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. The recovery rates of Saccharomyces cerevisiae and Escherichia coli DH5α single cell ejected by vortex beam are 89 and 78%, by flat-top beam are 85 and 57%. When employing Gaussian beam sorting, the receiving range extends to 400 µm, with cultivation success rates of S. cerevisiae and E. coli DH5α single cell are 48 and 19%, respectively. This marks the first application of different mode beams in the ejection and cultivation of single cells, providing a novel and effective approach for the precise isolation and improving the viability of single cells.

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 0.1 and 8 s, respectively, and the average accuracy of using long-short memory neural network to identify the spectra of 0.1 s was 95%, and the average accuracy of identifying the spectra of 8 s was 99.8%. At the same time, we performed data enhancement of 0.1 s spectral data by real-valued non-volume preserving method, and the recognition average accuracy of long-short memory neural networks recognition of the enhanced spectral data was improved to 96.2%. With this method, we shorten the acquisition time of Raman spectra to 1/80 of the original one, which greatly improves the efficiency of cell identification.

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. The results demonstrated that the utilization of optical tweezers to securely hold the bacteria significantly enhanced the stability of the SERS spectra. Furthermore, SVM and XGBoost machine learning algorithms were utilized to analyze the obtained Raman spectra for identification, and the identification accuracies exceeded 95% for all tested lactic acid bacteria. The findings of this study highlight the crucial role of optical tweezers in improving the stability of SERS spectra by capturing bacteria in a microfluidic environment, prove that this technology could be used in the rapid identification of lactic acid bacteria, and show great significance in expanding the applicability of the SERS technique for other bacterial testing purposes.

Climax forest has a higher soil bacterial diversity but lower soil nutrient contents than degraded forests in temperate northern China.

Bacteria are a crucial component of forest soil biodiversity and play an important role in numerous ecosystem processes. Here, we studied the patterns of soil bacterial community diversity and structure in a climax forest (Larix gmelinii; LG) compared with those in degraded forest ecosystems of four forest vegetation types (BD, Betula dahurica; BP, Betula platyphylla; QM, Quercus mongolica; and LGQM, a mixed coniferous-broadleaved forest composed of Larix gmelinii and Quercus mongolica) in the Heilongjiang Zhongyangzhan Black-billed Capercaillie Nature Reserve in northern China, using Illumina MiSeq sequencing of 16 S rRNA genes. Soil physicochemical properties (pH, soil organic carbon = SOC, total nitrogen = TN, carbon/nitrogen = C/N, total phosphorous = TP, available nitrogen = AN, available phosphorous = AP) differed significantly (p < .05) among the five forests. SOC, C/N, TP, AN, and AP were highest in QM, whereas SOC was lowest in LGQM. Soil pH was lowest in BD and highest in LGQM. α diversity was highest in LG and lowest in QM. The soil bacterial community composition in the climax forest was significantly different from that in the four degraded forests (p < .05). The dominant bacterial phyla were Acidobacteria, Proteobacteria, Verrucomicrobia, Bacteroidetes, Actinobacteria, Gemmatimonadetes, Firmicutes, Chloroflexi, and Rokubacteria. The highest relative abundances of these phyla were: 30.7% for Acidobacteria in LGQM, 42.6% for Proteobacteria in LG, 17.6% for Verrucomicrobia in BD, 5.5% for Firmicutes in BP, and 6.9% for Actinobacteria in QM. The dominant bacterial genera across the five forest vegetation types were Bryobacter and some poorly characterized taxa (e.g., Candidatus_Udaeobacter and Candidatus_Solibacter). Redundancy analysis indicated that SOC, C/N, TP, AN, and AP were the main soil physicochemical properties that shaped the bacterial communities. Our study revealed distinct bacterial diversity and composition in the climax forest compared with values in degraded forests, suggesting that the biotic and abiotic factors associated with climax ecosystems play an important role in shaping soil bacterial community structure and thus biogeochemical functions. The results of this study contribute to a deeper understanding and better predictions of the network among belowground systems and of the functions and services of degraded forests compared with climax ecosystems.

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. To maximize the increase in the SERS signal, an aluminized chip was created. We merged the t-SNE reduced dimensional analysis algorithm, and SVM, KNN, and LDA machine learning algorithms to further investigate the effect of the approach on the final identification rate. The results demonstrate that SERS spectra had an increased intensity and signal-to-noise ratio. The machine learning classification accuracy rates were all above 90%, indicating that the bacteria were correctly classified and identified.