In vivo CRISPR screening for novel noncoding RNA functional targets in glioblastoma models.

CRISPR (clustered regularly interspaced short palindromic repeat)-based genetic screens offer unbiased and powerful tools for systematic and specific evaluation of phenotypes associated with specific target genes. CRISPR screens have been utilized heavily in vitro to identify functional coding and noncoding genes in a large number of cell types, including glioblastoma (GB), though no prior study has described the evaluation of CRISPR screening in GB in vivo. Here, we describe a protocol for targeting and transcriptionally repressing GB-specific long noncoding RNAs (lncRNAs) by CRISPR interference (CRISPRi) system in vivo, with tumor growth in the mouse cerebral cortex. Given the target-specific parameters of each individual screen, we list general steps involved in transducing guide RNA libraries into GB tumor lines, maintaining sufficient coverage, as well as cortically injecting and subsequently isolating transduced screen tumor cell populations for analysis. Finally, in order to demonstrate the use of this technique to discern an essential lncRNA, HOTAIR, from a nonessential lncRNA, we injected a 1:1 (HOTAIR:control nonessential lncRNA knockdown) mixture of fluorescently tagged U87 GB cells into the cortex of eight mice, evaluating selective depletion of HOTAIR-tagged cells at 2 weeks of growth. Fluorescently tagged populations were analyzed via flow cytometry for hiBFP (control knockdown) and green fluorescent protein (HOTAIR knockdown), revealing 17% (p = 0.007) decrease in fluorescence associated with HOTAIR knockdown relative to control. The described in vivo CRISPR screening methodology thus appears to be an effective option for identifying noncoding (and coding) genes affecting GB growth within the mouse cortex.

Phylogenetic inference from single-cell RNA-seq data.

Tumors are comprised of subpopulations of cancer cells that harbor distinct genetic profiles and phenotypes that evolve over time and during treatment. By reconstructing the course of cancer evolution, we can understand the acquisition of the malignant properties that drive tumor progression. Unfortunately, recovering the evolutionary relationships of individual cancer cells linked to their phenotypes remains a difficult challenge. To address this need, we have developed PhylinSic, a method that reconstructs the phylogenetic relationships among cells linked to their gene expression profiles from single cell RNA-sequencing (scRNA-Seq) data. This method calls nucleotide bases using a probabilistic smoothing approach and then estimates a phylogenetic tree using a Bayesian modeling algorithm. We showed that PhylinSic identified evolutionary relationships underpinning drug selection and metastasis and was sensitive enough to identify subclones from genetic drift. We found that breast cancer tumors resistant to chemotherapies harbored multiple genetic lineages that independently acquired high K-Ras and ß-catenin, suggesting that therapeutic strategies may need to control multiple lineages to be durable. These results demonstrated that PhylinSic can reconstruct evolution and link the genotypes and phenotypes of cells across monophyletic tumors using scRNA-Seq.

A machine learning framework for scRNA-seq UMI threshold optimization and accurate classification of cell types.

Recent advances in single cell RNA sequencing (scRNA-seq) technologies have been invaluable in the study of the diversity of cancer cells and the tumor microenvironment. While scRNA-seq platforms allow processing of a high number of cells, uneven read quality and technical artifacts hinder the ability to identify and classify biologically relevant cells into correct subtypes. This obstructs the analysis of cancer and normal cell diversity, while rare and low expression cell populations may be lost by setting arbitrary high cutoffs for UMIs when filtering out low quality cells. To address these issues, we have developed a novel machine-learning framework that: 1. Trains cell lineage and subtype classifier using a gold standard dataset validated using marker genes 2. Systematically assess the lowest UMI threshold that can be used in a given dataset to accurately classify cells 3. Assign accurate cell lineage and subtype labels to the lower read depth cells recovered by setting the optimal threshold. We demonstrate the application of this framework in a well-curated scRNA-seq dataset of breast cancer patients and two external datasets. We show that the minimum UMI threshold for the breast cancer dataset could be lowered from the original 1500 to 450, thereby increasing the total number of recovered cells by 49%, while achieving a classification accuracy of >0.9. Our framework provides a roadmap for future scRNA-seq studies to determine optimal UMI threshold and accurately classify cells for downstream analyses.