Biology subjects

Zinn, N.

Publications and source records attributed to Zinn, N..

5 recordsLinked to original sources

bioRxiv2026

High-throughput platforms for genetic perturbation screening using CRISPR/Cas9 in human iPSC-derived macrophages for drug discovery

Human induced Pluripotent Stem Cell (hiPSC) models have revolutionised drug discovery, offering high translational relevance for recapitulating disease biology and thereby the potential to help reduce drug attrition. Macrophages are pivotal for maintaining tissue homeostasis and orchestrating immune responses; their dysregulation underpins several diseases, including autoinflammatory disorders, neurodegeneration, and cancer. Therapeutically targeting this cell type presents an attractive strategy to simultaneously influence multiple cellular mediators and functions. We have established a scalable, semi-automated, and physiologically relevant hiPSC-derived macrophage model, rigorously characterised through deep comparative multi-omics. We have also integrated our hiPSC-derived macrophage platform with large-scale CRISPR screening workflows designed for parallel genetic interrogation of thousands of gene targets, in both arrayed and pooled formats. In this manuscript, we apply those genetic screening methods to hiPSC-derived macrophages and showcase how genetic perturbations alter pro- and anti-inflammatory transcriptional signatures and significantly impact functional phenotypes in this cell model. This integrated approach allows for the exploration of novel genetic insights linked to causal disease biology, advancing myeloid cell-associated target discovery across a broad spectrum of therapeutic areas.

immunology↗

bioRxiv2025

Discovery of an orally available potent ER aminopeptidase 1 (ERAP1) inhibitor that enhances anti-tumor responses and limits inflammatory autoimmunity in vivo.

Endoplasmic reticulum aminopeptidase 1 (ERAP1) is an intracellular enzyme that can regulate immune responses primarily by proteolytically processing peptides before loading and presentation on the cell surface by major histocompatibility class I molecules (MHC-I). ERAP1 activity can either reduce the immunogenicity of cancer cells by over-trimming cancer-associated antigenic peptides or contribute to autoimmunity by generating self-antigenic peptides. As a result, ERAP1 inhibition has emerged as a tractable approach for cancer immunotherapy and specific classes of autoimmunity. Here, we describe the discovery, after hit-to-lead optimization, of a potent and selective ERAP1 inhibitor based on the pyrrolidine 3-carboxylic acid scaffold that targets the regulatory allosteric site. The compound has favourable in vivo pharmacokinetics, including oral bioavailability, and can regulate the immunopeptidome of cancer cells and enhance cancer cell antigenicity in vivo in a dose-dependent manner, controlling tumor growth. In addition, when administered in the murine collagen-induced arthritis model, it does not induce any exacerbation of autoimmune responses but rather results in a dose-dependent therapeutic benefit. Our results demonstrate that ERAP1 inhibition can constitute a tractable approach to modulating immune responses for therapeutic applications, providing mechanistic insight and a valuable lead and in vivo tool for further drug development efforts and for interrogating ERAP1 biology.

immunology↗

bioRxiv2022

PeptideForest: Semisupervised machine learning integrating multiple search engines for peptide identification

The first step in bottom-up proteomics is the assignment of measured fragmentation mass spectra to peptide sequences, also known as peptide spectrum matches. In recent years novel algorithms have pushed the assignment to new heights, unfortunately, different algorithms come with different strengths and weaknesses and choosing the appropriate algorithm poses a challenge for the user. Here we introduce PeptideForest, a semi-supervised machine learning approach that integrates the assignments of multiple algorithms to train a random forest classifier to elevate that issue. Additionally, PeptideForest increases the number of peptide-to-spectrum matches that exhibit a q-value lower than 1% by 25.2 {+/-} 1.6% compared to MS-GF+ data on samples containing mixed HEK and E. coli proteomes. However, an increase in quantity does not necessarily reflect an increase in quality and this is why we devised a novel approach to determine the quality of the assigned spectra through TMT quantification of samples with known ground truths. Thereby, we could show that the increase in PSMs below 1% q-value does not come with a decrease in quantification quality and as such PeptideForest offers a possibility to gain deeper insights into bottom-up proteomics. PeptideForest has been integrated into our pipeline framework Ursgal and can therefore be combined with a wide array of algorithms.

bioinformatics↗

bioRxiv2019

Multiomic and quantitative label-free microscopy-based analysis of ex vivo culture and TGFbeta1 stimulation of human precision-cut lung slices

Fibrosis can affect any organ resulting in the loss of tissue architecture and function with often life-threatening consequences. Pathologically, fibrosis is characterised by expansion of connective tissue due to excessive deposition of extracellular matrix proteins (ECM), including the fibrillar forms of collagen. A significant hurdle for discovering cures for fibrosis is the lack of suitable models and techniques to quantify mature collagen deposition in tissues. Here we have extensively characterized an ex-vivo cultured human lung derived, precision-cut lung slices model (hPCLS) using live fluorescence light microscopy as well as mass spectrometry-based techniques to obtain a proteomic and metabolomic fingerprint. Using an integrated approach of multiple readouts such as quantitative label-free Second Harmonic Generation (SHG) imaging to measure fibrillar collagen in the extracellular matrix and ELISA-based methods to measure soluble ECM biomarkers, we investigated TGFbeta1-mediated pro-fibrotic signalling in hPCLS. We demonstrate that hPCLS are viable and metabolically active with mesenchymal, epithelial, endothelial, and immune cells surviving for at least two weeks in ex vivo culture. Analysis of hPCLS-conditioned supernatants showed strong induction of ECM synthesis proteins P1NP and fibronectin upon TGFb stimulation. Importantly, this effect translated into an increased deposition of fibrillar collagen in ECM of cultured hPCLS as measured by a novel quantitative SHG-based imaging method only following addition of a metalloproteinase inhibitor (GM6001). Together the data show that an integrated approach of measuring soluble pro-fibrotic markers and quantitative SHG-based analysis of fibrillar collagen is a valuable tool for studying pro-fibrotic signalling and testing anti-fibrotic agents.

physiology↗

bioRxiv2019

Biological Plasticity Rescues Target Activity in CRISPR Knockouts

Gene knockouts (KOs) are efficiently engineered through CRISPR-Cas9-induced frameshift mutations. While DNA editing efficiency is readily verified by DNA sequencing, a systematic understanding of the efficiency of protein elimination has been lacking. Here, we devised an experimental strategy combining RNA-seq and triple-stage mass spectrometry (SPS-MS3) to characterize 193 genetically verified deletions targeting 136 distinct genes generated by CRISPR-induced frameshifts in HAP1 cells. We observed residual protein expression for about one third of the quantified targets, at variable levels from low to original, and identified two causal mechanisms, translation reinitiation leading to N-terminally truncated target proteins, or skipping of the edited exon leading to protein isoforms with internal sequence deletions. Detailed analysis of three truncated targets, BRD4, DNMT1 and NGLY1, revealed partial preservation of protein function. Our results imply that systematic characterization of residual protein expression or function in CRISPR-Cas9 generated KO lines is necessary for phenotype interpretation.

bioengineering↗