Graph Fourier transform for spatial omics representation and analyses of complex organs.

Spatial omics technologies are capable of deciphering detailed components of complex organs or tissue in cellular and subcellular resolution. A robust, interpretable, and unbiased representation method for spatial omics is necessary to illuminate novel investigations into biological functions, whereas a mathematical theory deficiency still exists. We present SpaGFT (Spatial Graph Fourier Transform), which provides a unique analytical feature representation of spatial omics data and elucidates molecular signatures linked to critical biological processes within tissues and cells. It outperformed existing tools in spatially variable gene prediction and gene expression imputation across human/mouse Visium data. Integrating SpaGFT representation into existing machine learning frameworks can enhance up to 40% accuracy of spatial domain identification, cell type annotation, cell-to-spot alignment, and subcellular hallmark inference. SpaGFT identified immunological regions for B cell maturation in human lymph node Visium data, characterized secondary follicle variations from in-house human tonsil CODEX data, and detected extremely rare subcellular organelles such as Cajal body and Set1/COMPASS. This new method lays the groundwork for a new theoretical model in explainable AI, advancing our understanding of tissue organization and function.

A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset.

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10%-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, microbial graph attention (MEGA), to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of nine cancer centers in the Oncology Research Information Exchange Network. This package has three unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2,704 tumor RNA sequencing samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors. SIGNIFICANCE: Studying the tumor microbiome in high-throughput sequencing data is challenging because of the extremely sparse data matrices, heterogeneity, and high likelihood of contamination. We present a new deep learning tool, MEGA, to refine the organisms that interact with tumors.

Comparing recurrence between cautery techniques in pediatric epistaxis.

OBJECTIVE: To compare the risk of recurrent epistaxis between children treated with silver nitrate (SN) in the office or electrocautery (EC) in the operating room (OR). METHODS: Patients aged 2-18 diagnosed with epistaxis (ICD R04.0) in 2018 and treated with SN or EC were retrospectively reviewed. Epistaxis laterality, history of nasal trauma, and personal or family history of a bleeding disorder were recorded. Patients with prior cautery or epistaxis secondary to a procedure were excluded. Recurrence was defined as initial encounter after cautery with documented epistaxis. Patients were followed up into 2022 to track onset of recurrence. Time to recurrence between SN and EC was compared with hazard curves with predictors for recurrence analyzed via Cox's proportional hazard regression. RESULTS: Among 291 patients cauterized for epistaxis, 62 % (n = 181) received SN compared to 38 % (n = 110) who underwent EC. There was significantly higher risk of recurrence when treated with SN compared to EC (Hazard ratio 2.45, 95 % CI: 1.57-3.82, P < 0.0001). Median time to recurrence was not statistically different between techniques (6.39 months (SN) (IQR: 2.33, 14.82) vs. 4.11 months (EC) (IQR: 1.18, 20.86), P = 0.4154). Complication rates were low for both groups (1.16 % (SN) vs. 0 % (EC), P > 0.05). CONCLUSION: Among patients with epistaxis, risk of recurrence is significantly higher in those cauterized with SN compared to EC. Time to recurrence is not significantly different between cautery techniques.

ssREAD: A Single-cell and Spatial RNA-seq Database for Alzheimer's Disease.

Alzheimer's Disease (AD) is a neurodegenerative malady predominantly affecting the elderly and exhibits its debilitating effects on a dementia-prone population. Recently, the advent of innovative technologies, such as single-cell and single-nucleus RNA-sequencing (scRNA-seq & snRNA-seq) and spatial transcriptomics (ST), has reformed our investigative approaches toward comprehending AD's neuropathological intricacies and underpinning regulatory mechanisms, encompassing sub-cellular, cellular, and spatial dimensions. In light of the overwhelming proliferation of single-cell and ST data associated with AD, the imperative for a comprehensive, user-friendly database that addresses the scientific community's analytical demands has never been more paramount. Introduced initially in 2020, scREAD presented itself as a pioneering repository that systematized publicly available scRNA-seq and snRNA-seq datasets derived from post-mortem human brain tissues and mouse models mirroring AD pathology. Here, we introduce ssREAD, a substantial upgrade over scREAD, enriching the platform with a broader spectrum of datasets, an optimized analytical pipeline, and enhanced usability and visibility. Specifically, ssREAD amalgamates an impressive portfolio of over 189 datasets extracted from 35 distinct AD-related scRNA-seq and snRNA-seq studies, encompassing a staggering 2,572,355 cells. In addition, we have diligently curated and archived 300 ST datasets, originating from 12 human and mouse brain studies, which include two focused on AD and ten control studies. Every dataset within our repository is meticulously annotated, bearing critical identifiers including species, gender, brain region, disease/control status, age, and AD stages. Besides the collection of above datasets in ssREAD, it delivers an exhaustive analysis suite offering cell clustering and annotation, inference of differentially expressed and spatially variable genes, identification of cell-type-specific marker genes and regulons, and spot deconvolution for integrative analysis of ST and scRNA-seq & snRNA-seq data from public domains. All these resources are freely accessible through a user-friendly, consolidated web portal available at https://bmblx.bmi.osumc.edu/ssread/.

A bioinformatics tool for identifying intratumoral microbes from the ORIEN dataset.

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, MEGA, to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of 9 cancer centers in the Oncology Research Information Exchange Network (ORIEN). This package has 3 unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2704 tumor RNA-seq samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors.

Morphological and cellular changes in secondary epidermal laminae of horses with insulin-induced laminitis.

OBJECTIVE: To determine cellular changes associated with secondary epidermal laminae (SEL) in forefeet and hind feet of ponies with insulin-induced laminitis. ANIMALS: 8 ponies. PROCEDURES: Laminitis was induced in 4 ponies by IV administration of insulin and glucose; 4 control ponies received saline (0.9% NaCl) solution IV. Laminar tissue samples obtained from the dorsal aspects of the hooves were histologically evaluated. Primary epidermal lamina (PEL) length and width and SEL length, width, and angle were determined. Numbers of epidermal cell nuclei per micrometer and per total length of SEL and numbers of apoptotic and proliferative cells in axial, middle, and abaxial laminar regions were determined. RESULTS: SEL in treatment group ponies were significantly longer, were significantly narrower, and had a smaller angle relative to PEL in all laminar regions versus control ponies. In treatment group ponies, the number of epidermal cell nuclei per SEL was typically higher and the number of cells per micrometer of SEL was lower in laminar regions, apoptotic cell numbers were higher in abaxial and middle regions in forefeet and hind feet, and proliferating cell numbers were higher in axial laminar regions in forefeet and all laminar regions in hind feet, versus control ponies. CONCLUSIONS AND CLINICAL RELEVANCE: Results indicated SEL elongation, narrowing, and alteration in orientation developed in all feet of ponies with insulin-induced laminitis. This was primarily attributable to cell stretching that developed at the same time as an accelerated cell death-proliferation cycle; differences in cell cycle responses among laminar regions between forefeet and hind feet may have been attributable to differences in load bearing.