MALDI mass spectrometry imaging shows a gradual change in the proteome landscape during mouse ovarian folliculogenesis

Abstract

Our knowledge regarding the role proteins play in the mutual relationship among oocytes, surrounding follicle cells, stroma, and the vascular network inside the ovary is still poor and obtaining insights into this context would significantly aid our understanding of folliculogenesis. Here, we describe a spatial proteomics approach to characterize the proteome of individual follicles at different growth stages in a whole prepubertal 25-day-old mouse ovary. A total of 401 proteins were identified by nano-scale liquid chromatography–electrospray ionization–tandem mass spectrometry (nLC-ESI-MS/MS), 69 with a known function in ovary biology, as demonstrated by earlier proteomics studies. Enrichment analysis highlighted significant KEGG and Reactome pathways, with apoptosis, developmental biology, PI3K-Akt, epigenetic regulation of gene expression, and extracellular matrix organization being well represented. Then, correlating these data with the spatial information provided by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) on 276 follicles enabled the protein profiles of single follicle types to be mapped within their native context, highlighting 94 proteins that were detected throughout the secondary to the pre-ovulatory transition. Statistical analyses identified a group of 37 proteins that showed a gradual quantitative change during follicle differentiation, comprising 10 with a known role in follicle growth (NUMA1, TPM2), oocyte germinal vesicle-to-metaphase II transition (SFPQ, ACTBL, MARCS, NUCL), ovulation (GELS, CO1A2), and preimplantation development (TIF1B, KHDC3). The proteome landscape identified includes molecules of known function in the ovary, but also those whose specific role is emerging. Altogether, this work demonstrates the utility of performing spatial proteomics in the context of the ovary and offers sound bases for more in-depth investigations that aim to further unravel its spatial proteome.

Introduction

Inside the mammalian ovary, follicles undergo a process of growth and differentiation known as folliculogenesis. In mice, folliculogenesis proceeds through eight main stages, from primordial Type 1 and 2 (T1–T2) to fully grown preovulatory T8 (Pedersen and Peters, 1968). Groups of small T1–T2 follicles are activated from an initial ovarian reserve and go through gradual morphological and functional changes of both the somatic and germinal components. Thus, primordial follicles become primary T3 and secondary T4, with one or two layers of surrounding cuboidal follicle cells, respectively. After FSH-dependent recruitment, preantral (T4–T5) follicles grow further to the antral (T6–T7), and fully grown preovulatory (T8) stage. Only a minority of follicles will complete maturation and will be ovulated; instead, all the others will be eliminated through a process of atresia (Greenwald and Terranova, 1988; Pangas and Rajkovic, 2015).

The progression of folliculogenesis as well as the acquisition of oocyte developmental competence occur thanks to an exchange of molecular information among the gamete, its surrounding follicle cells, the stroma, and the vascular network (Zuccotti et al., 2011). However, this mutual relationship is still poorly understood and, in this respect, a critical point remains the scarcity of proteomic investigations that could help to highlight the landscape of molecules playing a regulatory role during folliculogenesis. Up until now, most of the proteomics studies have been performed after whole-organ homogenization (mouse: Xiong et al., 2019; pig: Hou et al., 2018; Henning et al., 2019; monkey: He et al., 2014; human: Bothun et al., 2018; Ouni et al., 2019) or after its disaggregation into single follicles or oocytes. In particular, some studies investigated the protein profile of isolated fully grown or metaphase-II (MII) mouse oocytes (Ma et al., 2008; Zhang et al., 2009; Wang et al., 2010; Pfeiffer et al., 2011; Demant et al., 2012; Cao et al., 2020), on the entire ovulated cumulus-oocyte complex (Meng et al., 2007), or on isolated and cultured follicles (Anastácio et al., 2017), reporting an increasing list of proteins specific to each stage of maturation.

None of these studies, however, have analyzed follicle growth as it occurs inside the ovary, maintaining the morphological and functional interactions with the surrounding stromal tissue and the vasculature. The proteome landscape at the different stages of follicle growth in a native ovarian context would reflect not only the molecular features of the follicle itself, but also the results of the interaction with the surrounding environment.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a powerful tool that facilitates the proteomic complexity of tissue environments to be mapped entirely in situ, even within small morphological structures (Lagarrigue et al., 2012; Ly et al., 2016). Given that the native tissue distribution of the proteins is maintained, the molecular images can then be overlaid with their histological counterpart to investigate proteomic alterations in precise sub-regions of the tissue architecture. Thus, MALDI-MSI may represent an ideal approach when aiming to map follicle growth throughout an entire ovary. Moreover, this spatial proteomics approach can overcome limitations associated with classical ‘omics’ that occur as a result of the molecular information being diluted during extraction from the tissue, thus lacking its regiospecificity. Owing to this capability, spatial proteomics using MALDI-MSI has been applied in a number of biomedical studies, such as, among others, to investigate the molecular heterogeneity of cancers (Smith et al., 2017; Neagu, 2019; Eveque-Mourroux et al., 2021).

In the field of reproduction, this approach has been used to study rodent testes, allowing the spatial localization and quantitation of androgens (Shimma et al., 2016; Cobice et al., 2016), the distribution of endogenous peptides within interstitial regions (Lahiri et al., 2021), and the distinct proteomic signatures associated with spermatogenetic stages (Lagarrigue et al., 2011) to be highlighted. For the ovary, MALDI-MSI has only been used to determine changes in the lipid content of follicular fluid and of somatic cells during bovine antral follicle maturation (Bertevello et al., 2020) or in porcine follicular and extra-follicular compartments (Uzbekova et al., 2015). Proteomic investigation using this approach, instead, has been restricted to the validation of cancer biomarkers in human ovarian biopsies (Klein et al., 2019; Kulbe et al., 2020; Kassuhn et al., 2021).

In this pilot study, we investigated the potential of performing spatial proteomics, by combining nano-scale liquid chromatography–electrospray ionization–tandem mass spectrometry (nLC-ESI-MS/MS) with the spatial capabilities of MALDI-MSI, to identify the proteomic landscape of an entire prepubertal 25-day-old mouse ovary and to map the changes occurring throughout folliculogenesis.

Materials and methods

Animals

CD1 female mice were purchased from Charles River (Como, Italy). Animals were maintained under controlled conditions of 21°C, 60% air humidity and a light/dark cycle of 12:12 h. Research on mice was conducted with permission from the Ministry of Health (No. 1100/2016-PR) in accordance with the guiding principles of European (No. 2010/63/UE) and Italian (No. 26/2014) laws protecting animals used for scientific research.

Pipeline for protein identification in single follicles

An overview of the entire pipeline used to identify proteins in single follicles is presented in Fig. 1. The following provides a detailed description of each step.

Ovary fixation and slide preparation

A 25-day-old mouse ovary was fixed in 10% formalin, dehydrated, embedded in paraffin wax, and a total of 133 6-μm serial sections were cut and mounted onto four conductive indium tin oxide (ITO) glass slides (Bruker Daltonik GmbH, Bremen, Germany).

MALDI-MSI

After paraffin removal (De Sio et al., 2015), trypsin digestion (Merck KGaA, Darmstadt, Germany, 20 ng/µl) was performed using the iMatrixSpray (Tardo Gmbh, Subingen, Switzerland) automated spraying system and then slides were left in a humid chamber overnight at 40°C. Finally, matrix deposition for MALDI-MSI analysis was performed by spraying six layers of α-cyano-4-hydroxycinnamic acid (10 mg/ml in 50:50 acetonitrile:water w/0.4% trifluoroacetic acid) using the iMatrixSpray (Tardo Gmbh, Subingen, Switzerland) with an optimized method and an incorporated heat bed set at 40°C. For each tissue section, mass spectra were acquired in reflectron positive mode, within the m/z 700–3000 mass range, using a rapifleX MALDI Tissuetyper™ (Bruker Daltonik GmbH, Bremen, Germany) MALDI-TOF/TOF MS equipped with a Smartbeam 3D laser operating at 5 kHz frequency. A mixture of standard peptides within the mass range of m/z 750–3150 (PepMix I, Bruker Daltonik, Germany) was used for external calibration. MALDI-MS images were acquired with a single spot laser setting of 18 µm and a raster sampling of 20 μm in both x and y dimensions.

nLC-ESI-MS/MS

Following MALDI-MSI, the tryptic peptides were extracted from the tissue sections using a 50:50 acetonitrile:water w/0.4% trifluoroacetic acid solution and pooled. Using an HETO vacuum concentrator (Thermo Scientific, Milan, Italy), this extract was concentrated to an elution volume of 20 μl, and then resuspended in 100 μl of phase A (98/2/0.1; water/acetonitrile/trifluoroacetic acid) to reach a final volume of 120 μl, before storage at −20°C prior to the nLC-ESI-MS/MS analysis.

nLC-ESI-MS/MS analysis was performed using a Dionex UltiMate 3000 rapid separation (RS) LC nano system coupled with an Impact HD™ UHR-QqToF (Bruker Daltonik GmbH). Matrix desalting and concentration were achieved by using a pre-column (Dionex, Acclaim PepMap 100 C18, cartridge, 300 μm) and peptides were separated by using a 50-cm column (Dionex, ID 0.075 mm, Acclaim PepMap100, C18) with a 120 min gradient at 40°C from 96% to 2% of Phase A (0.1% formic acid), while in Phase B, 0.08% formic acid:acetonitrile (80:20) was used.

Protein identification

Protein identities were obtained using the Mascot software (version 2.4.0, Matrix Science Inc, Boston, MA, USA) and the Swissprot protein database (accessed in January 2020; 561 568 sequences; 201 997 950 residues), setting a peptide tolerance of 20 parts per million (ppm), a fragment mass tolerance of 0.05 Da, and trypsin as the digestive enzyme. Variable modifications were considered as +12 and +30 Da, which represent methylene and methylol adducts, respectively, that form during fixation and embedding in paraffin, and an automatic decoy database search and a built-in Percolator algorithm were applied. Only peptide sequences matched to a putative protein with P ≤ 0.05 were considered as positive results.

Finally, these mass measurements for the MSI signals were then aligned with the mass values belonging to the positively identified tryptic peptide sequences obtained using nLC-ESI-MS/MS. A protein identification was putatively assigned to a signal if an error of less than ±100 ppm (https://warwick.ac.uk/fac/sci/chemistry/research/barrow/barrowgroup/calculators/mass_errors/) was observed between the m/z value observed in MALDI-MSI and the m/z of the related amino acid sequence determined by nLC-ESI-MS/MS.

Protein–protein interaction networks functional enrichment analysis

Protein–protein interaction networks functional enrichment analysis of multiple proteins was performed by using STRING (Search Tool for the Retrieval of Interacting Genes/Proteins; version 11.0) linked to the Kyoto Encyclopaedia of Genes and Genomes (KEGG) or Reactome databases.

Histology

After the MALDI-MSI and nLC-ESI-MS/MS analysis, sections were washed with ethanol (70% and 100%) and stained with Mayer’s haematoxylin and 0.5% Eosin Y. The slides were converted to digital format by scanning using a ScanScope CS digital scanner (Aperio, Park Center Dr, Vista, CA, USA) and each follicle was classified, from T1 to T8, according to Pedersen and Peters (1968). Briefly, starting from the equatorial section, which was identified as the largest containing the nucleus of the oocyte, each follicle was annotated using SCiLS Lab MVS 2019c Pro software (http://scils.de/; Bremen, Germany).

Data pre-processing

The individual spectra from each ovary tissue section were imported into SCiLS Lab MVS 2019c Pro software to perform pre-processing: baseline subtraction (Convolution algorithm), normalization (Total Ion Current algorithm), and spatial denoizing. Moreover, the digital histology images were also imported to define regions of interest (ROIs) that corresponded to the follicle annotations. These ROIs were then grouped together in their relative classes from T1 to T8. Average spectra, representative of the different follicle classes, were generated to display differences in the protein profiles. Peak picking and alignment were performed as feature extraction for statistical analysis.

PubMed search

The relevance that each protein has with respect to ovarian function was determined using PubMed by cross-referencing each individual protein name with the specific keywords ‘ovarian’ or ‘ovary’ or ‘follicle’ or ‘follicular’ or ‘oocyte’ or ‘meiosis’, or ‘cumulus’, excluding pathological conditions and focusing on mammals. Moreover, a PubMed search of transcriptomics studies was performed by cross-referencing the keywords ‘transcriptomics’ or ‘next-generation sequencing’, or ‘microarrays with ovary’ or ‘follicle’ or ‘oocyte’.

Statistical analysis

Within SCiLS Lab MVS 2019c Pro, unsupervised principal component analysis (PCA) was performed, using Pareto scaling, to reduce the high complexity of the data and visualize general differences in the protein profiles of the T4–T8 follicles. Moreover, ROC analysis was performed: statistically significant peaks were those with an AUC of ≥ 0.70 and a P-value of ≤0.05. These signals were curated to only include m/z values representative of the monoisotopic mass of a tryptic peptide.

Results and discussion

The process of folliculogensis occurs as a result of molecular communications among the gamete, its surrounding follicle cells, the stroma, and the vascular network. Our understanding of these mutual relationships is limited and obtaining proteomic insights would lead to a heightened comprehension. In this context, we applied a spatial proteomics workflow to study the proteome of a whole prepubertal 25-day-old mouse ovary, maintaining the spatial relationship of the molecules within this complex backdrop. This spatial proteomics pipeline, combined with a cross-check analysis of previous proteomics and transcriptomics studies, enabled the detection of proteins with known implications in several aspects of follicle growth, oocyte germinal vesicle-to-metaphase II (GV-to-MII) transition, ovulation, and preimplantation development. Moreover, we highlighted a smaller subset of 37 proteins whose gradual change during follicle growth may provide novel insights into the molecular basis of folliculogenesis.

nLC-ESI-MS/MS on histological sections of the whole mouse ovary identifies 401 proteins

nLC-ESI-MS/MS analysis led to the identification of 401 proteins and enrichment analysis of this dataset highlighted several highly significant KEGG and Reactome pathways, such as spliceosome (27 proteins; P < 0.0001), focal adhesion (20; P < 0.0001), extracellular matrix (ECM)-receptor interaction (11; P < 0.0001), apoptosis (11; P < 0.0001), cellular response to stress (eight; P < 0.0001), developmental biology (16; P < 0.004), phosphatidylinositol 3 kinase-protein kinase B (PI3K-Akt) (16; P < 0.005), epigenetic regulation of gene expression (four; P < 0.01), and ECM organization (six; P < 0.02) (Supplementary Table SI). These pathways confirm the results of an earlier study on prepubertal mouse ovaries (Xiong et al., 2019) and underline the reliability of the dataset. Furthermore, these results were strengthened by a cross-check analysis with previous studies that analyzed the transcriptome and proteome in folliculogenesis. A PubMed search of transcriptomics publications identified a total of 273 papers, which were further filtered down to 81 by excluding pathological conditions and focusing on mammals, specifically on rodents (19), bovines (26), humans (22), and others (14, including equines, ovines, primates). The results of these transcriptional studies described the presence during folliculogenesis, in specific follicle cell types, of transcripts matching with 335 (83.5%) out of the 401 proteins identified by nLC-ESI-MS/MS (Supplementary Table SII). Of these, 144 and 38 transcripts were found either in oocytes or in follicle cells, respectively; 134 were present in both (Fig. 2), whereas the remaining 19 transcripts were assigned either to the whole follicle or to the ovary.

As for the proteome, 69 (17.2%) out of 401 proteins were found to be expressed in specific ovarian cell types and have a known functional role in key ovarian processes (Table I), such as follicle growth (n = 18), oocyte GV-to-MII transition and acquisition of developmental competence (n = 32), ovulation (n = 5), fertilization (n = 3), luteolysis (n = 1), angiogenesis (n = 1), and ovary pathologies (n = 9).

The 401 proteins identified with the nLC-ESI-MS/MS analysis were then used as the reference dataset to be correlated with those proteins mapped in the ovary using MALDI-MSI.

MALDI-MSI highlights proteins involved in follicle growth, oocyte GV-to-MII transition, ovulation, and preimplantation development

Using the pipeline described in Fig. 1, throughout the 133 histological serial sections of the whole ovary, we annotated a total of 276 follicles (Fig. 3), from the primordial to the pre-ovulatory, resulting in 56 200 mass spectra, distributed as follows: T1, 32 spectra; T2, 60; T3, 288; T4, 1674; T5, 6991; T6, 7628; T7, 19 254; and T8, 20 276. Follicles T1, T2, and T3 (15–30 µm in diameter) were excluded from subsequent analyses given that, owing to their small size and a 20-µm pixel being employed by MALDI-MSI, the acquired spectra often comprised both the follicle and the extra-follicular tissue and were, therefore, not follicle specific. On the contrary, in larger follicles (T4–T8, 40–320 µm in diameter), the majority of spectra were acquired from the follicle perimeter in each section, thus facilitating follicle-specific data acquisition. The mass spectra analyzed for T7 and T8 follicles were limited to those of the histological sections comprising the oocyte, thus providing a number of spectra comparable among follicle types (Fig. 3B and C).

Following pre-processing of the mass spectra, the resulting 214 m/z features were matched with the peptides identified by nLC-ESI-MS/MS and 55 of them were putatively assigned to 94 proteins (mass error < 100 ppm). Enrichment analysis of these proteins confirmed the significantly represented pathways previously described with nLC-ESI-MS/MS, being related to focal adhesion, ECM-receptor interaction, apoptosis, PI3K-Akt signalling, and epigenetic regulation of gene expression (Supplementary Table SIII).

Cross-check analysis with transcriptomics studies highlighted the presence during folliculogenesis of transcripts matching 65 out of the 94 MALDI-MSI proteins detected in our study (Table II). Analogously, a comparison with proteomics studies showed that 16 of the 94 proteins have a known role in different aspects of follicle growth, oocyte GV-to-MII transition, ovulation, and preimplantation development (Table I, asterisks). More specifically, during follicle growth, nuclear mitotic apparatus protein 1 (NUMA1) is involved in the organization and stability of spindle microtubules and mitotic centrosomes of proliferating human granulosa cells (Can and Albertini, 1997). Six of these proteins play a role in the oocyte GV-to-MII transition: splicing factor, proline- and glutamine-rich protein (SFPQ) and beta-actin-like protein 2 (ACTBL) are up-regulated in developmentally competent versus incompetent human or porcine oocytes (McReynolds et al., 2012; Al-Omar et al., 2020). Filamin-A (FLNA) (Wang et al., 2017) and myristoylated alanine-rich C-kinase substrate (MARCS) (Michaut et al., 2005) bind to actin and regulate spindle migration and asymmetric division in mouse oocytes. 14-3-3 protein theta (1433T) is expressed in mouse oocytes and eggs and interacts with the m-phase inducer phosphatase 2 (CDC25B) cell cycle protein (Eisa et al., 2019). Nucleolin (NUCL) is a nuclear protein that binds to the membrane-associated progesterone receptor component 1 (PGRMC1) in the mechanism of oocyte response to stress and survival (Terzaghi et al., 2018).

DNA (cytosine-5)-methyltransferase 1 (DNMT1), transcription intermediary factor 1-beta (TIF1B), KH domain-containing protein 3 (KHDC3), and NACHT, LRR and PYD domains-containing protein 5 (NALP5) are maternal effect factors, produced and stored in the oocyte during folliculogenesis, but with a crucial role in early preimplantation by guiding the passage from a maternal to an embryonic control of development (Li et al., 2010; Condic, 2016; Messerschmidt et al., 2012; Wu and Dean, 2020).

Three other proteins are involved in cumulus expansion and ovulation: the versican core protein (CSPG2), a member of the hyaluronan-binding proteoglycans mainly secreted by granulosa cells in response to the LH surge (Russell et al., 2003; Robker et al., 2018), has a role in both oocyte maturation and cumulus expansion; Gelsolin (GELS), a calcium-dependent actin-modulating protein, functions in smooth-muscle-like cells of the theca externa modulating contractility during both follicle growth and ovulation (Teubner et al., 1994); Collagen alpha-1(II) chain (CO1A2) is an ECM collagen abundant in the theca of growing follicles, whose remodelling reduces the tensile strength around the follicle, and contributes to its rupture and ovulation (Lind et al., 2006; Bagavandoss, 2014). For laminin subunit alpha-2 (LAMA2) and tropomyosin beta chain (TPM2), evidence suggest their involvement in the organization of the cytoskeleton and kinetochore in oocytes and follicle cells (Li et al., 2016; Kranc et al., 2017).

Importantly, 3D PCA on the mass spectra obtained from T4 to T8 follicles enabled differences among their protein profiles to be visualized. The score chart of the first three components (PC1, PC2, and PC3) is displayed in Fig. 4A, with the mass spectra of each follicle type represented as red dots and those of the grouped T4–T8 follicles as grey dots. The 3D distribution present in the PCA score chart indicates a gradual shift in the protein profiles from T5 to T8, with 32 putative proteins being shown to contribute most to this change (weight ≥0.20) (Figs 4B and 5). This gradual change indicates variations in the follicle proteome during growth and differentiation.

Performing ROC analysis among the T5–T8 follicles did not reveal significant changes when comparing consecutive follicle stages, which may be explained by the small, gradual variations in protein levels; however, the comparison between T5 and T8 highlighted a statistically different abundance of 19 proteins (AUC ≥ 0.7) (Fig. 5), 15 down- (non-POU domain-containing octamer-binding protein (NONO), NUMA1, SFPQ, acetyl-CoA acetyltransferase, cytosolic (THIC), TIF1B, KHDC3, sister chromatid cohesion protein PDS5 homolog A (PDS5A), protein FAM136A (F136A), spectrin alpha chain, non-erythrocytic 1 (SPTN1), CO1A2, serine/arginine-rich splicing factor 4 (SRSF4), heat shock cognate 71 kDa protein (HSP7C), serine/arginine repetitive matrix protein 2 (SRRM2), calpastatin (ICAL), and spliceosome-associated protein CWC15 homolog (CWC15)) and four up- (NUCL, histone H4 (H4), microtubule-associated protein 4 (MAP4), and 60S ribosomal protein L29 (RL29)) regulated in the passage from the pre-antral to the pre-ovulatory follicle. Importantly, the dynamic expression of some of these proteins is consistent with that described in previous proteomics (CO1A2 and KHDC3) (Lind et al., 2006; Anastácio et al., 2017) and transcriptomics (Nono, Sfpq, and Col1a2) (Bonnet et al., 2008; Regassa et al., 2011; Gu et al., 2019) studies, showing their down-regulation in the comparison between pre-antral and fully grown antral follicles.

Together, PCA and ROC statistical analyses revealed a group of 37 proteins that showed a gradual quantitative change during follicle differentiation, comprising 10 described in proteomics studies (Table I) with a role in follicle growth (NUMA1 and TPM2), oocyte GV-to-MII transition (SFPQ, ACTBL, MARCS, and NUCL), ovulation (GELS and CO1A2), and preimplantation development (TIF1B and KHDC3). The remaining 27 proteins may represent key candidates for future omics analyses that could lead to the identification of novel players involved in the process of folliculogenesis. Among these, a review of transcriptomics and proteomics studies, extended also outside the mammalian class, showed a putative role for the eukaryotic translation initiation factor 3 subunit A (Eif3a) in preimplantation development, since its downregulation, caused by Eif3e depletion, leads to reduced global protein translation (Sadato et al., 2018) and cellular proliferation (Salilew-Wondim et al., 2021). In Xenopus oocytes, PDS5A modulates the interaction of cohesin during sister chromatids cohesion (Losada et al., 2005) and MAP4 is required for M-phase microtubule remodelling and spindle assembly (Ookata et al., 1995; Cha et al., 1999).

In conclusion, this study highlights the feasibility of combining nLC-ESI-MS/MS and MALDI-MSI, in a spatial proteomics workflow, to map molecular variations of individual follicles in the whole prepubertal mouse ovary. The spatial information provided by MALDI-MSI enabled the protein profile of single follicle types to be mapped within their native context and indicated that changes in the proteome landscape during folliculogenesis occur gradually, including proteins of known function in the female gonad and some whose specific role is emerging.

A limitation of the MALDI-MSI technology is its 20 µm/pixel resolution, which determines two drawbacks: it often leads to the acquisition of spectra comprising both follicular and extra-follicular tissue, particularly with T1–T3 follicles that, for their 15–30 µm size in diameter, were excluded from our analysis; and it does not allow a distinction among the different cell types that form an ovarian follicle, thus preventing its application for single-cell in situ proteomics. At this stage of our study, this latter constraint was partially overcome by cross-checking the emerged proteins with the results described in previous proteomic and transcriptomic studies, thus allowing their indirect mapping to the different cell types present in the follicle. As a future direction, our results show that MALDI-MSI is a powerful technique to compare and analyse the molecular signature of fixed and embedded ovary specimens, not only to study their proteome, but also we envisage its use for the analysis of the lipidic or metabolic profiles (Baker et al., 2017; Dannhorn et al., 2022).

Altogether, this work demonstrates the utility of performing spatial proteomics in the context of the ovary and offers a sound basis for more in-depth investigations of its spatial proteome under both physiological and pathological conditions.

Supplementary data

Supplementary data are available at Molecular Human Reproduction online.

Data availability

The data underlying this article are available in FigShare, at https://doi.org/10.6084/m9.figshare.21930855.v1.

Acknowledgements

G.F., G.N., R.B., S.G., and M.Z. thank the Center for Health Technologies of the University of Pavia for support.

Authors’ roles

Conception and design: G.F., A.S., F.M., S.G., and M.Z.; experiments execution, acquisition and analysis of data: G.F., A.S., G.N., R.B., and M.Z.; manuscript writing: G.F., A.S., G.N., R.B., F.M., S.G., and M.Z.

Funding

This work was made possible thanks to support from the Italian Ministry of University and Research (MUR) Dipartimenti di Eccellenza Program (2018–2022) to the Department of Biology and Biotechnology ‘L. Spallanzani’, University of Pavia, and a grant from the University of Pavia (FRG 2020).

Conflict of interest

The authors have no conflict of interest to declare.

References