Impact of Aging on the Ovarian Extracellular Matrix and Derived 3D Scaffolds

Pennarossa, G.; Iorio, Teresina De; Gandolfi, F.; Brevini, Tiziana A. L.

doi:10.3390/nano12030345

Cited by 20 publications

(20 citation statements)

References 51 publications

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“…Both proteins have been shown to change with age. Briley et al (2016) demonstrated a positive linear correlation between age and collagen staining in mouse ovary ( Briley et al , 2016 ), also reported in pigs ( Pennarossa et al , 2022 ) and in humans whose ovarian collagen gradually accumulates from prepuberty to menopause ( Ouni et al , 2020 ), rendering the ECM stiffer and increasingly dense ( Amargant et al , 2020 ). Elastin content is lower in aged porcine ovaries compared to young ones ( Pennarossa et al , 2022 ), and in the human displays a moderate increase from the prepubertal to reproductive-age ovary, before drastically declining in menopausal women ( Ouni et al , 2020 ).…”

Section: Discussionsupporting

confidence: 53%

Spatio-temporal remodelling of the composition and architecture of the human ovarian cortical extracellular matrix duringin vitroculture

et al. 2023

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STUDY QUESTION How does in vitro culture alter the human ovarian cortical extracellular matrix (ECM) network structure? SUMMARY ANSWER The ECM composition and architecture vary in the different layers of the ovarian cortex and are remodelled during in vitro culture. WHAT IS KNOWN ALREADY The ovarian ECM is the scaffold within which follicles and stromal cells are organized. Its composition and structural properties constantly evolve to accommodate follicle development and expansion. Tissue preparation for culture of primordial follicles within the native ECM involves mechanical loosening; this induces undefined modifications in the ECM network and alters cell–cell contact, leading to spontaneous follicle activation. STUDY DESIGN, SIZE, DURATION Fresh ovarian cortical biopsies were obtained from six women aged 28–38 years (mean ± SD: 32.7 ± 4.1 years) at elective caesarean section. Biopsies were cut into fragments of ∼4 × 1 × 1 mm and cultured for 0, 2, 4, or 6 days (D). PARTICIPANTS/MATERIALS, SETTING, METHODS Primordial follicle activation, stromal cell density, and ECM-related protein (collagen, elastin, fibronectin, laminin) positive area in the entire cortex were quantified at each time point using histological and immunohistological analysis. Collagen and elastin content, collagen fibre characteristics, and follicle distribution within the tissue were further quantified within each layer of the human ovarian cortex, namely the outer cortex, the mid-cortex, and the cortex–medulla junction regions. MAIN RESULTS AND THE ROLE OF CHANCE Primordial follicle activation occurred concomitantly with a loosening of the ovarian cortex during culture, characterized by an early decrease in stromal cell density from 3.6 ± 0.2 × 106 at day 0 (D0) to 2.8 ± 0.1 × 106 cells/mm3 at D2 (P = 0.033) and a dynamic remodelling of the ECM. Notably, collagen content gradually fell from 55.5 ± 1.7% positive area at D0 to 42.3 ± 1.1% at D6 (P = 0.001), while elastin increased from 1.1 ± 0.2% at D0 to 1.9 ± 0.1% at D6 (P = 0.001). Fibronectin and laminin content remained stable. Moreover, collagen and elastin distribution were uneven throughout the cortex and during culture. Analysis at the sub-region level showed that collagen deposition was maximal in the outer cortex and the lowest in the mid-cortex (69.4 ± 1.2% versus 53.8 ± 0.8% positive area, respectively, P < 0.0001), and cortical collagen staining overall decreased from D0 to D2 (65.2 ± 2.4% versus 60.6 ± 1.8%, P = 0.033) then stabilized. Elastin showed the converse distribution, being most concentrated at the cortex–medulla junction (3.7 ± 0.6% versus 0.9 ± 0.2% in the outer cortex, P < 0.0001), and cortical elastin peaked at D6 compared to D0 (3.1 ± 0.5% versus 1.3 ± 0.2%, P < 0.0001). This was corroborated by a specific signature of the collagen fibre type across the cortex, indicating a distinct phenotype of the ovarian cortical ECM depending on region and culture period that might be responsible for the spatio-temporal and developmental pattern of follicular distribution observed within the cortex. LARGE SCALE DATA N/A. LIMITATIONS, REASONS FOR CAUTION Ovarian cortical biopsies were obtained from women undergoing caesarean sections. As such, the data obtained may not accurately reflect the ECM distribution and structure of non-pregnant women. WIDER IMPLICATIONS OF THE FINDINGS Clarifying the composition and architecture signature of the human ovarian cortical ECM provides a foundation for further exploration of ovarian microenvironments. It is also critical for understanding the ECM–follicle interactions regulating follicle quiescence and awakening, leading to improvements in both in vitro activation and in vitro growth techniques. STUDY FUNDING/COMPETING INTEREST(S) Medical Research Council grant MR/R003246/1 and Wellcome Trust Collaborative Award in Science: 215625/Z/19/Z. The authors have no conflicts to declare. TRIAL REGISTRATION NUMBER N/A.

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Section: Discussionsupporting

confidence: 53%

Spatio-temporal remodelling of the composition and architecture of the human ovarian cortical extracellular matrix duringin vitroculture

et al. 2023

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“…18 ovaries from 6-month ( n = 9, Young) and 5 years old ( n = 9, Aged) swine were collected at the local abattoir and subjected to whole-organ decellularization protocol [ 2 , 30 – 32 ]. Briefly, organs were frozen at − 80 °C for at least 24 h, thawed in a 37 °C water bath for 30 min, treated with 0.5% sodium dodecyl sulfate (SDS; Bio-Rad) for 3 h, and immersed overnight in 1% Triton X-100 (Sigma-Aldrich).…”

Section: Methodsmentioning

confidence: 99%

“…Among them, aging is one of the most critical and emerging problems in public health, due to the exponential increase of elderly patients suffering with chronic age-onset diseases, often with multiple co-morbidities. Aging is indeed defined as a complex, multifaceted process characterized by a progressive accumulation of macroscopic and microscopic modifications that are accompanied by molecular and cellular damages, negatively affecting organ, tissue, cell, and subcellular organelle functions [ 1 , 2 ]. These senescence processes are influenced by environmental factors as well as by genetic and epigenetic dysregulations that induce a gradual organism decline [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Synergistic Effect of miR-200 and Young Extracellular Matrix-based Bio-scaffolds to Reduce Signs of Aging in Senescent Fibroblasts

Pennarossa

Iorio

Arcuri

et al. 2022

Stem Cell Rev and Rep

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Aging is defined as a complex, multifaceted degenerative process that causes a gradual decline of physiological functions and a rising mortality risk with time. Stopping senescence or even rejuvenating the body represent one of the long-standing human dreams. Somatic cell nuclear transfer as well as cell reprogramming have suggested the possibility to slow or even reverse signs of aging. We exploited miR-200 family ability to induce a transient high plasticity state in human skin fibroblasts isolated from old individuals and we investigated whether this ameliorates cellular and physiological hallmarks of senescence. In addition, based on the assumption that extracellular matrix (ECM) provides biomechanical stimuli directly influencing cell behavior, we examine whether ECM-based bio-scaffolds, obtained from decellularized ovaries of young swine, stably maintain the rejuvenated phenotype acquired by cells after miR-200 exposure. The results show the existence of multiple factors that cooperate to control a unique program, driving the cell clock. In particular, miR-200 family directly regulates the molecular mechanisms erasing cell senescence. However, this effect is transient, reversible, and quickly lost. On the other hand, the use of an adequate young microenvironment stabilizes the miR-200-mediated rejuvenating effects, suggesting that synergistic interactions occur among molecular effectors and ECM-derived biomechanical stimuli. The model here described is a useful tool to better characterize these complex regulations and to finely dissect the multiple and concurring biochemical and biomechanical cues driving the cell biological clock. Graphical Abstract

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“…Principally that connection between age and survival is well-known in ovarian cancer ( 39 ). But it is also possible that the age of the patient also influences the quality of the tissue itself and so the performance of the deep learning algorithm decreases: For older patients, it is known that the composition of the ovarian extracellular matrix (e.g., increase of collagen and decrease of elastin) changes with age ( 40 ), and so the cutting properties of the tissue is altered. Which in turn could lead to unevenly distributed tissue thicknesses or different amounts of cracks.…”

Section: Discussionmentioning

confidence: 99%

How to learn with intentional mistakes: NoisyEnsembles to overcome poor tissue quality for deep learning in computational pathology

et al. 2022

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There is a lot of recent interest in the field of computational pathology, as many algorithms are introduced to detect, for example, cancer lesions or molecular features. However, there is a large gap between artificial intelligence (AI) technology and practice, since only a small fraction of the applications is used in routine diagnostics. The main problems are the transferability of convolutional neural network (CNN) models to data from other sources and the identification of uncertain predictions. The role of tissue quality itself is also largely unknown. Here, we demonstrated that samples of the TCGA ovarian cancer (TCGA-OV) dataset from different tissue sources have different quality characteristics and that CNN performance is linked to this property. CNNs performed best on high-quality data. Quality control tools were partially able to identify low-quality tiles, but their use did not increase the performance of the trained CNNs. Furthermore, we trained NoisyEnsembles by introducing label noise during training. These NoisyEnsembles could improve CNN performance for low-quality, unknown datasets. Moreover, the performance increases as the ensemble become more consistent, suggesting that incorrect predictions could be discarded efficiently to avoid wrong diagnostic decisions.

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Impact of Aging on the Ovarian Extracellular Matrix and Derived 3D Scaffolds

Cited by 20 publications

References 51 publications

Spatio-temporal remodelling of the composition and architecture of the human ovarian cortical extracellular matrix duringin vitroculture

Spatio-temporal remodelling of the composition and architecture of the human ovarian cortical extracellular matrix duringin vitroculture

Synergistic Effect of miR-200 and Young Extracellular Matrix-based Bio-scaffolds to Reduce Signs of Aging in Senescent Fibroblasts

How to learn with intentional mistakes: NoisyEnsembles to overcome poor tissue quality for deep learning in computational pathology

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