CITEViz: interactively classify cell populations in CITE-Seq via a flow cytometry-like gating workflow using R-Shiny.

BACKGROUND: The rapid advancement of new genomic sequencing technology has enabled the development of multi-omic single-cell sequencing assays. These assays profile multiple modalities in the same cell and can often yield new insights not revealed with a single modality. For example, Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) simultaneously profiles the RNA transcriptome and the surface protein expression. The surface protein markers in CITE-Seq can be used to identify cell populations similar to the iterative filtration process in flow cytometry, also called "gating", and is an essential step for downstream analyses and data interpretation. While several packages allow users to interactively gate cells, they often do not process multi-omic sequencing datasets and may require writing redundant code to specify gate boundaries. To streamline the gating process, we developed CITEViz which allows users to interactively gate cells in Seurat-processed CITE-Seq data. CITEViz can also visualize basic quality control (QC) metrics allowing for a rapid and holistic evaluation of CITE-Seq data. RESULTS: We applied CITEViz to a peripheral blood mononuclear cell CITE-Seq dataset and gated for several major blood cell populations (CD14 monocytes, CD4 T cells, CD8 T cells, NK cells, B cells, and platelets) using canonical surface protein markers. The visualization features of CITEViz were used to investigate cellular heterogeneity in CD14 and CD16-expressing monocytes and to detect differential numbers of detected antibodies per patient donor. These results highlight the utility of CITEViz to enable the robust classification of single cell populations. CONCLUSIONS: CITEViz is an R-Shiny app that standardizes the gating workflow in CITE-Seq data for efficient classification of cell populations. Its secondary function is to generate basic feature plots and QC figures specific to multi-omic data. The user interface and internal workflow of CITEViz uniquely work together to produce an organized workflow and sensible data structures for easy data retrieval. This package leverages the strengths of biologists and computational scientists to assess and analyze multi-omic single-cell datasets. In conclusion, CITEViz streamlines the flow cytometry gating workflow in CITE-Seq data to help facilitate novel hypothesis generation.

Predicting transcription factor activity using prior biological information.

Dysregulation of normal transcription factor activity is a common driver of disease. Therefore, the detection of aberrant transcription factor activity is important to understand disease pathogenesis. We have developed Priori, a method to predict transcription factor activity from RNA sequencing data. Priori has two key advantages over existing methods. First, Priori utilizes literature-supported regulatory information to identify transcription factor-target gene relationships. It then applies linear models to determine the impact of transcription factor regulation on the expression of its target genes. Second, results from a third-party benchmarking pipeline reveals that Priori detects aberrant activity from 124 single-gene perturbation experiments with higher sensitivity and specificity than 11 other methods. We applied Priori and other top-performing methods to predict transcription factor activity from two large primary patient datasets. Our work demonstrates that Priori uniquely discovered significant determinants of survival in breast cancer and identified mediators of drug response in leukemia.

A framework for considering prior information in network-based approaches to omics data analysis.

For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks (PKNs) in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of PKNs is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.

Disruption of the MYC Superenhancer Complex by Dual Targeting of FLT3 and LSD1 in Acute Myeloid Leukemia.

Mutations in Fms-like tyrosine kinase 3 (FLT3) are common drivers in acute myeloid leukemia (AML) yet FLT3 inhibitors only provide modest clinical benefit. Prior work has shown that inhibitors of lysine-specific demethylase 1 (LSD1) enhance kinase inhibitor activity in AML. Here we show that combined LSD1 and FLT3 inhibition induces synergistic cell death in FLT3-mutant AML. Multi-omic profiling revealed that the drug combination disrupts STAT5, LSD1, and GFI1 binding at the MYC blood superenhancer, suppressing superenhancer accessibility as well as MYC expression and activity. The drug combination simultaneously results in the accumulation of repressive H3K9me1 methylation, an LSD1 substrate, at MYC target genes. We validated these findings in 72 primary AML samples with the nearly every sample demonstrating synergistic responses to the drug combination. Collectively, these studies reveal how epigenetic therapies augment the activity of kinase inhibitors in FLT3-ITD (internal tandem duplication) AML. IMPLICATIONS: This work establishes the synergistic efficacy of combined FLT3 and LSD1 inhibition in FLT3-ITD AML by disrupting STAT5 and GFI1 binding at the MYC blood-specific superenhancer complex.

GoPeaks: histone modification peak calling for CUT&Tag.

Genome-wide mapping of histone modifications is critical to understanding transcriptional regulation. CUT&Tag is a new method for profiling histone modifications, offering improved sensitivity and decreased cost compared with ChIP-seq. Here, we present GoPeaks, a peak calling method specifically designed for histone modification CUT&Tag data. We compare the performance of GoPeaks against commonly used peak calling algorithms to detect histone modifications that display a range of peak profiles and are frequently used in epigenetic studies. We find that GoPeaks robustly detects genome-wide histone modifications and, notably, identifies a substantial number of H3K27ac peaks with improved sensitivity compared to other standard algorithms.

PU.1 and MYC transcriptional network defines synergistic drug responses to KIT and LSD1 inhibition in acute myeloid leukemia.

Responses to kinase-inhibitor therapy in AML are frequently short-lived due to the rapid development of resistance, limiting the clinical efficacy. Combination therapy may improve initial therapeutic responses by targeting pathways used by leukemia cells to escape monotherapy. Here we report that combined inhibition of KIT and lysine-specific demethylase 1 (LSD1) produces synergistic cell death in KIT-mutant AML cell lines and primary patient samples. This drug combination evicts both MYC and PU.1 from chromatin driving cell cycle exit. Using a live cell biosensor for AKT activity, we identify early adaptive changes in kinase signaling following KIT inhibition that are reversed with the addition of LSD1 inhibitor via modulation of the GSK3a/b axis. Multi-omic analyses, including scRNA-seq, ATAC-seq and CUT&Tag, confirm these mechanisms in primary KIT-mutant AML. Collectively, this work provides rational for a clinical trial to assess the efficacy of KIT and LSD1 inhibition in patients with KIT-mutant AML.