (GSE28750 and GSE57065) specifically related to sepsis were employed to filter hub SAGs, based on which a diagnostic model predictive of the incidence of sepsis was developed. The association between the expression of the SAGs identified and immune-related modules was analyzed employing Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and Microenvironment Cell Populations-counter (MCP-counter) analysis. The identified genes in each cohort were clustered by unsupervised agreement clustering analysis and weighted gene correlation network analysis (WGCNA). Results: A diagnostic model for sepsis established based on hub genes (IGFBP7, GMFG, IL10, IL18, ETS2, HGF, CD55, and MMP9) exhibited a strong clinical reliability (AUC = 0.989). Sepsis patients were randomly assigned and classified by WGCNA into two clusters with distinct immune statuses. Analysis on the single-cell RNA sequencing (scRNA-seq) data revealed high scores of SAGs in the natural killer (NK) cells of the sepsis cohort than the healthy cohort. Conclusion: These findings suggested a close association between SAGs and sepsis alterations. The identified hub genes had potential to serve as a viable diagnostic marker for sepsis.

Dysfunctional Epithelial Barrier Is Characterized by Reduced E-Cadherin in Idiopathic Subglottic Stenosis

OBJECTIVES

To aim of the study was to characterize the molecular profile and functional phenotype of idiopathic subglottic stenosis (iSGS)-scar epithelium.

METHODS

Human tracheal biopsies from iSGS scar (n = 6) and matched non-scar (n = 6) regions were analyzed using single-cell RNA sequencing (scRNA-seq). Separate specimens were used for epithelial cell expansion in vitro to assess average growth rate and functional capabilities using transepithelial-electrical resistance (TEER), fluorescein isothiocyanate-dextran flux permeability assay, ciliary coverage, and cilia beating frequency (CBF). Finally, epithelial tight junction protein expression of cultured cells was quantified using immunoblot assay (n = 4) and immunofluorescence (n = 6).

RESULTS

scRNA-seq analysis revealed a decrease in goblet, ciliated, and basal epithelial cells in the scar iSGS cohort. Furthermore, mRNA expression of proteins E-cadherin, claudin-3, claudin-10, occludin, TJP1, and TJP2 was also reduced (p < 0.001) in scar epithelium. Functional assays demonstrated a decrease in TEER (paired 95% confidence interval [CI], 195.68-890.83 Ω × cm2 , p < 0.05), an increase in permeability (paired 95% CI, -6116.00 to -1401.99 RFU, p < 0.05), and reduced epithelial coverage (paired 95% CI, 0.1814-1.766, fold change p < 0.05) in iSGS-scar epithelium relative to normal controls. No difference in growth rate (p < 0.05) or CBF was found (paired 95% CI, -2.118 to 3.820 Hz, p > 0.05). Immunoblot assay (paired 95% CI, 0.0367-0.605, p < 0.05) and immunofluorescence (paired 95% CI, 13.748-59.191 mean grey value, p < 0.05) revealed E-cadherin reduction in iSGS-scar epithelium.

CONCLUSION

iSGS-scar epithelium has a dysfunctional barrier and reduced structural protein expression. These results are consistent with dysfunctional epithelium seen in other airway pathology. Further studies are warranted to delineate the causality of epithelial dysfunction on the downstream fibroinflammatory cascade in iSGS.

LEVEL OF EVIDENCE

NA Laryngoscope, 134:374-381, 2024.

Single-cell sequencing of head and neck carcinoma: Transcriptional landscape and prognostic model based on malignant epithelial cell features

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignancy worldwide, and the development of novel therapeutic strategies for HNSCC requires a profound understanding of tumor cells and the tumor microenvironment (TME). Additionally, HNSCC has a poor prognosis, necessitating the use of genetic markers for predicting clinical outcomes in HNSCC. In this study, we performed single-cell sequencing analysis on tumor tissues from seven HNSCC patients, along with one adjacent normal tissue. Firstly, the analysis of epithelial cell clusters revealed two clusters of malignant epithelial cells, characterized by unique gene expression patterns and dysregulated signaling pathways compared to normal epithelial cells. Secondly, the examination of the TME unveiled extensive crosstalk between fibroblasts and malignant epithelial cells, potentially mediated through ligand-receptor interactions such as COL1A1-SDC1, COL1A1-CD44, and COL1A2-SDC1. Furthermore, transcriptional heterogeneity was observed in immune cells present in the TME, including macrophages and dendritic cells. Finally, leveraging the gene expression profiles of malignant epithelial cells, we developed a prognostic model comprising six genes, which we validated using two independent datasets. These findings shed light on the heterogeneity within HNSCC tumors and the intricate interplay between malignant cells and the TME. Importantly, the developed prognostic model demonstrates high efficacy in predicting the survival outcomes of HNSCC patients.

Systematic analysis of apoptosis-related genes in the prognosis of lung squamous cell carcinoma: a combined single-cell RNA sequencing study

Background

Lung squamous cell carcinoma (LUSC) has a poor prognosis and lacks appropriate diagnostic and treatment strategies. Apoptosis dysregulation is associated with tumor occurrence and drug resistance, but the prognostic value of apoptosis-related genes (ARGs) in LUSC remains unclear.

Methods

Using univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analysis based on differentially expressed ARGs, we constructed an ARG-related prognostic model for LUSC survival rates. We conducted correlation analysis of prognostic ARGs by incorporating the dataset of normal lung tissue from the Genotype-Tissue Expression (GTEx) database. We then constructed a risk model, and the predictive ability of the model was evaluated using receiver operating characteristic (ROC) curve analysis. Non-small cell lung cancer (NSCLC) single-cell RNA sequencing (scRNA-seq) data were downloaded from the Gene Expression Omnibus (GEO) database. Subsequently, these data were subjected to single-cell analysis. Cell subgroups were determined and annotated by dimensionality reduction clustering, and the cell subgroups in disease development were identified via pseudotemporal analysis with the Monocle 2 algorithm.

Results

We identified four significantly prognostic ARGs and constructed a stable prognostic risk model. Kaplan-Meier curve analysis showed that the high-risk group had a poorer prognosis (P<0.05). Furthermore, the ROC analysis of 3-, 5- and 7-year survival rates confirmed that the model had good predictive value for patients with LUSC. Single-cell RNA sequencing showed the prognostic ARGS were enriched in epithelial cells, smooth muscle cells, and T cells. Pseudotime analysis was used to infer the differentiation process and time sequence of cells.

Conclusions

This study identified ARGs that are associated with prognosis in LUSC, and a risk model based on these prognostic genes was constructed that could accurately predict the prognosis of LUSC. Single-cell sequencing analysis provided new insights into the cellular-level development of tumors. These findings provide more guidance for the diagnosis and treatment of patients with LUSC.

Chaos in a bacterial stress response

Cellular responses to environmental changes are often highly heterogeneous and exhibit seemingly random dynamics. The astonishing insight of chaos theory is that such unpredictable patterns can, in principle, arise without the need for any random processes, i.e., purely deterministically without noise. However, while chaos is well understood in mathematics and physics, its role in cell biology remains unclear because the complexity and noisiness of biological systems make testing difficult. Here, we show that chaos explains the heterogeneous response of Escherichia coli cells to oxidative stress. We developed a theoretical model of the gene expression dynamics and demonstrate that chaotic behavior arises from rapid molecular feedbacks that are coupled with cell growth dynamics and cell-cell interactions. Based on theoretical predictions, we then designed single-cell experiments to show we can shift gene expression from periodic oscillations to chaos on demand. Our work suggests that chaotic gene regulation can be employed by cell populations to generate strong and variable responses to changing environments.

Aged intestinal stem cells propagate cell-intrinsic sources of inflammaging in mice

Low-grade chronic inflammation is a hallmark of ageing, associated with impaired tissue function and disease development. However, how cell-intrinsic and -extrinsic factors collectively establish this phenotype, termed inflammaging, remains poorly understood. We addressed this question in the mouse intestinal epithelium, using mouse organoid cultures to dissect stem cell-intrinsic and -extrinsic sources of inflammaging. At the single-cell level, we found that inflammaging is established differently along the crypt-villus axis, with aged intestinal stem cells (ISCs) strongly upregulating major histocompatibility complex class II (MHC-II) genes. Importantly, the inflammaging phenotype was stably propagated by aged ISCs in organoid cultures and associated with increased chromatin accessibility at inflammation-associated loci in vivo and ex vivo, indicating cell-intrinsic inflammatory memory. Mechanistically, we show that the expression of inflammatory genes is dependent on STAT1 signaling. Together, our data identify that intestinal inflammaging in mice is promoted by a cell-intrinsic mechanism, stably propagated by ISCs, and associated with a disbalance in immune homeostasis.

Thinking process templates for constructing data stories with SCDNEY

Background

Globally, scientists now have the ability to generate a vast amount of high throughput biomedical data that carry critical information for important clinical and public health applications. This data revolution in biology is now creating a plethora of new single-cell datasets. Concurrently, there have been significant methodological advances in single-cell research. Integrating these two resources, creating tailor-made, efficient, and purpose-specific data analysis approaches can assist in accelerating scientific discovery.

Methods

We developed a series of living workshops for building data stories, using Single-cell data integrative analysis (scdney). scdney is a wrapper package with a collection of single-cell analysis R packages incorporating data integration, cell type annotation, higher order testing and more.

Results

Here, we illustrate two specific workshops. The first workshop examines how to characterise the identity and/or state of cells and the relationship between them, known as phenotyping. The second workshop focuses on extracting higher-order features from cells to predict disease progression.

Conclusions

Through these workshops, we not only showcase current solutions, but also highlight critical thinking points. In particular, we highlight the Thinking Process Template that provides a structured framework for the decision-making process behind such single-cell analyses. Furthermore, our workshop will incorporate dynamic contributions from the community in a collaborative learning approach, thus the term 'living'.

A review of skin immune processes in acne

Acne vulgaris is one of the most prevalent skin conditions, affecting almost all teenagers worldwide. Multiple factors, including the excessive production of sebum, dysbiosis of the skin microbiome, disruption of keratinization within hair follicles, and local inflammation, are believed to trigger or aggravate acne. Immune activity plays a crucial role in the pathogenesis of acne. Recent research has improved our understanding of the immunostimulatory functions of microorganisms, lipid mediators, and neuropeptides. Additionally, significant advances have been made in elucidating the intricate mechanisms through which cutaneous innate and adaptive immune cells perceive and transmit stimulatory signals and initiate immune responses. However, our understanding of precise temporal and spatial patterns of immune activity throughout various stages of acne development remains limited. This review provides a comprehensive overview of the current knowledge concerning the immune processes involved in the initiation and progression of acne. Furthermore, we highlight the significance of detailed spatiotemporal analyses, including analyses of temporal dynamics of immune cell populations as well as single-cell and spatial RNA sequencing, for the development of targeted therapeutic and prevention strategies.

Recent developments in ionization techniques for single-cell mass spectrometry

The variation among individual cells plays a significant role in many biological functions. Single-cell analysis is advantageous for gaining insight into intricate biochemical mechanisms rarely accessible when studying tissues as a whole. However, measurement on a unicellular scale is still challenging due to unicellular complex composition, minute substance quantities, and considerable differences in compound concentrations. Mass spectrometry has recently gained extensive attention in unicellular analytical fields due to its exceptional sensitivity, throughput, and compound identification abilities. At present, single-cell mass spectrometry primarily concentrates on the enhancement of ionization methods. The principal ionization approaches encompass nanoelectrospray ionization (nano-ESI), laser desorption ionization (LDI), secondary ion mass spectrometry (SIMS), and inductively coupled plasma (ICP). This article summarizes the most recent advancements in ionization techniques and explores their potential directions within the field of single-cell mass spectrometry.

An oncogenic phenoscape of colonic stem cell polarization

Cancer cells are regulated by oncogenic mutations and microenvironmental signals, yet these processes are often studied separately. To functionally map how cell-intrinsic and cell-extrinsic cues co-regulate cell fate, we performed a systematic single-cell analysis of 1,107 colonic organoid cultures regulated by (1) colorectal cancer (CRC) oncogenic mutations, (2) microenvironmental fibroblasts and macrophages, (3) stromal ligands, and (4) signaling inhibitors. Multiplexed single-cell analysis revealed a stepwise epithelial differentiation phenoscape dictated by combinations of oncogenes and stromal ligands, spanning from fibroblast-induced Clusterin (CLU)+ revival colonic stem cells (revCSCs) to oncogene-driven LRIG1+ hyper-proliferative CSCs (proCSCs). The transition from revCSCs to proCSCs is regulated by decreasing WNT3A and TGF-β-driven YAP signaling and increasing KRASG12D or stromal EGF/Epiregulin-activated MAPK/PI3K flux. We find that APC loss and KRASG12D collaboratively limit access to revCSCs and disrupt stromal-epithelial communication-trapping epithelia in the proCSC fate. These results reveal that oncogenic mutations dominate homeostatic differentiation by obstructing cell-extrinsic regulation of cell-fate plasticity.

Analyzing the cellular and molecular atlas of ovarian mesenchymal cells provides a strategy against female reproductive aging

Ovarian mesenchymal cells (oMCs) constitute a distinct microenvironment that supports folliculogenesis under physiological conditions. Supplementation of exogenous non-ovarian mesenchymal-related cells has been reported to be an efficient approach to improve ovarian functions. However, the development and cellular and molecular characteristics of endogenous oMCs remain largely unexplored. In this study, we surveyed the single-cell transcriptomic landscape to dissect the cellular and molecular changes associated with the aging of oMCs in mice. Our results showed that the oMCs were composed of five ovarian differentiated MC (odMC) populations and one ovarian mesenchymal progenitor (oMP) cell population. These cells could differentiate into various odMCs via an oMP-derived route to construct the ovarian stroma structures. Comparative analysis revealed that ovarian aging was associated with decreased quantity of oMP cells and reduced quality of odMCs. Based on the findings of bioinformatics analysis, we designed different strategies involving supplementation with young oMCs to examine their effects on female fertility and health. Our functional investigations revealed that oMCs supplementation prior to ovarian senescence was the optimal method to improve female fertility and extend the reproductive lifespan of aged females in the long-term.

Improved protocol for single-nucleus RNA-sequencing of frozen human bladder tumor biopsies

This paper provides a laboratory workflow for single-nucleus RNA-sequencing (snRNA-seq) including a protocol for gentle nuclei isolation from fresh frozen tumor biopsies, making it possible to analyze biobanked material. To develop this protocol, we used non-frozen and frozen human bladder tumors and cell lines. We tested different lysis buffers (IgePal and Nuclei EZ) and incubation times in combination with different approaches for tissue and cell dissection: sectioning, semi-automated dissociation, manual dissociation with pestles, and semi-automated dissociation combined with manual dissociation with pestles. Our results showed that a combination of IgePal lysis buffer, tissue dissection by sectioning, and short incubation time was the best conditions for gentle nuclei isolation applicable for snRNA-seq, and we found limited confounding transcriptomic changes based on the isolation procedure. This protocol makes it possible to analyze biobanked material from patients with well-described clinical and histopathological information and known clinical outcomes with snRNA-seq.

Identifying Explainable Machine Learning Models and a Novel SFRP2+ Fibroblast Signature as Predictors for Precision Medicine in Ovarian Cancer

Ovarian cancer (OC) is a type of malignant tumor with a consistently high mortality rate. The diagnosis of early-stage OC and identification of functional subsets in the tumor microenvironment are essential to the development of patient management strategies. However, the development of robust models remains unsatisfactory. We aimed to utilize artificial intelligence and single-cell analysis to address this issue. Two independent datasets were screened from the Gene Expression Omnibus (GEO) database and processed to obtain overlapping differentially expressed genes (DEGs) in stage II-IV vs. stage I diseases. Three explainable machine learning algorithms were integrated to construct models that could determine the tumor stage and extract important characteristic genes as diagnostic biomarkers. Correlations between cancer-associated fibroblast (CAF) infiltration and characteristic gene expression were analyzed using TIMER2.0 and their relationship with survival rates was comprehensively explored via the Kaplan-Meier plotter (KM-plotter) online database. The specific expression of characteristic genes in fibroblast subsets was investigated through single-cell analysis. A novel fibroblast subset signature was explored to predict immune checkpoint inhibitor (ICI) response and oncogene mutation through Tumor Immune Dysfunction and Exclusion (TIDE) and artificial neural network algorithms, respectively. We found that Support Vector Machine-Shapley Additive Explanations (SVM-SHAP), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) successfully diagnosed early-stage OC (stage I). The area under the receiver operating characteristic curves (AUCs) of these models exceeded 0.990. Their overlapping characteristic gene, secreted frizzled-related protein 2 (SFRP2), was a risk factor that affected the overall survival of OC patients with stage II-IV disease (log-rank test: p < 0.01) and was specifically expressed in a fibroblast subset. Finally, the SFRP2+ fibroblast signature served as a novel predictor in evaluating ICI response and exploring pan-cancer tumor protein P53 (TP53) mutation (AUC = 0.853, 95% confidence interval [CI]: 0.829-0.877). In conclusion, the models based on SVM-SHAP, XGBoost, and RF enabled the early detection of OC for clinical decision making, and SFRP2+ fibroblast signature used in diagnostic models can inform OC treatment selection and offer pan-cancer TP53 mutation detection.

Single-Cell Genotyping of Single-Nucleotide Mutations Using In Situ Allele-Specific Loop-Mediated Isothermal Amplification

Single-nucleotide mutations (SNMs) in the bacterial genome may cause antibiotic resistance. The visualization of SNMs can indicate antibiotic resistance phenotypes at the single-cell level but remains challenging. Herein, we proposed an in situ allele-specific isothermal amplification proceeded inside cells, allowing us to image bacterial genes with single-nucleotide resolution. The primer for loop-mediated isothermal amplification (LAMP) was designed with artificial mismatch bases to serve as an allele-specific probe, endowing LAMP to specifically amplify genes with SNMs. Due to the high amplification efficiency of LAMP, the method termed AlleLAMP can generate high gain for imaging SNMs and precisely quantify mutated quinolone-resistant Salmonella in bacterial mixture. We utilized AlleLAMP to survey the selection of antibiotic resistance under the preservative stress and found that the mutant quinolone-resistant strain owned a survival advantage over the wild-type quinolone-sensitive strain under the stress of preservatives. AlleLAMP can serve as a single-cell tool for analyzing the relationship between bacterial genotype and phenotype.

Cancer-Associated Fibroblasts in Gastrointestinal Cancers: Unveiling Their Dynamic Roles in the Tumor Microenvironment

Gastrointestinal cancers are highly aggressive malignancies with significant mortality rates. Recent research emphasizes the critical role of the tumor microenvironment (TME) in these cancers, which includes cancer-associated fibroblasts (CAFs), a key component of the TME that have diverse origins, including fibroblasts, mesenchymal stem cells, and endothelial cells. Several markers, such as α-SMA and FAP, have been identified to label CAFs, and some specific markers may serve as potential therapeutic targets. In this review article, we summarize the literature on the multifaceted role of CAFs in tumor progression, including their effects on angiogenesis, immune suppression, invasion, and metastasis. In addition, we highlight the use of single-cell transcriptomics to understand CAF heterogeneity and their interactions within the TME. Moreover, we discuss the dynamic interplay between CAFs and the immune system, which contributes to immunosuppression in the TME, and the potential for CAF-targeted therapies and combination approaches with immunotherapy to improve cancer treatment outcomes.

Single-Cell Analysis Differentiates the Effects of p53 Mutation and p53 Loss on Cell Compositions of Oncogenic Kras-Driven Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is a devastating malignant disease with a dismal prognosis. In the past decades, a plethora of genetically engineered mouse models (GEMMs) with autochthonous pancreatic tumor development have greatly facilitated studies of pancreatic cancer. Commonly used GEMMs of PDAC often harbor the oncogenic KRAS driver mutation (KrasG12D), in combination with either p53 mutation by knock-in strategy (Trp53R172H) or p53 loss by conditional knockout (Trp53cKO) strategy, in pancreatic cell lineages. However, the systematic comparison of the tumor microenvironment between KrasG12D; Trp53R172H (KPmut) mouse models and KrasG12D; Trp53cKO (KPloss) mouse models is still lacking. In this study, we conducted cross-dataset single-cell RNA-sequencing (scRNA-seq) analyses to compare the pancreatic tumor microenvironment from KPmut mouse models and KPloss mouse models, especially focusing on the cell compositions and transcriptomic phenotypes of major cell types including cancer cells, B cells, T cells, granulocytes, myeloid cells, cancer-associated fibroblasts, and endothelial cells. We identified the similarities and differences between KPmut and KPloss mouse models, revealing the effects of p53 mutation and p53 loss on oncogenic KRAS-driven pancreatic tumor progression.

Single-Cell RNA-Sequencing Analysis of Colonic Lamina Propria Immune Cells Reveals the Key Immune Cell-Related Genes of Ulcerative Colitis

Background

Ulcerative colitis (UC) is a severe threat to humans worldwide. Single-cell RNA sequencing (scRNA-seq) can be used to screen gene expression patterns of each cell in the intestine, provide new insights into the potential mechanism of UC, and analyze the development of immune cell changes. These findings can provide new ideas for the diagnosis and treatment of intestinal diseases. In this study, bioinformatics analysis combined with experiments applied in dextran sulfate sodium (DSS)-induced colitis mice was used to explore new diagnostic genes for UC and their potential relationship with immune cells.

Methods

We downloaded microarray datasets (GSE75214, GSE87473, GSE92415) from the Gene Expression Omnibus and used these datasets to screen differentially expressed genes (DEGs) and conduct Weighted Gene Co-expression Network Analysis (WGCNA) after quality control. The hub genes were screened, and ROC curves were drawn to verify the reliability of the results in both training set (GSE75214, GSE87473, GSE92415) and validation cohort (GSE87466). Also, we explored the relation of diagnostic genes and immune cells by CIBERSORT algorithm and single-cell analysis. Finally, the expression of hub genes and their relation with immune cells were verified in DSS-induced colitis mice.

Results

Diagnostic genes (ANXA5, MMP7, NR1H4, CYP3A4, ABCG2) were identified. In addition, we found these five genes firmly related to immune infiltration. The DSS-induced colitis mice confirm that the expression of ANXA5 mainly increased in the intestinal macrophages and had a strong negative correlation with M2 macrophages, which indicated its possible influence on the polarization of macrophages in UC patients.

Conclusion

We identified ANXA5, MMP7, NR1H4, CYP3A4, and ABCG2 as diagnostic genes of UC that are closely related to immune infiltration and ANXA5 maintains a negative correlation with M2 macrophages which indicated its possible influence on the polarization of macrophage in UC patients.

Single-cell analysis reveals immune cellular components in odontogenic keratocysts

OBJECTIVES

Various types of cells comprising a complex and diverse cell population are required for the biological activities of odontogenic keratocyst (OKC). Immune and non-immune cells collaborate via cytokine- or chemokine-mediated communication and direct cell-cell interactions. This study aimed to characterize the immune ecosystem and understand the potential chemotactic role of OKC fibroblasts in immune cell migration.

MATERIALS AND METHODS

Mass cytometry of 41 markers was employed for the classification of OKC cells from six OKC samples. Immunofluorescence staining and single-cell RNA sequencing (GSE176351) were used for the detection of fibroblast subpopulations. Enzyme-linked immunosorbent assay and immunofluorescence staining were employed for chemokine detection in hypoxia- and/or HIF-1α inhibitor-treated OKC fibroblasts and tissues. Chemotaxis assay was employed to determine the chemotactic effect of fibroblasts via co-culture with peripheral blood mononuclear cells. A cell communication network was constructed based on the single-cell RNA sequencing data.

RESULTS

The characterization of the immune cell types of OKC evidenced the enrichment of macrophages, neutrophils and B cells. The majority (41.5%) of fibroblast subsets consisted of chemokine ligand-enriched myofibroblasts. The activation of the HIF-1α signaling pathway in fibroblasts was associated with chemokine release. The chemokines released by OKC fibroblasts remarkably promoted the migration of peripheral blood mononuclear cells in the co-culture system. Close interactions between myofibroblasts and immune cells were validated by cell-cell interaction analysis. Increased RANKL expression was detected in OKC fibroblasts in the co-culture system with peripheral blood mononuclear cells.

CONCLUSIONS

Our results provided deep insights into the immune ecosystem and highlighted the potential chemotactic effects of chemokine-enriched myofibroblasts within OKCs. The close interaction between immune cells and fibroblasts demonstrated in this study may be responsible for the osteoclastogenic effects of OKC fibroblasts.

Interactions of Indoleamine 2,3-dioxygenase-expressing LAMP3+ dendritic cells with CD4+ regulatory T cells and CD8+ exhausted T cells: synergistically remodeling of the immunosuppressive microenvironment in cervical cancer and therapeutic implications

BACKGROUND

Cervical cancer (CC) is the fourth most common cancer in women worldwide. Although immunotherapy has been applied in clinical practice, its therapeutic efficacy remains far from satisfactory, necessitating further investigation of the mechanism of CC immune remodeling and exploration of novel treatment targets. This study aimed to investigate the mechanism of CC immune remodeling and explore potential therapeutic targets.

METHODS

We conducted single-cell RNA sequencing on a total of 17 clinical specimens, including normal cervical tissues, high-grade squamous intraepithelial lesions, and CC tissues. To validate our findings, we conducted multicolor immunohistochemical staining of CC tissues and constructed a subcutaneous tumorigenesis model in C57BL/6 mice using murine CC cell lines (TC1) to evaluate the effectiveness of combination therapy involving indoleamine 2,3-dioxygenase 1 (IDO1) inhibition and immune checkpoint blockade (ICB). We used the unpaired two-tailed Student's t-test, Mann-Whitney test, or Kruskal-Wallis test to compare continuous data between two groups and one-way ANOVA with Tukey's post hoc test to compare data between multiple groups.

RESULTS

Malignant cervical epithelial cells did not manifest noticeable signs of tumor escape, whereas lysosomal-associated membrane protein 3-positive (LAMP3+ ) dendritic cells (DCs) in a mature state with immunoregulatory roles were found to express IDO1 and affect tryptophan metabolism. These cells interacted with both tumor-reactive exhausted CD8+ T cells and CD4+ regulatory T cells, synergistically forming a vicious immunosuppressive cycle and mediating CC immune escape. Further validation through multicolor immunohistochemical staining showed co-localization of neoantigen-reactive T cells (CD3+ , CD4+ /CD8+ , and PD-1+ ) and LAMP3+ DCs (CD80+ and PD-L1+ ). Additionally, a combination of the IDO1 inhibitor with an ICB agent significantly reduced tumor volume in the mouse model of CC compared with an ICB agent alone.

CONCLUSIONS

Our study suggested that a combination treatment consisting of targeting IDO1 and ICB agent could improve the therapeutic efficacy of current CC immunotherapies. Additionally, our results provided crucial insights for designing drugs and conducting future clinical trials for CC.