Type I interferon (IFN) is a key cytokine that curbs viral infection and cell malignancy. Previously, we demonstrated a potent IFN immunogenicity of nucleic acid–containing (NA-containing) amyloid fibrils in the periphery. Here, we investigated whether IFN is associated with β-amyloidosis inside the brain and contributes to neuropathology. An IFN-stimulated gene (ISG) signature was detected in the brains of multiple murine Alzheimer disease (AD) models, a phenomenon also observed in WT mouse brain challenged with generic NA-containing amyloid fibrils. In vitro, microglia innately responded to NA-containing amyloid fibrils. In AD models, activated ISG-expressing microglia exclusively surrounded NA⁺ amyloid β plaques, which accumulated in an age-dependent manner. Brain administration of rIFN-β resulted in microglial activation and complement C3-dependent synapse elimination in vivo. Conversely, selective IFN receptor blockade effectively diminished the ongoing microgliosis and synapse loss in AD models. Moreover, we detected activated ISG-expressing microglia enveloping NA-containing neuritic plaques in postmortem brains of patients with AD. Gene expression interrogation revealed that IFN pathway was grossly upregulated in clinical AD and significantly correlated with disease severity and complement activation. Therefore, IFN constitutes a pivotal element within the neuroinflammatory network of AD and critically contributes to neuropathogenic processes.

Background: Cellular heterogeneity is present in almost all gene expression profiles. However, transcriptome analysis of tissue specimens often ignores the cellular heterogeneity present in these samples. Standard deconvolution algorithms require prior knowledge of the cell type frequencies within a tissue or their in vitro expression profiles. Furthermore, these algorithms tend to report biased estimations. Results: Here, we describe a Digital Sorting Algorithm (DSA) for extracting cell-type specific gene expression profiles from mixed tissue samples that is unbiased and does not require prior knowledge of cell type frequencies. Conclusions: The results suggest that DSA is a specific and sensitivity algorithm in gene expression profile deconvolution and will be useful in studying individual cell types of complex tissues.

Meningiomas account for one-third of all primary brain tumors. Although typically benign, about 20% of meningiomas are aggressive, and despite the rigor of the current histopathological classification system there remains considerable uncertainty in predicting tumor behavior. Here, we analyzed 160 tumors from all 3 World Health Organization (WHO) grades (I through III) using clinical, gene expression, and sequencing data. Unsupervised clustering analysis identified 3 molecular types (A, B, and C) that reliably predicted recurrence. These groups did not directly correlate with the WHO grading system, which classifies more than half of the tumors in the most aggressive molecular type as benign. Transcriptional and biochemical analyses revealed that aggressive meningiomas involve loss of the repressor function of the DREAM complex, which results in cell-cycle activation; only tumors in this category tend to recur after full resection. These findings should improve our ability to predict recurrence and develop targeted treatments for these clinically challenging tumors.

RNA sequencing (RNA‐seq) is a high‐throughput technology that provides unique insights into the transcriptome. It has a wide variety of applications in quantifying genes/isoforms and in detecting non‐coding RNA, alternative splicing, and splice junctions. It is extremely important to comprehend the entire transcriptome for a thorough understanding of the cellular system. Several RNA‐seq analysis pipelines have been proposed to date. However, no single analysis pipeline can capture dynamics of the entire transcriptome. Here, we compile and present a robust and commonly used analytical pipeline covering the entire spectrum of transcriptome analysis, including quality checks, alignment of reads, differential gene/transcript expression analysis, discovery of cryptic splicing events, and visualization. Challenges, critical parameters, and possible downstream functional analysis pipelines associated with each step are highlighted and discussed. This unit provides a comprehensive understanding of state‐of‐the‐art RNA‐seq analysis pipeline and a greater understanding of the transcriptome. © 2017 by John Wiley & Sons, Inc.

Deconvolution is a mathematical process of resolving an observed function into its constituent elements. In the field of biomedical research, deconvolution analysis is applied to obtain single cell-type or tissue specific signatures from a mixed signal and most of them follow the linearity assumption. Although recent development of next generation sequencing technology suggests RNA-seq as a fast and accurate method for obtaining transcriptomic profiles, few studies have been conducted to investigate best RNA-seq quantification methods that yield the optimum linear space for deconvolution analysis. Using a benchmark RNA-seq dataset, we investigated the linearity of abundance estimated from seven most popular RNA-seq quantification methods both at the gene and isoform levels. Linearity is evaluated through parameter estimation, concordance analysis and residual analysis based on a multiple linear regression model. Results show that count data gives poor parameter estimations, large intercepts and high inter-sample variability; while TPM value from Kallisto and Salmon shows high linearity in all analyses. Salmon and Kallisto TPM data gives the best fit to the linear model studied. This suggests that TPM values estimated from Salmon and Kallisto are the ideal RNA-seq measurements for deconvolution studies.

Clinical trials are currently underway to assess the efficacy of forniceal deep brain stimulation (DBS) for improvement of memory in Alzheimer’s patients, and forniceal DBS has been shown to improve learning and memory in a mouse model of Rett syndrome (RTT), an intellectual disability disorder caused by loss-of-function mutations in MECP2. The mechanism of DBS benefits has been elusive, however, so we assessed changes in gene expression, splice isoforms, DNA methylation, and proteome following acute forniceal DBS in wild-type mice and mice lacking Mecp2. We found that DBS upregulates genes involved in synaptic function, cell survival, and neurogenesis and normalized expression of ~25% of the genes altered in Mecp2-null mice. Moreover, DBS induced expression of 17–24% of the genes downregulated in other intellectual disability mouse models and in post-mortem human brain tissue from patients with Major Depressive Disorder, suggesting forniceal DBS could benefit individuals with a variety of neuropsychiatric disorders.