Leveraging gene correlations in single cell transcriptomic data.

BACKGROUND: Many approaches have been developed to overcome technical noise in single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data-looking for rare cell types, subtleties of cell states, and details of gene regulatory networks-there is a growing need for algorithms with controllable accuracy and fewer ad hoc parameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data in which ground truth about biological variation is unknown (i.e., usually). RESULTS: We approach this problem analytically, assuming that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We analyze scRNAseq data without normalization-a step that skews distributions, particularly for sparse data-and calculate p values associated with key statistics. We develop an improved method for selecting features for cell clustering and identifying gene-gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads), captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify thousands of correlations that, when clustered without supervision into gene communities, align with known cellular components and biological processes, and highlight potentially novel cell biological relationships. CONCLUSIONS: New insights into functionally relevant gene regulatory networks can be obtained using a statistically grounded approach to the identification of gene-gene correlations.

CellChat for systematic analysis of cell-cell communication from single-cell transcriptomics.

Recent advances in single-cell sequencing technologies offer an opportunity to explore cell-cell communication in tissues systematically and with reduced bias. A key challenge is integrating known molecular interactions and measurements into a framework to identify and analyze complex cell-cell communication networks. Previously, we developed a computational tool, named CellChat, that infers and analyzes cell-cell communication networks from single-cell transcriptomic data within an easily interpretable framework. CellChat quantifies the signaling communication probability between two cell groups using a simplified mass-action-based model, which incorporates the core interaction between ligands and receptors with multisubunit structure along with modulation by cofactors. Importantly, CellChat performs a systematic and comparative analysis of cell-cell communication using a variety of quantitative metrics and machine-learning approaches. CellChat v2 is an updated version that includes additional comparison functionalities, an expanded database of ligand-receptor pairs along with rich functional annotations, and an Interactive CellChat Explorer. Here we provide a step-by-step protocol for using CellChat v2 on single-cell transcriptomic data, including inference and analysis of cell-cell communication from one dataset and identification of altered intercellular communication, signals and cell populations from different datasets across biological conditions. The R implementation of CellChat v2 toolkit and its tutorials together with the graphic outputs are available at https://github.com/jinworks/CellChat . This protocol typically takes ~5 min depending on dataset size and requires a basic understanding of R and single-cell data analysis but no specialized bioinformatics training for its implementation.

LMNA-Related Dilated Cardiomyopathy: Single-Cell Transcriptomics during Patient-Derived iPSC Differentiation Support Cell Type and Lineage-Specific Dysregulation of Gene Expression and Development for Cardiomyocytes and Epicardium-Derived Cells with Lamin A/C Haploinsufficiency.

LMNA-related dilated cardiomyopathy (DCM) is an autosomal-dominant genetic condition with cardiomyocyte and conduction system dysfunction often resulting in heart failure or sudden death. The condition is caused by mutation in the Lamin A/C (LMNA) gene encoding Type-A nuclear lamin proteins involved in nuclear integrity, epigenetic regulation of gene expression, and differentiation. The molecular mechanisms of the disease are not completely understood, and there are no definitive treatments to reverse progression or prevent mortality. We investigated possible mechanisms of LMNA-related DCM using induced pluripotent stem cells derived from a family with a heterozygous LMNA c.357-2A>G splice-site mutation. We differentiated one LMNA-mutant iPSC line derived from an affected female (Patient) and two non-mutant iPSC lines derived from her unaffected sister (Control) and conducted single-cell RNA sequencing for 12 samples (four from Patients and eight from Controls) across seven time points: Day 0, 2, 4, 9, 16, 19, and 30. Our bioinformatics workflow identified 125,554 cells in raw data and 110,521 (88%) high-quality cells in sequentially processed data. Unsupervised clustering, cell annotation, and trajectory inference found complex heterogeneity: ten main cell types; many possible subtypes; and lineage bifurcation for cardiac progenitors to cardiomyocytes (CMs) and epicardium-derived cells (EPDCs). Data integration and comparative analyses of Patient and Control cells found cell type and lineage-specific differentially expressed genes (DEGs) with enrichment, supporting pathway dysregulation. Top DEGs and enriched pathways included 10 ZNF genes and RNA polymerase II transcription in pluripotent cells (PP); BMP4 and TGF Beta/BMP signaling, sarcomere gene subsets and cardiogenesis, CDH2 and EMT in CMs; LMNA and epigenetic regulation, as well as DDIT4 and mTORC1 signaling in EPDCs. Top DEGs also included XIST and other X-linked genes, six imprinted genes (SNRPN, PWAR6, NDN, PEG10, MEG3, MEG8), and enriched gene sets related to metabolism, proliferation, and homeostasis. We confirmed Lamin A/C haploinsufficiency by allelic expression and Western blot. Our complex Patient-derived iPSC model for Lamin A/C haploinsufficiency in PP, CM, and EPDC provided support for dysregulation of genes and pathways, many previously associated with Lamin A/C defects, such as epigenetic gene expression, signaling, and differentiation. Our findings support disruption of epigenomic developmental programs, as proposed in other LMNA disease models. We recognized other factors influencing epigenetics and differentiation; thus, our approach needs improvement to further investigate this mechanism in an iPSC-derived model.

Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics.

From single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST), one can extract high-dimensional gene expression patterns that can be described by intercellular communication networks or decoupled gene modules. These two descriptions of information flow are often assumed to occur independently. However, intercellular communication drives directed flows of information that are mediated by intracellular gene modules, in turn triggering outflows of other signals. Methodologies to describe such intercellular flows are lacking. We present FlowSig, a method that infers communication-driven intercellular flows from scRNA-seq or ST data using graphical causal modeling and conditional independence. We benchmark FlowSig using newly generated experimental cortical organoid data and synthetic data generated from mathematical modeling. We demonstrate FlowSig's utility by applying it to various studies, showing that FlowSig can capture stimulation-induced changes to paracrine signaling in pancreatic islets, demonstrate shifts in intercellular flows due to increasing COVID-19 severity and reconstruct morphogen-driven activator-inhibitor patterns in mouse embryogenesis.

Preventive and reparative potentials of heat-inactivated and viable commensal Bacillus pumilus SE5 in ameliorating the adverse impacts of high soybean meal in grouper (Epinephelus coioides).

Probiotic Bacillus pumilus SE5, heat-inactivated (HSE5) or active (ASE5), were supplemented to high soybean meal (HSM) (36 %) diet at whole term (0-56 days) and middle term (29-56 days) to investigate the preventing and repairing effects of B. pumilus SE5 in ameliorating the adverse effects of HSM in Epinephelus coioides. The results suggested that the HSM significantly decreased the weight gain rate (WGR), specific growth rate (SGR), and increased the feed conversion rate (FCR) at day 56 (P < 0.05), while HSE5 and ASE5 promoted the growth performance. The HSE5 and ASE5 showed preventive and reparative functions on the antioxidant capacity and serum immunity, with significantly increased the total antioxidant capacity (T-AOC), superoxide dismutase (SOD), glutathione (GSH), glutathione peroxidase (GSH-PX) activities, and reduced malondialdehyde (MDA) level, and increased acid phosphatase (ACP), alkaline phosphatase (AKP), immunoglobulin M (IgM) and complement 3 (C3). The HSM impaired the intestinal health (destroyed the intestinal structure, significantly increased the contents of serum D-lactic acid and diamine oxidase, and reduced the expressions of claudin-3 and occludin), while HSE5 and ASE5 improved them at whole term and middle term. The HSM impaired the intestinal microbiota and reduced its diversity, and the HSE5 or ASE5 improved the intestinal microbiota (especially at whole term). HSE5 and ASE5 improved the intestinal mRNA expressions of anti-inflammatory genes (il-10 and tgf-ß1) and reduced the expressions of pro-inflammatory genes (il-1ß, il-8, il-12), and promoted the expressions of humoral immune factor-related genes (cd4, igm, mhcII-α) and antimicrobial peptide genes (ß-defensin, epinecidin-1 and hepcidin-1), and decreased the expressions of NF-κB/MAPK signaling pathway-related genes (ikk-α, nf-κb, erk-1), and improved the expressions of MAPK signaling pathway-related gene p38-α (P < 0.05). In conclusion, the heat-inactivated and active B. pumilus SE5 effectively prevented and repaired the suppressive effects of soybean meal in E. coioides, which underscored the potential of B. pumilus SE5 as a nutritional intervention agent in HSM diet in aquaculture.

Small data methods in omics: the power of one.

Over the last decade, biology has begun utilizing 'big data' approaches, resulting in large, comprehensive atlases in modalities ranging from transcriptomics to neural connectomics. However, these approaches must be complemented and integrated with 'small data' approaches to efficiently utilize data from individual labs. Integration of smaller datasets with major reference atlases is critical to provide context to individual experiments, and approaches toward integration of large and small data have been a major focus in many fields in recent years. Here we discuss progress in integration of small data with consortium-sized atlases across multiple modalities, and its potential applications. We then examine promising future directions for utilizing the power of small data to maximize the information garnered from small-scale experiments. We envision that, in the near future, international consortia comprising many laboratories will work together to collaboratively build reference atlases and foundation models using small data methods.

Hydrogel crosslinking modulates macrophages, fibroblasts, and their communication, during wound healing.

Biomaterial wound dressings, such as hydrogels, interact with host cells to regulate tissue repair. This study investigates how crosslinking of gelatin-based hydrogels influences immune and stromal cell behavior and wound healing in female mice. We observe that softer, lightly crosslinked hydrogels promote greater cellular infiltration and result in smaller scars compared to stiffer, heavily crosslinked hydrogels. Using single-cell RNA sequencing, we further show that heavily crosslinked hydrogels increase inflammation and lead to the formation of a distinct macrophage subpopulation exhibiting signs of oxidative activity and cell fusion. Conversely, lightly crosslinked hydrogels are more readily taken up by macrophages and integrated within the tissue. The physical properties differentially affect macrophage and fibroblast interactions, with heavily crosslinked hydrogels promoting pro-fibrotic fibroblast activity that drives macrophage fusion through RANKL signaling. These findings suggest that tuning the physical properties of hydrogels can guide cellular responses and improve healing, offering insights for designing better biomaterials for wound treatment.

The Potential Correlation between Bacterial Diversity and the Characteristic Volatile Flavor Compounds of Sichuan Sauce-Flavored Sausage.

The distinctive taste of Sichuan sauce-flavored sausage comes from an intricate microbial metabolism. The correlation between microbial composition and distinct flavor components has not been researched. The study used headspace solid-phase microextraction action with gas chromatography mass spectrometry to find flavor components and high-throughput sequencing of 16S rRNA to look at the diversity and succession of microbial communities. The correlation network model forecasted the connection between essential bacteria and the development of flavors. The study revealed that the primary flavor compounds in Sichuan sauce-flavored sausages were alcohols, aldehydes, and esters. The closely related microbes were Leuconostoc, Pseudomonas, Psychrobacter, Flavobacterium, and Algoriella. The microbes aided in the production of various flavor compounds, such as 1-octen-3-ol, benzeneacetaldehyde, hexanal, (R,R)-2,3-butanediol, and ethyl caprylate. This work has enhanced our comprehension of the diverse functions that bacteria serve in flavor development during the fermentation of Sichuan sauce-flavored sausage.

Single-cell and spatial transcriptomics of vulvar lichen sclerosus reveal multi-compartmental alterations in gene expression and signaling cross-talk.

Vulvar diseases are a critical yet often neglected area of women's health, profoundly affecting patients' quality of life and frequently resulting in long-term physical and psychological challenges. Lichen sclerosus (LS) is a chronic inflammatory skin disorder that predominantly affects the vulva, leading to severe itching, pain, scarring, and an increased risk of malignancy. Despite its profound impact on affected individuals, the molecular pathogenesis of vulvar LS (VLS) is not well understood, hindering the development of FDA-approved therapies. Here, we utilize single-cell and spatial transcriptomics to analyze lesional and non-lesional skin from VLS patients, as well as healthy control vulvar skin. Our findings demonstrate histologic, cellular, and molecular heterogeneities within VLS, yet highlight unifying molecular changes across keratinocytes, fibroblasts, immune cells, and melanocytes in lesional skin. They reveal cellular stress and damage in fibroblasts and keratinocytes, enhanced T cell activation and cytotoxicity, aberrant cell-cell signaling, and increased activation of the IFN, JAK/STAT, and p53 pathways in specific cell types. Using both monolayer and organotypic culture models, we also demonstrate that knockdown of select genes, which are downregulated in VLS lesional keratinocytes, partially recapitulates VLS-like stress-associated changes. Collectively, these data provide novel insights into the pathogenesis of VLS, identifying potential biomarkers and therapeutic targets for future research.

Integrated Single-Cell Analysis Reveals Spatially and Temporally Dynamic Heterogeneity in Fibroblast States during Wound Healing.

Wound healing is a dynamic process over temporal and spatial scales. Key to repair outcomes are fibroblasts; yet, how they modulate healing across time and in different wound regions remains incompletely understood. By integrating single-cell RNA-sequencing datasets of mouse skin and wounds, we infer that fibroblasts are the most transcriptionally dynamic skin-resident cells, evolving during postnatal skin maturation and rapidly after injury toward distinct late scar states. We show that transcriptional dynamics in fibroblasts are largely driven by genes encoding extracellular matrix and signaling factors. Lineage trajectory inference and spatial gene mapping reveal that Prg4-expressing fibroblasts transiently emerge along early wound edges. Within days, they become replaced by long-lasting and likely noninterconverting fibroblast populations, including Col25a1-expressing and Pamr1-expressing fibroblasts that occupy subepidermal and deep scar regions, respectively, where they engage in reciprocal signaling with immune cells. Signaling inference shows that fibroblast-immune crosstalk repeatedly uses some signaling pathways across wound healing time, whereas use of other signaling pathways is time and space limited. Collectively, we uncovered high transcriptional plasticity by wound fibroblasts, with early states transiently forming distinct microniches along wound edges and in the fascia, followed by stable states that stratify scar tissue into molecularly dissimilar upper and lower layers.

DRML-Ensemble: drug repurposing method based on feature construction of multi-layer ensemble.

CONTEXT: Computational drug repurposing methods have been continuously developed in recent years to alleviate the high costs associated with drug development. As drug targets or the products of disease-related genes, proteins play an important role in drug repurposing. Although the potential has been demonstrated, heterogeneous graphs with proteins as independent nodes have yet to be studied, where extracting high-quality protein features from heterogeneous graphs poses a significant challenge. A novel drug repurposing model based on the feature construction of multi-layer ensemble (DRML-Ensemble) is proposed in this study. The performance of DRML-Ensemble, as evaluated on publicly available datasets, achieves an AUPR value of 0.93 and an AUROC value of 0.92, surpassing those of existing state-of-the-art methods. Additionally, DRML-Ensemble demonstrates its notable ability for drug repurposing in Alzheimer's disease. METHODS: DRML-Ensemble is primarily composed of multiple layers of heterogeneous graph feature construction (HGFC). Each HGFC can extract protein features by leveraging the relationships between drugs, diseases, and proteins. These protein features are then utilized in subsequent layers to build drug and disease features, facilitating drug repurposing. By stacking multiple layers, optimal protein features can be obtained from the heterogeneous graph, consequently improving the accuracy of drug repurposing. However, an excessive· stacking of layers usually affect the model's training process, for example, causing problems such as overfitting; a multi-layer ensemble prediction module is designed to further improve the model's performance.

Sequential emergence and contraction of epithelial subtypes in the prenatal human choroid plexus revealed by a stem cell model.

Despite the major roles of choroid plexus epithelial cells (CPECs) in brain homeostasis and repair, their developmental lineage and diversity remain undefined. In simplified differentiations from human pluripotent stem cells, derived CPECs (dCPECs) displayed canonical properties and dynamic multiciliated phenotypes that interacted with Aß uptake. Single dCPEC transcriptomes over time correlated well with human organoid and fetal CPECs, while pseudotemporal and cell cycle analyses highlighted the direct CPEC origin from neuroepithelial cells. In addition, time series analyses defined metabolic (type 1) and ciliogenic dCPECs (type 2) at early timepoints, followed by type 1 diversification into anabolic-secretory (type 1a) and catabolic-absorptive subtypes (type 1b) as type 2 cells contracted. These temporal patterns were then confirmed in independent derivations and mapped to prenatal stages using human tissues. In addition to defining the prenatal lineage of human CPECs, these findings suggest new dynamic models of ChP support for the developing human brain.

Polyunsaturated Fatty Acid - mediated Cellular Rejuvenation for Reversing Age-related Vision Decline.

The retina is uniquely enriched in polyunsaturated fatty acids (PUFAs), which are primarily localized in cell membranes, where they govern membrane biophysical properties such as diffusion, permeability, domain formation, and curvature generation. During aging, alterations in lipid metabolism lead to reduced content of very long-chain PUFAs (VLC-PUFAs) in the retina, and this decline is associated with normal age-related visual decline and pathological age-related macular degeneration (AMD). ELOVL2 (Elongation of very-long-chain fatty acids-like 2) encodes a transmembrane protein that produces precursors to docosahexaenoic acid (DHA) and VLC-PUFAs, and methylation level of its promoter is currently the best predictor of chronological age. Here, we show that mice lacking ELOVL2-specific enzymatic activity (Elovl2 C234W ) have impaired contrast sensitivity and slower rod response recovery following bright light exposure. Intravitreal supplementation with the direct product of ELOVL2, 24:5n-3, in aged animals significantly improved visual function and reduced accumulation of ApoE, HTRA1 and complement proteins in sub-RPE deposits. At the molecular level, the gene expression pattern observed in retinas supplemented with 24:5n-3 exhibited a partial rejuvenation profile, including decreased expression of aging-related genes and a transcriptomic signature of younger retina. Finally, we present the first human genetic data showing significant association of several variants in the human ELOVL2 locus with the onset of intermediate AMD, underlying the translational significance of our findings. In sum, our study identifies novel therapeutic opportunities and defines ELOVL2 as a promising target for interventions aimed at preventing age-related vision loss.

Interruption of the intratumor CD8+ T cell:Treg crosstalk improves the efficacy of PD-1 immunotherapy.

PD-1 blockade unleashes potent antitumor activity in CD8+ T cells but can also promote immunosuppressive T regulatory (Treg) cells, which may worsen the response to immunotherapy. Tumor-Treg inhibition is a promising strategy to improve the efficacy of checkpoint blockade immunotherapy; however, our understanding of the mechanisms supporting tumor-Tregs during PD-1 immunotherapy is incomplete. Here, we show that PD-1 blockade increases tumor-Tregs in mouse models of melanoma and metastatic melanoma patients. Mechanistically, Treg accumulation is not caused by Treg-intrinsic inhibition of PD-1 signaling but depends on an indirect effect of activated CD8+ T cells. CD8+ T cells produce IL-2 and colocalize with Tregs in mouse and human melanomas. IL-2 upregulates the anti-apoptotic protein ICOS on tumor-Tregs, promoting their accumulation. Inhibition of ICOS signaling before PD-1 immunotherapy improves control over immunogenic melanoma. Thus, interrupting the intratumor CD8+ T cell:Treg crosstalk represents a strategy to enhance the therapeutic efficacy of PD-1 immunotherapy.

LMNA -Related Dilated Cardiomyopathy: Single-Cell Transcriptomics during Patient-derived iPSC Differentiation Support Cell type and Lineage-specific Dysregulation of Gene Expression and Development for Cardiomyocytes and Epicardium-Derived Cells with Lamin A/C Haploinsufficiency.

LMNA -Related Dilated Cardiomyopathy (DCM) is an autosomal-dominant genetic condition with cardiomyocyte and conduction system dysfunction often resulting in heart failure or sudden death. The condition is caused by mutation in the Lamin A/C ( LMNA ) gene encoding Type-A nuclear lamin proteins involved in nuclear integrity, epigenetic regulation of gene expression, and differentiation. Molecular mechanisms of disease are not completely understood, and there are no definitive treatments to reverse progression or prevent mortality. We investigated possible mechanisms of LMNA -Related DCM using induced pluripotent stem cells derived from a family with a heterozygous LMNA c.357-2A>G splice-site mutation. We differentiated one LMNA mutant iPSC line derived from an affected female (Patient) and two non-mutant iPSC lines derived from her unaffected sister (Control) and conducted single-cell RNA sequencing for 12 samples (4 Patient and 8 Control) across seven time points: Day 0, 2, 4, 9, 16, 19, and 30. Our bioinformatics workflow identified 125,554 cells in raw data and 110,521 (88%) high-quality cells in sequentially processed data. Unsupervised clustering, cell annotation, and trajectory inference found complex heterogeneity: ten main cell types; many possible subtypes; and lineage bifurcation for Cardiac Progenitors to Cardiomyocytes (CM) and Epicardium-Derived Cells (EPDC). Data integration and comparative analyses of Patient and Control cells found cell type and lineage differentially expressed genes (DEG) with enrichment to support pathway dysregulation. Top DEG and enriched pathways included: 10 ZNF genes and RNA polymerase II transcription in Pluripotent cells (PP); BMP4 and TGF Beta/BMP signaling, sarcomere gene subsets and cardiogenesis, CDH2 and EMT in CM; LMNA and epigenetic regulation and DDIT4 and mTORC1 signaling in EPDC. Top DEG also included: XIST and other X-linked genes, six imprinted genes: SNRPN , PWAR6 , NDN , PEG10 , MEG3 , MEG8 , and enriched gene sets in metabolism, proliferation, and homeostasis. We confirmed Lamin A/C haploinsufficiency by allelic expression and Western blot. Our complex Patient-derived iPSC model for Lamin A/C haploinsufficiency in PP, CM, and EPDC provided support for dysregulation of genes and pathways, many previously associated with Lamin A/C defects, such as epigenetic gene expression, signaling, and differentiation. Our findings support disruption of epigenomic developmental programs as proposed in other LMNA disease models. We recognized other factors influencing epigenetics and differentiation; thus, our approach needs improvement to further investigate this mechanism in an iPSC-derived model.

Study on simulation effect of physical and chemical characteristics of sausage by sausage model system.

Introduction: The incorporation of Staphylococcus xylosus in sausage production is hypothesized to affect various physicochemical properties and flavor profiles of sausages. This study aimed to evaluate the simulation of these features in a sausage model and establish its applicability for in vitro studies. Methods: Both a control and an experimental model, inclusive of Staphylococcus xylosus, were assessed for changes in physicochemical indexes (pH and water activity, Aw) and the concentration of flavoring components (esters and aldehydes). Thiobarbituric acid reactive substances (TBARS) values were also measured to evaluate lipid oxidation. Results: The introduction of Staphylococcus xylosus resulted in no significant changes in pH and Aw between the sausage and the model. However, there was a considerable increase in the content of volatile flavor compounds, specifically esters and aldehydes, in the experimental groups compared to the control. Additionally, the TBARS values in experimental groups were significantly lower than those in the control group at the end of the testing period. Discussion: The findings indicate that Staphylococcus xylosus plays a critical role in enhancing the flavor profile of sausages through the increased synthesis of volatile compounds and inhibiting fat oxidation. The sausage model effectively simulated the physicochemical and flavor index responses, demonstrating its potential utility for further in vitro research on sausage fermentation and preservation techniques.

Giant cavernous aneurysms occluded by aneurysmal thrombosis, calcification, parent artery occlusion: A case report and review of literature.

BACKGROUND: Patients with giant intracranial aneurysms (GIAs) are at a high risk of rupture, morbidity, and mortality even after surgical or endovascular treatment. We described a case of a spontaneously occluded GIA secondary to gradual growth of the GIA, continuously progressed aneurysmal thrombosis, complete aneurysmal calcification and complete occlusion of the parent artery-the right internal carotid artery (RICA). CASE SUMMARY: A 72-year-old female patient complained of sudden pain in her right eye upon admission to our hospital. She had been diagnosed with a GIA [30 mm (axial) × 38 mm (coronal) × 28 mm (sagittal)] containing an aneurysmal thrombus located in the cavernous sinus segment of RICA diagnosed by magnetic resonance imaging (MRI), enhanced MRI, and magnetic resonance angiography more than 14 years ago. Later, with slow growth of the cavernous carotid GIA, aneurysmal thrombosis progressed continuously, spontaneous occlusion of the RICA, complete aneurysmal calcification, and occlusion of the GIA occurred gradually. She had no history of subarachnoid hemorrhage but missed the chance for endovascular therapy at an early stage. As a result, she was left with severe permanent sequelae from the injuries to the right cranial nerves II, III, IV, V1/V2, and VI. CONCLUSION: The risk of rupture of the cavernous carotid GIAs was relatively low and possibly further be reduced by the stasis flow and spontaneous occlusion of the parent artery internal carotid artery (ICA) induced by the mass effect of the cavernous carotid GIAs and the extremely rare aneurysmal calcification. However, nowadays, it is advisable to recommend early endovascular treatment for the cavernous carotid GIAs to prevent injuries to the surrounding intracranial nerves and occlusion of the ICA, mainly caused by the mass effect of the cavernous carotid GIAs.

Analysis of changes in inter-cellular communications during Alzheimer's Disease pathogenesis reveals conserved changes in glutamatergic transmission in mice and humans.

Analysis of system-wide cellular communication changes in Alzheimer's disease (AD) has recently been enabled by single nucleus RNA sequencing (snRNA-seq) and new computational methods. Here, we combined these to analyze data from postmortem human tissue from the entorhinal cortex of AD patients and compared our findings to those from multiomic data from the 5xFAD amyloidogenic mouse model at two different time points. Using the cellular communication inference tool CellChat we found that disease-related changes were largely related to neuronal excitability as well as synaptic communication, with specific signaling pathways including BMP, EGF, and EPHA, and relatively poor conservation of glial-related changes during disease. Further analysis using the neuron-specific NeuronChat revealed changes relating to metabotropic glutamate receptors as well as neuronal adhesion molecules including neurexins and neuroligins. Our results that cellular processes relating to excitotoxicity are the best conserved between 5xFAD mice and AD suggest that excitotoxicity is the main common feature between pathogenesis in 5xFAD mice and AD patients.

Coexistence of Parkinson's disease and myasthenia gravis: A case report and literature review.

The coexistence of Parkinson's disease (PD) and myasthenia gravis (MG) is rare. When similar symptoms of both diseases overlap, it is challenging to make a concomitant diagnosis of PD and MG. The present study describes the case of a patient with concomitant PD and MG. In addition, a systematic literature review was conducted by searching PubMed and Embase for reports on all patients with concomitant PD and MG, which were then grouped and compared according to different preexisting diseases. Finally, a total of 47 cases of concomitant PD and MG (35 men; 12 women), including the present case, were analyzed. The median age of the patients at first diagnosis was 66.59±9.91 years. The interval between the two diseases varied from 2 months to 22 years. Based on the sequential occurrence of these two diseases, the patients were categorized into three groups: The prePD-MG (30 cases), preMG-PD (12 cases), and coPD-MG (5 cases) groups. In the prePD-MG group, the onset age of MG was older and head drop was more common. In the preMG-PD group, the patients were more likely to have comorbid immune diseases.

Dissecting Spatiotemporal Structures in Spatial Transcriptomics via Diffusion-Based Adversarial Learning.

Recent advancements in spatial transcriptomics (ST) technologies offer unprecedented opportunities to unveil the spatial heterogeneity of gene expression and cell states within tissues. Despite these capabilities of the ST data, accurately dissecting spatiotemporal structures (e.g., spatial domains, temporal trajectories, and functional interactions) remains challenging. Here, we introduce a computational framework, PearlST (partial differential equation [PDE]-enhanced adversarial graph autoencoder of ST), for accurate inference of spatiotemporal structures from the ST data using PDE-enhanced adversarial graph autoencoder. PearlST employs contrastive learning to extract histological image features, integrates a PDE-based diffusion model to enhance characterization of spatial features at domain boundaries, and learns the latent low-dimensional embeddings via Wasserstein adversarial regularized graph autoencoders. Comparative analyses across multiple ST datasets with varying resolutions demonstrate that PearlST outperforms existing methods in spatial clustering, trajectory inference, and pseudotime analysis. Furthermore, PearlST elucidates functional regulations of the latent features by linking intercellular ligand-receptor interactions to most contributing genes of the low-dimensional embeddings, as illustrated in a human breast cancer dataset. Overall, PearlST proves to be a powerful tool for extracting interpretable latent features and dissecting intricate spatiotemporal structures in ST data across various biological contexts.