Single-cell fucosylation breakdown: Switching fucose to europium

A CH4-Driven Ion Cloud-Stretched Approach Enables ICP-qMS for Multiplex Single-Cell Analysis

In the last 40 years, inductively coupled plasma quadrupole (q) mass spectrometry (ICP-qMS) has been recognized as one of the best tools for the quantification of multiple elements/isotopes and even the biomolecules they labeled in a homogeneous solution sample. However, it meets a tough challenge when acquiring multi-m/z signals from an intact single-cell dispersed in a cell suspension, since the single-cell ion cloud generated in ICP presents an intermittently transient event with a duration time of hundreds of microseconds while the dwell time plus settling time of the q is at the similar time scale when peak-hopping between different m/z. Herein, we report CH4 is able to stretch the single-cell ion cloud duration time to more than 7,000 μs in collision-reaction-cell (CRC), allowing multi-m/z signals acquisition by ICP-qMS. Quantification of single-cell's multiple phenotype protein markers can thus be achieved on ICP-(CH4-CRC)-qMS, not only revealing the heterogeneity between the single cells but also enabling an unambiguous cell-classification of their subtypes. CH4-driven ion cloud-stretched approach breaks through the long-standing bottleneck limited single-cell multiplex analysis on ICP-qMS, paving a path for more important applications of ICP-qMS in the fields related to single-cell analysis.

Design of a dual Ir-Eu tag for fluorescent visualization and ICP-MS quantification of SIRPα and its host cells

With the expansion of ICP-MS application into the field of bioanalysis, there is an urgent need for novel element tags today. Here, we report the design of a dual-element Ir-Eu tag, opening the door to simultaneous fluorescent imaging and ICP-MS quantification. The ratio of 153Eu/193Ir may serve as a precision control of the labeling process, allowing internal validation of the quantitative results obtained. As for SIRPα and its host cell analysis exemplified here, the Ir-Eu tag demonstrated superior figures of ICP-MS quantification with the LOD (3σ) down to 0.5 (153Eu) and 1.1 (193Ir) pM SIRPα and 220 (153Eu) and 830 (193Ir) RAW264.7 cells more than 130 times more sensitive compared with the LOD (3σ) of 65.2 pM SIRPα at 612 nm using fluorometry. Not limited to these demonstrations, we believe that the design ideas of the dual Ir-Eu tags should be applicable to various cases of bioanalysis when dual optical profiling and ICP-MS quantification are indispensable.

Selenium-based metabolic oligosaccharide engineering strategy for quantitative glycan detection

Metabolic oligosaccharide engineering (MOE) is a classical chemical approach to perturb, profile and perceive glycans in physiological systems, but probes upon bioorthogonal reaction require accessibility and the background signal readout makes it challenging to achieve glycan quantification. Here we develop SeMOE, a selenium-based metabolic oligosaccharide engineering strategy that concisely combines elemental analysis and MOE,enabling the mass spectrometric imaging of glycome. We also demonstrate that the new-to-nature SeMOE probes allow for detection, quantitative measurement and visualization of glycans in diverse biological contexts. We also show that chemical reporters on conventional MOE can be integrated into a bifunctional SeMOE probe to provide multimodality signal readouts. SeMOE thus provides a convenient and simplified method to explore the glyco-world.

Single-cell multi-element analysis reveals element distribution pattern in human sperm

Multiple elements in human sperm have been demonstrated to play significant roles in the reproductive process, but their simultaneous detection in single cells remains challenging. We propose a novel analytical procedure using single-cell inductively coupled plasma-time of flight-mass spectrometry (scICP-TOF-MS) to simultaneously quantify multiple elements of individual sperm cells. A promising label-free cell identification strategy based on the endogenous element was developed to obtain valid data. The element contents exhibited varied degrees of heterogeneity in single cells. Machine learning-based analysis of the multi-dimension dataset indicated different distribution patterns and physiological roles among the simultaneously detected elements.