Nanometer Resolution Mass Spectro-Microtomography for In-Depth Anatomical Profiling of Single Cells

Advances in mass spectrometry imaging for plant metabolomics-Expanding the analytical toolbox

Mass spectrometry imaging (MSI) has become increasingly popular in plant science due to its ability to characterize complex chemical, spatial, and temporal aspects of plant metabolism. Over the past decade, as the emerging and unique features of various MSI techniques have continued to support new discoveries in studies of plant metabolism closely associated with various aspects of plant function and physiology, spatial metabolomics based on MSI techniques has positioned it at the forefront of plant metabolic studies, providing the opportunity for far higher resolution than was previously available. Despite these efforts, profound challenges at the levels of spatial resolution, sensitivity, quantitative ability, chemical confidence, isomer discrimination, and spatial multi-omics integration, undoubtedly remain. In this Perspective, we provide a contemporary overview of the emergent MSI techniques widely used in the plant sciences, with particular emphasis on recent advances in methodological breakthroughs. Having established the detailed context of MSI, we outline both the golden opportunities and key challenges currently facing plant metabolomics, presenting our vision as to how the enormous potential of MSI technologies will contribute to progress in plant science in the coming years.

AI-assisted mass spectrometry imaging with in situ image segmentation for subcellular metabolomics analysis

Subcellular metabolomics analysis is crucial for understanding intracellular heterogeneity and accurate drug-cell interactions. Unfortunately, the ultra-small size and complex microenvironment inside the cell pose a great challenge to achieving this goal. To address this challenge, we propose an artificial intelligence-assisted subcellular mass spectrometry imaging (AI-SMSI) strategy with in situ image segmentation. Based on the nanometer-resolution MSI technique, the protonated guanine and threonine ions were respectively employed as the nucleus and cytoplasmic markers to complete image segmentation at the subcellular level, avoiding mutual interference of signals from various compartments in the cell. With advanced AI models, the metabolites within the different regions could be further integrated and profiled. Through this method, we decrypted the distinct action mechanism of isomeric drugs, doxorubicin (DOX) and epirubicin (EPI), only with a stereochemical inversion at C-4'. Within the cytoplasmic region, fifteen specific metabolites were discovered as biomarkers for distinguishing the drug action difference between DOX and EPI. Moreover, we identified that the downregulations of glutamate and aspartate in the malate-aspartate shuttle pathway may contribute to the higher paratoxicity of DOX. Our current AI-SMSI approach has promising applications for subcellular metabolomics analysis and thus opens new opportunities to further explore drug-cell specific interactions for the long-term pursuit of precision medicine.

Electrochemiluminescence Analysis of Multiple Glycans on Single Living Cell with a Closed Bipolar Electrode Array Chip

The profiling of multiple glycans on a single cell is important for elucidating glycosylation mechanisms and accurately identifying disease states. Herein, we developed a closed bipolar electrode (BPE) array chip for live single-cell trapping and in situ galactose and sialic acid detection with the electrochemiluminescence (ECL) method. Methylene blue-DNA (MB-DNA) as well as biotin-DNA (Bio-DNA) codecorated AuNPs were prepared as nanoprobes, which were selectively labeled on the cell surface through chemoselective labeling techniques. The individual cell was captured and labeled in the microtrap of the cathodic chamber, under an appropriate potential, MB molecules on the cellular membrane underwent oxidation, triggering the reduction of [Ru(bpy)3]2+/TPA and consequently generating ECL signals in the anodic chamber. The abundance of MB groups on the single cell enabled selective monitoring of both sialic acid and galactosyl groups with high sensitivity using ECL. The sialic acid and galactosyl content per HepG2 cell were detected to be 0.66 and 0.82 fmol, respectively. Through comprehensive evaluation of these two types of glycans on a single cell, tumor cells, and normal cells could be effectively discriminated and the accuracy of single-cell heterogeneous analysis was improved. Additionally, dynamic monitoring of variations in galactosyl groups on the surface of the single cell was also achieved. This work introduced a straightforward and convenient approach for heterogeneity analysis among single cells.