Line image sensor-based colony fingerprinting system for rapid pathogenic bacteria identification.

The rapid identification of pathogenic bacteria is crucial across various industries, including food or beverage manufacturing. Bacterial microcolony image-based classification has emerged as a promising approach to expedite identification, automate inspections, and reduce costs. However, conventional imaging methods have significant practical limitations, namely low throughput caused by the limited imaging range and slow imaging speed. To address these challenges, we developed an imaging system based on a line image sensor for rapid and wide-field imaging compared to existing colony imaging methods. This system can image a standard Petri dish (92 mm in diameter) completely within 22 s, successfully acquiring bacterial microcolony images. This process yielded a set of discrimination parameters termed as colony fingerprints, which were employed for machine learning. We demonstrated the performance of our system by identifying Staphylococcus aureus in food products using a machine learning model trained on a colony fingerprint dataset of 15 species from 9 genera, including foodborne pathogens. While conventional mass spectrometry-based methods require 24 h of incubation, our colony fingerprinting approach achieved 96% accuracy in just 10 h of incubation. Line image sensor offer high imaging speeds and scalability, allowing for swift and straightforward microbiological testing, eliminating the need for specialized expertise and overcoming the limitations of conventional methods. This innovation marks a transformative shift in industrial applications.

Lensless imaging-based discrimination between tumour cells and blood cells towards circulating tumour cell cultivation.

Circulating tumour cells (CTCs) are recognized as important markers for cancer research. Nonetheless, the extreme rarity of CTCs in blood samples limits their availability for multiple characterization. The cultivation of CTCs is still technically challenging due to the lack of information of CTC proliferation, and it is difficult for conventional microscopy to monitor CTC cultivation owing to low throughput. In addition, for precise monitoring, CTCs need to be distinguished from the blood cells which co-exist with CTCs. Lensless imaging is an emerging technique to visualize micro-objects over a wide field of view, and has been applied for various cytometry analyses including blood tests. However, discrimination between tumour cells and blood cells was not well studied. In this study, we evaluated the potential of the lensless imaging system as a tool for monitoring CTC cultivation. Cell division of model tumour cells was examined using the lensless imaging system composed of a simple setup. Subsequently, we confirmed that tumour cells, JM cells (model lymphocytes), and erythrocytes exhibited cell line-specific patterns on the lensless images. After several discriminative parameters were extracted, discrimination between the tumour cells and other blood cells was demonstrated based on linear discriminant analysis. We also combined the highly efficient CTC recovery device, termed microcavity array, with the lensless-imaging to demonstrate recovery, monitoring and discrimination of the tumour cells spiked into whole blood samples. This study indicates that lensless imaging can be a powerful tool to investigate CTC proliferation and cultivation.