Kaya, A. N. | bioRxiv Science

bioRxiv2025

Extraction method dominates phytochemical profiles of aromatic medicinal herbs: a comparative HPTLC study

The extraction method is a critical determinant of the phytochemical profile obtained from medicinal plants, yet comparative evaluations across multiple extraction techniques and plant species remain limited. In this study, the qualitative influence of commonly used extraction methods on the phytochemical composition of aromatic medicinal herbs was investigated using high-performance thin-layer chromatography (HPTLC). Ten aromatic medicinal herbs belonging to the families Lamiaceae, Apiaceae, and Asteraceae were subjected to six extraction approaches: simple distillation, essential oil (essence) extraction, hydroalcoholic tincture, infusion, decoction, and decoction of distillate. All extracts were analyzed under standardized HPTLC conditions using silica gel 60 F254 plates, a toluene-ethyl acetate (93:7 v/v) mobile phase, anisaldehyde-sulfuric acid derivatization, and a standard as a reference compound. Across all investigated species, the extraction method exerted a stronger influence on band number, intensity, and polarity distribution than botanical differences among the herbs. Essential oil and tincture extracts consistently produced the highest diversity of bands, particularly in higher Rf regions corresponding to non-polar and semi-polar compounds. Aqueous methods, including infusion and decoction, predominantly yielded low-Rf polar bands with reduced overall diversity, while simple distillation showed minimal extraction efficiency. These findings demonstrate that extraction strategy is the primary factor governing qualitative phytochemical outcomes in HPTLC-based herbal analysis. The results highlight the importance of method selection in phytochemical screening and provide a comparative framework for choosing appropriate extraction techniques in pharmacological, cosmetic, and analytical applications.

pharmacology and toxicology↗

bioRxiv2021

Obtaining Spatially Resolved Tumor Purity Maps Using Deep Multiple Instance Learning In A Pan-cancer Study

Tumor purity is the proportion of cancer cells in the tumor tissue. An accurate tumor purity estimation is crucial for accurate pathologic evaluation and for sample selection to minimize normal cell contamination in high throughput genomic analysis. We developed a novel deep multiple instance learning model predicting tumor purity from H&E stained digital histopathology slides. Our model successfully predicted tumor purity from slides of fresh-frozen sections in eight different TCGA cohorts and formalin-fixed paraffin-embedded sections in a local Singapore cohort. The predictions were highly consistent with genomic tumor purity values, which were inferred from genomic data and accepted as the golden standard. Besides, we obtained spatially resolved tumor purity maps and showed that tumor purity varies spatially within a sample. Our analyses on tumor purity maps also suggested that pathologists might have chosen high tumor content regions inside the slides during tumor purity estimation in the TCGA cohorts, which resulted in higher values than genomic tumor purity values. In short, our model can be utilized for high throughput sample selection for genomic analysis, which will help reduce pathologists workload and decrease inter-observer variability. Moreover, spatial tumor purity maps can help better understand the tumor microenvironment as a key determinant in tumor formation and therapeutic response.

cancer biology↗