Spatial transcriptomics is a rapidly growing field that promises to comprehensively characterize tissue organization and architecture at the single-cell or subcellular resolution. Such information provides a solid foundation for mechanistic understanding of many biological processes in both health and disease that cannot be obtained by using traditional technologies. The development of computational methods plays important roles in extracting biological signals from raw data. Various approaches have been developed to overcome technology-specific limitations such as spatial resolution, gene coverage, sensitivity, and technical biases. Downstream analysis tools formulate spatial organization and cell–cell communications as quantifiable properties, and provide algorithms to derive such properties. Integrative pipelines further assemble multiple tools in one package, allowing biologists to conveniently analyze data from beginning to end. In this review, we summarize the state of the art of spatial transcriptomic data analysis methods and pipelines, and discuss how they operate on different technological platforms.

ImportanceRepetitive head impact (RHI) exposure is the chief risk factor for chronic traumatic encephalopathy (CTE). However, the occurrence and severity of CTE varies widely among those with similar RHI exposure. Limited evidence suggests that the APOEε4 allele may confer risk for CTE, but previous studies were small with limited scope.ObjectiveTo test the association between APOE genotype and CTE neuropathology and related endophenotypes.Design, Setting, and ParticipantsThis cross-sectional genetic association study analyzed brain donors from February 2008 to August 2019 from the Veterans Affairs–Boston University–Concussion Legacy Foundation Brain Bank. All donors had exposure to RHI from contact sports or military service. All eligible donors were included. Analysis took place between June 2020 and April 2022.ExposuresOne or more APOEε4 or APOEε2 alleles.Main Outcomes and MeasuresCTE neuropathological status, CTE stage (0-IV), semiquantitative phosphorylated tau (p-tau) burden in 11 brain regions (0-3), quantitative p-tau burden in the dorsolateral frontal lobe (log-transformed AT8+ pixel count per mm2), and dementia.ResultsOf 364 consecutive brain donors (100% male; 53 [14.6%] self-identified as Black and 311 [85.4%] as White; median [IQR] age, 65 [47-77] years) 20 years or older, there were 294 individuals with CTE and 70 controls. Among donors older than 65 years, APOEε4 status was significantly associated with CTE stage (odds ratio [OR], 2.34 [95% CI, 1.30-4.20]; false discovery rate [FDR]–corrected P = .01) and quantitative p-tau burden in the dorsolateral frontal lobe (β, 1.39 [95% CI, 0.83-1.94]; FDR-corrected P = 2.37 × 10−5). There was a nonsignificant association between APOEε4 status and dementia (OR, 2.64 [95% CI, 1.06-6.61]; FDR-corrected P = .08). Across 11 brain regions, significant associations were observed for semiquantitative p-tau burden in the frontal and parietal cortices, amygdala, and entorhinal cortex (OR range, 2.45-3.26). Among football players, the APOEε4 association size for CTE stage was similar to playing more than 7 years of football. Associations were significantly larger in the older half of the sample. There was no significant association for CTE status. Association sizes were similar when donors with an Alzheimer disease neuropathological diagnosis were excluded and were reduced but remained significant after adjusting for neuritic and diffuse amyloid plaques. No associations were observed for APOEε2 status. Models were adjusted for age at death and race.Conclusions and RelevanceAPOEε4 may confer increased risk for CTE-related neuropathological and clinical outcomes among older individuals with RHI exposure. Further work is required to validate these findings in an independent sample.

Single cell RNA-sequencing (scRNA-Seq) allows researchers to profile transcriptional activity in individual cells. However, the complex nature of these data and variability in study design and data generation requires sophisticated computational tools and informed analytical decisions. Here, we present the Single Cell Toolkit (SCTK), an interactive scRNA-Seq analysis package that enables users to perform scRNA-Seq analysis interactively using a command-line workflow or a graphical user interface (GUI) written in R/Shiny. Main TextSingle cell RNA-sequencing (scRNA-Seq) techniques allow researchers to explore the transcriptional landscape of a sample at the resolution of the individual cell. In the context of cancer, scRNA-Seq can identify the subclonality of a tumor sample to improve our ability to identify the cell-specific mechanisms that drive tumor growth and can characterize different cellular populations within the tumor microenvironment such as immune cells 1,2 . However, different optimizations of parameters and algorithms are required for filtration, normalization, clustering, and differential expression of scRNA-Seq data compared to bulk RNA-seq due to the low amount of starting material and technical bias introduced in the common scRNA-Seq library preparation techniques 3 . Tools for normalization and analysis of scRNA-Seq data exist to overcome these technical biases, but these tools are not integrated and require command line processing of samples and knowledge of the many options available for each tool, which makes them difficult to use, especially for scientists without training in bioinformatics [4][5][6][7][8][9] . Even for more advanced users, there is still a need to interactively explore scRNA-Seq results during processing to help make dataset specific decisions that can affect downstream analysis.Here, we present the Single Cell Toolkit (SCTK), an R/Shiny 10 based package for both command line and interactive scRNA-Seq processing. Users can upload count and annotation data and interactively explore and perform analyses. Data and results can be saved in a convenient object for downstream command line analysis, or to reload into the GUI in another session. With the SCTK, it is possible to perform a full analysis workflow from uploading raw data to downloading processed results. While other tools can perform specific scRNA-Seq analysis steps, the SCTK is the first fully interactive scRNA-Seq analysis workflow available within the R language ( Table 1).. CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. The SCTK is organized into several analysis modules ( Fig. 1). Analysis modules include data summary and filtering, dimensionality reduction, clustering, batch correction, differential expression, pathway activity analysis, and power calculations to evaluate the tradeoff between sample size, cell numbers, and sequencing depths. All analysis modules can be...

Head and neck cancers are a complex malignancy comprising multiple anatomical sites, with cancer of the oral cavity ranking among the deadliest and the most disfiguring cancers globally. Oral cancer (OC) constitutes a subset of head and neck cancer cases, presenting primarily as tobacco- and alcohol-associated oral squamous cell carcinoma (OSCC), with a 5-year survival rate of ~ 65%, partly due to the lack of early detection and effective treatments. OSCC arises from premalignant lesions (PMLs) in the oral cavity through a multi-step series of clinical and histopathological stages, including varying degrees of epithelial dysplasia. To gain insights into the molecular mechanisms associated with the progression of PMLs to OSCC, we profiled the whole transcriptome of 66 human PMLs comprising leukoplakia with dysplasia and hyperkeratosis non-reactive (HkNR) pathologies, alongside healthy controls and OSCC. Our data revealed that PMLs were enriched in gene signatures associated with cellular plasticity, such as partial EMT (p-EMT) phenotypes, and with immune response. Integrated analyses of the host transcriptome and microbiome further highlighted a significant association between differential microbial abundance and PML pathway activity, suggesting a contribution of the oral microbiome toward PML evolution to OSCC. Collectively, this study reveals molecular processes associated with PML progression that may help early diagnosis and disease interception at an early stage. Graphical abstract

Head and neck cancers are a complex malignancy comprising multiple anatomical sites, with cancer of the oral cavity ranking among the deadliest and most disfiguring cancers globally. Oral cancer (OC) constitutes a subset of head and neck cancer cases, presenting primarily as tobacco- and alcohol-associated oral squamous cell carcinoma (OSCC), with a 5-year survival rate of ~65%, partly due to the lack of early detection and effective treatments. OSCC arises from premalignant lesions (PMLs) in the oral cavity through a multi-step series of clinical and histopathological stages, including varying degrees of epithelial dysplasia. To gain insights into the molecular mechanisms associated with the progression of PMLs to OSCC, we profiled the whole transcriptome of 66 human PMLs comprising leukoplakia with dysplasia and hyper­keratosis non-reactive (HkNR) pathologies, alongside healthy controls and OSCC. Our data revealed that PMLs were enriched in gene signatures associated with cellular plasticity, such as partial EMT (p-EMT) phenotypes, and with immune response. Integrated analyses of the host transcriptome and microbiome further highlighted a significant association between differential microbial abundance and PML pathway activity, suggesting a contribution of the oral microbiome towards PML evolution to OSCC. Collectively, this study reveals molecular processes associated with PML progression that may help early diagnosis and disease interception at an early stage.