A Topological Data Analysis Mapper of the Ovarian Folliculogenesis Based on MALDI Mass Spectrometry Imaging Proteomics
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Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI), also referred to as molecular histology, is an emerging omics, which allows the simultaneous, label-free, detection of thousands of peptides in their tissue localization, and generates highly dimensional data. This technology requires the development of advanced computational methods to deepen our knowledge on relevant biological processes, such as those involved in reproductive biology.
The mammalian ovary cyclically undergoes morpho-functional changes. From puberty, at each ovarian cycle, a group of pre-antral follicles (type 4, T4) is recruited and grows to the pre-ovulatory (T8) stage, until ovulation of mature oocytes. The correct follicle growth and acquisition of oocyte developmental competence are strictly related to a continuous, but still poorly understood, molecular crosstalk between the gamete and the surrounding follicle cells.
Here, we tested the use of advanced clustering and visual analytics approaches on MALDI-MSI data for the in-situ identification of the protein signature of growing follicles, from the pre-antral T4 to the pre-ovulatory T8. Specifically, we first analyzed follicles MALDI-MSI data with PCA, tSNE and UMAP approaches, and then we developed a framework that employs Topological Data Analysis (TDA) Mapper to detect spatial and temporal related clusters and to pinpoint differentially expressed proteins. TDA Mapper is an unsupervised Machine Learning method suited to the analysis of high-dimensional data that are embedded into a graph model. Interestingly, the graph structure revealed protein patterns in clusters containing different follicle types, highlighting putative factors that drive follicle growth.
KeywordsTopological Data Analysis Histology MALDI-MSI
- 4.Lagarrigue, M., et al.: Matrix-assisted laser desorption/ionization imaging mass spectrometry: a promising technique for reproductive research. Biol. Reprod. 86 (2012)Google Scholar
- 7.Li, L., et al.: Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Sci. Transl. Med. 7, 311ra174 (2015)Google Scholar
- 9.Clauset, A., et al.: Finding community structure in very large networks. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 70, 066111 (2005)Google Scholar