Biology subjects

Thiel, K.

Publications and source records attributed to Thiel, K..

5 recordsLinked to original sources

bioRxiv2025

ASSET: A Framework for Decoding Aptamer Specificity of an Enriched Library by Next-Generation Sequencing of Experimental Samples

The SELEX process to identify RNA and DNA aptamers relies on sequencing selection rounds to detect highly specific aptamers through patterns of aptamer accumulation or enrichment. However, this approach infers rather than quantify aptamer specificity. Here we present a novel strategy for directly quantifying aptamer specificity within enriched libraries termed Aptamer Specificity Sequencing for Efficient Targeting (ASSET). The ASSET framework takes experimental samples and replicates testing the specificity of an aptamer library and prepares them for next-generation sequencing (NGS) with a known internal reference sequence. This enables robust data normalization, calculation of aptamer specificity scores with statistical significance, and the creation of specificity profiles of individual aptamers across multiple targets and non-targets. By integrating ASSET specificity scores with conventional selection round sequencing data, aptamers can be easily classified as true or false positives and negatives, allowing for easy separation of true positive aptamers. Compared to conventional methods for identifying aptamer candidates, such as measuring abundance or enrichment, ASSET specificity scores show a strong correlation with experimentally measured specificity. This supports ASSET as a more effective metric for selecting lead candidates following SELEX. ASSET is an easily implemented framework that accelerates the identification of highly specific aptamers, thereby expediting aptamer discovery for therapeutic and diagnostic applications.

molecular biology↗

bioRxiv2024

Brain Structural Correlates of an Impending Initial Major Depressive Episode

BackgroundNeuroimaging research has yet to elucidate, whether reported gray matter volume (GMV) alterations in major depressive disorder (MDD) exist already before the onset of the first episode. Recruitment of presently healthy individuals with a known future transition to MDD (converters) is extremely challenging but crucial to gain insights into neurobiological vulnerability. Hence, we compared converters to patients with MDD and sustained healthy controls (HC) to distinguish pre-existing neurobiological markers from those emerging later in the course of depression. MethodsCombining two clinical cohorts (n=1709), voxel-wise GMV of n=45 converters, n=748 patients with MDD, and n=916 HC were analyzed in regions-of-interest approaches. By contrasting the subgroups and considering both remission state and reported recurrence at a 2-year clinical follow-up, we stepwise disentangled effects of 1) vulnerability, 2) the acute depressive state, and 3) an initial vs. a recurrent episode. ResultsAnalyses revealed higher amygdala GMV in converters relative to HC (pTFCE-FWE=.037, d=0.447) and patients (pTFCE-FWE=.005, d=0.508), remaining significant when compared to remitted patients with imminent recurrence. Lower GMV in the dorsolateral prefrontal cortex (pTFCE-FWE<.001, d=0.188) and insula (pTFCE-FWE=.010, d=0.186) emerged in patients relative to HC but not to converters, driven by patients with acute MDD. ConclusionBy examining one of the largest available converter samples in psychiatric neuroimaging, this study allowed a first determination of neural markers for an impending initial depressive episode. Our findings suggest a temporary vulnerability, which in combination with other common risk factors might facilitate prediction and in turn improve prevention of depression.

neuroscience↗

bioRxiv2023

Cross-validation for the estimation of effect size generalizability in mass-univariate brain-wide association studies

IntroductionStatistical effect sizes are systematically overestimated in small samples, leading to poor generalizability and replicability of findings in all areas of research. Due to the large number of variables, this is particularly problematic in neuroimaging research. While cross-validation is frequently used in multivariate machine learning approaches to assess model generalizability and replicability, the benefits for mass-univariate brain analysis are yet unclear. We investigated the impact of cross-validation on effect size estimation in univariate voxel-based brain-wide associations, using body mass index (BMI) as an exemplary predictor. MethodsA total of n=3401 adults were pooled from three independent cohorts. Brain-wide associations between BMI and gray matter structure were tested using a standard linear mass-univariate voxel-based approach. First, a traditional non-cross-validated analysis was conducted to identify brain-wide effect sizes in the total sample (as an estimate of a realistic reference effect size). The impact of sample size (bootstrapped samples ranging from n=25 to n=3401) and cross-validation on effect size estimates was investigated across selected voxels with differing underlying effect sizes (including the brain-wide lowest effect size). Linear effects were estimated within training sets and then applied to unseen test set data, using 5-fold cross-validation. Resulting effect sizes (explained variance) were investigated. ResultsAnalysis in the total sample (n=3401) without cross-validation yielded mainly negative correlations between BMI and gray matter density with a maximum effect size of R2p=.036 (peak voxel in the cerebellum). Effects were overestimated exponentially with decreasing sample size, with effect sizes up to R2p=.535 in samples of n=25 for the voxel with the brain-wide largest effect and up to R2p=.429 for the voxel with the brain-wide smallest effect. When applying cross-validation, linear effects estimated in small samples did not generalize to an independent test set. For the largest brain-wide effect a minimum sample size of n=100 was required to start generalizing (explained variance >0 in unseen data), while n=400 were needed for smaller effects of R2p =.005 to generalize. For a voxel with an underlying null effect, linear effects found in non-cross-validated samples did not generalize to test sets even with the maximum sample size of n=3401. Effect size estimates obtained with and without cross-validation approached convergence in large samples. DiscussionCross-validation is a useful method to counteract the overestimation of effect size particularly in small samples and to assess the generalizability of effects. Train and test set effect sizes converge in large samples which likely reflects a good generalizability for models in such samples. While linear effects start generalizing to unseen data in samples of n>100 for large effect sizes, the generalization of smaller effects requires larger samples (n>400). Cross-validation should be applied in voxel-based mass-univariate analysis to foster accurate effect size estimation and improve replicability of neuroimaging findings. We provide open-source python code for this purpose (https://osf.io/cy7fp/?view_only=a10fd0ee7b914f50820b5265f65f0cdb).

neuroscience↗

bioRxiv2022

Genetic mapping of craniofacial traits in the Mexican tetra reveals loci associated with bite differences between cave and surface fish

The Mexican tetra, Astyanax mexicanus, includes interfertile surface-dwelling and cave-dwelling morphs, enabling powerful studies aimed at uncovering genes involved in the evolution of cave-associated traits. Compared to surface fish, cavefish harbor several extreme traits within their skull, such as a protruding lower jaw, a wider gape, and an increase in tooth number. These features are highly variable between individual cavefish and even across different cavefish populations. To investigate these traits, we created a novel feeding behavior assay wherein bite impressions could be obtained. We determined that fish with an underbite leave larger bite impressions with an increase in the number of tooth marks. Capitalizing on the ability to produce hybrids from surface and cavefish crosses, we investigated genes underlying these segregating orofacial traits by performing Quantitative Trait Loci (QTL) analysis with F2 hybrids. We discovered significant QTL for bite (underbite vs. overbite) that mapped to a single region of the Astyanax genome. This work highlights cavefish as a valuable genetic model for orofacial patterning and will provide insight into the genetic regulators of jaw and tooth development.

evolutionary biology↗

bioRxiv2019

Short-term oxaliplatin exposure according to established hyperthermic intraperitoneal chemotherapy (HIPEC) protocols lacks effectiveness in vitro and ex vivo

Cytotoxicity of oxaliplatin-containing solutions (OCS), sampled during patient treatment with hyperthermic intraperitoneal chemotherapy (HIPEC), was assessed by well-established continuous impedance-based real-time cell analysis (RTCA) ex vivo. HIPEC treatment was replicated by exposing OAW-42 cancer cells to OCS for 30 or 60 minutes at 42 {degrees}C. In contrast to previous observations with continuous exposure, where cytotoxicity was proven, identical OCS obtained during HIPEC did not induce cell death reproducibly and showed strongly attenuated effects after only 30 minutes of application. Based on these unexpected findings, spike-ins of oxaliplatin (OX) into peritoneal dialysis solution (PDS) or dextrose 5 % in water (D5W) were used to replicate HIPEC conditions, as used in either our own protocols or the recently presented randomized controlled PRODIGE 7 trial, where OX HIPEC for 30 minutes failed to produce survival benefits in colorectal carcinoma patients. With OX-spiked into D5W or PDS at identical concentrations as used for PRODIGE 7 or conforming with own HIPEC protocols, we did not observe the expectable cytotoxic effects in RTCA, after replicating OX HIPEC for 30 minutes. These results were corroborated for both solvents at relevant drug concentrations by classical end-point assays for cytotoxicity in two cancer cell lines. Further results suggest that penetration depth, drug dosage, exposure time and drug solvents may constitute critical factors for HIPEC effectiveness. Accordingly, we witnessed substantial cell shrinkage with both PDS and D5W, potentially contributing to reduced drug effects. Based on these results, intensified pharmacological research seems warranted to establish effective HIPEC protocols.\n\nKey PointsO_LIOxaliplatin (OX)-containing solutions obtained during patient treatment with Hyperthermic intraperitoneal chemotherapy (HIPEC) unexpectedly showed low cytotoxicity in an impedance-based ex vivo cytotoxicity cell assay.\nC_LIO_LIOX cytotoxicity under HIPEC conditions could be enhanced by extending drug exposure to one hour by an impedance-based ex vivo cytotoxicity cell assay.\nC_LIO_LIHIPEC failed to show survival benefits in the randomized controlled PRODIGE 7 trial and was questioned in the aftermath.\nC_LIO_LIClinically relevant OX concentrations applied in conjunction with hyperthermia (42 {degrees}C) for 30 minutes, as used either at our own medical center or according to the PRODIGE 7 trial, proved predominantly ineffective, when used according to HIPEC routines in an impedance-based in vitro cytotoxicity cell assay.\nC_LIO_LIRespective findings were corroborated in two different cell lines and by two established end-point assays, showing that 50 % cell death could not be reached by the same HIPEC treatment with OX, in contrast to continuous drug exposure.\nC_LIO_LIAs potentially relevant factor, the thickness of the exposed cell layer was identified, requiring at least ~100 {micro}m penetration depth for our model to indicate effectiveness.\nC_LIO_LIAdditionally, we show relevant cell shrinkage by two drug diluents used either at our own medical center or according to the PRODIGE 7 trial, potentially associated with fluid shifts out of the cell and impaired drug effects.\nC_LIO_LIOur own as well as recent findings by Ubink et al. (Br J Surg. 2019. doi: 10.1002/bjs.11206) support the notion that lacking effectiveness of OX HIPEC may explain the negative PRODIGE 7 trial results.\nC_LI

pharmacology and toxicology↗