Biology subjects

Holdt, L. M.

Publications and source records attributed to Holdt, L. M..

3 recordsLinked to original sources

bioRxiv2025

A platform of robust patient-derived leukemia models covering subgroups for which no cell lines exist

Preclinical cancer research requires robust model systems, especially for poor prognosis entities like acute myeloid leukemia (AML), a highly aggressive blood cancer. Here, primary tumor cells from 137 AML patients of all age groups were transplanted into immune compromised mice to generate patient-derived xenografts (PDX). From these, 23 models enable robust, virtually endless serial re-transplantation and are amenable to lentiviral genetic engineering (*PDX AML models). These models primarily originate from patients with highly aggressive, relapsed disease. Comprehensive genomic, transcriptomic, and epigenomic analyses confirmed that they replicate primary AML biology more faithfully than conventional cell lines. Notably, *PDX AML models include AML subgroups that are underrepresented or absent in existing model systems, such as cytogenetically normal or IDH1/2-mutant AML. They withstand freeze-thaw cycles, making them suitable for broad distribution and reproducibility across research institutions. Luciferase-based in vivo imaging enables real-time monitoring of tumor progression and treatment responses in preclinical trials. Surprisingly, long-term treatment, including repeated cytarabine therapy over a period of one year, showed a gradual reduction in leukemia cell proliferation, which decreased continuously after each treatment block. Collectively, our *PDX models represent a robust, versatile, and relevant platform that holds great promise to accelerate translational research for the benefit of cancer patients. Visual Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=123 SRC="FIGDIR/small/677299v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1ead31eorg.highwire.dtl.DTLVardef@1ca702corg.highwire.dtl.DTLVardef@18a7137org.highwire.dtl.DTLVardef@edbe6_HPS_FORMAT_FIGEXP M_FIG C_FIG Key PointsO_LIWe present new robust AML PDX models covering subgroups for which no cell lines exist for use in various ex vivo and in vivo applications. C_LIO_LI*PDX models enable serial transplantation, genetic engineering and better representation of primary AML biology than cell lines. C_LIO_LIOne-year in vivo trials mimicking clinical chemotherapy showed surprising gradual decline in leukemia growth after each treatment block. C_LI

cancer biology↗

bioRxiv2025

Pre-Analytical Drivers of Bias in Bead-Enriched Plasma Proteomics

Bead-based enrichment has emerged as a promising strategy to improve depth in plasma proteomics by overcoming the dynamic range barrier. However, its robustness against pre-analytical variation has not been sufficiently characterized. Here, we systematically evaluate five plasma proteomics workflows--including three bead-based methods and two conventional protocols--using controlled spike-ins of low-abundance proteins and defined cellular contaminants. We find that bead-based approaches enhance detection of low-abundance proteins but can be highly susceptible to systematic bias from platelet and PBMC contamination, even at low levels. This can easily inflate results by thousands of proteins, likely accounting for some of the very high literature-reported numbers. In contrast, a perchloric acid-based workflow shows notable resistance to erythrocyte and platelet-derived contamination. We further investigate how centrifugation conditions, anticoagulant choice, and buffer-bead combinations modulate contamination profiles and demonstrate that bias can partially be mitigated by optimized sample handling. In total, we identify more than 13,000 different protein groups from various conditions, including cellular components from the circulating proteome. Our results provide a quantitative framework for assessing workflow performance under variable sample quality and offer guidance for both biomarker discovery and quality control in clinical proteomics studies.

biochemistry↗

bioRxiv2018

Plasma proteome profiling to detect and avoid sample-related biases in biomarker studies

Plasma and serum are rich sources of information regarding an individuals health state and protein tests inform medical decision making. Despite major investments, few new biomarkers have reached the clinic. Mass spectrometry (MS)-based proteomics now allows highly specific and quantitative read-out of the plasma proteome. Here we employ Plasma Proteome Profiling to define contamination marker panels to assess plasma samples and the likelihood that suggested biomarkers are instead artifacts related to sample handling and processing. We acquire deep reference proteomes of erythrocytes, platelets, plasma and whole blood of 20 individuals (>6000 proteins), and compare serum and plasma proteomes. Based on spike-in experiments we determine contamination-associated proteins, many of which have been reported as biomarker candidates as revealed by a comprehensive literature survey. We provide sample preparation guidelines and an online resource (www.plasmaproteomeprofiling.org) to assess overall sample-related bias in clinical studies and to prevent costly miss-assignment of biomarker candidates.

epidemiology↗