Biology subjects

Jens, M.

Publications and source records attributed to Jens, M..

5 recordsLinked to original sources

bioRxiv2026

Circulating extracellular vesicles display immunotherapeutic targets and complement circulating tumor DNA in high-risk neuroblastoma

Extracellular vesicles (EVs) offer a minimally invasive window into tumor biology through surface molecules reflecting their cellular origin. Here, we investigated whether neuroblastoma-derived EVs retain tumor-associated surface markers and provide complementary information to established biomarkers in children with neuroblastoma. EVs from neuroblastoma cell lines were characterized using orthogonal biochemical, morphological and biophysical approaches, followed by analysis of GD2, L1CAM and NCAM1 surface expression. EV-associated markers were subsequently evaluated in blood plasma-derived EVs from children with neuroblastoma and pediatric controls and compared with clinical and circulating cell-free DNA (cfDNA) parameters. Neuroblastoma EVs displayed GD2, L1CAM and NCAM1 in patterns reflecting donor-cell surface expression, supported by a positive correlation between cellular and EV marker abundance (Spearman {rho}=0.74, P<0.0001). Genetic disruption of GD2 biosynthesis resulted in corresponding loss of EV-associated GD2, further supporting its cellular origin. In plasma, GD2-, L1CAM- and NCAM1-positive EVs were enriched in patients with high-risk neuroblastoma and correlated with LDH level, soft tissue disease burden (GD2, L1CAM) and cfDNA concentration (GD2, NCAM1). Longitudinal analyses demonstrated dynamic and partially discordant changes in EV- and cfDNA-markers. These findings identify tumor-associated EV surface phenotypes as complementary circulating biomarkers and support their further evaluation for minimally invasive monitoring of neuroblastoma evolution.

cancer biology↗

bioRxiv2025

Personalized CRISPR Knock-In Cytokine Gene Therapy to Remodel the Tumor Microenvironment and Enhance CAR T Cell Therapy in Solid Tumors

The immunosuppressive tumor microenvironment (TME) remains a central barrier to effective immunotherapy in solid tumors. To address this, we developed a novel gene therapeutic strategy that enables localized remodeling of the TME via tumor-intrinsic cytokine expression. Central to this approach is CancerPAM, a multi-omics bioinformatics pipeline that identifies and ranks patient-specific, tumor-exclusive CRISPR-Cas9 knock-in sites with high specificity and integration efficiency. Using neuroblastoma--a pediatric solid tumor with a suppressive TME--as a model, we applied CancerPAM to sequencing data from cell lines and patients to identify optimal integration sites for pro-inflammatory cytokines (CXCL10, CXCL11, IFNG). CRISPR-mediated CXCL10 knock-in into tumor cells significantly enhanced CAR T cell infiltration and antitumor efficacy both in vitro and in vivo. In vivo, CXCL10-expressing tumors showed significantly increased early CAR T cell infiltration and prolonged survival compared to controls. CancerPAM rankings correlated strongly with target-site specificity and knock-in efficiency, validating its predictive performance. Our findings establish CancerPAM as a powerful tool for safe and effective CRISPR-based interventions and provide a conceptual framework for integrating cytokine-driven TME remodeling with cellular immunotherapies. This personalized strategy holds promise for enhancing CAR T cells and other immunotherapies across immune-refractory solid tumors.

bioengineering↗

bioRxiv2022

RBPamp: Quantitative Modeling of Protein-RNA Interactions in vitro Predicts in vivo Binding

RNA-binding proteins (RBPs) control the processing and function of cellular transcripts to effect post-transcriptional gene regulation. Sequence-specific binding of RBPs to millions of synthetic RNAs has been probed in vitro by RNA Bind-n-Seq (RBNS). Here we describe RBPamp, a bio-physically-based model of protein-RNA interactions and associated algorithm that inferred affinity spectra of 79 diverse human RBPs from RBNS data. RBPamp supports multiple motifs per RBP, models RBP concentration and binding site saturation, and accounts for the effects of RNA secondary structure. RBPamp affinities along transcripts are predictive of in vivo binding, as measured by eCLIP density. For many RBPs, average local eCLIP density increases monotonically with predicted affinity, and the shape of this relationship can suggest free protein concentrations and potential cooperativity. Together, these analyses demonstrate a powerful integrative approach for the quantitative dissection of RBP function.

bioinformatics↗

bioRxiv2021

Spacemake: processing and analysis of large-scale spatial transcriptomics data

Spatial sequencing methods increasingly gain popularity within RNA biology studies. State-of-the-art techniques can read mRNA expression levels from tissue sections and at the same time register information about the original locations of the molecules in the tissue. The resulting datasets are processed and analyzed by accompanying software which, however, is incompatible across inputs from different technologies. Here, we present spacemake, a modular, robust and scalable spatial transcriptomics pipeline built in snakemake and python. Spacemake is designed to handle all major spatial transcriptomics datasets and can be readily configured to run on other technologies. It can process and analyze several samples in parallel, even if they stem from different experimental methods. Spacemakes unified framework enables reproducible data processing from raw sequencing data to automatically generated downstream analysis reports. Moreover, spacemake is built with a modular design and offers additional functionality such as sample merging, saturation analysis and analysis of long-reads as separate modules. Moreover, spacemake employs novoSpaRc to integrate spatial and single-cell transcriptomics data, resulting in increased gene counts for the spatial dataset. Spacemake is open-source, extendable and can be readily integrated with existing computational workflows.

bioinformatics↗

bioRxiv2019

Secondary motifs enable concentration-dependent regulation by Rbfox family proteins

The Rbfox family of splicing factors regulate alternative splicing during animal development and in disease, impacting thousands of exons in the maturing brain, heart, and muscle. Rbfox proteins have long been known to bind to the RNA sequence GCAUG with high affinity, but just half of Rbfox CLIP peaks contain a GCAUG motif. We incubated recombinant RBFOX2 with over 60,000 transcriptomic sequences to reveal significant binding to several moderate-affinity, non-GCAYG sites at a physiologically relevant range of RBFOX concentrations. We find that many of these "secondary motifs" bind Rbfox robustly in vivo and that several together can exert regulation comparable to a GCAUG in a trichromatic splicing reporter assay. Furthermore, secondary motifs regulate RNA splicing in neuronal development and in neuronal subtypes where cellular Rbfox concentrations are highest, enabling a second wave of splicing changes as Rbfox levels increase.

biochemistry↗