Biology subjects

Wollmann, T.

Publications and source records attributed to Wollmann, T..

4 recordsLinked to original sources

bioRxiv2026

Next-generation all-in-one CRISPR/Cas9 multiply-edited CD30CAR-T cells:Potency despite risk of translocations

BackgroundChimeric antigen receptor (CAR)-T cells are therapeutic breakthroughs against advanced non-Hodgkin lymphomas and myelomas. On the other hand, no CAR-T cell product has been so far clinically approved for therapy of Hodgkin Lymphoma (HL), T cell lymphoma (TCL), or Epstein-Barr-Virus (EBV)-associated lymphoproliferative diseases (EBV-LPDs). CD30 (TNFRSF8) is commonly expressed on HL and on subsets of TCL and EBV-LPDs. CD30CAR-T cells generated via transduction with viral vectors have been tested in clinical trials, showing overall good responses against HL. CAR-T cells produced entirely with locus-specific gene editing methods are emerging as attractive next-generation engineered cell products for ease of multiple seamless cell modifications. MethodsUsing CRISPR/Cas9-mediated techniques, we optimized homology-directed repair templates (HDRTs) and performed all-in-one multiplex editing to knock-in (KI) CD30CAR within the TCR constant (TRAC) locus and to simultaneously knock-out (KO) PD-1 or/and {beta}2M. CD30CAR-T cells were tested in CD30+ cell models of HL, TCL, and EBV-LPDs. ResultsWe compared mouse versus human anti-CD30 scFv designs in HDRTs incorporating TRAC homology arms, FcIg spacer/detection domain, and CD28 / CD3{zeta} signaling domains. We obtained an average of 30% TRACKICD30CAR-T cells and efficient in vitro cytotoxicity with CD30+ cell targets. CARs incorporating the high-affinity humanized 5F11 scFv showed the highest CAR expression, and the editing templates were further modified to incorporate a truncated CD34 (tCD34) spacer/detection domain. 5F11-CD30CAR-tCD34-T cells showed high CAR-KI rates (approx. 50-80% 12-14 days after editing) and potency in vitro and in vivo. Subsequently, we tested all-in-one CAR KI with additional KOs by co-electroporation of guide RNAs (gRNAs) targeting the genes encoding PD-1 or /and {beta}2M to improve function and allow for improved cell persistence in allogeneic recipients, respectively. Compared with CD30CAR-T cells, CD30CAR-{beta}2MKO-T cells were similarly viable and functional and showed low risk of translocations. PD1KO enabled CD30CAR-T cells to produce higher levels of cytotoxic features upon exposure to targets. However, simultaneous {beta}2MKO and PD-1KO compromised the expansion capacity of CD30CAR-T cells and resulted in detectable translocations. ConclusionsNon-virally engineered 5F11-CD30CAR-T cells represent a novel cell therapy modality against CD30+ lymphomas. Multiplex editing remains to be optimized to avoid unwanted genomic alterations and chromosomal translocations.

cancer biology↗

bioRxiv2024

DeepRod: A human-in-the-loop system for automatic rodent behavior analysis

We present a human-in-the-loop system for efficient rodent behavior analysis in drug development. Addressing the time-consuming and labor-intensive nature of manual behavior categorization, this UX-optimized platform integrates AI for complex behavior prediction and active learning to identify rare events. The proposed solution leverages a cloud-native data processing pipeline, AI-based novelty behavior recognition and multi-class classification, demonstrating significant improvements in behavior labeling and discovery.

animal behavior and cognition↗

bioRxiv2022

Galaxy Training: A Powerful Framework for Teaching!

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis and stewardship are still rarely taught in life science educational programs [1], resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org); an open access, community-driven framework for the collection of FAIR training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform [2]. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments.

bioinformatics↗

bioRxiv2019

Accessible and reproducible mass spectrometry imaging data analysis in Galaxy

BackgroundMass spectrometry imaging is increasingly used in biological and translational research as it has the ability to determine the spatial distribution of hundreds of analytes in a sample. Being at the interface of proteomics/metabolomics and imaging, the acquired data sets are large and complex and often analyzed with proprietary software or in-house scripts, which hinder reproducibility. Open source software solutions that enable reproducible data analysis often require programming skills and are therefore not accessible to many MSI researchers.\n\nFindingsWe have integrated 18 dedicated mass spectrometry imaging tools into the Galaxy framework to allow accessible, reproducible, and transparent data analysis. Our tools are based on Cardinal, MALDIquant, and scikit-image and enable all major MSI analysis steps such as quality control, visualization, preprocessing, statistical analysis, and image co-registration. Further, we created hands-on training material for use cases in proteomics and metabolomics. To demonstrate the utility of our tools, we re-analyzed a publicly available N-linked glycan imaging dataset. By providing the entire analysis history online, we highlight how the Galaxy framework fosters transparent and reproducible research.\n\nConclusionThe Galaxy framework has emerged as a powerful analysis platform for the analysis of MSI data with ease of use and access together with high levels of reproducibility and transparency.

bioinformatics↗