Biology subjects

Martens, J. W.

Publications and source records attributed to Martens, J. W..

4 recordsLinked to original sources

bioRxiv2026

Label-free Isolation of Heterogeneous Breast Cancer Cell Populations via Insulator-Based Dielectrophoresis

Circulating tumor cells (CTCs) are valuable biomarkers for cancer diagnosis and monitoring, yet their isolation from blood remains challenging due to their phenotypic heterogeneity and rarity. Label-free microfluidic technologies offer a promising alternative to affinity-based approaches by exploiting intrinsic biophysical differences between cell types. Here, we developed a microfluidic platform for label-free cell separation based on insulator-based dielectrophoresis (iDEP). The microfluidic device employs an array of triangular insulating structures that generate strong electric field gradients in response to an externally applied alternating current (AC) electric field, enabling selective isolation of breast cancer cells from blood cells based on their dielectric properties. Hydrodynamic focusing is used to confine the sample stream and precisely control cell trajectories within the separation region. Numerical simulations were performed to optimize the electric field distribution and fluid flow characteristics within the device. Experimental validation using breast cancer cell lines (mesenchymal-like MDA-MB-231 cells and epithelial-like MCF-7 cells) spiked into peripheral blood mononuclear cells (PBMCs) demonstrates selective dielectrophoretic deflection of cancer cells while PBMCs largely follow the central streamline. The platform achieves recovery rates exceeding 98% and a separation purity above 65% within the optimized operating conditions. The proposed system provides a simple label-free approach to separate heterogeneous cell populations and represents a promising tool for microfluidic liquid biopsy enrichment applications.

cancer biology↗

bioRxiv2025

From Background to Signal: Simultaneously derived Copy Numbers from Methylation-Dependent Sequencing cell-free DNA

Cell-free DNA (cfDNA) analysis offers a powerful, minimally invasive approach to improve cancer care by measuring tumor-specific genomic and epigenetic alterations. Here, we demonstrate the versatility of MeD-seq, a methylation-dependent sequencing assay, for comprehensive cfDNA analysis, including DNA methylation profiling, chromosomal copy number (CN) alterations, and tumor fraction (TF) estimation. MeD-seq-derived CN profiles and TF estimates from 38 colorectal cancer with liver metastases (CRLM) and 5 ovarian cancer patients were highly comparable to shallow whole-genome sequencing (sWGS) validating our approach. For 120 CRLM patients we used MeD-seq CN and TF information in an improved Differential Methylation Model which detected additional significantly Differentially Methylated Regions (DMRs) correlating with TF estimates. Using the identified DMR sets we were subsequently able to distinguish healthy blood donors from CRLM patients with low amounts of circulating tumor DNA (ctDNA) as well. These findings show MeD-seq as an affordable platform for detecting cancer-specific signals directly from plasma without prior tissue-based information. Future work could expand its application to other cancer types, solidifying MeD-seq as a versatile tool for cfDNA profiling. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=159 SRC="FIGDIR/small/633371v2_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@bd440eorg.highwire.dtl.DTLVardef@1befbb7org.highwire.dtl.DTLVardef@17fe9d2org.highwire.dtl.DTLVardef@d0101a_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract of MeD-seq and Copy Number Informed Differential Methylation Analysis Liquid biopsy samples are collected, followed by cfDNA extraction, methylation-dependent enzymatic digestion, Whole Genome Sequencing (WGS) and in silico read selection. Background reads (lacking the recognition site of the methylation-dependent restriction ezyme) are utilized for CN profiling and TF estimation. Methylated reads (with the recognition site of the methylation-dependent restriction enzyme) are counted in CpG islands and integrated with CN information to perform Differential Methylation profiling, enabling a comprehensive assessment of methylation in cfDNA. C_FIG

bioinformatics↗

bioRxiv2024

TCR-engineered T-cells directed against Ropporin-1 constitute a safe and effective treatment for triple-negative breast cancer in near-clinical models

Triple-negative breast cancer (TNBC) shows an urgent need for new therapies. We discovered Ropporin-1 (ROPN1) as a target to treat TNBC with T-cells. ROPN1 showed high and homogenous expression in 90% of primary and metastatic TNBC but not in healthy tissues. HLA-A2-binding peptides were detected via immunopeptidomics and predictions and used to retrieve T-cell receptors (TCRs) from naive repertoires. Following gene introduction into T-cells and stringent selection, we retrieved a highly specific TCR directed against the epitope FLYTYIAKV that did not recognize non-cognate epitopes from alternative source proteins. Notably, this TCR mediated killing of three-dimensional tumoroids in vitro and tumor cells in vivo and outperformed standard-of-care drugs. Finally, the T-cell product expressing this TCR and manufactured using a clinical protocol fulfilled standard safety and efficacy assays. Collectively, we have identified and preclinically validated ROPN1 as a target and anti-ROPN1 TCR T-cells as a treatment for the vast majority of TNBC patients.

immunology↗

bioRxiv2021

High throughput and affordable genome-wide methylation profiling of circulating cell-free DNA by Methylated DNA sequencing (MeD-seq) of LpnPI digested fragments

BackgroundDNA methylation detection in liquid biopsies provides a highly promising and much needed means for real-time monitoring of disease load in advanced cancer patient care. Compared to the often-used somatic mutations, tissue- and cancer-type specific epigenetic marks affect a larger part of the cancer genome and generally have a high penetrance throughout the tumour. Here we describe the successful application of the recently described MeD-seq assay for genome-wide DNA methylation profiling on cell-free DNA (cfDNA). The compatibility of the MeD-seq assay with different types of blood collection tubes, cfDNA input amounts, cfDNA isolation methods, and vacuum-concentration of samples was evaluated using plasma from both metastatic cancer patients and healthy blood donors (HBDs). To investigate the potential value of cfDNA methylation profiling for tumour load monitoring, we profiled paired samples from 8 patients with resectable colorectal liver metastases (CRLM) before and after surgery. ResultsThe MeD-seq assay worked on plasma-derived cfDNA from both EDTA and CellSave blood collection tubes when at least 10 ng of cfDNA was used. From the 3 evaluated cfDNA isolation methods, both the manual QIAamp Circulating Nucleic Acid Kit (Qiagen) and the semi-automated Maxwell(R) RSC ccfDNA Plasma Kit (Promega) were compatible with MeD-seq analysis, whereas the QIAsymphony DSP Circulating DNA Kit (Qiagen) yielded significantly fewer reads when compared to the QIAamp kit (P<0.001). Vacuum-concentration of samples before MeD-seq analysis was possible with samples in AVE buffer (QIAamp) or water, but yielded inconsistent results for samples in EDTA-containing Maxwell buffer. Principal component analysis showed that pre-surgical samples from CRLM patients were very distinct from HBDs, whereas post-surgical samples were more similar. Several described methylation markers for colorectal cancer monitoring in liquid biopsies showed differential methylation between pre-surgical CRLM samples and HBDs in our data, supporting the validity of our approach. Results for MSC, ITGA4, GRIA4, and EYA4, were validated by quantitative methylation specific PCR. ConclusionsThe MeD-seq assay provides a promising new method for cfDNA methylation profiling. Potential future applications of the assay include marker discovery specifically for liquid biopsy analysis as well as direct use as a disease load monitoring tool in advanced cancer patients.

genomics↗