An autoinhibitory switch of the LSD1 disordered region controls enhancer silencing.

Transcriptional coregulators and transcription factors (TFs) contain intrinsically disordered regions (IDRs) that are critical for their association and function in gene regulation. More recently, IDRs have been shown to promote multivalent protein-protein interactions between coregulators and TFs to drive their association into condensates. By contrast, here we demonstrate how the IDR of the corepressor LSD1 excludes TF association, acting as a dynamic conformational switch that tunes repression of active cis-regulatory elements. Hydrogen-deuterium exchange shows that the LSD1 IDR interconverts between transient open and closed conformational states, the latter of which inhibits partitioning of the protein's structured domains with TF condensates. This autoinhibitory switch controls leukemic differentiation by modulating repression of active cis-regulatory elements bound by LSD1 and master hematopoietic TFs. Together, these studies unveil alternative mechanisms by which disordered regions and their dynamic crosstalk with structured regions can shape coregulator-TF interactions to control cis-regulatory landscapes and cell fate.

SIMBA: single-cell embedding along with features.

Most current single-cell analysis pipelines are limited to cell embeddings and rely heavily on clustering, while lacking the ability to explicitly model interactions between different feature types. Furthermore, these methods are tailored to specific tasks, as distinct single-cell problems are formulated differently. To address these shortcomings, here we present SIMBA, a graph embedding method that jointly embeds single cells and their defining features, such as genes, chromatin-accessible regions and DNA sequences, into a common latent space. By leveraging the co-embedding of cells and features, SIMBA allows for the study of cellular heterogeneity, clustering-free marker discovery, gene regulation inference, batch effect removal and omics data integration. We show that SIMBA provides a single framework that allows diverse single-cell problems to be formulated in a unified way and thus simplifies the development of new analyses and extension to new single-cell modalities. SIMBA is implemented as a comprehensive Python library ( https://simba-bio.readthedocs.io ).

CRISPR-suppressor scanning reveals a nonenzymatic role of LSD1 in AML.

Understanding the mechanism of small molecules is a critical challenge in chemical biology and drug discovery. Medicinal chemistry is essential for elucidating drug mechanism, enabling variation of small molecule structure to gain structure-activity relationships (SARs). However, the development of complementary approaches that systematically vary target protein structure could provide equally informative SARs for investigating drug mechanism and protein function. Here we explore the ability of CRISPR-Cas9 mutagenesis to profile the interactions between lysine-specific histone demethylase 1 (LSD1) and chemical inhibitors in the context of acute myeloid leukemia (AML). Through this approach, termed CRISPR-suppressor scanning, we elucidate drug mechanism of action by showing that LSD1 enzyme activity is not required for AML survival and that LSD1 inhibitors instead function by disrupting interactions between LSD1 and the transcription factor GFI1B on chromatin. Our studies clarify how LSD1 inhibitors mechanistically operate in AML and demonstrate how CRISPR-suppressor scanning can uncover novel aspects of target biology.

Defining and benchmarking open problems in single-cell analysis.

With the growing number of single-cell analysis tools, benchmarks are increasingly important to guide analysis and method development. However, a lack of standardisation and extensibility in current benchmarks limits their usability, longevity, and relevance to the community. We present Open Problems, a living, extensible, community-guided benchmarking platform including 10 current single-cell tasks that we envision will raise standards for the selection, evaluation, and development of methods in single-cell analysis.

A molecular glue approach to control the half-life of CRISPR-based technologies.

Cas9 is a programmable nuclease that has furnished transformative technologies, including base editors and transcription modulators (e.g., CRISPRi/a), but several applications of these technologies, including therapeutics, mandatorily require precision control of their half-life. For example, such control can help avert any potential immunological and adverse events in clinical trials. Current genome editing technologies to control the half-life of Cas9 are slow, have lower activity, involve fusion of large response elements (> 230 amino acids), utilize expensive controllers with poor pharmacological attributes, and cannot be implemented in vivo on several CRISPR-based technologies. We report a general platform for half-life control using the molecular glue, pomalidomide, that binds to a ubiquitin ligase complex and a response-element bearing CRISPR-based technology, thereby causing the latter's rapid ubiquitination and degradation. Using pomalidomide, we were able to control the half-life of large CRISPR-based technologies (e.g., base editors, CRISPRi) and small anti-CRISPRs that inhibit such technologies, allowing us to build the first examples of on-switch for base editors. The ability to switch on, fine-tune and switch-off CRISPR-based technologies with pomalidomide allowed complete control over their activity, specificity, and genome editing outcome. Importantly, the miniature size of the response element and favorable pharmacological attributes of the drug pomalidomide allowed control of activity of base editor in vivo using AAV as the delivery vehicle. These studies provide methods and reagents to precisely control the dosage and half-life of CRISPR-based technologies, propelling their therapeutic development.

singlecellVR: Interactive Visualization of Single-Cell Data in Virtual Reality.

Single-cell assays have transformed our ability to model heterogeneity within cell populations. As these assays have advanced in their ability to measure various aspects of molecular processes in cells, computational methods to analyze and meaningfully visualize such data have required matched innovation. Independently, Virtual Reality (VR) has recently emerged as a powerful technology to dynamically explore complex data and shows promise for adaptation to challenges in single-cell data visualization. However, adopting VR for single-cell data visualization has thus far been hindered by expensive prerequisite hardware or advanced data preprocessing skills. To address current shortcomings, we present singlecellVR, a user-friendly web application for visualizing single-cell data, designed for cheap and easily available virtual reality hardware (e.g., Google Cardboard, ∼$8). singlecellVR can visualize data from a variety of sequencing-based technologies including transcriptomic, epigenomic, and proteomic data as well as combinations thereof. Analysis modalities supported include approaches to clustering as well as trajectory inference and visualization of dynamical changes discovered through modelling RNA velocity. We provide a companion software package, scvr to streamline data conversion from the most widely-adopted single-cell analysis tools as well as a growing database of pre-analyzed datasets to which users can contribute.

Assessment of computational methods for the analysis of single-cell ATAC-seq data.

BACKGROUND: Recent innovations in single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) enable profiling of the epigenetic landscape of thousands of individual cells. scATAC-seq data analysis presents unique methodological challenges. scATAC-seq experiments sample DNA, which, due to low copy numbers (diploid in humans), lead to inherent data sparsity (1-10% of peaks detected per cell) compared to transcriptomic (scRNA-seq) data (10-45% of expressed genes detected per cell). Such challenges in data generation emphasize the need for informative features to assess cell heterogeneity at the chromatin level. RESULTS: We present a benchmarking framework that is applied to 10 computational methods for scATAC-seq on 13 synthetic and real datasets from different assays, profiling cell types from diverse tissues and organisms. Methods for processing and featurizing scATAC-seq data were compared by their ability to discriminate cell types when combined with common unsupervised clustering approaches. We rank evaluated methods and discuss computational challenges associated with scATAC-seq analysis including inherently sparse data, determination of features, peak calling, the effects of sequencing coverage and noise, and clustering performance. Running times and memory requirements are also discussed. CONCLUSIONS: This reference summary of scATAC-seq methods offers recommendations for best practices with consideration for both the non-expert user and the methods developer. Despite variation across methods and datasets, SnapATAC, Cusanovich2018, and cisTopic outperform other methods in separating cell populations of different coverages and noise levels in both synthetic and real datasets. Notably, SnapATAC is the only method able to analyze a large dataset (> 80,000 cells).