Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 May 19;28(10):4202.
doi: 10.3390/molecules28104202.

Lipidomic Characterization of Oocytes at Single-Cell Level Using Nanoflow Chromatography-Trapped Ion Mobility Spectrometry-Mass Spectrometry

Pujia Zhu  1 , Guowei Bu  2 , Ruifeng Hu  2 , Xianqin Ruan  1 , Rongrong Fu  1 , Zhourui Zhang  1 , Qiongqiong Wan  1 , Xin Liu  2 , Yiliang Miao  2 , Suming Chen  1
Affiliations

Lipidomic Characterization of Oocytes at Single-Cell Level Using Nanoflow Chromatography-Trapped Ion Mobility Spectrometry-Mass Spectrometry

Pujia Zhu et al. Molecules. .

Abstract

Mass spectrometry (MS)-based lipidomic has become a powerful tool for studying lipids in biological systems. However, lipidome analysis at the single-cell level remains a challenge. Here, we report a highly sensitive lipidomic workflow based on nanoflow liquid chromatography and trapped ion mobility spectrometry (TIMS)-MS. This approach enables the high-coverage identification of lipidome landscape at the single-oocyte level. By using the proposed method, comprehensive lipid changes in porcine oocytes during their maturation were revealed. The results provide valuable insights into the structural changes of lipid molecules during porcine oocyte maturation, highlighting the significance of sphingolipids and glycerophospholipids. This study offers a new approach to the single-cell lipidomic.

Keywords: lipidomic; liquid chromatography; mass spectrometry; oocytes; single-cell.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Overview of the lipidomic at single-cell level. (A) Schematic overview of the workflow for lipidomic profiling in oocytes. (B) Sequential data analysis steps used to identify unique lipids from the total number of detected 4D features in single-cell level oocytes. The analysis was performed in both ionization modes using either a nanoLC or an HPLC system. (C) Candidate features identified via matching using CCS versus matching using only MS/MS when using the nanoLC system.
Figure 2
Figure 2
Single-cell lipidomic analysis of porcine oocytes at different maturation stages. (A) Principal component analysis (PCA) of individual samples. (B) Percentage of total variance explained by the top 10 components. (C) The top 10 variables contributing to PC-1 are shown separately in the bar plot. (D) Radar diagrams illustrate the distribution of fatty acyls of different carbon atom numbers and double bond numbers in major lipid classes for porcine oocyte lipidome. The numbers at the circumferential boundary indicate the number of quantitated lipid species within each lipid class.
Figure 3
Figure 3
Differences of the lipids in porcine oocytes with different maturation stages. (A–C) Volcano plots display top lipids that were most significantly different in each pairwise comparison between mitochondrial lipidome at GVBD relative to GV, MII relative to GVBD, and MII relative to GV, based on magnitudes of q-value and fold changes. Two-sided Nemenyi test was used for post hoc pairwise comparisons, n = 24 independent oocytes for each maturation stage. (D) The number of lipids that produced significant differences between two comparisons at GV, GVBD, and MII periods.
Figure 4
Figure 4
Differential correlation network analyses of lipids during oocyte maturation. (a–i) Nine of the divided independent correlation networks. Lipid pairs with significant differential correlations (q-value < 0.05) were connected by lines of different colors according to their changes in correlation patterns.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Feng G., Gao M., Wang L., Chen J., Hou M., Wan Q., Lin Y., Xu G., Qi X., Chen S. Dual-resolving of positional and geometric isomers of C=C bonds via bifunctional photocycloaddition-photoisomerization reaction system. Nat. Commun. 2022;13:2652. doi: 10.1038/s41467-022-30249-z. - DOI - PMC - PubMed
    1. Han X. Lipidomics for studying metabolism. Nat. Rev. Endocrinol. 2016;12:668–679. doi: 10.1038/nrendo.2016.98. - DOI - PubMed
    1. Xie P., Zhang J., Wu P., Wu Y., Hong Y., Wang J., Cai Z. Multicellular tumor spheroids bridge the gap between two-dimensional cancer cells and solid tumors: The role of lipid metabolism and distribution. Chin. Chem. Lett. 2023;34:107349. doi: 10.1016/j.cclet.2022.03.072. - DOI
    1. Hao Y., Zhang Z., Feng G., Chen M., Wan Q., Lin J., Wu L., Nie W., Chen S. Distinct lipid metabolic dysregulation in asymptomatic COVID-19. iScience. 2021;24:102974. doi: 10.1016/j.isci.2021.102974. - DOI - PMC - PubMed
    1. Murphy R.C. Tandem Mass Spectrometry of Lipids: Molecular Analysis of Complex Lipids. 1st ed. Volume 4 Royal Society of Chemistry; Cambridge, UK: 2014.

LinkOut - more resources