Fiber array-based large spot confocal Raman system for rapid in situ detection of pathogenic bacterial colonies.

Pathogenic bacteria infections are a major public health problem in current society. Rapid and reliable identification of these pathogens can help avoid the misuse of antibiotics and enable precision therapy. In this study, we present a large-spot confocal Raman system based on fiber array (LSCR-FA) for the in situ detection of microbial colonies on agar plates.

Siamese network for classification of Raman spectroscopy with inter-instrument variation for biological applications.

Raman spectroscopy has emerged as a highly sensitive, rapid, and label-free detection method, extensively utilized in biological research. Presently, it is frequently paired with artificial intelligence (AI) algorithms to facilitate identification and classification tasks. However, variations in the settings across different Raman spectrometers, along with the sensitive and continuous nature of biological Raman signals, can subtly alter the acquisition of these signals.

Raman cell sorting for single-cell research.

Cells constitute the fundamental units of living organisms. Investigating individual differences at the single-cell level facilitates an understanding of cell differentiation, development, gene expression, and cellular characteristics, unveiling the underlying laws governing life activities in depth. In recent years, the integration of single-cell manipulation and recognition technologies into detection and sorting systems has emerged as a powerful tool for advancing single-cell research.

Precision isolation and cultivation of single cells by vortex and flat-top laser ejection.

Single-cell isolation stands as a critical step in single-cell studies, and single-cell ejection technology based on laser induced forward transfer technology (LIFT) is considered one of the most promising methods in this regard for its ability of visible isolating single cell from complex samples. In this study, we improve the LIFT technology and introduce optical vortex laser-induced forward transfer (OV-LIFT) and flat-top laser-induced forward transfer (FT-LIFT) by utilizing spatial light modulator (SLM), aiming to enhance the precision of single-cell sorting and the cell's viability after ejection. Experimental results demonstrate that applying vortex and flat-top beams during the sorting and collection process enables precise retrieval of single cells within diameter ranges of 50 μm and 100 μm, respectively.

Stable SERS Detection of Lactobacillus fermentum Using Optical Tweezers in a Microfluidic Environment.

Rapid identification of fermented lactic acid bacteria has long been a challenge in the brewing industry. This study combined label-free surface-enhanced Raman scattering (SERS) and optical tweezer technology to construct a test platform within a microfluidic environment. Six kinds of lactic acid bacteria common in industry were tested to prove the stability of the SERS spectra.

Deep learning-based ultra-fast identification of Raman spectra with low signal-to-noise ratio.

Ensuring the correct use of cell lines is crucial to obtaining reliable experimental results and avoiding unnecessary waste of resources. Raman spectroscopy has been confirmed to be able to identify cell lines, but the collection time is usually 10-30 s. In this study, we acquired Raman spectra of five cell lines with integration times of 0.

Building an ensemble learning model for gastric cancer cell line classification via rapid raman spectroscopy.

Cell misuse and cross-contamination can affect the accuracy of cell research results and result in wasted time, manpower and material resources. Thus, cell line identification is important and necessary. At present, the commonly used cell line identification methods need cell staining and culturing.

Classification of pathogenic bacteria by Raman spectroscopy combined with variational auto-encoder and deep learning.

Rapid and early identification of pathogens is critical to guide antibiotic therapy. Raman spectroscopy as a noninvasive diagnostic technique provides rapid and accurate detection of pathogens. Raman spectrum of single cells serves as the "fingerprint" of the cell, revealing its metabolic characteristics.

Multi-point scanning confocal Raman spectroscopy for accurate identification of microorganisms at the single-cell level.

Raman spectroscopy has been widely used for microbial analysis due to its exceptional qualities as a rapid, simple, non-invasive, reproducible, and real-time monitoring tool. The Raman spectrum of a cell is a superposition of the spectral information of all biochemical components in the laser focus. In the case where the microbial size is larger than the laser spot size, the Raman spectrum measured from a single-point within a cell cannot capture all biochemical information due to the spatial heterogeneity of microorganisms.

Rapid detection of beer spoilage bacteria based on label-free SERS technology.

Beer spoilage bacteria have been a headache for major breweries. In order to rapidly identify spoilage bacteria and improve the sensitivity and signal-to-noise ratio of bacterial SERS detection, the label-free SERS technique was used as a starting point, and we found eight bacteria species that led to beer spoilage. The impact of AgNP concentration and AgNP and bacterial binding time on the final results were thoroughly investigated.

Laser induced forward transfer isolating complex-shaped cell by beam shaping.

Beam shaping techniques have been widely used in holographic optical tweezers to accurately manipulate tiny particles and hologram optimization algorithms have also been widely reported to improve the optical trapping performance. In this paper, we presented a beam shaping laser induced forward transfer (BS-LIFT) technique to isolate complex-shaped cells. To do this, we built up a BS-LIFT instrument which combined beam shaping methods and laser induced forward transfer using liquid-crystal-on-silicon spatial light modulator.