Hada, A. | bioRxiv Science

bioRxiv2025

Searching for Acrolein-Scavenging Compounds in Plants: Isoalliin from Onion as an Excellent Scavenger

Reactive carbonyl species (RCS), such as acrolein (Acr), are generated through the degradation of lipid peroxides and exert cytotoxic effects. To identify natural RCS scavengers, we examined 80% ethanol extracts from 46 angiosperm species for Acr-trapping activity using an HPLC-based assay. Strong activities were observed in several taxa, including garlic, spinach, avocado, broccoli, and lotus. In garlic, the active metabolite was identified as S-allyl-L-cysteine sulfoxide (alliin), a characteristic Allium amino acid. Alliin and its S-(1E)-propenyl and S-methyl derivatives (isoalliin and methiin, respectively) trapped up to two Acr molecules at the amino group and exhibited higher activities than known scavengers such as carnosine and epigallocatechin gallate. These findings highlight S-alk(en)yl-L-cysteine sulfoxides as potent secondary antioxidants and suggest that structurally diverse RCS scavengers remain to be discovered in plants.

plant biology↗

bioRxiv2024

micronuclAI: Automated quantification of micronuclei for assessment of chromosomal instability.

Chromosomal instability (CIN) is a hallmark of cancer that drives metastasis, immune evasion and treatment resistance. CIN results from chromosome mis-segregation events during anaphase, as excessive chromatin is packaged in micronuclei (MN), that can be enumerated to quantify CIN. Despite recent advancements in automation through computer vision and machine learning, the assessment of CIN remains a predominantly manual and time-consuming task, thus hampering important work in the field. Here, we present micronuclAI, a novel pipeline for automated and reliable quantification of MN of varying size, morphology and location from DNA-only stained images. In micronucleAI, single-cell crops are extracted from high-resolution microscopy images with the help of segmentation masks, which are then used to train a convolutional neural network (CNN) to output the number of MN associated with each cell. The pipeline was evaluated against manual single-cell level counts by experts and against routinely used MN ratio within the complete image. The classifier was able to achieve a weighted F1 score of 0.937 on the test dataset and the complete pipeline can achieve close to human-level performance on various datasets derived from multiple human and murine cancer cell lines. The pipeline achieved a root-mean-square deviation (RMSE) value of 0.0041, an R2 of 0.87 and a Pearsons correlation of 0.938 on images obtained at 10X magnification. We tested the approach in otherwise isogenic cell lines in which we genetically dialed up or down CIN rates, and also on a publicly available image data set (obtained at 100X) and achieved an RMSE value of 0.0159, an R2 of 0.90, and a Pearsons correlation of 0.951. Given the increasing interest in developing therapies for CIN-driven cancers, this method provides an important, scalable, and rapid approach to quantifying CIN on routinely obtained images. We release a GUI-implementation for easy access and utilization of the pipeline.

systems biology↗