Biology subjects

Mertens, M.

Publications and source records attributed to Mertens, M..

4 recordsLinked to original sources

bioRxiv2025

Who am I? Optimal Tissue for Germline Genetic Testing Post-Stem Cell Transplantation

With advancements in genetic diagnostics and genotype-based therapeutics, the demand for germline genetic testing in post-hematopoietic stem cell transplantation patients is increasing. Due to genetic chimerism, blood samples can no longer be used for germline testing after transplantation. This study aims to identify the most suitable tissue for germline analysis following stem cell transplantation by investigating alternative tissue sources. Buccal swab, eyebrow hair, and nail samples were analyzed for donor-derived DNA using next-generation sequencing and short tandem repeat analysis, with linear regression used for evaluation. Factors such as HLA match, transplantation type, sex, and time after transplantation were also evaluated for their effect on donor-derived DNA share. Buccal swab and nail samples exhibited 25% and 22% higher proportions of donor-derived DNA compared to eyebrow hair follicles, respectively. The median donor DNA share in eyebrow hair follicles was 1% for NGS and 3% for STR. Factors such as matched related donors, higher HLA match, different donor-recipient sex, and longer time post-transplantation correlated with lower donor DNA shares. Eyebrow hair follicles are a promising tissue for accurate germline genetic testing in post-SCT patients. Patient characteristics like donor relatedness, HLA match, sex match, and time after transplantation should be considered.

genetics↗

bioRxiv2020

CRISPAltRations: a validated cloud-based approach for interrogation of double-strand break repair mediated by CRISPR genome editing

CRISPR systems enable targeted genome editing in a wide variety of organisms by introducing single- or double-strand DNA breaks, which are repaired using endogenous molecular pathways. Characterization of on- and off-target editing events from CRISPR proteins can be evaluated using targeted genome resequencing. We characterized DNA repair footprints that result from non-homologous end joining (NHEJ) after double stranded breaks (DSBs) were introduced by Cas9 or Cas12a for >500 paired treatment/control experiments. We found that building our understanding into a novel analysis tool (CRISPAltRations) improved results quality. We validated our software using simulated rhAmpSeq amplicon sequencing data (11 gRNAs and 603 on- and off-target locations) and demonstrate that CRISPAltRations outperforms other publicly available software tools in accurately annotating CRISPR-associated indels and homology directed repair (HDR) events. We enable non-bioinformaticians to use CRISPAltRations by developing a web-accessible, cloud-hosted deployment, which allows rapid batch processing of samples in a graphical user-interface (GUI) and complies with HIPAA security standards. By ensuring that our software is thoroughly tested, version controlled, and supported with a UI we enable resequencing analysis of CRISPR genome editing experiments to researchers no matter their skill in bioinformatics.

bioinformatics↗

bioRxiv2019

Volume-wise analysis of fMRI time series allows accurate prediction of language lateralization

Using fMRI as a clinical tool, for example for lateralizing language, requires that it provides accurate results on the individual level. However, using a single voxel-wise activity map per patient limits how well the uncertainty associated with a decision can be estimated. Here, we explored how using a "volume-wise" analysis, where the lateralization of each time point of a patients fMRI session is evaluated independently, could support clinical decision making. Ninety-six patients with epilepsy who performed a language fMRI were analyzed retrospectively. Results from Wada testing were used as an indication of true language lateralization. Each patients 200 fMRI volumes were correlated with an independent template of prototypical lateralization. Depending on the strength of correlation with the template, each volume was classified as indicating either left-lateralized, bilateral or right-lateralized language. A decision about the patients language lateralization was then made based on how most volumes were classified. The results show that, using a simple majority vote, accuracies of 84% were reached in a sample of 63 patients with high-quality data. When 33 patients with datasets previously deemed inconclusive were added, the same accuracy was reached when more than 43% of a patients volumes were in agreement with each other. Increasing this cutoff to 51% volumes with agreeing classifications allowed for excluding all inconclusive cases and reaching accuracies over 90% for the remaining cases. Further increasing the cutoff to 65% agreeing volumes resulted in correct predictions for all remaining patients. The study confirms the usefulness of fMRI for language lateralization in patients with epilepsy, by demonstrating high accuracies. Furthermore, it illustrates how the diagnostic yield of individual volumes of fMRI data can be increased using simple similarity measures. The accuracy of our approach increased with the number of agreeing volumes, and thus allowed estimating the uncertainty associated with each individual diagnosis.

neuroscience↗

bioRxiv2019

Quantifying the Confidence in fMRI-based Language Lateralisation through Laterality Index Deconstruction

In epilepsy patients, language lateralisation is an important part of the presurgical diagnostic process. Using task-based fMRI, language lateralisation can be determined by visual inspection of activity patterns or by quantifying the difference in left- and right-hemisphere activity using variations of a basic formula [(L-R)/(L+R)]. However, the values of this laterality index (LI) depend on the choice of activity thresholds and regions of interest. The diagnostic utility of the LI also depends on how its continuous values are translated into categorical decisions about a patients language lateralisation. Here, we analysed fMRI data from 712 epilepsy patients who performed a verbal fluency task. Each fMRI data set was evaluated by a trained human rater as depicting left-sided, right-sided, or bilateral lateralisation or as being inconclusive. We used data-driven methods to define the activity thresholds and regions of interest used for LI computation and to define a classification scheme that allowed us to translate the LI values into categorical decisions. By deconstructing the LI into measures of laterality (L-R) and strength (L+R), we also modelled the relationship between activation strength and conclusiveness of a data set. In a held-out data set, predictions reached 91% correct when using only conclusive data and 82% when inconclusive data were included. Although only trained on human evaluations of fMRIs, the approach generalised to the prediction of language Wada test results, allowing for significant above-chance accuracies. Compared against different existing methods of LI-computation, our approach improved the identification of inconclusive cases and increased the accuracy with which decisions for the remaining data could be made. We discuss how this approach can support clinicians in assessing fMRI data on a single-case level, deciding whether lateralisation can be determined with sufficient certainty or whether additional information is needed.

neuroscience↗