Biology subjects

Parlatan, U.

Publications and source records attributed to Parlatan, U..

8 recordsLinked to original sources

bioRxiv2026

Glio-SERS: Label-Free Molecular Profiling of Plasma Extracellular Vesicles in Brain Tumors Using SERS and Artificial Intelligence

Extracellular vesicles are increasingly recognized as important carriers of disease-associated molecular information, yet robust methods for their isolation and molecular characterization from limited clinical samples remain challenging. Here, we present an integrated approach combining standardized EV isolation, label-free Surface-Enhanced Raman Spectroscopy (SERS), and artificial intelligence (AI) for comprehensive molecular profiling of small extracellular vesicles (sEVs) from human plasma. Here, we show systematically isolated and characterized plasma sEVs using ExoTIC in accordance with MISEV2023 guidelines, with SERS analysis revealing quantifiable spectral differences across samples from patients with glioblastoma (n=20) and meningioma (n=23) compared to healthy controls (n=30). Among the evaluated AI models, the convolutional neural network most effectively captured group-level spectral differences in sEVs, achieving accuracies up to 88% in this pilot cohort. Further, an EGFR-based spectral regression model was explored to examine molecular variability across sEV samples. Parallel proteomic analysis presented statistically significant differences in several proteins elevated in glioblastoma or meningioma. This label-free, rapid approach provides a proof-of-concept framework for sEV molecular profiling establishing the basis for broad validation studies across diverse diseases.

bioengineering↗

bioRxiv2026

Analysis of Plasma Extracellular Vesicles in Normal-Weight and Overweight Type 2 Diabetes Using Multimodal SERS and RNA-Seq

AimsTo characterize subtype-associated heterogeneity in type 2 diabetes mellitus (T2DM), particularly normal-weight diabetes, using extracellular vesicle (EV)-associated molecular features in a clinically stratified cohort. MethodsEVs were isolated from plasma using ExoTIC and validated by transmission electron microscopy, nanoparticle tracking analysis, flow cytometry, and Western blotting. EVs from Asian normal-weight (A-NWD), Asian overweight (A-OWD), Non-Hispanic White normal-weight (W-NWD), and Non-Hispanic White overweight (W-OWD) T2DM patients were analyzed by multimodal surface-enhanced Raman spectroscopy (SERS; n=65) and EV-RNA sequencing (n=39). ResultsSERS identified subgroup-associated spectral fingerprints that distinguished the four BMI- and race/ethnicity-defined groups in this cohort. EV-RNA sequencing revealed differential microRNA expression across subgroups, with higher miR-208a and miR-132 in A-OWD and higher miR-484 in A-NWD. Unsupervised analyses also showed partially overlapping EV-associated molecular features between A-NWD and W-OWD, suggesting that BMI-based subgrouping alone may not fully capture shared metabolic states. ConclusionsMultimodal EV profiling identified subgroup-associated spectral and miRNA features in clinically stratified T2DM and provides a framework for studying diabetes heterogeneity, including molecular patterns associated with normal-weight diabetes.

bioengineering↗

bioRxiv2025

Broad-Spectrum HIV-1 Detection and Neutralization via Multivalent Designer DNA Nanostructures

Early and accurate detection of HIV-1 remains a critical unmet need, particularly during the acute phase of infection when viral loads are low and transmission risk is highest. Here, we report a modular diagnostic and antiviral platform based on designer DNA nanostructures engineered for high-affinity recognition of the HIV-1 envelope glycoprotein (GP120). A custom DNA aptamer, termed HINT, was developed to bind GP120 across major HIV-1 subtypes (Groups M and P; subtypes A and B) with nanomolar affinity. To amplify binding strength, HINT aptamers were spatially patterned onto a net-shaped DNA nanostructure (DNA-NetHINT) that geometrically matches the trimeric GP120 spikes on the viral surface. Using multivalent interactions, the nanostructure enabled up to 104-fold improvement in binding affinity (sub-picomolar KD), confirmed by surface plasmon resonance. Integration of DNA-NetHINT into a paper-based lateral flow assay produced a low-cost, saliva-compatible self-testing device capable of detecting intact HIV-1 virions at concentrations as low as 328 viral copies per test, outperforming commercial fourth-generation rapid diagnostic tests. In addition to its diagnostic capabilities, the DNA-NetHINT construct exhibited potent antiviral activity, reducing pseudovirus infection with an EC50 of [~]1.8 nM, nearly 1,000-fold more effective than free aptamers. This work demonstrates a dual-function DNA nanotechnology platform that enables both ultrasensitive HIV-1 detection and entry inhibition. The approach is broadly applicable to other enveloped viruses and represents a promising step toward next-generation molecular theranostics for infectious disease management.

bioengineering↗

bioRxiv2025

SHINE: SERS-based Hepatotoxicity detection using Inference from Nanoscale Extracellular vesicle content

Extracellular vesicles (EV) are becoming crucial tools in liquid biopsy, diagnostics, and therapeutic applications, yet their nanoscale characterization remains challenging. In this context, the detection of drug-induced liver injury, i.e., hepatotoxicity, through extracellular vesicle molecular content remains an unexplored frontier. To this end, we present a label-free surface-enhanced Raman (SERS) spectroscopy approach, which provides rapid EV content analysis under ten minutes and requires only 1.3 microliters of sample. Using hepatic cultures as a model, our platform captures distinct and reproducible EV molecular changes in response to acetaminophen-induced hepatoxicity. Our platform achieves exceptional accuracy with root mean squared error values as low as 3.80%, establishing strong correlations between EV spectra and conventional toxicity biomarkers. Unlike previous EV-SERS studies limited to vesicle identification and disease markers, this approach reveals EV drug-response signatures strongly correlated with conventional toxicity markers. These findings establish EVs as dynamic reporters of cellular drug responses and demonstrate use of SERS-based EV detection of hepatotoxicity.

biophysics↗

bioRxiv2023

Label-free differentiation of functional zones in mature mouse placenta using micro-Raman imaging

In histopathology, it is highly crucial to have chemical and structural information about tissues. Additionally, the segmentation of zones within a tissue plays an important role in investigating the functions of these regions for better diagnosis and treatment. The placenta plays an important role in embryonic and fetal development and in the diagnosis of some diseases associated with its dysfunction. This study provides a label-free approach to obtain the images of mature mouse placenta together with the chemical differences between the tissue compartments using Raman spectroscopy. To generate the Raman images, spectra of placental tissue were collected using a custom-built optical setup. The pre-processed spectra were analyzed using statistical and machine learning methods to acquire the Raman maps. We found that the placental regions called decidua and the labyrinth zone are biochemically distinct from the junctional zone. Comparison and evaluation of the Raman maps with histological images of the placental tissue were performed by a histologist and they are found to be in agreement. The results of this study show that Raman spectroscopy offers the possibility of label-free monitoring of the placental tissue from mature mice while revealing important structural information about the zones at the same time.

biophysics↗

bioRxiv2022

Comparison of the human's and camel's erythrocyte deformability by optical tweezers and Raman spectroscopy

The evolution of red blood cells (RBCs) or erythrocytes has led to variation in morphological and mechanical properties of these cells among many species today. Camelids have the most different RBC characteristics among the vertebrates. As a result of adaptation to the desert environment, camelid RBCs can expand twice as much of their total volume in the case of rapid hydration yet are almost undeformable under mechanical stress. In this work, the difference between cell features of the human and the camelid species was explored both mechanically and chemically with optical tweezers and Raman spectroscopy, respectively. We measured the deformability of camel RBCs relative to the human RBCs at the single-cell level using optical tweezers. We found that the deformability index (DI) of the camel and the human RBCs were 0.024{+/-}0.0188 and 0.215{+/-}0.061, respectively. Raman spectral analysis of the whole blood of these two species indicated that some of the Raman peaks observed on the camels blood spectrum were absent on the human bloods spectrum, which further points to the difference in chemical contents of these two species.

biophysics↗

bioRxiv2022

Quantification of salt stress in wheat leaves by Raman spectroscopy and machine learning

The salinity level of the growing medium has diverse effects on the development of plants, including both physical and biochemical changes. To determine the salt stress level of a plant endures, one can measure these structural and chemical changes. Raman spectroscopy and biochemical analysis are some of the most common techniques in the literature. Here, we present a combination of machine learning and Raman spectroscopy with which we can both find out the biochemical change that occurs while the medium salt concentration changes and predict the level of salt stress a wheat sample experiences accurately using our trained regression models. In addition, by applying different machine learning algorithms, we compare the level of success for different algorithms and determine the best method to use in this application. Production units can take actions based on the quantitative information they get from the trained machine learning models related to salt stress, which can potentially increase efficiency and avoid the loss of crops.

biophysics↗

bioRxiv2022

Raman Signal Denoising Using Fully Convolutional Encoder Decoder Network

Raman spectroscopy is a vibrational method that gives molecular information rapidly and non-invasively. Despite its advantages, the weak intensity of Raman spectroscopy leads to low-quality signals, particularly with tissue samples. The requirement of high exposure times makes Raman a time-consuming process and diminishes its non-invasive property while studying living tissues. Novel denoising techniques using convolutional neural networks (CNN) have achieved remarkable results in image processing. Here, we propose a similar approach for noise reduction for the Raman spectra acquired with 10x lower exposure times. In this work, we developed fully convolutional encoder-decoder architecture (FCED) and trained them with noisy Raman signals. The results demonstrate that our model is superior (p-value < 0.0001) to the conventional denoising techniques such as the Savitzky-Golay filter and wavelet denoising. Improvement in the signal-to-noise ratio values ranges from 20% to 80%, depending on the initial signal-to-noise ratio. Thus, we proved that tissue analysis could be done in a shorter time without any need for instrumental enhancement.

biochemistry↗