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Background: Periodontitis is a common complication of type II diabetes (T2D). However, the existing research cannot fully elucidate the association between them, let alone identify therapeutic targets for precise treatment of diabetic periodontitis. Therefore, we employed integrated genetic approaches such as single-cell analysis, Mendelian randomization (MR) analysis and colocalization analysis to uncover novel therapeutic targets for T2D and periodontitis. Methods: This study integrated single-cell analysis, MR analysis, colocalization analysis, phenotype scanning, cell-cell communication analysis and metabolic pathway activity analysis to unveil novel therapeutic targets for periodontitis and T2D. We firstly identified core cell clusters of T2D and periodontitis, and important marker genes were selected. The causal associations between these genes and the two diseases were evaluated through MR analysis. Reverse MR analysis, colocalization analysis, additional validation and phenotype scanning further supported our findings. Finally, cell-cell communication analysis and metabolic pathway activity analysis were employed to preliminarily investigate the mechanisms of the observed causal associations. Results: Through analysis of scRNA-seq data, we identified classical monocytes and intermediate monocytes as core cell subclusters. Differential analysis identified 221 differentially expressed genes (DEGs). MR analysis identified 13 genes exhibiting causal associations with T2D, and 11 causal genes with periodontitis. Colocalization analysis, reverse MR analysis, additional validation and phenotype scanning further enhanced the robustness of our results. Finally, we identified NCF1 as the core therapeutic target for T2D (OR = 1.09, 95% CI: 1.03-1.14, p = 1.85 × 10 − 3 ) and LRRC25 for T2D (OR = 0.96, 95% CI: 0.93-0.99, p = 3.44 × 10 − 2 ) and periodontitis (OR = 0.92, 95% CI: 0.84-0.99, p = 4.45 × 10 − 2 ). At last, cell-cell communication analysis indicated significant differences in functions and metabolic pathway activity between monocytes expressing or not expressing the core causal genes, which preliminarily interpreted the observed causal associations. Conclusion: This study integrated single-cell analysis, MR analysis and colocalization analysis to identified novel therapeutic targets for T2D and periodontitis. 13 causal genes were identified for T2D, and 11 for periodontitis. Among them, NCF1 and LRRC25 were regarded as core therapeutic targets. Our findings bridge the gap in the understanding of the association between T2D and periodontitis, and pave the way for targeted therapy of the two diseases. [ABSTRACT FROM AUTHOR]

Testicular tumors represent the most common malignancy among young men. Nevertheless, the pathogenesis and molecular underpinning of testicular tumors remain largely elusive. We aimed to delineate the intricate intra‐tumoral heterogeneity and the network of intercellular communication within the tumor microenvironment. A total of 40,760 single‐cell transcriptomes were analyzed, encompassing samples from six individuals with seminomas, two patients with mixed germ cell tumors, one patient with a Leydig cell tumor, and three healthy donors. Five distinct malignant subclusters were identified in the constructed landscape. Among them, malignant 1 and 3 subclusters were associated with a more immunosuppressive state and displayed worse disease‐free survival. Further analysis identified that APP–CD74 interactions were significantly strengthened between malignant 1 and 3 subclusters and 14 types of immune subpopulations. In addition, we established an aberrant spermatogenesis trajectory and delineated the global gene alterations of somatic cells in seminoma testes. Sertoli cells were identified as the somatic cell type that differed the most from healthy donors to seminoma testes. Cellular communication between spermatogonial stem cells and Sertoli cells is disturbed in seminoma testes. Our study delineates the intra‐tumoral heterogeneity and the tumor immune microenvironment in testicular tumors, offering novel insights for targeted therapy. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer is one of the leading causes of cancer-related deaths in men. Its molecular pathogenesis is closely linked to various genetic and epigenetic alterations, including posttranslational modifications like SUMOylation. Identifying biomarkers that predict outcomes and specific therapeutic targets depends on a comprehensive understanding of these processes. With growing interest in SUMOylation as a mechanism affecting prostate cancer-related genes, this study aimed to investigate the central role of SUMOylation in prostate cancer prognostics, focusing on the significance of NOP58. Methods: We conducted a comprehensive bioinformatics analysis, integrating differential expression analysis, survival analysis, gene set enrichment analysis (GSEA), and single-cell transcriptomic analyses using data from The Cancer Genome Atlas (TCGA). Key genes were identified through intersections of Venn diagrams, Boralta algorithm signatures, and machine learning models. These signaling mechanisms were validated through experimental studies, including immunohistochemical staining and gene ontology analyses. Results: The dual-gene molecular subtype analysis with SUMO1, SUMO2, and XPO1 genes revealed significant differences in survival outcomes across molecular subtypes, further emphasizing the potential impact of NOP58 on SUMOylation, a key post-translational modification, in prostate cancer. NOP58 overexpression was strongly associated with shorter overall survival (OS), progression-free interval (PFI), and disease-specific death in prostate cancer patients. Immunohistochemical analysis confirmed that NOP58 was significantly overexpressed in prostate cancer tissues compared to normal tissues. ROC curve analysis demonstrated that NOP58 could distinguish prostate cancer from control samples with high diagnostic accuracy. Gene Ontology analysis, along with GSVA and GSEA, suggested that NOP58 may be involved in cell cycle regulation and DNA repair pathways. Moreover, NOP58 knockdown led to increased BCL2 expression and decreased Ki67 levels, promoting apoptosis and inhibiting cell proliferation. Colony formation assays further showed that NOP58 knockdown inhibited, while its overexpression promoted, colony formation, highlighting the critical role of NOP58 in prostate cancer cell growth and survival. Additionally, NOP58 was linked to drug responses, including Methotrexate, Rapamycin, Sorafenib, and Vorinostat. Conclusion: NOP58 is a key regulator of prostate cancer progression through its mediation of the SUMOylation pathway. Its expression level serves as a reliable prognostic biomarker and an actionable therapeutic target, advancing precision medicine for prostate cancer. Targeting NOP58 may enhance therapeutic efficacy and improve outcomes in oncology. [ABSTRACT FROM AUTHOR]

Background: Abnormal sphingolipid metabolism (SM) is closely linked to the incidence of cancers. However, the role of SM in pancreatic cancer (PC) remains unclear. This study aims to explore the significance of SM in the prognosis, immune microenvironment, and treatment of PC. Methods: Single-cell and bulk transcriptome data of PC were acquired via TCGA and GEO databases. SM-related genes (SMRGs) were obtained via MSigDB database. Consensus clustering was utilized to construct SM-related molecular subtypes. LASSO and Cox regression were utilized to build SM-related prognostic signature. ESTIMATE and CIBERSORT algorithms were employed to assess the tumour immune microenvironment. OncoPredict package was used to predict drug sensitivity. CCK-8, scratch, and transwell experiments were performed to analyze the function of ANKRD22 in PC cell line PANC-1 and BxPC-3. Results: A total of 153 SMRGs were acquired, of which 48 were linked to PC patients' prognosis. Two SM-related subtypes (SMRGcluster A and B) were identified in PC. SMRGcluster A had a poorer outcome and more active SM process compared to SMRGcluster B. Immune analysis revealed that SMRGcluster B had higher immune and stromal scores and CD8 + T cell abundance, while SMRGcluster A had a higher tumour purity score and M0 macrophages and activated dendritic cell abundance. PC with SMRGcluster B was more susceptible to gemcitabine, paclitaxel, and oxaliplatin. Then SM-related prognostic model (including ANLN, ANKRD22, and DKK1) was built, which had a very good predictive performance. Single-cell analysis revealed that in PC microenvironment, macrophages, epithelial cells, and endothelial cells had relatively higher SM activity. ANKRD22, DKK1, and ANLN have relatively higher expression levels in epithelial cells. Cell subpopulations with high expression of ANKRD22, DKK1, and ANLN had more active SM activity. In vitro experiments showed that ANKRD22 knockdown can inhibit the proliferation, migration, and invasion of PC cells. Conclusion: This study revealed the important significance of SM in PC and identified SM-associated molecular subtypes and prognostic model, which provided novel perspectives on the stratification, prognostic prediction, and precision treatment of PC patients. [ABSTRACT FROM AUTHOR]

Objective: Flow cytometry (FC) is probably the most important technique for single-cell analysis. It's precisely, rapid, and suitable for multidimensional single-cell analysis. The commonly used side scatter (SSC) intensity determined by FC is often interpreted as a measure of the internal cellular complexity of cells. In simple terms, the more structured a cell is, the higher the SSC intensity quantified by FC. Nevertheless, most of the studies that support this interpretation are based on data derived from animal or human cell lines and while it is assumed that the results can also be transferred to plant or algal cell lines, the details remain unclear. The objective of the recent work is to clarify the interpretation of the SSC signal from algal cells. Results: Algal lipid droplets and their starch play an inherent role to shape the popular SSC signal derived from FC. This was shown by a theoretical approach based on Lorenz–Mie theory. These results were supported by experiments with different model cultures of Chlamydomonas reinhardtii in which a high linear correlation was observed between the SSC signal and the 'physical' starch quantity. [ABSTRACT FROM AUTHOR]

Introduction: Human peripheral blood mononuclear cells (hPBMCs) are widely used in fundamental research and clinical applications as studying their responses to in vitro activation is an effective way to uncover functional alterations and disease associated phenotypes. However, the availability of samples in large numbers at a specific time and location remains challenging, hence they often might preferably be collected and cryopreserved for later analysis. While the effect of cryopreservation on viability and cell surface expression is well established, changes in activity and cytokine secretion still lead to conflicting results as it is often measured in bulk or within the cells. Methods: Here, we used our platform for dynamic single-cell multiplexed cytokine secretion measurement and compared it to a traditional intracellular cytokine staining to quantify the effect of cryopreservation on cytokine secretion and expression of individual hPBMCs. Results: Following stimulation with LPS or anti-CD3/CD28 antibodies for up to 36 or 72 h incubation, we observed distinct alterations in cytokine responses due to cryopreservation when comparing to fresh samples, but also remarkable consistencies for some cytokines and parameters. In short, the frequencies of cytokine-secreting cells in cryopreserved samples were lower for IL-6 (LPS), IL1-b (CD3/CD28) and IFN-g (CD3/CD28), while the frequency and dynamics of IL-8 secretion were strongly impacted in all cases. We observed a large disconnect between cytokine expression and secretion for TNF-a, where the expression dramatically increased after cryopreservation, but actual secretion was, in comparison, remarkably stable. The polyfunctionality of single cells was altered by cryopreservation in specific co-secreting populations led by the effects on IL-6 or IL-8 secretion. Among immune cells, cryopreservation seemed to affect lymphocytes and monocytes differently as effects appeared early on in lymphocytes while generally observed in later time points in monocytes. Conclusion: Together, this study offers an in-depth quantitative insight into the biological behavior of immune cells in response to cryopreservation and stimulation, further providing some insights into conflicting results in the literature as well as guidelines for researchers planning to assess cytokine-secreting from frozen hPBMCs in immunological research or clinical applications. [ABSTRACT FROM AUTHOR]

Interaction between tumor‐associated macrophages and tumor cells is crucial for tumor development, metastasis, and the related immune process. However, the macrophages are highly heterogeneous spanning from anti‐tumorigenic to pro‐tumorigenic, which needs to be understood at the single‐cell level. Herein, a sessile microdroplet system designed for monitoring cellular behavior and analyzing intercellular interaction, demonstrated with macrophage‐tumor cell pairs is presented. An automatic procedure based on the inkjet printing method is utilized for the precise pairing and co‐encapsulation of heterotypic cells within picoliter droplets. The sessile nature of microdroplets ensures controlled fusion and provides stable environments conducive to adherent cell culture. The nitric oxide generation and morphological changes over incubation are explored to reveal the complicated interactions from a single‐cell perspective. The immune response of macrophages under distinct cellular microenvironments is recorded. The results demonstrate that the tumor microenvironment displays a modulating role in polarizing macrophages from anti‐tumorigenic into pro‐tumorigenic phenotype. The approach provides a versatile and compatible platform to investigate intercellular interaction at the single‐cell level, showing promising potential for advancing single‐cell behavior studies. [ABSTRACT FROM AUTHOR]

This study investigates the impact of Hashimoto’s thyroiditis (HT), an autoimmune disorder, on the papillary thyroid cancer (PTC) microenvironment using a dataset of 140,456 cells from 11 patients. By comparing PTC cases with and without HT, we identify HT-specific cell populations (HASCs) and their role in creating a TSH-suppressive environment via mTE3, nTE0, and nTE2 thyroid cells. These cells facilitate intricate immune–stromal communication through the MIF–(CD74+CXCR4) axis, emphasizing immune regulation in the TSH context. In the realm of personalized medicine, our HASC-focused analysis within the TCGA-THCA dataset validates the utility of HASC profiling for guiding tailored therapies. Moreover, we introduce a novel, objective method to determine K- means clustering coefficients in copy number variation inference from bulk RNA- seq data, mitigating the arbitrariness in conventional coefficient selection. Collectively, our research presents a detailed single-cell atlas illustrating HT– PTC interactions, deepening our understanding of HT’s modulatory effects on PTC microenvironments. It contributes to our understanding of autoimmunity– carcinogenesis dynamics and charts a course for discovering new therapeutic targets in PTC, advancing cancer genomics and immunotherapy research. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Esophageal squamous cell carcinoma (ESCC) stands as a significant global health challenge, distinguished by its aggressive progression from the esophageal epithelium. Central to this malignancy is sphingolipid metabolism, a critical pathway that governs key cellular processes, including apoptosis and immune regulation, thereby influencing tumor behavior. The advent of single-cell and transcriptome sequencing technologies has catalyzed significant advancements in oncology research, offering unprecedented insights into the molecular underpinnings of cancer. Methods: We explored sphingolipid metabolism-related genes in ESCC using scRNA-seq data from GEO and transcriptome data from TCGA. We assessed 97 genes in epithelial cells with AUCell, UCell, and singscore algorithms, followed by bulk RNA-seq and differential analysis to identify prognosis-related genes. Immune infiltration and potential immunotherapeutic strategies were also investigated, and tumor gene mutations and drug treatment strategies were analyzed. Result: Our study identified distinct gene expression patterns, highlighting ARSD, CTSA, DEGS1, and PPTQ's roles in later cellular stages. We identified seven independent prognostic genes and created a precise nomogram for prognosis. Conclusion: This study integrates single-cell and transcriptomic data to provide a reliable prognostic model associated with sphingolipid metabolism and to inform immunotherapy and pharmacotherapy for ESCC at the genetic level. The findings have significant implications for precision therapy in esophageal cancer. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer (PCa) is a prevalent malignancy among men, primarily originating from the prostate epithelium. It ranks first in global cancer incidence and second in mortality rates, with a rising trend in China. PCa's subtle initial symptoms, such as urinary issues, necessitate diagnostic measures like digital rectal examination, prostate-specific antigen (PSA) testing, and tissue biopsy. Advanced PCa management typically involves a multifaceted approach encompassing surgery, radiation, chemotherapy, and hormonal therapy. The involvement of aging genes in PCa development and progression, particularly through the mTOR pathway, has garnered increasing attention. Methods: This study aimed to explore the association between aging genes and biochemical PCa recurrence and construct predictive models. Utilizing public gene expression datasets (GSE70768, GSE116918, and TCGA), we conducted extensive analyses, including Cox regression, functional enrichment, immune cell infiltration estimation, and drug sensitivity assessments. The constructed risk score model, based on aging-related genes (ARGs), demonstrated superior predictive capability for PCa prognosis compared to conventional clinical features. High-risk genes positively correlated with risk, while low-risk genes displayed a negative correlation. Results: An ARGs-based risk score model was developed and validated for predicting prognosis in prostate adenocarcinoma (PRAD) patients. LASSO regression analysis and cross-validation plots were employed to select ARGs with prognostic significance. The risk score outperformed traditional clinicopathological features in predicting PRAD prognosis, as evidenced by its high AUC (0.787). The model demonstrated good sensitivity and specificity, with AUC values of 0.67, 0.675, 0.696, and 0.696 at 1, 3, 5, and 8 years, respectively, in the GEO cohort. Similar AUC values were observed in the TCGA cohort at 1, 3, and 5 years (0.67, 0.659, 0.667, and 0.743). The model included 12 genes, with high-risk genes positively correlated with risk and low-risk genes negatively correlated. Conclusions: This study presents a robust ARGs-based risk score model for predicting biochemical recurrence in PCa patients, highlighting the potential significance of aging genes in PCa prognosis and offering enhanced predictive accuracy compared to traditional clinical parameters. These findings open new avenues for research on PCa recurrence prediction and therapeutic strategies. [ABSTRACT FROM AUTHOR]

Metastasis is the major cause of treatment failure in patients with prostate adenocarcinoma (PRAD). Diverse programmed cell death (PCD) patterns play an important role in tumor metastasis and hold promise as predictive indicators for PRAD metastasis. Using the LASSO Cox regression method, we developed PCD score (PCDS) based on differentially expressed genes (DEGs) associated with PCD. Clinical correlation, external validation, functional enrichment analysis, mutation landscape analysis, tumor immune environment analysis, and immunotherapy analysis were conducted. The role of Prostaglandin D2 Synthase (PTGDS) in PRAD was examined through in vitro experiments, single-cell, and Mendelian randomization (MR) analysis. PCDS is elevated in patients with higher Gleason scores, higher T stage, biochemical recurrence (BCR), and higher prostate-specific antigen (PSA) levels. Individuals with higher PCDS are prone to metastasis, metastasis after BCR, BCR, and castration resistance. Moreover, PRAD patients with low PCDS responded positively to immunotherapy. Random forest analysis and Mendelian randomization analysis identified PTGDS as the top gene associated with PRAD metastasis and in vitro experiments revealed that PTGDS was considerably downregulated in PRAD cells against normal prostate cells. Furthermore, the overexpression of PTGDS was found to suppress the migration, invasion, proliferationof DU145 and LNCaP cells. To sum up, PCDS may be a useful biomarker for forecasting the possibility of metastasis, recurrence, castration resistance, and the efficacy of immunotherapy in PRAD patients. Additionally, PTGDS was identified as a viable therapeutic target for the management of PRAD. [ABSTRACT FROM AUTHOR]

Background: With the advance in single-cell RNA sequencing (scRNA-seq) technology, deriving inherent biological system information from expression profiles at a single-cell resolution has become possible. It has been known that network modeling by estimating the associations between genes could better reveal dynamic changes in biological systems. However, accurately constructing a single-cell network (SCN) to capture the network architecture of each cell and further explore cell-to-cell heterogeneity remains challenging. Results: We introduce SINUM, a method for constructing the SIngle-cell Network Using Mutual information, which estimates mutual information between any two genes from scRNA-seq data to determine whether they are dependent or independent in a specific cell. Experiments on various scRNA-seq datasets with different cell numbers based on eight performance indexes (e.g., adjusted rand index and F-measure index) validated the accuracy and robustness of SINUM in cell type identification, superior to the state-of-the-art SCN inference method. Additionally, the SINUM SCNs exhibit high overlap with the human interactome and possess the scale-free property. Conclusions: SINUM presents a view of biological systems at the network level to detect cell-type marker genes/gene pairs and investigate time-dependent changes in gene associations during embryo development. Codes for SINUM are freely available at https://github.com/SysMednet/SINUM. [ABSTRACT FROM AUTHOR]

The global spread of flaviviruses has triggered major outbreaks worldwide, significantly impacting public health, society, and economies. This has intensified research efforts to understand how flaviviruses interact with their hosts and manipulate the immune system, underscoring the need for advanced research tools. RNA-sequencing (RNA-seq) technologies have revolutionized our understanding of flavivirus infections by offering transcriptome analysis to dissect the intricate dynamics of virus-host interactions. Bulk RNA-seq provides a macroscopic overview of gene expression changes in virus-infected cells, offering insights into infection mechanisms and host responses at the molecular level. Single-cell RNA sequencing (scRNAseq) provides unprecedented resolution by analyzing individual infected cells, revealing remarkable cellular heterogeneity within the host response. A particularly innovative advancement, virus-inclusive single-cell RNA sequencing (viscRNA-seq), addresses the challenges posed by non-polyadenylated flavivirus genomes, unveiling intricate details of virus-host interactions. In this review, we discuss the contributions of bulk RNA-seq, scRNA-seq, and viscRNA-seq to the field, exploring their implications in cell line experiments and studies on patients infected with various flavivirus species. Comprehensive transcriptome analyses from RNA-seq technologies are pivotal in accelerating the development of effective diagnostics and therapeutics, paving the way for innovative treatments and enhancing our preparedness for future outbreaks. [ABSTRACT FROM AUTHOR]

One of the goals of tissue engineering and regenerative medicine is restoring primary living tissue function by manufacturing a 3D microenvironment. One of the main challenges is protecting implanted non-autologous cells or tissues from the host immune system. Cell encapsulation has emerged as a promising technique for this purpose. It involves entrapping cells in biocompatible and semi-permeable microcarriers made from natural or synthetic polymers that regulate the release of cellular secretions. In recent years, droplet-based microfluidic systems have emerged as powerful tools for cell encapsulation in tissue engineering and regenerative medicine. These systems offer precise control over droplet size, composition, and functionality, allowing for creating of microenvironments that closely mimic native tissue. Droplet-based microfluidic systems have extensive applications in biotechnology, medical diagnosis, and drug discovery. This review summarises the recent developments in droplet-based microfluidic systems and cell encapsulation techniques, as well as their applications, advantages, and challenges in biology and medicine. The integration of these technologies has the potential to revolutionise tissue engineering and regenerative medicine by providing a precise and controlled microenvironment for cell growth and differentiation. By overcoming the immune system's challenges and enabling the release of cellular secretions, these technologies hold great promise for the future of regenerative medicine. [ABSTRACT FROM AUTHOR]

Adipose tissue macrophages (ATMs) are key regulators of adipose tissue (AT) inflammation and insulin resistance in obesity, and the traditional M1/M2 characterization of ATMs is inadequate for capturing their diversity in obese conditions. Single-cell transcriptomic profiling has revealed heterogeneity among ATMs that goes beyond the old paradigm and identified new subsets with unique functions. Furthermore, explorations of their developmental origins suggest that multiple differentiation pathways contribute to ATM variety. These advances raise concerns about how to define ATM functions, how they are regulated, and how they orchestrate changes in AT. This review provides an overview of the current understanding of ATMs and their updated categorization in both mice and humans during obesity. Additionally, diverse ATM functions and contributions in the context of obesity are discussed. Finally, potential strategies for targeting ATM functions as therapeutic interventions for obesity-induced metabolic diseases are addressed. [ABSTRACT FROM AUTHOR]

Simple Summary: Although liquid biopsy has emerged as a viable substitute, bone marrow (BM) is still the gold standard for the diagnosis and follow-up of patients with multiple myeloma (MM) and smouldering MM (SMM). The potential involvement of circulating MM cells (CMMCs), counted via CELLSEARCH®, in monitoring disease dynamics was assessed by measuring them during treatment and correlating the results with the prognoses of the patients. For MM and SMM patients, the median numbers of CMMCs counted at diagnosis were 349 (1 to 39,940) and 327 (range 22–2463), respectively. Among SMM patients, higher CMMCs were associated with a greater propensity to evolve (p = 0.042). The CMMC counts in the MM patients showed a significant correlation (p < 0.04) with serum albumin and monoclonal component concentration. Under therapy, CMMCs were consistently detectable in 15/40 patients (coMMstant = 1), and correlated with lower responses (p = 0.04) and survival probability (p = 0.047), suggesting that CMMC persistence is linked to poor prognoses. In recent years, liquid biopsy has emerged as a promising alternative to the bone marrow (BM) examination, since it is a minimally invasive technique allowing serial monitoring. Circulating multiple myeloma cells (CMMCs) enumerated using CELLSEARCH® were correlated with patients' prognosis and measured under treatment to assess their role in monitoring disease dynamics. Forty-four MM and seven smouldering MM (SMM) patients were studied. The CMMC medians at diagnosis were 349 (1 to 39,940) and 327 (range 22–2463) for MM and SMM, respectively. In the MM patients, the CMMC count was correlated with serum albumin, calcium, β2-microglobulin, and monoclonal components (p < 0.04). Under therapy, the CMMCs were consistently detectable in 15/40 patients (coMMstant = 1) and were undetectable or decreasing in 25/40 patients (coMMstant = 0). High-quality response rates were lower in the coMMstant = 1 group (p = 0.04), with a 7.8-fold higher risk of death (p = 0.039), suggesting that continuous CMMC release is correlated with poor responses. In four MM patients, a single-cell DNA sequencing analysis on residual CMMCs confirmed the genomic pattern of the aberrations observed in the BM samples, also highlighting the presence of emerging clones. The CMMC kinetics during treatment were used to separate the patients into two subgroups based on the coMMstant index, with different responses and survival probabilities, providing evidence that CMMC persistence is associated with a poor disease course. [ABSTRACT FROM AUTHOR]

Rates of microbial growth are fundamental to understanding environmental geochemistry and ecology. However, measuring the heterogeneity of microbial activity at the single-cell level, especially within complex populations and environmental matrices, remains a forefront challenge. Stable isotope probing (SIP) is a method for assessing microbial growth and involves measuring the incorporation of an isotopic label into microbial biomass. Here, we assess Raman microspectroscopy as a SIP technique, specifically focusing on the measurement of deuterium (2H), a tracer of microbial biomass production. We correlatively measured cells grown in varying concentrations of deuterated water with both Raman spectroscopy and nanoscale secondary ion mass spectrometry (nanoSIMS), generating isotopic calibrations of microbial 2H. Relative to Raman, we find that nanoSIMS measurements of 2H are subject to substantial dilution due to rapid exchange of H during sample washing. We apply our Raman-derived calibration to a numerical model of microbial growth, explicitly parameterizing the factors controlling growth rate quantification and demonstrating that Raman–SIP can sensitively measure the growth of microorganisms with doubling times ranging from hours to years. The measurement of single-cell growth with Raman spectroscopy, a rapid, nondestructive technique, represents an important step toward application of single-cell analysis into complex sample matrices or cellular assemblages. [ABSTRACT FROM AUTHOR]

Patients with systemic lupus erythematosus (SLE), a heterogeneous and chronic autoimmune disease, exhibit unique changes in the complex composition and transcriptional signatures of peripheral blood mononuclear cells (PBMCs). While the mechanism of pathogenesis for both childhood-onset SLE (cSLE) and adult-onset SLE (aSLE) remains unclear, cSLE patients are considered more unpredictable and dangerous than asle patients. in this study, we analysed single-cell RNA sequencing data (scRNA-seq) to profile the PBMc clusters of cSLE/aSLE patients and matched healthy donors and compared the PBMC composition and transcriptional variations between the two groups. Our analysis revealed that the PBMC composition and transcriptional variations in cSLE patients were similar to those in asle patients. comparative single-cell transcriptome analysis between healthy donors and sle patients revealed IFITM3, ISG15, IFI16 and LY6E as potential therapeutic targets for both asle and csle patients. additionally, we observed that the percentage of pre-B cells (CD34-) was increased in csle patients, while the percentage of neutrophil cells was upregulated in asle patients. Notably, we found decreased expression of TPM2 in csle patients, and similarly, TMEM150B, IQSEC2, CHN2, LRP8 and USP46 were significantly downregulated in neutrophil cells from aSLE patients. Overall, our study highlights the differences in complex PBMc composition and transcriptional profiles between cSLE and asle patients, providing potential biomarkers that could aid in diagnosing sle. [ABSTRACT FROM AUTHOR]

Background: The metastasis of cancer cells is influenced by both their intrinsic characteristics and the tumor microenvironment (TME). However, the molecular mechanisms underlying pre-nodal metastases of breast cancer remain unclear. Methods: We integrated a total of 216,963 cells from 54 samples across 6 single-cell datasets to profile the cellular landscape differences between primary tumors and pre-nodal metastases. Results: We revealed three distinct metastatic epithelial cell subtypes (Epi1, Epi2 and Epi3), which exhibited different metastatic mechanisms. Specifically, the marker gene KCNK15 of the Epi1 subtype exhibited increased gene expression along the cell differentiation trajectory and was specifically regulated by the transcription factor ASCL1. In the Epi3 subtype, we highlighted NR2F1 as a regulator targeting the marker gene MUCL1. Additionally, we found that the Epi2 and Epi3 subtypes shared some regulons, such as ZEB1 and NR2C1. Similarly, we identified specific subtypes of stromal and immune cells in the TME, and discovered that vascular cancer-associated fibroblasts might promote capillary formation through CXCL9+ macrophages in pre-nodal metastases. All three subtypes of metastatic epithelial cells were associated with poor prognosis. Conclusions: In summary, this study dissects the intratumoral heterogeneity and remodeling of the TME in pre-nodal metastases of breast cancer, providing novel insights into the mechanisms underlying breast cancer metastasis. [ABSTRACT FROM AUTHOR]

Background: The regulator of calcineurin 1 (RCAN1) is expressed in multiple organs, including the heart, liver, brain, and kidney, and is closely linked to the pathogenesis of cardiovascular diseases, Down syndrome, and Alzheimer's disease. It is also implicated in the development of various organ tumors; however, its potential role in hepatocellular carcinoma (HCC) remains poorly understood. Therefore, the objective of this study was to investigate the potential mechanisms of RCAN1 in HCC through bioinformatics analysis. Methods: We conducted a joint analysis based on the NCBI and TCGA databases, integrating both bulk transcriptome and single-cell analyses to examine the principal biological functions of RCAN1 in HCC, as well as its roles related to phenotype, metabolism, and cell communication. Subsequently, an RCAN1-overexpressing cell line was established, and the effects of RCAN1 on tumor cells were validated through in vitro experiments. Moreover, we endeavored to identify potential related drugs using molecular docking and molecular dynamics simulations. Results: The expression of RCAN1 was found to be downregulated in 19 types of cancer tissues and upregulated in 11 types of cancer tissues. Higher levels of RCAN1 expression were associated with improved patient survival. RCAN1 was predominantly expressed in hepatocytes, macrophages, endothelial cells, and monocytes, and its high expression not only closely correlated with the distribution of cells related to the HCC phenotype but also with the distribution of HCC cells themselves. Additionally, Rcan1 may directly or indirectly participate in metabolic pathways such as alanine, aspartate, and glutamate metabolism, as well as butanoate metabolism, thereby influencing tumor cell proliferation and migration. In vitro experiments confirmed that RCAN1 overexpression promoted apoptosis while inhibiting proliferation and invasion of HCC cells. Through molecular docking of 1615 drugs, we screened brompheniramine as a potential target drug and verified our results by molecular dynamics. Conclusion: In this study, we revealed the relationship between RCAN1 and HCC through bioinformatics methods, verified that RCAN1 can affect the progress of the disease through experiments, and finally identified potential therapeutic drugs through drug molecular docking and molecular dynamics. [ABSTRACT FROM AUTHOR]

Introduction: Breast cancer (BRCA) is a significant cause of cancer-associated mortality across the globe. Current therapeutic approaches face challenges such as drug resistance and metastasis. Immune signaling is triggered by chromosomal instability (CIN) generates misplaced DNA structures that activate the cyclic GMP-AMP synthase-stimulator of interferon genes (cGASSTING) pathway, triggering. Studies have linked STING activation to BRCA treatment. Methods: The bulk RNA-seq data for patients with BRCA were collected from the TCGA-BRCA cohort, GSE20685, and GSE96058 cohorts. STING pathway-related genes (SRGs) were obtained from the Reactome database. Differentially expressed genes were analyzed using the limma package. Immune cell infiltration was analyzed using the IOBR package. Gene Ontology biological processes, Kyoto Encyclopedia of Genes and Genomes pathways, and cancer hallmark pathways were analyzed using the MSigDB database. Prognostic models were prepared using the least absolute shrinkage and selection operator and multiple-factor Cox regression analysis. Single-cell analysis was performed using the Seurat and SCP pipeline. Results: The expression patterns and clinical relevance of SRGs were analyzed in patients with BRCA. Transcriptional differences in the SRGs were observed between normal and tumorous tissues, with global down-regulated STING1 and up-regulated TBK1 in BRCA tissue. Tumor tissues were classified through consensus clustering analysis into two distinct groups, with differences in clinical characteristics and immune infiltration. A prognostic model related to the differences in STING pathway activity--high prognostic stratification potency--was developed and validated. Correlation analysis revealed suppressed overall immune activation in patients with BRCA having higher risk scores. Gemcitabine had a more favorable outcome in the low-risk group. The activity of the prognostic model at the single-cell level was confirmed through single-cell analysis, particularly in CD8 T cells and intratumor natural killer cells. Conclusion: A STING pathway-related prognostic model developed and validated and the model could accurately predict BRCA patient outcomes. These findings have important implications for the personalized treatment and management of patients with BRCA. [ABSTRACT FROM AUTHOR]

Background: Endometriosis (EM) is a prevalent gynecological disorder frequently associated with irregular menstruation and infertility. Programmed cell death (PCD) is pivotal in the pathophysiological mechanisms underlying EM. Despite this, the precise pathogenesis of EM remains poorly understood, leading to diagnostic delays. Consequently, identifying biomarkers associated with PCD is critical for advancing the diagnosis and treatment of EM. Methods: This study used datasets from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) following preprocessing. By crossreferencing these DEGs with genes associated with PCD, differentially expressed PCD-related genes (DPGs) were identified. Enrichment analyses for KEGG and GO pathways were conducted on these DPGs. Additionally, Mendelian randomization and machine learning techniques were applied to identify biomarkers strongly associated with EM. Results: The study identified three pivotal biomarkers: TNFSF12, AP3M1, and PDK2, and established a diagnostic model for EM based on these genes. The results revealed a marked upregulation of TNFSF12 and PDK2 in EM samples, coupled with a significant downregulation of AP3M1. Single-cell analysis further underscored the potential of TNFSF12, AP3M1, and PDK2 as biomarkers for EM. Additionally, molecular docking studies demonstrated that these genes exhibit significant binding affinities with drugs currently utilized in clinical practice. Conclusion: This study systematically elucidated the molecular characteristics of PCD in EM and identified TNFSF12, AP3M1, and PDK2 as key biomarkers. These findings provide new directions for the early diagnosis and personalized treatment of EM. [ABSTRACT FROM AUTHOR]

Background: pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a very poor prognosis and a complex tumor microenvironment, which plays a key role in tumor progression and treatment resistance. Glycosylation plays an important role in processes such as cell signaling, immune response and protein stability. Materials and methods: single-cell RNA sequencing data and spatial transcriptome data were obtained from GSE197177 and GSE224411, respectively, and RNA-seq data and survival information were obtained from UCSC Xena and TCGA. Multiple transcriptomic data were comprehensively analyzed to explore the role of glycosylation processes in tumor progression, and functional experiments were performed to assess the effects of MGAT1 overexpression on PDAC cell proliferation and migration. Results: In PDAC tumor samples, the glycosylation level of macrophages was significantly higher than that of normal samples. MGAT1 was identified as a key glycosylation-related gene, and its high expression was associated with better patient prognosis. Overexpression of MGAT1 significantly inhibited the proliferation and migration of PDAC cells and affected intercellular interactions in the tumor microenvironment. Conclusion: MGAT1 plays an important role in PDAC by regulating glycosylation levels in macrophages, influencing tumor progression and improving prognosis.MGAT1 is a potential therapeutic target for PDAC and further studies are needed to develop targeted therapeutic strategies against MGAT1 to improve clinical outcomes. [ABSTRACT FROM AUTHOR]

Ulcerative colitis (UC) is a chronic inflammatory disorder of the colon, and its pathogenesis remains unclear. Polyamine metabolic enzymes play a crucial role in UC. In this study, we aimed to identify pivotal polyamine-related genes (PRGs) and explore the underlying mechanism between PRGs and the disease status and therapeutic response of UC. We analyzed mRNA-sequencing data and clinical information of UC patients from the GEO database and identified NNMT, PTGS2, TRIM22, TGM2, and PPARG as key PRGs associated with active UC using differential expression analysis and weighted gene co-expression network analysis (WCGNA). Receiver operator characteristic curve (ROC) analysis confirmed the accuracy of these key genes in UC and colitis-associated colon cancer (CAC) diagnosis, and we validated their relationship with therapeutic response in external verification sets. Additionally, single-cell analysis revealed that the key PRGs were specific to certain immune cell types, emphasizing the vital role of intestinal tissue stem cells in active UC. The results were validated in vitro and in vivo experiments, including the colitis mice model and CAC mice model. In conclusion, these key PRGs effectively predict the progression of UC patients and could serve as new pharmacological biomarkers for the therapeutic response of UC. [ABSTRACT FROM AUTHOR]

Analysis of single cancer cells is critical to obtain accurate patient diagnosis and prognosis. In this work, we report the selective dielectrophoretic capture and electrochemical analysis of single melanoma cells at an array of interlocked spiral bipolar electrodes (iBPEs). Following dielectrophoretic capture, individual melanoma cells are hydrodynamically transferred into picoliter‐scale chambers for subsequent analysis. The interlocked spiral end of the iBPE (the sensing pole) is utilized to read out an electrochemical enzyme‐linked immunosorbent assay (eELISA), which quantifies the expression of a cell surface antigen, melanoma cell adhesion marker (MCAM). The opposite pole of each BPE is located in a fluidically isolated compartment containing reagents for electrogenerated chemiluminescence (ECL), such that luminescence reports iBPE current. In a preliminary device design, the ECL intensity was insufficient to detect MCAM expression on single cells. To achieve single‐cell analysis, we decreased the gap size between the interlocked spirals tenfold (5.0 μm to 0.5 μm), thereby creating a more sensitive biosensor by enhanced redox cycling of the product of eELISA. This work is significant because it allows for the selective isolation and sensitive analysis of individual melanoma cells in a device amenable to point‐of‐care (POC) application by combining dielectrophoresis (DEP) with interdigitated bipolar electrodes (IDBPEs). [ABSTRACT FROM AUTHOR]

A novel optically induced dielectrophoresis (ODEP) system that can operate under flow conditions is designed for automatic trapping of cells and subsequent induction of 2D multi‐frequency cell trajectories. Like in a "ping‐pong" match, two virtual electrode barriers operate in an alternate mode with varying frequencies of the input voltage. The so‐derived cell motions are characterized via time‐lapse microscopy, cell tracking, and state‐of‐the‐art machine learning algorithms, like the wavelet scattering transform (WST). As a cell‐electrokinetic fingerprint, the dynamic of variation of the cell displacements happening, over time, is quantified in response to different frequency values of the induced electric field. When tested on two biological scenarios in the cancer domain, the proposed approach discriminates cellular dielectric phenotypes obtained, respectively, at different early phases of drug‐induced apoptosis in prostate cancer (PC3) cells and for differential expression of the lectine‐like oxidized low‐density lipoprotein receptor‐1 (LOX‐1) transcript levels in human colorectal adenocarcinoma (DLD‐1) cells. The results demonstrate increased discrimination of the proposed system and pose an additional basis for making ODEP‐based assays addressing cancer heterogeneity for precision medicine and pharmacological research. [ABSTRACT FROM AUTHOR]

The encapsulation of cells within droplets is a crucial aspect of various cell analysis applications. Current research has focused on accurately detecting and identifying cell types or cell counts within droplets using object detection in bright-field images. However, there are only a few in-depth investigations into the impact of the image data quality acquired from optical systems on computer vision models. This study examines several popular machine learning object detection models to analyze scenarios complicating the identification of bead locations within a droplet, posing challenges for computer vision models. A microfluidic droplet generation system was developed and implemented, coupled with optical devices to capture images of encapsulated beads within the droplet. To identify the most efficient model, a specific dataset was meticulously selected from the overall data, encompassing images depicting overlapping beads and edge-drifting scenarios. The proposed method achieved up to 98.2% accuracy on the testing dataset and 95% in real-time testing with the YOLOv8 model, enhancing bead count precision within droplets and clarifying the correlation between accuracy and frame recognition thresholds. This work holds particular importance in single-cell sorting, where precision is critical in ensuring meaningful outcomes, particularly concerning rare cell types such as cancer cells. [ABSTRACT FROM AUTHOR]

Autofluorescence lifetime imaging microscopy (FLIM) is sensitive to metabolic changes in single cells based on changes in the protein‐binding activities of the metabolic co‐enzymes NAD(P)H. However, FLIM typically relies on time‐correlated single‐photon counting (TCSPC) detection electronics on laser‐scanning microscopes, which are expensive, low‐throughput, and require substantial post‐processing time for cell segmentation and analysis. Here, we present a fluorescence lifetime‐sensitive flow cytometer that offers the same TCSPC temporal resolution in a flow geometry, with low‐cost single‐photon excitation sources, a throughput of tens of cells per second, and real‐time single‐cell analysis. The system uses a 375 nm picosecond‐pulsed diode laser operating at 50 MHz, alkali photomultiplier tubes, an FPGA‐based time tagger, and can provide real‐time phasor‐based classification (i.e., gating) of flowing cells. A CMOS camera produces simultaneous brightfield images using far‐red illumination. A second PMT provides two‐color analysis. Cells are injected into the microfluidic channel using a syringe pump at 2–5 mm/s with nearly 5 ms integration time per cell, resulting in a light dose of 2.65 J/cm2 that is well below damage thresholds (25 J/cm2 at 375 nm). Our results show that cells remain viable after measurement, and the system is sensitive to autofluorescence lifetime changes in Jurkat T cells with metabolic perturbation (sodium cyanide), quiescent versus activated (CD3/CD28/CD2) primary human T cells, and quiescent versus activated primary adult mouse neural stem cells, consistent with prior studies using multiphoton FLIM. This TCSPC‐based autofluorescence lifetime flow cytometer provides a valuable label‐free method for real‐time analysis of single‐cell function and metabolism with higher throughput than laser‐scanning microscopy systems. [ABSTRACT FROM AUTHOR]

Genetic gains made by plant breeders are limited by generational cycling rates and flowering time. Several efforts have been made to reduce the time to switch from vegetative to reproductive stages in plants, but these solutions are usually species-specific and require flowering. The concept of in vitro nurseries is that somatic plant cells can be induced to form haploid cells that have undergone recombination (creating artificial gametes), which can then be used for cell fusion to enable breeding in a Petri dish. The induction of in vitro meiosis, however, is the largest current bottleneck to in vitro nurseries. To help overcome this, we previously described a high-throughput, bi-fluorescent, single cell system in Arabidopsis thaliana, which can be used to test the meiosis-like induction capabilities of candidate factors. In this present work, we validated the system using robust datasets (>4M datapoints) from extensive simulated meiosis induction tests. Additionally, we determined false-detection rates of the fluorescent cells used in this system as well as the ideal tissue source for factor testing. [ABSTRACT FROM AUTHOR]

Melanoma, a malignant skin cancer arising from melanocytes, exhibits rapid metastasis and a high mortality rate, especially in advanced stages. Current treatment modalities, including surgery, radiation, and immunotherapy, offer limited success, with immunotherapy using immune checkpoint inhibitors (ICIs) being the most promising. However, the high mortality rate underscores the urgent need for robust, non-invasive biomarkers to predict patient response to adjuvant therapies. The immune microenvironment of melanoma comprises various immune cells, which influence tumor growth and immune response. Melanoma cells employ multiple mechanisms for immune escape, including defects in immune recognition and epithelial-mesenchymal transition (EMT), which collectively impact treatment efficacy. Single-cell analysis technologies, such as single-cell RNA sequencing (scRNA-seq), have revolutionized the understanding of tumor heterogeneity and immune microenvironment dynamics. These technologies facilitate the identification of rare cell populations, co-expression patterns, and regulatory networks, offering deep insights into tumor progression, immune response, and therapy resistance. In the realm of biomarker discovery for melanoma, single-cell analysis has demonstrated significant potential. It aids in uncovering cellular composition, gene profiles, and novel markers, thus advancing diagnosis, treatment, and prognosis. Additionally, tumor-associated antibodies and specific genetic and cellular markers identified through single-cell analysis hold promise as predictive biomarkers. Despite these advancements, challenges such as RNA-protein expression discrepancies and tumor heterogeneity persist, necessitating further research. Nonetheless, single-cell analysis remains a powerful tool in elucidating the mechanisms underlying therapy response and resistance, ultimately contributing to the development of personalized melanoma therapies and improved patient outcomes. [ABSTRACT FROM AUTHOR]

The study aims to explore the central genes that Kawasaki disease (KD) and Obesity (OB) may jointly contribute to coronary artery disease. Investigating single-cell datasets (GSE168732 and GSE163830) from a comprehensive gene expression database, we identified characteristic immune cell subpopulations in KD and OB. B cells emerged as the common immune cell characteristic subgroup in both conditions. Subsequently, we analyzed RNA sequencing datasets (GSE18606 and GSE87493) to identify genes associated with B-cell subpopulations in KD and OB. Lastly, a genome-wide association study and Mendelian randomization were conducted to substantiate the causal impact of these core genes on myocardial infarction. Quantitative real-time PCR (qRT-PCR) to validate the expression levels of hub genes in KD and OB. The overlapping characteristic genes of B cell clusters in both KD and OB yielded 70 shared characteristic genes. PPI analysis led to the discovery of eleven key genes that significantly contribute to the crosstalk. Employing receiver operating characteristic analysis, we evaluated the specificity and sensitivity of these core genes and scored them using Cytoscape software. The inverse variance weighting analysis suggested an association between TNFRSF17 and myocardial infarction risk, with an odds ratio of 0.9995 (95% CI = 0.9990–1.0000, p = 0.049). By employing a single-cell combined transcriptome data analysis, we successfully pinpointed central genes associated with both KD and OB. The implications of these findings extend to shedding light on the increased risk of coronary artery disease resulting from the co-occurrence of OB and KD. [ABSTRACT FROM AUTHOR]

Gastric cancer, the fifth most prevalent cancer worldwide, is often diagnosed in advanced stages with limited treatment options. Examining the tumor microenvironment (TME) and its metabolic reprogramming can provide insights for better diagnosis and treatment. This study investigates the link between TME factors and metabolic activity in gastric cancer using bulk and single-cell RNA-sequencing data. We identified two molecular subtypes in gastric cancer by analyzing the distinct expression patterns of 81 prognostic genes related to the TME and metabolism, which exhibited significant protein-level interactions. The high-risk subtype had increased stromal content, fibroblast and M2 macrophage infiltration, elevated glycosaminoglycans/glycosphingolipids biosynthesis, and fat metabolism, along with advanced clinicopathological features. It also exhibited low mutation rates and microsatellite instability, associating it with the mesenchymal phenotype. In contrast, the low-risk group showed higher tumor content and upregulated protein and sugar metabolism. We identified a 15-gene prognostic signature representing these characteristics, including CPVL, KYNU, CD36, and GPX3, strongly correlated with M2 macrophages, validated through single-cell analysis and an internal cohort. Despite resistance to immunotherapy, the high-risk group showed sensitivity to molecular targeted agents directed at IGF-1R (BMS-754807) and the PI3K-mTOR pathways (AZD8186, AZD8055). We experimentally validated these promising drugs for their inhibitory effects on MKN45 and MKN28 gastric cells. This study unveils the intricate interplay between TME and metabolic pathways in gastric cancer, offering potential for enhanced diagnosis, patient stratification, and personalized treatment. Understanding molecular features in each subtype enriches our comprehension of gastric cancer heterogeneity and potential therapeutic targets. [ABSTRACT FROM AUTHOR]

Conventional magnetophoresis techniques for manipulating biocarriers and cells predominantly rely on large‐scale electromagnetic systems, which is a major obstacle to the development of portable and miniaturized cell‐on‐chip platforms. Herein, a novel magnetic engineering approach by tailoring a nanoscale notch on a disk micromagnet using two‐step optical and thermal lithography is developed. Versatile manipulations are demonstrated, such as separation and trapping, of carriers and cells by mediating changes in the magnetic domain structure and discontinuous movement of magnetic energy wells around the circumferential edge of the micromagnet caused by a locally fabricated nano‐notch in a low magnetic field system. The motion of the magnetic energy well is regulated by the configuration of the nanoscale notch and the strength and frequency of the magnetic field, accompanying the jump motion of the carriers. The proposed concepts demonstrate that multiple carriers and cells can be manipulated and sorted using optimized nanoscale multi‐notch gates for a portable magnetophoretic system. This highlights the potential for developing cost‐effective point‐of‐care testing and lab‐on‐chip systems for various single‐cell‐level diagnoses and analyses. [ABSTRACT FROM AUTHOR]

Objective The process of spermatogenesis is complex involving multiple regulatory mechanisms and requiring complex interactions between germ cells and somatic cells. The mechanisms of Sertoli cells involving in spermatogenesis remain to be explored. Methods We collected single-cell RNA sequencing data from a high-throughput gene expression database (GEO: GSE113293) from wild-type C57BIL6J male mice with normal spermatogenesis and four different mutant mouse strains (knockout Mlh3, Hormadl, and Cul4a; Cnp transgene). Then bioinformatics analyses such as gene function enrichment analysis (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enriched pathway analysis, protein- protein interaction (PPI) network analysis, transcription factor regulation analysis (SCENIC), and gene set variation analysis (GSVA) on the Sertoli cells were further indentified. Results We identified nine subtypes of spermatogenic cell clusters (undiferentiated spermatogonia, diferentiating sperrmatogonia, leptotene/zygotene, pachytene, diplotene, round sperrmatids, elongating spermatids, and spermatozoa), and two types of somatic cell clusters (Leydig cells, and Sertoli cells), and one unknown type of cell cluster. Compared to the normal spermatogenesis model, 276 genes were upregulated and 234 genes were downregulated in the Sertoli cells of the mouse spermatogenesis defect model. GO analysis showed that differential genes in Sertoli cells were significantly enriched in spermatogenesis and energy metabolism. KEGG enrichment analyses showed that the homogeneous ribosomal pathway, glycolytic pathway, and oxidative phosphorylation pathway were significantly enriched in Sertoli cells. Protein-protein interaction (PPI) network analysis of diferentially expressed genes identified 20 hub genes, all of which belonged to ribosomal genes, and GSVA analysis revealed that the upregulated genes in Sertoli cells were closely related to the Notch signaling pathway. Conclusion The hub genes and the Notch signaling pathway in Sertoli cells are involved in the process of spermatogenesis. This study provides new perspectives on infertility caused by spermatogenesis dis orders. [ABSTRACT FROM AUTHOR]

Background: The involvement of cytotoxic CD4+ T cells (CD4+ CTLs) and their potential role in dictating the response to immune checkpoint inhibitors (ICIs) in patients with metastatic renal cell carcinoma (mRCC) remains an unexplored area of research. Methods: Utilizing single-cell RNA sequencing, we analyzed the immunophenotype and expression patterns of CD4+ T lymphocyte subtypes in mRCC patients, followed by preliminary validation via multi-immunofluorescent staining. In addition, we obtained a comprehensive immunotherapy dataset encompassing single-cell RNA sequencing datasets and bulk RNA-seq cohorts from the European Genome-Phenome Archive and ArrayExpress database. Utilizing the CIBERSORTx deconvolution algorithms, we derived a signature score for CD4+ CTLs from the bulk-RNA-seq datasets of the CheckMate 009/025 clinical trials. Results: Single-cell analysis of CD4+ T lymphocytes in mRCC reveals several cancer-specific states, including diverse phenotypes of regulatory T cells. Remarkably, we observe that CD4+ CTLs cells constitute a substantial proportion of all CD4+ T lymphocyte sub-clusters in mRCC patients, highlighting their potential significance in the disease. Furthermore, within mRCC patients, we identify two distinct cytotoxic states of CD4+ T cells: CD4+GZMK+ T cells, which exhibit a weaker cytotoxic potential, and CD4+GZMB+ T cells, which demonstrate robust cytotoxic activity. Both regulatory T cells and CD4+ CTLs originate from proliferating CD4+ T cells within mRCC tissues. Intriguingly, our trajectory analysis indicates that the weakly cytotoxic CD4+GZMK+ T cells differentiate from their more cytotoxic CD4+GZMB+ counterparts. In comparing patients with lower CD4+ CTLs levels to those with higher CD4+ CTLs abundance in the CheckMate 009 and 25 immunotherapy cohorts, the latter group exhibited significantly improved OS and PFS probability. Conclusion: Our study underscores the pivotal role that intratumoral CD4+ CTLs may play in bolstering anti-tumor immunity, suggesting their potential as a promising biomarker for predicting response to ICIs in patients with mRCC. [ABSTRACT FROM AUTHOR]

The progression of lung adenocarcinoma (LUAD) from atypical adenomatous hyperplasia (AAH) to invasive adenocarcinoma (IAC) involves a complex evolution of tumour cell clusters, the mechanisms of which remain largely unknown. By integrating single‐cell datasets and using inferCNV, we identified and analysed tumour cell clusters to explore their heterogeneity and changes in abundance throughout LUAD progression. We applied gene set variation analysis (GSVA), pseudotime analysis, scMetabolism, and Cytotrace scores to study biological functions, metabolic profiles and stemness traits. A predictive model for prognosis, based on key cluster marker genes, was developed using CoxBoost and plsRcox (CPM), and validated across multiple cohorts for its prognostic prediction capabilities, tumour microenvironment characterization, mutation landscape and immunotherapy response. We identified nine distinct tumour cell clusters, with Cluster 6 indicating an early developmental stage, high stemness and proliferative potential. The abundance of Clusters 0 and 6 increased from AAH to IAC, correlating with prognosis. The CPM model effectively distinguished prognosis in immunotherapy cohorts and predicted genomic alterations, chemotherapy drug sensitivity, and immunotherapy responsiveness. Key gene S100A16 in the CPM model was validated as an oncogene, enhancing LUAD cell proliferation, invasion and migration. The CPM model emerges as a novel biomarker for predicting prognosis and immunotherapy response in LUAD patients, with S100A16 identified as a potential therapeutic target. [ABSTRACT FROM AUTHOR]

The link between ferroptosis, a form of cell death mediated by iron and acute kidney injury (AKI) is recently gaining widespread attention. However, the mechanism of the crosstalk between cells in the pathogenesis and progression of acute kidney injury remains unexplored. In our research, we performed a non-negative matrix decomposition (NMF) algorithm on acute kidney injury single-cell RNA sequencing data based specifically focusing in ferroptosis-associated genes. Through a combination with pseudo-time analysis, cell–cell interaction analysis and SCENIC analysis, we discovered that proximal tubular cells, macrophages, and fibroblasts all showed associations with ferroptosis in different pathways and at various time. This involvement influenced cellular functions, enhancing cellular communication and activating multiple transcription factors. In addition, analyzing bulk expression profiles and marker genes of newly defined ferroptosis subtypes of cells, we have identified crucial cell subtypes, including Egr1 + PTC-C1, Jun + PTC-C3, Cxcl2 + Mac-C1 and Egr1 + Fib-C1. All these subtypes which were found in AKI mice kidneys and played significantly distinct roles from those of normal mice. Moreover, we verified the differential expression of Egr1, Jun, and Cxcl2 in the IRI mouse model and acute kidney injury human samples. Finally, our research presented a novel analysis of the crosstalk of proximal tubular cells, macrophages and fibroblasts in acute kidney injury targeting ferroptosis, therefore, contributing to better understanding the acute kidney injury pathogenesis, self-repairment and acute kidney injury-chronic kidney disease (AKI-CKD) progression. [ABSTRACT FROM AUTHOR]

Multiplexed in situ RNA imaging offers new opportunities for gene expression profiling by providing high-throughput spatial information. In this work, we present a cyclic combinatorial fluorescent in situ hybridization (combFISH) assay to achieve multiplexed detection of RNA in cell cultures and tissues. Specifically, multiplexing is achieved through cyclic interrogation of barcode sequences on the rolling circle amplicons generated from the padlock probe assay by using sets of combinatorial detection probes. Theoretically, combFISH can detect 64 genes in three hybridization cycles by combinatorial barcoding using 12 fluorescently labeled detection probes. Our method eliminates sequencing-by-ligation (SBL) chemistry in the in situ sequencing protocol and directly uses RNA as targets for ligation, making it more straightforward. We showed that our method works in fresh-frozen and formalin-fixed paraffin-embedded tissue sections. With its straightforward protocols, we expect our method to be adopted by the scientific community and extended to clinical settings. Multiplexed RNA in situ imaging by combinatorial FISH decoding [ABSTRACT FROM AUTHOR]

Flow cytometry and fluorescence‐activated cell sorting are widely used to study endothelial cells, for which the generation of viable single‐cell suspensions is an essential first step. Two enzymatic approaches, collagenase A and dispase, are widely employed for endothelial cell isolation. In this study, the utility of both enzymatic approaches, alone and in combination, for endothelial cell isolation from juvenile and adult mouse lungs was assessed, considering the number, viability, and subtype composition of recovered endothelial cell pools. Collagenase A yielded an 8‐12‐fold superior recovery of viable endothelial cells from lung tissue from developing mouse pups, compared to dispase, although dispase proved superior in efficiency for epithelial cell recovery. Single‐cell RNA‐Seq revealed that the collagenase A approach yielded a diverse endothelial cell subtype composition of recovered endothelial cell pools, with broad representation of arterial, capillary, venous, and lymphatic lung endothelial cells; while the dispase approach yielded a recovered endothelial cell pool highly enriched for one subset of general capillary endothelial cells, but poor representation of other endothelial cells subtypes. These data indicate that tissue dissociation markedly influences the recovery of endothelial cells, and the endothelial subtype composition of recovered endothelial cell pools, as assessed by single‐cell RNA‐Seq. [ABSTRACT FROM AUTHOR]

Progress in the discovery of potent antibodies for therapeutic and research purposes has been accelerated by mining the antibody repertoire of plasma cells and plasmablasts. Magnetic activated cell sorting (MACS) is a well recognized method for cell sorting, however, secreted IgGs rapidly diffuse away from cells, therefore requiring specialized technique to compartmentalize individual cells and capture their secretions for MACS isolation. To this end, a scalable method to prepare hydrogel‐based microcontainers for single cell culture and subsequent sorting based on their secreted IgG products at high‐throughput level are described. In situ crosslinkable microgels featuring tunable viscosity are fabricated to act as soft compartments that accommodate delicate primary cells and capture secreted proteins, while providing solid supports after solgel transition to enable immune recognition with chemically functionalized magnetic nanorods in aqueous medium for MACS separation. Splenocytes are sorted based on antigen‐specific IgG production from mice immunized with an antibiotic target ceftriaxone sodium, enriching a population of 220 000 000 to 800 000 prevalence following sorting. This enrichment ratio is substantially higher than conventional fluorescence activated cell sorting (FACS) methods, likely due to the multivalency of the antigen‐conjugated magnetic nanorods. [ABSTRACT FROM AUTHOR]

An intriguing field of research has recently emerged in which biological cells can be demonstrated to behave as photonics devices, such as optical microlenses. This research highlights yeast cells as promising candidates for engineering biolenses with customizable focal properties. Typically serving as positive biolenses due to their quasi‐spherical shape and positive refractive index (RI) contrast, yeast cells can achieve a negative lensing effect by manipulating their intracellular content, without altering their overall morphology. In fact, it shows that exposure to hypotonic conditions induces the formation of a large, roundish vacuole with lower RI values with respect to the surrounding cytoplasm, thus altering the cell's optical properties. This behavior is investigated by using 3D RI tomograms obtained through a holo‐tomographic phase imaging flow cytometry system, demonstrating that the anisotropic nature of yeast cells results in focal properties dependent on their specific orientations in respect to the incident light. Furthermore, Zemax OpticStudio is utilized to perform comprehensive assessments for optical design analysis of the obtained cell biolenses. The methods described here and the related results represent a pioneering investigation of biological cells' metamorphoses via intracellular content manipulation for biolens applications. [ABSTRACT FROM AUTHOR]

Objective: To investigate the association between liver enzymes and ovarian cancer (OC), and to validate their potential as biomarkers and their mechanisms in OC. Methods Genome-wide association studies for OC and levels of enzymes such as Alkaline phosphatase (ALP), Aspartate aminotransferase (AST), Alanine aminotransferase, and gamma-glutamyltransferase were analyzed. Univariate and multivariate Mendelian randomization (MR), complemented by the Steiger test, identified enzymes with a potential causal relationship to OC. Single-cell transcriptomics from the GSE130000 dataset pinpointed pivotal cellular clusters, enabling further examination of enzyme-encoding gene expression. Transcription factors (TFs) governing these genes were predicted to construct TF-mRNA networks. Additionally, liver enzyme levels were retrospectively analyzed in healthy individuals and OC patients, alongside the evaluation of correlations with cancer antigen 125 (CA125) and Human Epididymis Protein 4 (HE4). Results: A total of 283 single nucleotide polymorphisms (SNPs) and 209 SNPs related to ALP and AST, respectively. Using the inverse-variance weighted method, univariate MR (UVMR) analysis revealed that ALP (P = 0.050, OR = 0.938) and AST (P = 0.017, OR = 0.906) were inversely associated with OC risk, suggesting their roles as protective factors. Multivariate MR (MVMR) confirmed the causal effect of ALP (P = 0.005, OR = 0.938) on OC without reverse causality. Key cellular clusters including T cells, ovarian cells, endothelial cells, macrophages, cancer-associated fibroblasts (CAFs), and epithelial cells were identified, with epithelial cells showing high expression of genes encoding AST and ALP. Notably, TFs such as TCE4 were implicated in the regulation of GOT2 and ALPL genes. OC patient samples exhibited decreased ALP levels in both blood and tumor tissues, with a negative correlation between ALP and CA125 levels observed. Conclusion: This study has established a causal link between AST and ALP with OC, identifying them as protective factors. The increased expression of the genes encoding these enzymes in epithelial cells provides a theoretical basis for developing novel disease markers and targeted therapies for OC. [ABSTRACT FROM AUTHOR]

Kidney renal clear cell carcinoma (KIRC) pathogenesis intricately involves immune system dynamics, particularly the role of T cells within the tumour microenvironment. Through a multifaceted approach encompassing single‐cell RNA sequencing, spatial transcriptome analysis and bulk transcriptome profiling, we systematically explored the contribution of infiltrating T cells to KIRC heterogeneity. Employing high‐density weighted gene co‐expression network analysis (hdWGCNA), module scoring and machine learning, we identified a distinct signature of infiltrating T cell‐associated genes (ITSGs). Spatial transcriptomic data were analysed using robust cell type decomposition (RCTD) to uncover spatial interactions. Further analyses included enrichment assessments, immune infiltration evaluations and drug susceptibility predictions. Experimental validation involved PCR experiments, CCK‐8 assays, plate cloning assays, wound‐healing assays and Transwell assays. Six subpopulations of infiltrating and proliferating T cells were identified in KIRC, with notable dynamics observed in mid‐ to late‐stage disease progression. Spatial analysis revealed significant correlations between T cells and epithelial cells across varying distances within the tumour microenvironment. The ITSG‐based prognostic model demonstrated robust predictive capabilities, implicating these genes in immune modulation and metabolic pathways and offering prognostic insights into drug sensitivity for 12 KIRC treatment agents. Experimental validation underscored the functional relevance of PPIB in KIRC cell proliferation, invasion and migration. Our study comprehensively characterizes infiltrating T‐cell heterogeneity in KIRC using single‐cell RNA sequencing and spatial transcriptome data. The stable prognostic model based on ITSGs unveils infiltrating T cells' prognostic potential, shedding light on the immune microenvironment and offering avenues for personalized treatment and immunotherapy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Sporadic parathyroid adenoma (PA) is the most common cause of hyperparathyroidism, yet the mechanisms involved in its pathogenesis remain incompletely understood. Methods: Surgically removed PA samples, along with normal parathyroid gland (PG) tissues that were incidentally dissected during total thyroidectomy, were analysed using single‐cell RNA‐sequencing with the 10× Genomics Chromium Droplet platform and Cell Ranger software. Gene set variation analysis was conducted to characterise hallmark pathway gene signatures, and single‐cell regulatory network inference and clustering were utilised to analyse transcription factor regulons. Immunohistochemistry and immunofluorescence were performed to validate cellular components of PA tissues. siRNA knockdown and gene overexpression, alongside quantitative polymerase chain reaction, Western blotting and cell proliferation assays, were conducted for functional investigations. Results: There was a pervasive increase in gene transcription in PA cells (PACs) compared with PG cells. This is associated with high expression of histone‐lysine N‐methyltransferase 2A (KMT2A). High KMT2A levels potentially contribute to promoting PAC proliferation through upregulation of the proto‐oncogene CCND2, which is mediated by the transcription factors signal transducer and activator of transcription 3 (STAT3) and GATA binding protein 3 (GATA3). PA tissues are heavily infiltrated with myeloid cells, while fibroblasts, endothelial cells and macrophages in PA tissues are commonly enriched with proinflammatory gene signatures relative to their counterparts in PG tissues. Conclusions: We revealed the previously underappreciated involvement of the KMT2A‒STAT3/GATA3‒CCND2 axis and chronic inflammation in the pathogenesis of PA. These findings underscore the therapeutic promise of KMT2A inhibition and anti‐inflammatory strategies, highlighting the need for future investigations to translate these molecular insights into practical applications. Highlights: Single‐cell RNA‐sequencing reveals a transcriptome catalogue comparing sporadic parathyroid adenomas (PAs) with normal parathyroid glands.PA cells show a pervasive increase in gene expression linked to KMT2A upregulation.KMT2A‐mediated STAT3 and GATA3 upregulation is key to promoting PA cell proliferation via cyclin D2.PAs exhibit a proinflammatory microenvironment, suggesting a potential role of chronic inflammation in PA pathogenesis. [ABSTRACT FROM AUTHOR]

Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high dimensional, sparse, and heterogeneous and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, deep learning often demonstrates superior performance compared to traditional machine learning methods. In this work, we give a comprehensive survey on deep learning in single-cell analysis. We first introduce background on single-cell technologies and their development, as well as fundamental concepts of deep learning including the most popular deep architectures. We present an overview of the single-cell analytic pipeline pursued in research applications while noting divergences due to data sources or specific applications. We then review seven popular tasks spanning different stages of the single-cell analysis pipeline, including multimodal integration, imputation, clustering, spatial domain identification, cell-type deconvolution, cell segmentation, and cell-type annotation. Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages. Deep learning tools and benchmark datasets are also summarized for each task. Finally, we discuss the future directions and the most recent challenges. This survey will serve as a reference for biologists and computer scientists, encouraging collaborations. [ABSTRACT FROM AUTHOR]

Mitochondrial RNA modification (MRM) plays a crucial role in regulating the expression of key mitochondrial genes and promoting tumor metastasis. Despite its significance, comprehensive studies on MRM in lower grade gliomas (LGGs) remain unknown. Single-cell RNA-seq data (GSE89567) was used to evaluate the distribution functional status, and correlation of MRM-related genes in different cell types of LGG microenvironment. We developed an MRM scoring system by selecting potential MRM-related genes using LASSO regression analysis and the Random Survival Forest algorithm, based on multiple bulk RNA-seq datasets from TCGA, CGGA, GSE16011, and E-MTAB-3892. Analysis was performed on prognostic and immunological features, signaling pathways, metabolism, somatic mutations and copy number variations (CNVs), treatment responses, and forecasting of potential small-molecule agents. A total of 35 MRM-related genes were selected from the literature. Differential expression analysis of 1120 normal brain tissues and 529 LGGs revealed that 22 and 10 genes were upregulated and downregulated, respectively. Most genes were associated with prognosis of LGG. METLL8, METLL2A, TRMT112, and METTL2B were extensively expressed in all cell types and different cell cycle of each cell type. Almost all cell types had clusters related to mitochondrial RNA processing, ribosome biogenesis, or oxidative phosphorylation. Cell–cell communication and Pearson correlation analyses indicated that MRM may promoting the development of microenvironment beneficial to malignant progression via modulating NCMA signaling pathway and ICP expression. A total of 11 and 9 MRM-related genes were observed by LASSO and the RSF algorithm, respectively, and finally 6 MRM-related genes were used to establish MRM scoring system (TRMT2B, TRMT11, METTL6, METTL8, TRMT6, and TRUB2). The six MRM-related genes were then validated by qPCR in glioma and normal tissues. MRM score can predict the malignant clinical characteristics, abundance of immune infiltration, gene variation, clinical outcome, the enrichment of signaling pathways and metabolism. In vitro experiments demonstrated that silencing METTL8 significantly curbs glioma cell proliferation and enhances apoptosis. Patients with a high MRM score showed a better response to immunotherapies and small-molecule agents such as arachidonyl trifluoromethyl ketone, MS.275, AH.6809, tacrolimus, and TTNPB. These novel insights into the biological impacts of MRM within the glioma microenvironment underscore its potential as a target for developing precise therapies, including immunotherapeutic approaches. [ABSTRACT FROM AUTHOR]

Single-cell joint analysis of methylome and transcriptome reveals how the methylation regulates the transcriptional activity. However, traditional bench-top protocols for single-cell DNA methylation and RNA transcription co-detection are labor-intensive, cost-ineffective and contaminant-prone. Herein, we establish the DMF-scMT-seq, a highly-efficient and cost-effective method to simultaneously analyze single-cell DNA methylation and transcriptional activity based on digital microfluidics. DMF-scMT-seq automates the workflow of single-cell isolation, cellular hypotonic lysis, nucleic acid separation and methylome/transcriptome library construction in a contactless and addressable way. The system ensures high accuracy (R>0.85), high gene detection ability (14,697 genes per cell at 4 million sequencing depth), and high CpG coverage (677,198 CpG sites per cell at 1 million sequencing depth). By using DMF-scMT-seq, the relationship of DNA methylation and RNA transcription under different genomic contexts is resolved. We further apply DMF-scMT-seq to study the dynamics of transcription regulation with methylation-inhibiting anti-tumor Decitabine, and identify the methylated promoter/gene body driven genes in response to Decitabine treatment. DMF-scMT-seq facilitates the construction of the correlation of DNA methylation and transcriptional activity at the single-cell level in a flexible, sensitive and accurate way, which is anticipated to be a powerful tool in studying single-cell biological systems. [ABSTRACT FROM AUTHOR]

Pollution from toxic spores has caused us a lot of problems because spores are extremely resistant and can survive most disinfectants. Therefore, the detection of spore response to disinfectant is of great significance for the development of effective decontamination strategies. In this work, we investigated the effect of 0.5% sodium hypochlorite on the molecular and morphological properties of single spores of Bacillus subtilis using single‐cell techniques. Laser tweezers Raman spectroscopy showed that sodium hypochlorite resulted in Ca2+‐dipicolinic acid release and nucleic acid denaturation. Atomic force microscopy showed that the surface of treated spores changed from rough to smooth, protein shells were degraded at 10 min, and the permeability barrier was destroyed at 15 min. The spore volume decreased gradually over time. Live‐cell imaging showed that the germination and growth rates decreased with increasing treatment time. These results provide new insight into the response of spores to sodium hypochlorite. [ABSTRACT FROM AUTHOR]