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Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

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Published on: November 10, 2023

Single-Cell Simultaneous Metabolome and Transcriptome Profiling Revealing Metabolite-Gene Correlation Network.

Xiying Mao¹, Dandan Xia², Miao Xu¹

¹Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, P. R. China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|December 4, 2024

Summary

This study introduces scMeT-seq, a novel method for simultaneous single-cell metabolome and transcriptome profiling. This technique provides a more complete view of cellular metabolism and its link to cell phenotype.

Keywords:

RNA sequencing functional metabolomics mass spectroscopy multi‐omics single‐cell

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A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Area of Science:

Cellular Biology
Metabolomics
Genomics

Background:

Single-cell metabolome (SCM) studies are crucial for understanding cellular phenotypes in physiological and disease states.
Current SCM methods using mass spectrometry offer incomplete metabolic views and struggle to link metabolism with transcriptomics accurately.
Inconsistencies between transcriptomic and metabolic data limit the prediction of metabolism-phenotype relationships.

Purpose of the Study:

To develop a novel method for simultaneous single-cell metabolome and transcriptome profiling (scMeT-seq).
To enable a comprehensive analysis of metabolic activity and its correlation with gene expression within individual cells.
To advance the functional interpretation of metabolomics data in cellular research.

Main Methods:

Developed scMeT-seq, a method involving sub-picoliter sampling for initial metabolome profiling.
Performed single-cell transcriptome sequencing on the same cell after metabolome analysis.
Ensured cellular viability for accurate transcriptomic analysis post-metabolome sampling.

Main Results:

scMeT-seq successfully provided simultaneous metabolome and transcriptome data from single cells.
Integrative analysis revealed dynamic and cell state-specific associations between metabolome and transcriptome in macrophages.
Metabolite signatures were mapped to single-cell trajectories and gene networks, enabling unsupervised functional interpretation.

Conclusions:

The scMeT-seq method overcomes limitations of current SCM studies by providing a complete metabolic profile alongside transcriptomic data.
This approach facilitates a deeper understanding of the interplay between metabolism and gene expression at the single-cell level.
scMeT-seq transforms metabolomics from a static snapshot to a dynamic, functional approach for cellular research.