Efficacy and safety of a SOD1-targeting artificial miRNA delivered by AAV9 in mice are impacted by miRNA scaffold selection.

Toxic gain-of-function mutations in superoxide dismutase 1 (SOD1) contribute to approximately 2%-3% of all amyotrophic lateral sclerosis (ALS) cases. Artificial microRNAs (amiRs) delivered by adeno-associated virus (AAV) have been proposed as a potential treatment option to silence SOD1 expression and mitigate disease progression. Primary microRNA (pri-miRNA) scaffolds are used in amiRs to shuttle a hairpin RNA into the endogenous miRNA pathway, but it is unclear whether different primary miRNA (pri-miRNA) scaffolds impact the potency and safety profile of the expressed amiR in vivo. In our process to develop an AAV amiR targeting SOD1, we performed a preclinical characterization of two pri-miRNA scaffolds, miR155 and miR30a, sharing the same guide strand sequence. We report that, while the miR155-based vector, compared with the miR30a-based vector, leads to a higher level of the amiR and more robust suppression of SOD1 in vitro and in vivo, it also presents significantly greater risks for CNS-related toxicities in vivo. Despite miR30a-based vector showing relatively lower potency, it can significantly delay the development of ALS-like phenotypes in SOD1-G93A mice and increase survival in a dose-dependent manner. These data highlight the importance of scaffold selection in the pursuit of highly efficacious and safe amiRs for RNA interference gene therapy.

scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing.

BACKGROUND: Single-cell RNA sequencing is a state-of-the-art technology to understand gene expression in complex tissues. With the growing amount of data being generated, the standardization and automation of data analysis are critical to generating hypotheses and discovering biological insights. RESULTS: Here, we present scRNASequest, a semi-automated single-cell RNA-seq (scRNA-seq) data analysis workflow which allows (1) preprocessing from raw UMI count data, (2) harmonization by one or multiple methods, (3) reference-dataset-based cell type label transfer and embedding projection, (4) multi-sample, multi-condition single-cell level differential gene expression analysis, and (5) seamless integration with cellxgene VIP for visualization and with CellDepot for data hosting and sharing by generating compatible h5ad files. CONCLUSIONS: We developed scRNASequest, an end-to-end pipeline for single-cell RNA-seq data analysis, visualization, and publishing. The source code under MIT open-source license is provided at https://github.com/interactivereport/scRNASequest . We also prepared a bookdown tutorial for the installation and detailed usage of the pipeline: https://interactivereport.github.io/scRNAsequest/tutorial/docs/ . Users have the option to run it on a local computer with a Linux/Unix system including MacOS, or interact with SGE/Slurm schedulers on high-performance computing (HPC) clusters.

RNASequest: An End-to-End Reproducible RNAseq Data Analysis and Publishing Framework.

We present RNASequest, a customizable RNA sequencing (RNAseq) analysis, app management, and result publishing framework. Its three-in-one RNAseq data analysis ecosystem consists of (1) a reproducible, configurable expression analysis (EA) module, (2) multi-faceted result presentation in R Shiny, a Bookdown document and an online slide deck, and (3) a centralized data management system. In principle, following up our well-received omics data visualization tool Quickomics, RNASequest automates the differential gene expression analysis step, eases statistical model design by built-in covariates testing module, and further provides a web-based tool, ShinyOne, to manage apps powered by Quickomics and reports generated by running the pipeline on multiple projects in one place. Researchers can experience the functionalities by exploring demo data sets hosted at http://shinyone.bxgenomics.com or following the tutorial, https://interactivereport.github.io/RNASequest/tutorial/docs/introduction.html to set up the framework locally to process private RNAseq datasets. The source code released under MIT open-source license is provided at https://github.com/interactivereport/RNASequest.

Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases.

Identification of therapeutic targets from genome-wide association studies (GWAS) requires insights into downstream functional consequences. We harmonized 8,613 RNA-sequencing samples from 14 brain datasets to create the MetaBrain resource and performed cis- and trans-expression quantitative trait locus (eQTL) meta-analyses in multiple brain region- and ancestry-specific datasets (n ≤ 2,759). Many of the 16,169 cortex cis-eQTLs were tissue-dependent when compared with blood cis-eQTLs. We inferred brain cell types for 3,549 cis-eQTLs by interaction analysis. We prioritized 186 cis-eQTLs for 31 brain-related traits using Mendelian randomization and co-localization including 40 cis-eQTLs with an inferred cell type, such as a neuron-specific cis-eQTL (CYP24A1) for multiple sclerosis. We further describe 737 trans-eQTLs for 526 unique variants and 108 unique genes. We used brain-specific gene-co-regulation networks to link GWAS loci and prioritize additional genes for five central nervous system diseases. This study represents a valuable resource for post-GWAS research on central nervous system diseases.

In vivo genome editing using novel AAV-PHP variants rescues motor function deficits and extends survival in a SOD1-ALS mouse model.

CRISPR-based gene editing technology represents a promising approach to deliver therapies for inherited disorders, including amyotrophic lateral sclerosis (ALS). Toxic gain-of-function superoxide dismutase 1 (SOD1) mutations are responsible for ~20% of familial ALS cases. Thus, current clinical strategies to treat SOD1-ALS are designed to lower SOD1 levels. Here, we utilized AAV-PHP.B variants to deliver CRISPR-Cas9 guide RNAs designed to disrupt the human SOD1 (huSOD1) transgene in SOD1G93A mice. A one-time intracerebroventricular injection of AAV.PHP.B-huSOD1-sgRNA into neonatal H11Cas9 SOD1G93A mice caused robust and sustained mutant huSOD1 protein reduction in the cortex and spinal cord, and restored motor function. Neonatal treatment also reduced spinal motor neuron loss, denervation at neuromuscular junction (NMJ) and muscle atrophy, diminished axonal damage and preserved compound muscle action potential throughout the lifespan of treated mice. SOD1G93A treated mice achieved significant disease-free survival, extending lifespan by more than 110 days. Importantly, a one-time intrathecal or intravenous injection of AAV.PHP.eB-huSOD1-sgRNA in adult H11Cas9 SOD1G93A mice, immediately before symptom onset, also extended lifespan by at least 170 days. We observed substantial protection against disease progression, demonstrating the utility of our CRISPR editing preclinical approach for target evaluation. Our approach uncovered key parameters (e.g., AAV capsid, Cas9 expression) that resulted in improved efficacy compared to similar approaches and can also serve to accelerate drug target validation.

Characterizing the composition of iPSC derived cells from bulk transcriptomics data with CellMap.

Induced pluripotent stem cell (iPSC) derived cell types are increasingly employed as in vitro model systems for drug discovery. For these studies to be meaningful, it is important to understand the reproducibility of the iPSC-derived cultures and their similarity to equivalent endogenous cell types. Single-cell and single-nucleus RNA sequencing (RNA-seq) are useful to gain such understanding, but they are expensive and time consuming, while bulk RNA-seq data can be generated quicker and at lower cost. In silico cell type decomposition is an efficient, inexpensive, and convenient alternative that can leverage bulk RNA-seq to derive more fine-grained information about these cultures. We developed CellMap, a computational tool that derives cell type profiles from publicly available single-cell and single-nucleus datasets to infer cell types in bulk RNA-seq data from iPSC-derived cell lines.

Multiscale, multimodal analysis of tumor heterogeneity in IDH1 mutant vs wild-type diffuse gliomas.

Glioma is recognized to be a highly heterogeneous CNS malignancy, whose diverse cellular composition and cellular interactions have not been well characterized. To gain new clinical- and biological-insights into the genetically-bifurcated IDH1 mutant (mt) vs wildtype (wt) forms of glioma, we integrated data from protein, genomic and MR imaging from 20 treatment-naïve glioma cases and 16 recurrent GBM cases. Multiplexed immunofluorescence (MxIF) was used to generate single cell data for 43 protein markers representing all cancer hallmarks, Genomic sequencing (exome and RNA (normal and tumor) and magnetic resonance imaging (MRI) quantitative features (protocols were T1-post, FLAIR and ADC) from whole tumor, peritumoral edema and enhancing core vs equivalent normal region were also collected from patients. Based on MxIF analysis, 85,767 cells (glioma cases) and 56,304 cells (GBM cases) were used to generate cell-level data for 24 biomarkers. K-means clustering was used to generate 7 distinct groups of cells with divergent biomarker profiles and deconvolution was used to assign RNA data into three classes. Spatial and molecular heterogeneity metrics were generated for the cell data. All features were compared between IDH mt and IDHwt patients and were finally combined to provide a holistic/integrated comparison. Protein expression by hallmark was generally lower in the IDHmt vs wt patients. Molecular and spatial heterogeneity scores for angiogenesis and cell invasion also differed between IDHmt and wt gliomas irrespective of prior treatment and tumor grade; these differences also persisted in the MR imaging features of peritumoral edema and contrast enhancement volumes. A coherent picture of enhanced angiogenesis in IDHwt tumors was derived from multiple platforms (genomic, proteomic and imaging) and scales from individual proteins to cell clusters and heterogeneity, as well as bulk tumor RNA and imaging features. Longer overall survival for IDH1mt glioma patients may reflect mutation-driven alterations in cellular, molecular, and spatial heterogeneity which manifest in discernable radiological manifestations.

Computational de novo discovery of distinguishing genes for biological processes and cell types in complex tissues.

Bulk tissue samples examined by gene expression studies are usually heterogeneous. The data gained from these samples display the confounding patterns of mixtures consisting of multiple cell types or similar cell types in various functional states, which hinders the elucidation of the molecular mechanisms underlying complex biological phenomena. A realistic approach to compensate for the limitations of experimentally separating homogenous cell populations from mixed tissues is to computationally identify cell-type specific patterns from bulk, heterogeneous measurements. We designed the CellDistinguisher algorithm to analyze the gene expression data of mixed samples, identifying genes that best distinguish biological processes and cell types. Coupled with a deconvolution algorithm that takes cell type specific gene lists as input, we show that CellDistinguisher performs as well as partial deconvolution algorithms in predicting cell type composition without the need for prior knowledge of cell type signatures. This approach is also better in predicting cell type signatures than the one-step traditional complete deconvolution methods. To illustrate its wide applicability, the algorithm was tested on multiple publicly available data sets. In each case, CellDistinguisher identified genes reflecting biological processes typical for the tissues and development stages of interest and estimated the sample compositions accurately.

Characterizing the heterogeneity of tumor tissues from spatially resolved molecular measures.

BACKGROUND: Tumor heterogeneity can manifest itself by sub-populations of cells having distinct phenotypic profiles expressed as diverse molecular, morphological and spatial distributions. This inherent heterogeneity poses challenges in terms of diagnosis, prognosis and efficient treatment. Consequently, tools and techniques are being developed to properly characterize and quantify tumor heterogeneity. Multiplexed immunofluorescence (MxIF) is one such technology that offers molecular insight into both inter-individual and intratumor heterogeneity. It enables the quantification of both the concentration and spatial distribution of 60+ proteins across a tissue section. Upon bioimage processing, protein expression data can be generated for each cell from a tissue field of view. RESULTS: The Multi-Omics Heterogeneity Analysis (MOHA) tool was developed to compute tissue heterogeneity metrics from MxIF spatially resolved tissue imaging data. This technique computes the molecular state of each cell in a sample based on a pathway or gene set. Spatial states are then computed based on the spatial arrangements of the cells as distinguished by their respective molecular states. MOHA computes tissue heterogeneity metrics from the distributions of these molecular and spatially defined states. A colorectal cancer cohort of approximately 700 subjects with MxIF data is presented to demonstrate the MOHA methodology. Within this dataset, statistically significant correlations were found between the intratumor AKT pathway state diversity and cancer stage and histological tumor grade. Furthermore, intratumor spatial diversity metrics were found to correlate with cancer recurrence. CONCLUSIONS: MOHA provides a simple and robust approach to characterize molecular and spatial heterogeneity of tissues. Research projects that generate spatially resolved tissue imaging data can take full advantage of this useful technique. The MOHA algorithm is implemented as a freely available R script (see supplementary information).

Microfluidic Tissue Mesodissection in Molecular Cancer Diagnostics.

We present a mesodissection platform that retains the advantages of laser-based dissection instrumentation with the speed and ease of manual dissection. Tissue dissection in clinical laboratories is often performed by manually scraping a physician-selected region from standard glass slide mounts. In this manner, costs associated with dissection remain low, but spatial resolution is compromised. In contrast, laser microdissection methods maintain spatial resolution that matches the requirements for analysis of important tissue heterogeneity but remains costly and labor intensive. We demonstrate a microfluidic tool for rapid extraction of histological regions of interest from formalin-fixed paraffin-embedded tissue, which uses a simple and automated method that is compatible with most downstream enzymatic reactions, including protocols used for next-generation DNA sequencing.