Transcriptome-wide characterization of genetic perturbations
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE264667 | GEO | 2024/05/05
REPOSITORIES: GEO
Similar Datasets
Project description:Pooled CRISPR screens with single-cell RNA-seq readout (Perturb-seq) have emerged as a key technique in functional genomics, but are limited in scale by cost and combinatorial complexity. Here, we reimagine Perturb-seq’s design through the lens of algorithms applied to random, low-dimensional observations. We present compressed Perturb-seq, in which we measure multiple perturbations per cell or multiple cells per droplet, and decompress these measurements by leveraging the sparse structure of regulatory circuits. Applying compressed Perturb-seq to 598 genes in the immune response to bacterial lipopolysaccharide, we achieve the same accuracy as conventional Perturb-seq at 4 to 20-fold reduced cost, with greater power to learn genetic interactions. We identify known and novel regulators of immune responses and uncover evolutionarily constrained genes with downstream targets enriched for immune disease heritability, including many missed by existing GWAS or trans-eQTL studies. Our framework enables new scales of interrogation for a foundational method in functional genomics.
2023-01-01 | GSE221321 | GEO
Project description:Compressed Perturb-seq: highly efficient screens for regulatory circuits using random composite perturbations
| PRJNA913903 | ENA
Project description:The United States Environmental Protection Agency has proposed a tiered testing strategy for chemical hazard evaluation based on new approach methods (NAMs). The first tier includes in vitro profiling assays applicable to many (human) cell types, such as high-throughput transcriptomics (HTTr) and high-throughput phenotypic profiling (HTPP). The goals of this study were to: (1) harmonize the seeding density of U-2 OS human osteosarcoma cells for use in both assays; (2) compare HTTr- versus HTPP-derived potency estimates for 11 mechanistically diverse chemicals; (3) identify candidate reference chemicals for monitoring assay performance in future screens; and (4) characterize the transcriptional and phenotypic changes in detail for all-trans retinoic acid (ATRA) as a model compound known for its adverse effects on osteoblast differentiation. The results of this evaluation showed that (1) HTPP conducted at low (400 cells/well) and high (3000 cells/well) seeding densities yielded comparable potency estimates and similar phenotypic profiles for the tested chemicals; (2) HTPP and HTTr resulted in comparable potency estimates for changes in cellular morphology and gene expression, respectively; (3) three test chemicals (etoposide, ATRA, dexamethasone) produced concentration-dependent effects on cellular morphology and gene expression that were consistent with known modes-of-action, demonstrating their suitability for use as reference chemicals for monitoring assay performance; and (4) ATRA produced phenotypic changes that were highly similar to other retinoic acid receptor activators (AM580, arotinoid acid) and some retinoid X receptor activators (bexarotene, methoprene acid). This phenotype was observed concurrently with autoregulation of the RARB gene. Both effects were prevented by pre-treating U-2 OS cells with pharmacological antagonists of their respective receptors. Thus, the observed phenotype could be considered characteristic of retinoic acid pathway activation in U-2 OS cells. These findings lay the groundwork for combinatorial screening of chemicals using HTTr and HTPP to generate complementary information for the first tier of a NAM-based chemical hazard evaluation strategy.
2022-04-18 | GSE200845 | GEO
Project description:Single cell perturb-seq. Abstract WIP.
2024-12-12 | GSE283614 | GEO
Project description:DNA methylation is an important component of epigenetic modifications that influences the transcriptional machinery and is aberrant in many human diseases. Several methods have been developed to map DNA methylation for either limited regions or genome-wide. In particular, antibodies specific for methylated CpG have been successfully applied in genome-wide studies. However, despite the relevance of the obtained results, the interpretation of antibody enrichment is not trivial. Of greatest importance, the coupling of antibody-enriched methylated fragments with microarrays generates DNA methylation estimates that are not linearly related to the true methylation level. Here, we present an experimental and analytical methodology to obtain enhanced estimates which better describe the true values of DNA methylation level throughout the genome. We propose an experimental scenario for evaluating the true relationship in a high-throughput setting and a model-based analysis to predict the absolute and relative DNA methylation levels. We successfully applied this model to evaluate DNA methylation status of normal human melanocytes compared to a melanoma cell strain. Despite the low resolution typical of methods based on immunoprecipitation, we show that model-derived estimates of DNA methylation provide relatively high correlation with measured absolute and relative levels, as validated by bisulfite genomic DNA sequencing. Importantly, the model-derived DNA methylation estimates simplify the interpretation of the results both at single-loci and at chromosome-wide levels. The MEDME R library as well as installation instructions and a PDF tutorial are available online at the website below.
2008-07-21 | GSE12096 | GEO
Project description:CRISPR technology has demonstrated broad utility for controlling target gene expression. However, there remains a need for strategies capable of modulating expression via the precise editing of non-coding regulatory elements. Here we demonstrate that CRISPR base editors, a class of gene-modifying proteins capable of inducing single-base substitutions in DNA, can be harnessed to perturb target gene expression via the targeted mutagenesis of cis-acting sequences. Using the promoter region of the human huntingtin (HTT) gene as an initial target, we show that editing of the binding site for the transcription factor NF-κB led to a marked reduction in HTT gene expression. We find that these gene perturbations were persistent and specific, as a transcriptome-wide RNA-seq analysis revealed virtually no off-target effects. We further show that this base-editing platform could lower HTT in vivo, as its delivery to a mouse model of Huntington’s disease decreased HTT expression in striatal neurons, an outcome that we show also increased survival. Finally, we use this approach to target the amyloid-beta precursor protein, demonstrating that multiplexed editing of its promoter region could significantly perturb its expression, supporting the applicability of this method. These findings thus demonstrate the potential for base editors to regulate target gene expression.
2022-08-24 | GSE204833 | GEO
Project description:High-throughput phenotypic screens leveraging biochemical perturbations and high-content readouts are poised to advance therapeutic discovery, yet they remain constrained by limitations of scale. To address this, we establish a method of pooling exogenous perturbations followed by computational deconvolution to compress a screen’s required sample, labor, and financial input. We benchmark the approach with a bioactive small molecule library and a high-content imaging readout, demonstrating the feasibility and increased efficiency of compressed experimental designs compared to conventional approaches. To prove generalizability, we apply compressed screening in two different biological discovery campaigns. In the first, we use early-passage pancreatic cancer organoids to map transcriptional responses to alibrary oftumor-microenvironmentrecombinant protein ligands. We uncover reproducible phenotypic shifts induced by specific ligands that are distinct from canonical reference signatures and uniquely correlate with clinical outcome.In the second, we examine the modulatory effects of a known mechanism of action chemical compound library on primary human peripheral blood mononuclear cell immune responses. Through contrastive analyses, we identify molecules that potentiate and/or inhibit cell-type specific transcriptional features, uncover pleiotropic effects for individual compounds across diverse cell types, and realize a systems-level view of drug responses. In sum, our approach empowers phenotypic screens with information-rich readouts to advance drug discovery efforts as well as basic biological inquiry.
2024-05-13 | GSE267243 | GEO
Project description:Differential miRNA expression of inherently high- and low-active inbred mice
| PRJNA243091 | ENA
Project description:The cellular architecture of a tumor has a major impact on cancer outcome, and thus there is interest in identifying genes controlling the tumor microenvironment (TME). While CRISPR screens are helping uncover genes regulating many cell-intrinsic processes, existing approaches are suboptimal for identifying gene functions operating extracellularly or within a tissue context. To address this, we developed an approach for spatial functional genomics called Perturb-map, which utilizes protein barcodes (Pro-Code) to enable spatial detection of barcoded cells within tissue. We applied Perturb-map to knockout dozens of genes in parallel in a mouse model of lung cancer and simultaneously assessed how each knockout influenced tumor growth, histopathology, and immune composition. Additionally, we paired Perturb-map and spatial transcriptomics for unbiased molecular analysis of Pro-Code/CRISPR lesions. Our studies found in Tgfbr2 knockout lesions, the TME was converted to a fibro-mucinous state and T-cells excluded, concomitant with upregulated TGFb and TGFb-mediated stroma activation, suggesting Tgfbr2 loss on lung cancer cells increased TGFb bioavailability and enhanced its suppressive effects on the TME. These studies establish Perturb-map for functional genomics within a tissue at single cell-resolution with spatial architecture preserved.
2022-01-15 | GSE193460 | GEO
Project description:Circulating cell-free DNA (cfDNA) in the bloodstream originates from dying cells and is a promising non-invasive biomarker for cell death. Here, we propose an algorithm, CelFiE, to accurately estimate relative abundances of cell types and tissues contributing to cfDNA from epigenetic cfDNA sequencing. In contrast to previous work, CelFiE accommodates low coverage data, does not rely on CpG site curation, and estimates contributions from multiple unknown cell types that are not available in external reference data. In simulations, CelFiE accurately estimates known and unknown cell type proportions from low coverage and noisy cfDNA mixtures, including from rare cell types composing less than 1% of the total mixture. In a positive control of cfDNA extracted from pregnant and non-pregnant women, CelFiE correctly estimates a large placenta component specifically in pregnant women (p = 4.5x10^-5). In cfDNA from ALS patients and age-matched controls, we observed increased cfDNA concentrations in ALS patients (p = 5.0x10^-3), and CelFiE identified a corresponding increased skeletal muscle component (p = 2.4x10^-3), consistent with muscle impairment characterizing ALS. Together these results show CelFiE may be a useful tool for biomarker discovery and monitoring of degenerative disease progression.
2021-01-12 | GSE164600 | GEO