Biology subjects

Woehrer, A.

Publications and source records attributed to Woehrer, A..

3 recordsLinked to original sources

bioRxiv2025

Ultrasensitive saliva-based detection of early Alzheimer's disease biomarkers via nanoparticle-enhanced evanescent scattering microscopy

Alzheimers disease (AD) is the most common neurodegenerative disease, yet early diagnosis remains a major challenge. Current cerebrospinal fluid (CSF) assays are invasive and unsuitable for large-scale or repeated screening. Blood-based biomarkers have achieved sensitivities above 90%, but still face challenges of standardization, cost, technical complexity, and the need for sophisticated instrumentation. Saliva offers an attractive, non-invasive solution; however, the low concentrations of AD biomarkers have thus far hindered its clinical applicability. Here, we introduce a saliva-based diagnostic platform that combines Total Internal Reflection Scattering (TIRS) microscopy with antibody-functionalized metallic nanoparticles (NPs), for ultrasensitive, real-time quantification of salivary amyloid-{beta} (A{beta}) proteins. Using two established AD mouse models (APPsl and 5xFAD), we found that salivary A{beta}2 levels robustly distinguish transgenic from wild-type animals and correlate with brain amyloid deposition. Pooled and stratified analyses suggest these associations are primarily driven by the transgenic-wild-type contrast rather than linear changes within groups. Predictive modeling further confirmed diagnostic utility: in APPsl, Logistic Regression and Support Vector Machine (SVM) classifiers both achieved 92% accuracy with balanced sensitivity and specificity, while in 5xFAD, SVM reached 88% accuracy with perfect specificity. These results establish the NPs-enhanced TIRS sensor as a rapid, accurate and non-invasive tool for AD detection via saliva. By addressing a critical unmet need in neurodegenerative disease screening, this platform has strong potential to transform early diagnosis, enable timely interventions, and support biomarker-guided clinical trials. Its simplicity, speed, and scalability, make it well-suited for point-of-care diagnostics and large-scale screening initiatives. One Sentence SummaryUltrasensitive saliva test detects early Alzheimers biomarkers using nanoparticle-enhanced scattering microscopy

biochemistry↗

bioRxiv2025

TET CpG sequence context specific DNA demeth-ylation shapes progression of IDH-mutant gliomas

BackgroundTreatment decisions in IDH-mutant oligodendrogliomas are shaped by tumor aggressiveness, underscoring the need for objective grading of these malignant brain tumors. Material and MethodsWe collected 302 primary and recurrent resections from oligodendrogliomas and performed Ki-67 staining, proteomics and DNA methylation profiling. Results & conclusionDuring tumor progression, DNA methylation of oligodendrogliomas changed along a continuum. This continuum is linked to increased epigenetic aging, methylation of transcription factors and Ki-67+ cell density, and to large scale DNA demethylation. Demethylation was correlated with CpGs flanking sequences preferred by TET enzymes. We confirmed these findings in previously profiled astrocytomas, indicating IDH-mutant gliomas progress along a shared epigenetic axis. We developed an objective DNA methylation based prognostic continuous grading coefficient (CGC{psi}) that captured these changes and outperformed WHO grading for oligodendrogliomas. Our findings underscore the potential of DNA methylation-based grading to more accurately reflect tumor biology and inform clinical decision-making in IDH-mutant gliomas.

cancer biology↗

bioRxiv2024

Tissue clocks derived from histological signatures of biological aging enable tissue-specific aging predictions from blood

Aging, the leading risk factor for numerous diseases, manifests through diverse structural and architectural changes in human tissues, providing an opportunity to quantify and interpret tissue-specific aging. To address this, we present a comprehensive assessment of tissue changes occurring during human aging, utilizing a vast array of whole slide histopathological images from the Genotype-Tissue Expression Project (GTEx), primarily reflecting non-diseased tissue samples. Using deep learning, we analyzed 25,712 images from 40 distinct tissue types across 983 individuals, quantifying nuanced morphological changes that tissues undergo with age. We developed tissue clocks--predictors of biological age based on tissue images--which achieved a mean prediction error of 4.9 years. These clocks were associated with established aging markers, including telomere attrition, subclinical pathologies, and comorbidities. In a systematic assessment of biological age rates across organs, we identified pervasive non-uniform rates of aging across the human lifespan, with some organs exhibiting earlier changes (20-40 years old) and others showing bimodal patterns of age-related changes. We also uncovered several associations between demographic, lifestyle, and medical history factors and tissue-specific acceleration or deceleration of biological age, highlighting potential modifiable risk factors that influenced the aging process at the tissue level. Finally, by combining paired histological images and gene expression data, we developed a strategy to predict tissue-specific age gaps from blood samples. This approach was validated in independent cohorts covering eight diseases, ranging from acute conditions like stroke to chronic diseases such as cystic fibrosis and Alzheimers disease. It successfully recovered significant associations with disease-relevant organs and revealed patterns of systemic and tissue-specific aging that may reflect broader physiological changes in health and disease. This work offers a new perspective on the aging process by positioning tissue structure as an integrator of cellular and molecular changes that reflect the physiological state of organs in health and disease. It underscores the value of histopathological imaging as a tool for understanding human aging and provides a foundation for the monitoring of tissue-specific aging processes in age-associated diseases.

physiology↗