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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.

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Background: Mitophagy selectively eliminates potentially cytotoxic and damaged mitochondria and effectively prevents excessive cytotoxicity from damaged mitochondria, thereby attenuating inflammatory and oxidative responses. However, the potential role of mitophagy in intervertebral disc degeneration remains to be elucidated. Methods: The GSVA method, two machine learning methods (SVM-RFE algorithm and random forest), the CIBERSORT and MCPcounter methods, as well as the consensus clustering method and the WGCNA algorithm were used to analyze the involvement of mitophagy in intervertebral disc degeneration, the diagnostic value of mitophagy-associated genes in intervertebral disc degeneration, and the infiltration of immune cells, and identify the gene modules that were closely related to mitophagy. Single-cell analysis was used to detect mitophagy scores and TOMM22 expression, and pseudo-temporal analysis was used to explore the function of TOMM22 in nucleus pulposus cells. In addition, TOMM22 expression was compared between human normal and degenerated intervertebral disc tissue samples by immunohistochemistry and PCR. Results: This study identified that the mitophagy pathway score was elevated in intervertebral disc degeneration compared with the normal condition. A strong link was present between mitophagy genes and immune cells, which may be used to typify intervertebral disc degeneration. The single-cell level showed that mitophagy-associated gene TOMM22 was highly expressed in medullary cells of the disease group. Further investigations indicated the upregulation of TOMM22 expression in late-stage nucleus pulposus cells and its role in cellular communication. In addition, human intervertebral disc tissue samples established that TOMM22 levels were higher in disc degeneration samples than in normal samples. Conclusions: Our findings revealed that mitophagy may be used in the diagnosis of intervertebral disc degeneration and its typing, and TOMM22 is a molecule in this regard and may act as a potential diagnostic marker in intervertebral disc degeneration.

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Background: Ovarian cancer (OV) is regarded as one of the most lethal malignancies affecting the female reproductive system, with individuals diagnosed with OV often facing a dismal prognosis due to resistance to chemotherapy and the presence of an immunosuppressive environment. T cells serve as a crucial mediator for immune surveillance and cancer elimination. This study aims to analyze the mechanism of T cell-associated markers in OV and create a prognostic model for clinical use in enhancing outcomes for OV patients. Methods: Based on the single-cell dataset GSE184880, this study used single-cell data analysis to identify characteristic T cell subsets. Analysis of high dimensional weighted gene co-expression network analysis (hdWGCNA) is utilized to identify crucial gene modules along with their corresponding hub genes. A grand total of 113 predictive models were formed utilizing ten distinct machine learning algorithms along with the combination of the cancer genome atlas (TCGA)-OV dataset and the GSE140082 dataset. The most dependable clinical prognostic model was created utilizing the leave one out cross validation (LOOCV) framework. The validation process for the models was achieved by conducting survival curve analysis and receiver operating characteristic (ROC) analysis. The relationship between risk scores and immune cells was explored through the utilization of the Cibersort algorithm. Additionally, an analysis of drug sensitivity was carried out to anticipate chemotherapy responses across various risk groups. The genes implicated in the model were authenticated utilizing qRT-PCR, cell viability experiments, and EdU assay. Results: This study developed a clinical prognostic model that includes ten risk genes. The results obtained from the training set of the study indicate that patients classified in the low-risk group experience a significant survival advantage compared to those in the high-risk group. The ROC analysis demonstrates that the model holds significant clinical utility. These results were verified using an independent dataset, strengthening the model's precision and dependability. The risk assessment provided by the model also serves as an independent prognostic factor for OV patients. The study also unveiled a noteworthy relationship between the risk scores calculated by the model and various immune cells, suggesting that the model may potentially serve as a valuable tool in forecasting responses to both immune therapy and chemotherapy in ovarian cancer patients. Notably, experimental evidence suggests that PFN1, one of the genes included in the model, is upregulated in human OV cell lines and has the capacity to promote cancer progression in in vitro models. Conclusion: We have created an accurate and dependable clinical prognostic model for OV capable of predicting clinical outcomes and categorizing patients. This model effectively forecasts responses to both immune therapy and chemotherapy. By regulating the immune microenvironment and targeting the key gene PFN1, it may improve the prognosis for high-risk patients.

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Huntington's Disease (HD) is an inheritable neurodegenerative condition caused by an expanded CAG trinucleotide repeat in the HTT gene with a direct correlation between CAG repeats expansion and disease severity with earlier onset-of- disease. Previously we have shown that primary skin fibroblasts from HD patients exhibit unique phenotype disease features, including distinct nuclear morphology and perturbed actin cap linked with cell motility, that are correlated with the HD patient disease severity. Here we provide further evidence that mitochondrial fission-fusion morphology balance dynamics, classified using a custom image-based high-content analysis (HCA) machine learning tool, that improved correlation with HD severity status. This mitochondrial phenotype is supported by appropriate changes in fission-fusion biomarkers (Drp1, MFN1, MFN2, VAT1) levels in the HD patients' fibroblasts. These findings collectively point towards a dysregulation in mitochondrial dynamics, where both fission and fusion processes may be disrupted in HD cells compared to healthy controls. This study shows for the first time a methodology that enables identification of HD phenotype before patient's disease onset (Premanifest). Therefore, we believe that this tool holds a potential for improving precision in HD patient's diagnostics bearing the potential to evaluate alterations in mitochondrial dynamics throughout the progression of HD, offering valuable insights into the molecular mechanisms and drug therapy evaluation underlying biological differences in any disease stage.

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Extracellular matrix (ECM)-mimicking microsized cell carriers featuring a semi-isolated chamber facilitate the study of cellular heterogeneity as well as intercellular communication. However, the semiopen shaping of the designated gel mixture remains unattainable with current methods. We report an oil-phase freeze-shrink self-molding mechanism for generating size- and composition-tunable cradle-shaped microgels (microcradles) from water-in-oil droplets. The universality of this shape transition principle is demonstrated with six types of polysaccharides dispersed in a poly(ethylene glycol) diacrylate (PEGDA) or methacrylate gelatin (GelMA) matrix. By doping the microcradles with the major ECM component, hyaluronic acid sodium, we demonstrate a label-free selective culture of CD44 receptor-rich cells and the formation of cell spheroids within 3 days. This cryo-induced cradle-shaping strategy enables the functionalization of microcarriers for selective cell culture, thereby allowing them to be used for intercellular communication, drug delivery, and the construction of structural units for osteogenesis and 3D printing.

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Single-particle inductively coupled plasma-mass spectrometry (sp-ICP-MS) is one of the most powerful tools in the thriving field of nanomaterial analysis. Along the same lines, single-cell ICP-MS (sc-ICP-MS) has become an invaluable tool in the study of the variances of cell populations down to a per-cell basis. Their importance and application fields have been listed numerous times, across various reports and reviews. However, not enough attention has been paid to the immense and ongoing development of the tools that are currently available to the analytical community for the acquisition, and more importantly, the treatment of single-particle and single-cell-related data. Due to the ever-increasing demands of modern research, the efficient and dependable treatment of the data has become more important than ever. In addition, the field of single-particle and single-cell analysis suffers due to a large number of approaches for the generated data-with varying levels of specificity and applicability. As a result, finding the appropriate tool or approach, or even comparing results, can be challenging. This article will attempt to bridge these gaps, by covering the evolution and current state of the tools at the disposal of sp-ICP-MS users.

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Background: Pancreatic cancer (PC), characterized by its aggressive nature and low patient survival rate, remains a challenging malignancy. Anoikis, a process inhibiting the spread of metastatic cancer cells, is closely linked to cancer progression and metastasis through anoikis-related genes. Nonetheless, the precise mechanism of action of these genes in PC remains unclear. Methods: Study data were acquired from the Cancer Genome Atlas (TCGA) database, with validation data accessed at the Gene Expression Omnibus (GEO) database. Differential expression analysis and univariate Cox analysis were performed to determine prognostically relevant differentially expressed genes (DEGs) associated with anoikis. Unsupervised cluster analysis was then employed to categorize cancer samples. Subsequently, a least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted on the identified DEGs to establish a clinical prognostic gene signature. Using risk scores derived from this signature, patients with cancer were stratified into high-risk and low-risk groups, with further assessment conducted via survival analysis, immune infiltration analysis, and mutation analysis. External validation data were employed to confirm the findings, and Western blot and immunohistochemistry were utilized to validate risk genes for the clinical prognostic gene signature. Results: A total of 20 prognostic-related DEGs associated with anoikis were obtained. The TCGA dataset revealed two distinct subgroups: cluster 1 and cluster 2. Utilizing the 20 DEGs, a clinical prognostic gene signature comprising two risk genes (CDKN3 and LAMA3) was constructed. Patients with pancreatic adenocarcinoma (PAAD) were classified into high-risk and low-risk groups per their risk scores, with the latter exhibiting a superior survival rate. Statistically significant variation was noted across immune infiltration and mutation levels between the two groups. Validation cohort results were consistent with the initial findings. Additionally, experimental verification confirmed the high expression of CDKN3 and LAMA3 in tumor samples. Conclusion: Our study addresses the gap in understanding the involvement of genes linked to anoikis in PAAD. The clinical prognostic gene signature developed herein accurately stratifies patients with PAAD, contributing to the advancement of precision medicine for these patients.

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Background: Cancer stem cells (CSCs) are a subset of cells within tumors that possess the unique ability to self-renew and give rise to diverse tumor cells. These cells are crucial in driving tumor metastasis, recurrence, and resistance to treatment. The objective of this study was to pinpoint the essential regulatory genes associated with CSCs in prostate adenocarcinoma (PRAD) and assess their potential significance in the diagnosis, prognosis, and immunotherapy of patients with PRAD. Method: The study utilized single-cell analysis techniques to identify stem cell-related genes and evaluate their significance in relation to patient prognosis and immunotherapy in PRAD through cluster analysis. By utilizing diverse datasets and employing various machine learning methods for clustering, diagnostic models for PRAD were developed and validated. The random forest algorithm pinpointed HSPE1 as the most crucial prognostic gene among the stem cell-related genes. Furthermore, the study delved into the association between HSPE1 and immune infiltration, and employed molecular docking to investigate the relationship between HSPE1 and its associated compounds. Immunofluorescence staining analysis of 60 PRAD tissue samples confirmed the expression of HSPE1 and its correlation with patient prognosis in PRAD. Result: This study identified 15 crucial stem cell-related genes through single-cell analysis, highlighting their importance in diagnosing, prognosticating, and potentially treating PRAD patients. HSPE1 was specifically linked to PRAD prognosis and response to immunotherapy, with experimental data supporting its upregulation in PRAD and association with poorer prognosis. Conclusion: Overall, our findings underscore the significant role of stem cell-related genes in PRAD and unveil HSPE1 as a novel target related to stem cell.

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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 .

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In the realm of this study, obtaining a comprehensive understanding of ischemic brain injury and its molecular foundations is of paramount importance. Our study delved into single-cell data analysis, with a specific focus on sub-celltypes and differentially expressed genes in the aftermath of ischemic injury. Notably, we observed a significant enrichment of the "ATP METABOLIC PROCESS" and "ATP HYDROLYSIS ACTIVITY" pathways, featuring pivotal genes such as Pbx3, Dguok, and Kif21b. A remarkable finding was the consistent upregulation of genes like Fabp7 and Bcl11a within the MCAO group, highlighting their crucial roles in regulating the pathway of mitochondrial ATP synthesis coupled proton transport. Furthermore, our network analysis unveiled pathways like "Neuron differentiation" and "T cell differentiation" as central in the regulatory processes of sub-celltypes. These findings provide valuable insights into the intricate molecular responses and regulatory mechanisms that govern brain injury. The shared differentially expressed genes among sub-celltypes emphasize their significance in orchestrating responses post-ischemic injury. Our research, viewed from the perspective of a medical researcher, contributes to the evolving understanding of the molecular landscape underlying ischemic brain injury, potentially paving the way for targeted therapeutic strategies and improved patient outcomes.

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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, ß2-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.

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Background: Hypoxia has been found to cause cellular dysfunction and cell death, which are essential mechanisms in the development of acute myocardial infarction (AMI). However, the impact of hypoxia-related genes (HRGs) on AMI remains uncertain. Methods: The training dataset GSE66360, validation dataset GSE48060, and scRNA dataset GSE163956 were downloaded from the GEO database. We identified hub HRGs in AMI using machine learning methods. A prediction model for AMI occurrence was constructed and validated based on the identified hub HRGs. Correlations between hub HRGs and immune cells were explored using ssGSEA analysis. Unsupervised consensus clustering analysis was used to identify robust molecular clusters associated with hypoxia. Single-cell analysis was used to determine the distribution of hub HRGs in cell populations. RT-qPCR verified the expression levels of hub HRGs in the human cardiomyocyte model of AMI by oxygen-glucose deprivation (OGD) treatment in AC16 cells. Results: Fourteen candidate HRGs were identified by differential analysis, and the RF model and the nomogram based on 8 hub HRGs (IRS2, ZFP36, NFIL3, TNFAIP3, SLC2A3, IER3, MAFF, and PLAUR) were constructed, and the ROC curves verified its good prediction effect in training and validation datasets (AUC = 0.9339 and 0.8141, respectively). In addition, the interaction between hub HRGs and smooth muscle cells, immune cells was elucidated by scRNA analysis. Subsequently, the HRG pattern was constructed by consensus clustering, and the HRG gene pattern verified the accuracy of its grouping. Patients with AMI could be categorized into three HRG subclusters, and cluster A was significantly associated with immune infiltration. The RT-qPCR results showed that the hub HRGs in the OGD group were significantly overexpressed. Conclusion: A predictive model of AMI based on HRGs was developed and strongly associated with immune cell infiltration. Characterizing patients for hypoxia could help identify populations with specific molecular profiles and provide precise treatment.

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To gain insight into how an adjuvant impacts vaccination responses, we use systems immunology to study human H5N1 influenza vaccination with or without the adjuvant AS03, longitudinally assessing 14 time points including multiple time points within the first day after prime and boost. We develop an unsupervised computational framework to discover high-dimensional response patterns, which uncover adjuvant- and immunogenicity-associated early response dynamics, including some that differ post prime versus boost. With or without adjuvant, some vaccine-induced transcriptional patterns persist to at least 100 days after initial vaccination. Single-cell profiling of surface proteins, transcriptomes, and chromatin accessibility implicates transcription factors in the erythroblast-transformation-specific (ETS) family as shaping these long-lasting signatures, primarily in classical monocytes but also in CD8+ naive-like T cells. These cell-type-specific signatures are elevated at baseline in high-antibody responders in an independent vaccination cohort, suggesting that antigen-agnostic baseline immune states can be modulated by vaccine antigens alone to enhance future responses.

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Huntington's disease (HD) is a complex neurodegenerative disorder with considerable heterogeneity in clinical manifestations. While CAG repeat length is a known predictor of disease severity, this heterogeneity suggests the involvement of additional genetic and environmental factors. Previously we revealed that HD primary fibroblasts exhibit unique features, including distinct nuclear morphology and perturbed actin cap, resembling characteristics seen in Hutchinson-Gilford Progeria Syndrome (HGPS). This study establishes a link between actin cap deficiency and cell motility in HD, which correlates with the HD patient disease severity. Here, we examined single-cell motility imaging features in HD primary fibroblasts to explore in depth the relationship between cell migration patterns and their respective HD patients' clinical severity status (premanifest, mild and severe). The single-cell analysis revealed a decline in overall cell motility in correlation with HD severity, being most prominent in severe HD subgroup and HGPS. Moreover, we identified seven distinct spatial clusters of cell migration in all groups, which their proportion varies within each group becoming a significant HD severity classifier between HD subgroups. Next, we investigated the relationship between Lamin B1 expression, serving as nuclear envelope morphology marker, and cell motility finding that changes in Lamin B1 levels are associated with specific motility patterns within HD subgroups. Based on these data we present an accurate machine learning classifier offering comprehensive exploration of cellular migration patterns and disease severity markers for future accurate drug evaluation opening new opportunities for personalized treatment approaches in this challenging disorder.

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Entamoeba nuttalli is genetically the closest to Entamoeba histolytica, the causative agent of human amebiasis, and its natural host is Macaca species. A unique E. nuttalli specific surface protein (PTORS) containing 42 repeats of octapeptide was identified by comparative genomic analysis of Entamoeba species. We aimed to elucidate the function of this protein. When trophozoites from various E. nuttalli strains were examined by immunofluorescence microscopy and flow cytometry using a PTORS-specific monoclonal antibody, only a limited proportion of trophozoites were stained, indicating that the protein was not commonly expressed in all E. nuttalli trophozoite. The proportion of trophozoites expressing PTORS increased after passage in hamster livers, suggesting that the protein functions in the virulence of trophozoites in the liver tissue. Single-cell analysis revealed that in the cluster including trophozoites with PTORS gene expression, genes of virulence-related proteins were also upregulated. Trophozoites of E. histolytica transfected with PTORS showed enhanced adherence and subsequent phagocytic activity towards human Jurkat cells, independent of the lectin. E. histolytica trophozoites expressing PTORS formed larger liver abscesses in hamsters. These results demonstrate that PTORS is a novel virulence factor in Entamoeba species.

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Effective tools for exploration and analysis are needed to extract insights from large-scale single-cell measurement data. However, current techniques for handling single-cell studies performed across experimental conditions (e.g., samples, perturbations, or patients) require restrictive assumptions, lack flexibility, or do not adequately deconvolute condition-to-condition variation from cell-to-cell variation. Here, we report that the tensor decomposition method PARAFAC2 (Pf2) enables the dimensionality reduction of single-cell data across conditions. We demonstrate these benefits across two distinct contexts of single-cell RNA-sequencing (scRNA-seq) experiments of peripheral immune cells: pharmacologic drug perturbations and systemic lupus erythematosus (SLE) patient samples. By isolating relevant gene modules across cells and conditions, Pf2 enables straightforward associations of gene variation patterns across specific patients or perturbations while connecting each coordinated change to certain cells without pre-defining cell types. The theoretical grounding of Pf2 suggests a unified framework for many modeling tasks associated with single-cell data. Thus, Pf2 provides an intuitive universal dimensionality reduction approach for multi-sample single-cell studies across diverse biological contexts.

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Single-molecule imaging in living cells is an effective tool for elucidating the mechanisms of cellular phenomena at the molecular level. However, the analysis was not designed for throughput and requires high expertise, preventing it from reaching large scale, which is necessary when searching for rare cells that induce singularity phenomena. To overcome this limitation, we have automated the imaging procedures by combining our own focusing device, artificial intelligence, and robotics. The apparatus, called automated in-cell single-molecule imaging system (AiSIS), achieves a throughput that is a hundred-fold higher than conventional manual imaging operations, enabling the analysis of molecular events by individual cells across a large population. Here, using AiSIS, we demonstrate the single-molecule imaging of molecular behaviors and reactions related to tau protein aggregation, which is considered a singularity phenomenon in neurological disorders. Changes in the dynamics and kinetics of molecular events were observed inside and on the basal membrane of cells after the induction of aggregation. Additionally, to detect rare cells based on the molecular behavior, we developed a method to identify the state of individual cells defined by the quantitative distribution of molecular mobility and clustering. Using this method, cellular variations in receptor behavior were shown to decrease following ligand stimulation. This cell state analysis based on large-scale single-molecule imaging by AiSIS will advance the study of molecular mechanisms causing singularity phenomena.

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Motivation: Analyzing the local microenvironment of tumor cells can provide significant insights into their complex interactions with their cellular surroundings, including immune cells. By quantifying the prevalence and distances of certain immune cells in the vicinity of tumor cells through a neighborhood analysis, patterns may emerge that indicate specific associations between cell populations. Such analyses can reveal important aspects of tumor-immune dynamics, which may inform therapeutic strategies. This method enables an in-depth exploration of spatial interactions among different cell types, which is crucial for research in oncology, immunology, and developmental biology. Results: We introduce an R Markdown script called SNAQ™ (Single-cell Spatial Neighborhood Analysis and Quantification), which conducts a neighborhood analysis on immunofluorescent images without the need for extensive coding knowledge. As a demonstration, SNAQ™ was used to analyze images of pancreatic ductal adenocarcinoma. Samples stained for DAPI, PanCK, CD68, and PD-L1 were segmented and classified using QuPath. The resulting CSV files were exported into RStudio for further analysis and visualization using SNAQ™. Visualizations include plots revealing the cellular composition of neighborhoods around multiple cell types within a customizable radius. Additionally, the analysis includes measuring the distances between cells of certain types relative to others across multiple regions of interest. Availability and implementation: The R Markdown files that comprise the SNAQ™ algorithm and the input data from this paper are freely available on the web at https://github.com/AryehSilver1/SNAQ.

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Single-tube nested PCR (STnPCR) is a technique that improves nested PCR, reducing potential contamination and false-positive results, enhancing the amplification sensitivity. Despite being commonly used for the detection of microorganisms, STnPCR can be a valuable tool for bovine genotyping, encompassing essential targets as ROSA26 and TSPY, pivotal in the fields of animal reproduction, genetic improvement, and transgenic research. The objective of this study was to improve and innovate STnPCR for gene detection in cattle. We aimed to detect the ROSA26 and TSPY genes using low-concentration DNA samples, including single cells, small cell groups (one to five cells), in vitro-produced embryos, and bovine tissue samples. Moreover, we refined STnPCR for gene detection in up to single cells by conducting sensitivity testing with different concentration ratios of internal and external primers. Successful amplification of the ROSA26 and TSPY genes was achieved across all tested primer concentrations, even in single cells, with more consistent results observed at lower primer concentrations. Additionally, simultaneous gene amplification was achieved through STnPCR multiplexing, representing the first study of multiplex STnPCR in cattle. These outcomes not only confirm its effectiveness in detecting genetic markers for animal genetic improvement and transgenic elements but also pave the way for its widespread adoption in reproductive studies in bovines.

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Alzheimer's disease (AD) and Parkinson's disease (PD) cause significant neuronal loss and severely impair daily living. Despite different clinical manifestations, these disorders share common pathological molecular hallmarks, including mitochondrial dysfunction and synaptic degeneration. A detailed comparison of molecular changes at single-cell resolution in the cortex, as one of the main brain regions affected in both disorders, may reveal common susceptibility factors and disease mechanisms. We performed single-cell transcriptomic analyses of post-mortem cortical tissue from AD and PD subjects and controls to identify common and distinct disease-associated changes in individual genes, cellular pathways, molecular networks, and cell-cell communication events, and to investigate common mechanisms. The results revealed significant disease-specific, shared, and opposing gene expression changes, including cell type-specific signatures for both diseases. Hypoxia signaling and lipid metabolism emerged as significantly modulated cellular processes in both AD and PD, with contrasting expression alterations between the two diseases. Furthermore, both pathway and cell-cell communication analyses highlighted shared significant alterations involving the JAK-STAT signaling pathway, which has been implicated in the inflammatory response in several neurodegenerative disorders. Overall, the analyses revealed common and distinct alterations in gene signatures, pathway activities, and gene regulatory subnetworks in AD and PD. The results provide insights into coordinated changes in pathway activity and cell-cell communication that may guide future diagnostics and therapeutics.