sample-preparation-for-single-cell-mass-spectrometry-metabolomics

Sample Preparation for Single Cell Mass Spectrometry Metabolomics Studies: Combined Cell Washing, Quenching, Drying, and Storage

Single-cell mass spectrometry (SCMS) has become an indispensable tool for studying cellular metabolism. Owing to advancements in modern mass spectrometry ...

in-vivo-confocal-microscopy-diagnosis-management-dry-eye-focus-on

<em>In Vivo</em> Confocal Microscopy in the Diagnosis and Management of Dry Eye: A Focus on Imaging Protocols and Interpretation

In vivo confocal microscopy (IVCM) has gained significant attention for its non-invasive nature and high resolution. It enables in vivo observation and ...

single-cell-mass-spectrometry-imaging-using-desorption-electrospray

Single-Cell Mass Spectrometry Imaging Using Desorption Electrospray Ionization Coupled To Orbitrap Mass Spectrometers

Desorption electrospray ionization&#8211;mass spectrometry imaging (DESI&#8211;MSI) enables ambient, matrix-free molecular imaging with minimal sample ...

Single-cell mass spectrometry (SCMS) is an emerging tool for studying cell heterogeneity according to variation of molecular species in single cells. Although it has become increasingly common to employ machine learning models in SCMS data analysis, such as the classification of cell phenotypes, the existing machine learning models often suffer from low adaptability and transferability. In addition, SCMS studies of rare cells can be restricted by limited number of cell samples. To overcome these limitations, we performed SCMS analyses of melanoma cancer cell lines with two phenotypes (i.e., primary and metastatic cells). We then developed a meta-learning-based model, MetaPhenotype, that can be trained using a small amount of SCMS data to accurately classify cells into primary or metastatic phenotypes. Our results show that compared with standard transfer learning models, MetaPhenotype can rapidly predict and achieve a high accuracy of over 90% with fewer new training samples. Overall, our work opens the possibility of accurate cell phenotype classification based on fewer SCMS samples, thus lowering the demand for sample acquisition.

MetaPhenotype: A Transferable Meta-Learning Model for Single-Cell Mass Spectrometry-Based Cell Phenotype Prediction Using Limited Number of Cells.

Single-cell mass spectrometry (SCMS) has emerged as a powerful tool for analyzing metabolites in individual cells, including live cells. However, cell metabolites have a rapid turnover rate, whereas maintaining metabolites' profiles of live cells during sample transport, storage, or extended measurements can be challenging. In this study, a cell preparation method, which integrates cell washing by volatile salt solution, rapid liquid nitrogen (LN) quenching, freeze-drying in vacuum, and freezer storage at -80 °C, to preserve cell metabolites for SCMS measurement is discussed. Experimental results revealed that LN quenching preserved the overall cell metabolome, whereas storage at -80 °C for 48 h slightly changed the metabolite profiles in quenched cells. However, metabolites in unquenched cells were changed regardless of low-temperature storage. The influence of omission of quenching and low-temperature storage on cell metabolites and relevant pathways were investigated. Results from this work indicate that cell quenching is necessary, but low-temperature storage time should be minimized to preserve cell metabolites. The method developed in the current work can be readily adopted by SCMS techniques with storage remaining largely unaltered, allowing for extended SCMS studies.

Developing a Cell Quenching Method to Facilitate Single Cell Mass Spectrometry Metabolomics Studies.

To detect an earlier improvement in mild to moderate keratoconus following corneal cross-linking (CXL) with total corneal refractive power (TCRP) using ray tracing method.

ArticleTotal : 3	Year
Publication title Cited by 1	2025
Publication title	2025
Publication title	2026

Article	Year
MetaPhenotype: A Transferable Meta-Learning Model for Single-Cell Mass Spectrometry-Based Cell Phenotype Prediction Using Limited Number of Cells. Analytical chemistry\| PubMed ID: 39570119	2024
Developing a Cell Quenching Method to Facilitate Single Cell Mass Spectrometry Metabolomics Studies. JACS Au\| PubMed ID: 40443889	2025
A novel analysis of Scheimpflug total corneal refractive power following corneal cross-linking in mild to moderate keratoconus. International journal of ophthalmology\| PubMed ID: 35601182	2022

Dan Chen

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