Biology subjects

Lun, A.

Publications and source records attributed to Lun, A..

7 recordsLinked to original sources

bioRxiv2024

Systematic perturbation screens decode regulators of inflammatory macrophage states and identify a role for TNF mRNA m6A modification

Macrophages adopt dynamic cell states with distinct effector functions to maintain tissue homeostasis and respond to environmental challenges. During chronic inflammation, macrophage polarization is subverted towards sustained inflammatory states which contribute to disease, but there is limited understanding of the regulatory mechanisms underlying these disease-associated states. Here, we describe a systematic functional genomics approach that combines genome-wide phenotypic screening in primary murine macrophages with transcriptional and cytokine profiling of genetic perturbations in primary human monocyte-derived macrophages (hMDMs) to uncover regulatory circuits of inflammatory macrophage states. This process identifies regulators of five distinct inflammatory states associated with key features of macrophage function. Among these, the mRNA m6A writer components emerge as novel inhibitors of a TNF-driven cell state associated with multiple inflammatory pathologies. Loss of m6A writer components in hMDMs enhances TNF transcript stability, thereby elevating macrophage TNF production. A PheWAS on SNPs predicted to impact m6A installation on TNF revealed an association with cystic kidney disease, implicating an m6A-mediated regulatory mechanism in human disease. Thus, systematic phenotypic characterization of primary human macrophages describes the regulatory circuits underlying distinct inflammatory states, revealing post-transcriptional control of TNF mRNA stability as an immunosuppressive mechanism in innate immunity.

immunology↗

bioRxiv2023

Epiregulon: Inference of single-cell transcription factor activity to dissect mechanisms of lineage plasticity and drug response

Transcription factors (TFs) and transcriptional coregulators represent an emerging class of therapeutic targets in oncology. Gene regulatory networks (GRNs) can be used to evaluate pharmacological agents targeting these factors and to identify drivers of disease and drug resistance. However, GRN methods that rely solely on gene expression often fail to account for post-transcriptional modulation of TF function. We present Epiregulon, a method that constructs GRNs from single-cell ATAC-seq and RNA-seq data for accurate prediction of TF activity. This is achieved by considering the co-occurrence of TF expression and chromatin accessibility at TF binding sites in each cell. We leverage ChIP-seq data to extend inference to transcriptional coregulators lacking defined motifs or TF harboring neomorphic mutations. Epiregulon accurately predicted the effects of AR inhibition across various drug modalities including an AR antagonist and an AR degrader, delineated the mechanisms of a SMARCA4 degrader by identifying context-dependent interaction partners and prioritized known and novel drivers of lineage reprogramming and tumorigenesis. By mapping gene regulation across various cellular contexts, Epiregulon can accelerate the discovery of therapeutics targeting transcriptional regulators.

bioinformatics↗

bioRxiv2022

A comprehensive Bioconductor ecosystem for the design of CRISPR guide RNAs across nucleases and technologies

The success of CRISPR-mediated gene perturbation studies is highly dependent on the quality of gRNAs, and several tools have been developed to enable optimal gRNA design. However, these tools are not all adaptable to the latest CRISPR modalities or nucleases, nor do they offer comprehensive annotation methods for advanced CRISPR applications. Here, we present a new ecosystem of R packages, called crispr-Verse, that enables efficient gRNA design and annotation for a multitude of CRISPR technologies. This includes CRISPR knockout (CRISPRko), CRISPR activation (CRISPRa), CRISPR interference (CRISPRi), CRISPR base editing (CRISPRbe) and CRISPR knockdown (CRISPRkd). The core package, crisprDesign, offers a comprehensive, user-friendly, and unified interface to add on- and off-target annotations via several alignment methods, rich gene and SNP annotations, and a dozen on- and off-target activity scores. These functionalities are enabled for any RNA- or DNA-targeting nucleases, including Cas9, Cas12, and Cas13. We illustrate the general applicability of our tools by designing optimal gRNAs for three case studies: tiling CRISPRbe library for BRCA1 using the base editor BE4max, tiling RNA-targeting libraries for CD46 and CD55 using CasRx, and activation of MMP7 using CRISPRa. The crisprVerse ecosystem is open-source and deployed through the Bioconductor project to facilitate its use by the CRISPR community (https://github.com/crisprVerse).

bioinformatics↗

bioRxiv2022

Single-cell data analysis in the browser

We present kana, a web application for interactive single-cell omics data analysis in the browser. Like, literally, in the browser: kana leverages web technologies such as WebAssembly to efficiently perform the relevant computations on the users machine, avoiding the need to provision and maintain a backend service. The application provides a streamlined one-click workflow for the main steps in a typical single-cell analysis, starting from a count matrix and finishing with marker detection. Results are presented in an intuitive web interface for further exploration and iterative analysis. Testing on public datasets shows that kana can analyze over 100,000 cells within 5 minutes on a typical laptop.

bioinformatics↗

bioRxiv2018

Overcoming systematic errors caused by log-transformation of normalized single-cell RNA sequencing data

Applying a log-transformation to normalized expression values is one of the most common procedures in exploratory analyses of single-cell RNA sequencing (scRNA-seq) data. Normalization removes systematic biases in sequencing coverage between cells, while the log-transformation ensures that downstream computational procedures operate on relative rather than absolute differences in expression. We show that the log-transformation can introduce systematic errors when cells vary in sequencing coverage, leading to spurious non-zero differences in expression and artificial population structure in simulations. We observe similar effects in real scRNA-seq data where the difference in transformed values between groups of cells is not an accurate proxy for the log-fold change. We provide some practical recommendations to overcome this effect and analytically derive an expression for a larger pseudo-count that controls the transformation-induced error to a specified threshold.

bioinformatics↗

bioRxiv2018

Distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data

Droplet-based single-cell RNA sequencing protocols have dramatically increased the throughput and efficiency of single-cell transcriptomics studies. A key computational challenge when processing these data is to distinguish libraries for real cells from empty droplets. Existing methods for cell calling set a minimum threshold on the total unique molecular identifier (UMI) count for each library, which indiscriminately discards cell libraries with low UMI counts. Here, we describe a new statistical method for calling cells from droplet-based data, based on detecting significant deviations from the expression profile of the ambient solution. Using simulations, we demonstrate that our method has greater power than existing approaches for detecting cell libraries with low UMI counts, while controlling the false discovery rate among detected cells. We also apply our method to real data, where we show that the use of our method results in the retention of distinct cell types that would otherwise have been discarded.

bioinformatics↗

bioRxiv2017

Specificity of RNAi, LNA and CRISPRi as loss-of-function methods in transcriptional analysis

Loss-of-function (LOF) methods, such as RNA interference (RNAi), antisense oligonucleotides or CRISPR-based genome editing, provide unparalleled power for studying the biological function of genes of interest. When coupled with transcriptomic analyses, LOF methods allow researchers to dissect networks of transcriptional regulation. However, a major concern is nonspecific targeting, which involves depletion of transcripts other than those intended. The off-target effects of each of these common LOF methods have yet to be compared at the whole-transcriptome level. Here, we systematically and experimentally compared non-specific activity of RNAi, antisense oligonucleotides and CRISPR interference (CRISPRi). All three methods yielded non-negligible offtarget effects in gene expression, with CRISPRi exhibiting clonal variation in the transcriptional profile. As an illustrative example, we evaluated the performance of each method for deciphering the role of a long noncoding RNA (lncRNA) with unknown function. Although all LOF methods reduced expression of the candidate lncRNA, each method yielded different sets of differentially expressed genes upon knockdown as well as a different cellular phenotype. Therefore, to definitively confirm the functional role of a transcriptional regulator, we recommend the simultaneous use of at least two different LOF methods and the inclusion of multiple, specifically designed negative controls.

genomics↗