Biology subjects

Wilhelm, B.

Publications and source records attributed to Wilhelm, B..

5 recordsLinked to original sources

bioRxiv2026

Discovery of compounds targeting human acute myeloid leukemia stem cells via a novel high-throughput screen

Acute myeloid leukemia (AML) is sustained by leukemic stem cells (LSCs), which must be eradicated for durable remission yet remain therapy-resistant. Here, we present a scalable platform to identify compounds that eliminate LSCs by performing the first large high-throughput drug screen directly on human LSC-enriched cells. Using this system, we screened 11 142 compounds and identified 20 inhibitors selective for LSCs. For three candidates, BIO-acetoxime, SJB2-043 and UMxxxxx03, we confirmed anti-LSC activity across multiple primary patient samples and validated efficacy through xenotransplantation assays. Single-cell RNA sequencing uncovered convergent and distinct anti-LSC mechanisms of action, including USP1 inhibition, which suppressed stemness and cell-cycle programs while promoting metabolic and inflammatory stress responses. Collectively, the LSC-enriched fraction was eliminated through apoptosis or differentiation. Together, this work establishes the first platform to directly screen LSCs at scale and identifies novel vulnerabilities and therapeutics capable of eliminating the root of AML.

cancer biology↗

bioRxiv2026

A Chemical-Genetic Interaction Matrix Reveals Drug Mechanism and Genetic Architecture

To probe drug mechanism of action (MOA) and interrogate the genetic architecture of human cells, we carried out isogenic genome-wide CRISPR/Cas9 knockout screens against 310 diverse drugs, bioactive compounds, and stress conditions. Stringent statistical correction for gene knockout fitness defects yielded a large-scale matrix of >12,000 high confidence chemical-genetic interactions (CGIs). This dataset revealed many previously unappreciated off-target effects for well-characterized compounds and novel MOAs for uncharacterized compounds. The CGI matrix uncovered dense genetic modules that yielded new biological insights into phospholipidosis, mitotic regulation, metabolism, the DNA damage response, and mTOR signaling. The dataset allowed identification of multi-drug sensitization and resistance mechanisms, inference of gene function, elaboration of cross-process connectivity, evaluation of the cell type specificity of CGIs, prediction of chemical synergism, and extensive annotation of understudied genes. This resource provides a map of the genetic landscape in human cells and a framework to help guide drug discovery.

systems biology↗

bioRxiv2025

A platform for the systematic interrogation of genetic interactions in human cells: 26 gene by genome-wide double knockout screens as a proof of concept

Genetic interactions are typically studied by looking at the phenotype that results from disruption of pairs of genes, as well as from higher order combinations of perturbations. Systematically interrogating all pairwise combinations provides insights into how genes are organized into pathways and complexes to sustain cellular homeostasis and how interacting genes respond to stressors and external signals. Genetic interactions have been studied extensively in yeast, due, in part, to the availability of a systematic collection of gene knockouts, and the development of Synthetic Genetic Array (SGA) technology. In contrast, such approaches are more challenging in human cells and therefore comparable data for human cells is scarce. This study introduces an innovative approach to functionally characterize genetic interactions in human cells through CRISPR/Cas9 screens using a pooled genome-wide knockout library in NALM6 cells. By combining a single guide RNA (sgRNA) targeting the gene of interest (aka the query) in cells already infected with an inducible genome-wide sgRNA pool, it is possible to achieve near saturation of genome-wide double knockouts. We conducted 26 of these screens, which we term "gene by genome-wide" knockout screens. This approach can be rapidly performed, in part, because it bypasses the need to generate genotyped isogenic knockout clones. Data from these screens identified both expected and novel synthetic lethal and synthetic rescue interactions, demonstrating that this strategy is effective for large-scale genetic research in human cells. Additionally, we show that these GBGW screens can be combined with chemical perturbation to reveal new synthetic interactions that are not apparent without drug treatment. Finally, we show that cDNA overexpression can be incorporated with genome-wide knockouts to systematically explore gain-of-function scenarios. The complete dataset is accessible on the ChemoGenix website (URL: https://chemogenix.iric.ca).

genetics↗

bioRxiv2021

Transcriptomics data availability and reusability in the transition from microarray to next-generation sequencing

Over the last two decades, molecular biology has been changed by the introduction of high-throughput technologies. Data sharing requirements have prompted the establishment of persistent data archives. A standardized approach for recording and managing these data was first proposed in the Minimal Information About a Microarray Experiment (MIAME) guidelines. The Minimal Information about a high throughput nucleotide Sequencing Experiment (MINSEQE) proposal was introduced in 2008 as a logical extension of the guidelines to next-generation sequencing (NGS) technologies used for transcriptome analysis. We present a historical snapshot of the data-sharing situation focusing on transcriptomics data from both microarray and RNA-sequencing experiments published between 2009 and 2013, a period during which RNA-seq studies became increasingly popular for transcriptome analysis. We assess how much data from RNA-seq based experiments is actually available in persistent data archives, compared to data derived from microarray based experiments, and evaluate how these types of data differ. Based on this analysis, we provide recommendations to improve RNA-seq data availability, reusability, and reproducibility.

bioinformatics↗

bioRxiv2017

Content-Aware Image Restoration: Pushing the Limits of Fluorescence Microscopy

Fluorescence microscopy is a key driver of discoveries in the life-sciences, with observable phenomena being limited by the optics of the microscope, the chemistry of the fluorophores, and the maximum photon exposure tolerated by the sample. These limits necessitate trade-offs between imaging speed, spatial resolution, light exposure, and imaging depth. In this work we show how image restoration based on deep learning extends the range of biological phenomena observable by microscopy. On seven concrete examples we demonstrate how microscopy images can be restored even if 60-fold fewer photons are used during acquisition, how near isotropic resolution can be achieved with up to 10-fold under-sampling along the axial direction, and how tubular and granular structures smaller than the diffraction limit can be resolved at 20-times higher frame-rates compared to state-of-the-art methods. All developed image restoration methods are freely available as open source software in Python, FO_SCPLOWIJIC_SCPLOW, and KO_SCPLOWNIMEC_SCPLOW.

bioinformatics↗