Fluorescence time-lapse microscopy with automatic cell detection

Bibliographic Details
Title: Fluorescence time-lapse microscopy with automatic cell detection
Authors: Ulderico Wanderlingh, Rosa Musotto, Angela D’Ascola, Giovanni Pioggia, Sebastiano Vasi
Source: Review of Scientific Instruments. 96
Publisher Information: AIP Publishing, 2025.
Publication Year: 2025
Subject Terms: Fluorescence microscopy, Automation, Microscopy, Fluorescence, Application programming interfaces (API), Calcium, Error detection, Fluorescence, Molecular biology, Open source software, Open systems, Astrocytes, Image Processing, Computer-Assisted, Animals, Humans, Time-Lapse Imaging, Software
Description: Fluorescence microscopy is an indispensable tool in several scientific research fields, such as biology, medicine, and physics. This study presents a low-cost Raspberry Pi-based epifluorescence microscopy system optimized for capturing both fast and slow cellular processes, enabling detailed observation of cellular dynamics and molecular interactions. The system integrates advanced hardware and open-source software to facilitate the acquisition of high-fidelity technical images leveraging on the Picamera2 API for RAW data numerical acquisition. A novel approach, based on Numpy Python, for automatic peak identification in fluorescence images is implemented, allowing for efficient detection of regions of interest in cellular cultures. Additionally, the system enables precise extraction of the temporal evolution of calcium signals in in-vitro cultured astrocytes using a fluorophore. By combining affordability with advanced imaging and analysis capabilities, this system offers a robust solution for real-time cellular biophysical research, accessible to a wider range of laboratories.
Document Type: Article
File Description: application/pdf
Language: English
ISSN: 1089-7623
0034-6748
DOI: 10.1063/5.0262424
Access URL: https://pubmed.ncbi.nlm.nih.gov/40801787
https://hdl.handle.net/11570/3343950
https://doi.org/10.1063/5.0262424
Rights: CC 0
Accession Number: edsair.doi.dedup.....401e68f0ec6e742f7e0ee62f55afdb1a
Database: OpenAIRE
Description
ISSN:10897623
00346748
DOI:10.1063/5.0262424