Leveraging single-cell Raman spectroscopy and single-cell sorting for the detection and identification of yeast infections

Wang, Jingkai; Lin, Kaicheng; Yi, Xiaofei; Meng, Siyu; Sun, Yifei; Xu, Xiaogang; He, Ning; Zhang, Zhiqiang; Hu, Huijie; Qie, Xingwang; Zhang, Dayi; Tang, Yuguo; Huang, Wei E.; He, Jian; Song, Yizhi

doi:10.1016/j.aca.2022.340658

Cited by 9 publications

(6 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using a REPLI-g Single Cell Kit (Qiagen, Germany), the femtogram-level DNA in the collector was high-quality amplified to a microgram level for sequencing through multiple displacement amplification (MDA) [ 23 ]. Briefly following the procedure of the kit ( Figure 1 (iii)), all collectors arranged in sterile petri dishes were repeatedly frozen and thawed at −80 °C to accomplish as much lysis as possible of the target bacteria.…”

Section: Methodsmentioning

confidence: 99%

“…The aluminum-coated single-cell ejection chip resembled a two-dimensional rectangular coordinate and was divided into four regions, each of which was marked with a special shape to facilitate the localization of the target bacteria (Figure 1(i)). The Raman spectrum of each randomly picked cell was then recorded, and the SCRS of these unknown groups was recognized by well-established classifiers, while the coordinates of the above familiarized cells were registered for further sorting [23]. The chip was inverted and immobilized on a single-cell separation device equipped with a 532 nm Nd:YAG laser, 10× objective and CCD imaging system (PRECI SCS, HOOKE Instruments Ltd., Beijing, China).…”

Section: Identification and Ejection Of Bacteria With Unknown Tagsmentioning

confidence: 99%

See 1 more Smart Citation

Raman-Activated Cell Ejection for Validating the Reliability of the Raman Fingerprint Database of Foodborne Pathogens

Yan,

Guo,

Zong

et al. 2024

Foods

View full text Add to dashboard Cite

Raman spectroscopy for rapid identification of foodborne pathogens based on phenotype has attracted increasing attention, and the reliability of the Raman fingerprint database through genotypic determination is crucial. In the research, the classification model of four foodborne pathogens was established based on t-distributed stochastic neighbor embedding (t-SNE) and support vector machine (SVM); the recognition accuracy was 97.04%. The target bacteria named by the model were ejected through Raman-activated cell ejection (RACE), and then single-cell genomic DNA was amplified for species analysis. The accuracy of correct matches between the predicted phenotype and the actual genotype of the target cells was at least 83.3%. Furthermore, all anticipant sequencing results brought into correspondence with the species were predicted through the model. In sum, the Raman fingerprint database based on Raman spectroscopy combined with machine learning was reliable and promising in the field of rapid detection of foodborne pathogens.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Identification and Ejection Of Bacteria With Unknown Tagsmentioning

confidence: 99%

Raman-Activated Cell Ejection for Validating the Reliability of the Raman Fingerprint Database of Foodborne Pathogens

Yan,

Guo,

Zong

et al. 2024

Foods

View full text Add to dashboard Cite

show abstract

“…Raman spectra, which depict the relationship between the intensity of scattered light and frequency shift, provide unique molecular "fingerprints" for identification [26,[28][29][30]. Table 1 summarizes the biomolecular assignments of different Raman shifts [33,34]. This specificity, combined with its high sensitivity and non-destructive nature, makes Raman spectroscopy invaluable in fields like chemical analysis, materials science, and forensics [31,[35][36][37].…”

Section: Basics Of Raman Spectroscopymentioning

confidence: 99%

Raman Flow Cytometry and Its Biomedical Applications

Xu,

Chen,

Wang

et al. 2024

Biosensors

Self Cite

View full text Add to dashboard Cite

Raman flow cytometry (RFC) uniquely integrates the “label-free” capability of Raman spectroscopy with the “high-throughput” attribute of traditional flow cytometry (FCM), offering exceptional performance in cell characterization and sorting. Unlike conventional FCM, RFC stands out for its elimination of the dependency on fluorescent labels, thereby reducing interference with the natural state of cells. Furthermore, it significantly enhances the detection information, providing a more comprehensive chemical fingerprint of cells. This review thoroughly discusses the fundamental principles and technological advantages of RFC and elaborates on its various applications in the biomedical field, from identifying and characterizing cancer cells for in vivo cancer detection and surveillance to sorting stem cells, paving the way for cell therapy, and identifying metabolic products of microbial cells, enabling the differentiation of microbial subgroups. Moreover, we delve into the current challenges and future directions regarding the improvement in sensitivity and throughput. This holds significant implications for the field of cell analysis, especially for the advancement of metabolomics.

show abstract

“…However, excluding single wavenumbers would not provide insight into the network's sensitivity regarding the different spectral regions. This is due to shifts and spectra broadening due to various tissue processing effects [44][45][46][47].…”

Section: Sensitivity Analysismentioning

confidence: 99%

Deep learning analysis of mid‐infrared microscopic imaging data for the diagnosis and classification of human lymphomas

Zelger

Brunner

Zelger

et al. 2023

Journal of Biophotonics

View full text Add to dashboard Cite

The present study presents an alternative analytical workflow that combines mid‐infrared (MIR) microscopic imaging and deep learning to diagnose human lymphoma and differentiate between small and large cell lymphoma. We could show that using a deep learning approach to analyze MIR hyperspectral data obtained from benign and malignant lymph node pathology results in high accuracy for correct classification, learning the distinct region of 3900 cm−1 to 850 cm−1. The accuracy is above 95% for every pair of malignant lymphoid tissue and still above 90% for the distinction between benign and malignant lymphoid tissue for binary classification. These results demonstrate that a preliminary diagnosis and subtyping of human lymphoma could be streamlined by applying a deep learning approach to analyze MIR spectroscopic data.This article is protected by copyright. All rights reserved.

show abstract

Leveraging single-cell Raman spectroscopy and single-cell sorting for the detection and identification of yeast infections

Cited by 9 publications

References 59 publications

Raman-Activated Cell Ejection for Validating the Reliability of the Raman Fingerprint Database of Foodborne Pathogens

Raman-Activated Cell Ejection for Validating the Reliability of the Raman Fingerprint Database of Foodborne Pathogens

Raman Flow Cytometry and Its Biomedical Applications

Deep learning analysis of mid‐infrared microscopic imaging data for the diagnosis and classification of human lymphomas

Contact Info

Product

Resources

About