Biology subjects

Stanciu, S. G.

Publications and source records attributed to Stanciu, S. G..

3 recordsLinked to original sources

bioRxiv2024

A Coronaviral Pore-Replicase Complex Drives RNA Synthesis in Double Membrane Vesicles

Coronavirus-infected cells contain double-membrane vesicles (DMVs) that are key for viral RNA replication and transcription, perforated by hexameric pores connecting the vesicular lumen to the cytoplasm. How pores form and traverse two membranes, and how DMVs organize RNA synthesis, is unknown. Using structure prediction and functional assays, we show that the non-structural viral membrane protein nsp4 is the key DMV pore organizer, spanning the double membrane and forming most of the pore lining. Nsp4 interacts with nsp3 on the cytoplasmic side and with the viral replicase inside the DMV. Newly synthesized mRNAs exit the DMV into the cytoplasm, passing through a narrow ring of conserved nsp4 residues. Steric constraints imposed by the ring predict that modified nucleobases block mRNA transit, with broad spectrum anti-coronaviral activity.

biochemistry↗

bioRxiv2019

Investigating and Assessing the Dermoepidermal Junction with Multiphoton Microscopy and Deep Learning

Histopathological image analysis performed by a trained expert is currently regarded as the gold-standard in the case of many pathologies, including cancers. However, such approaches are laborious, time consuming and contain a risk for bias or human error. There is thus a clear need for faster, less intrusive and more accurate diagnostic solutions, requiring also minimal human intervention. Multiphoton Microscopy (MPM) can alleviate some of the drawbacks specific to traditional histopathology by exploiting various endogenous optical signals to provide virtual biopsies that reflect the architecture and composition of tissues, both in-vivo or ex-vivo. Here we show that MPM imaging of the dermoepidermal junction (DEJ) in unstained tissues provides useful cues for a histopathologist to identify the onset of non-melanoma skin cancers. Furthermore, we show that MPM images collected on the DEJ, besides being easy to interpret by a trained specialist, can be automatically classified into healthy and dysplastic classes with high precision using a Deep Learning method and existing pre-trained Convolutional Neural Networks. Our results suggest that Deep Learning enhanced MPM for in-vivo skin cancer screening could facilitate timely diagnosis and intervention, enabling thus more optimal therapeutic approaches.

pathology↗

bioRxiv2019

BIAFLOWS: A collaborative framework to benchmark bioimage analysis workflows

Automated image analysis has become key to extract quantitative information from scientific microscopy bioimages, but the methods involved are now often so refined that they can no longer be unambiguously described using written protocols. We introduce BIAFLOWS, a software tool with web services and a user interface specifically designed to document, interface, reproducibly deploy, and benchmark image analysis workflows. BIAFLOWS allows image analysis workflows to be compared fairly and shared in a reproducible manner, safeguarding research results and promoting the highest quality standards in bioimage analysis. A curated instance of BIAFLOWS is available online; it is currently populated with 34 workflows that can be triggered to process image datasets illustrating 15 common bioimage analysis problems organized in 9 major classes. As a complete case study, the open benchmarking of 7 nuclei segmentation workflows, including classical and deep learning techniques, was performed on this online instance. All the results presented can be reproduced online.

bioinformatics↗