IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-41656-1.html
   My bibliography  Save this article

Decoding the endometrial niche of Asherman’s Syndrome at single-cell resolution

Author

Listed:
  • Xavier Santamaria

    (Carlos Simon Foundation, INCLIVA Health Research Institute
    Department Ob/Gyn Vall d’Hebron Institut de Recerca)

  • Beatriz Roson

    (Carlos Simon Foundation, INCLIVA Health Research Institute)

  • Raul Perez-Moraga

    (Carlos Simon Foundation, INCLIVA Health Research Institute
    Igenomix R&D)

  • Nandakumar Venkatesan

    (University of Valencia)

  • Maria Pardo-Figuerez

    (Carlos Simon Foundation, INCLIVA Health Research Institute)

  • Javier Gonzalez-Fernandez

    (Carlos Simon Foundation, INCLIVA Health Research Institute)

  • Jaime Llera-Oyola

    (Carlos Simon Foundation, INCLIVA Health Research Institute)

  • Estefania Fernández

    (Carlos Simon Foundation, INCLIVA Health Research Institute)

  • Inmaculada Moreno

    (Carlos Simon Foundation, INCLIVA Health Research Institute)

  • Andres Salumets

    (University of Tartu
    Competence Centre on Health Technologies
    Karolinska Institute and Karolinska University Hospital)

  • Hugo Vankelecom

    (University of Leuven (KU Leuven))

  • Felipe Vilella

    (Carlos Simon Foundation, INCLIVA Health Research Institute)

  • Carlos Simon

    (Carlos Simon Foundation, INCLIVA Health Research Institute
    University of Valencia
    Beth Israel Deaconess Medical Center, Harvard Medical School)

Abstract

Asherman’s Syndrome is characterized by intrauterine adhesions or scarring, which cause infertility, menstrual abnormalities, and recurrent pregnancy loss. The pathophysiology of this syndrome remains unknown, with treatment restricted to recurrent surgical removal of intrauterine scarring, which has limited success. Here, we decode the Asherman’s Syndrome endometrial cell niche by analyzing data from over 200,000 cells with single-cell RNA-sequencing in patients with this condition and through in vitro analyses of Asherman’s Syndrome patient-derived endometrial organoids. Our endometrial atlas highlights the loss of the endometrial epithelium, alterations to epithelial differentiation signaling pathways such as Wnt and Notch, and the appearance of characteristic epithelium expressing secretory leukocyte protease inhibitor during the window of implantation. We describe syndrome-associated alterations in cell-to-cell communication and gene expression profiles that support a dysfunctional pro-fibrotic, pro-inflammatory, and anti-angiogenic environment.

Suggested Citation

  • Xavier Santamaria & Beatriz Roson & Raul Perez-Moraga & Nandakumar Venkatesan & Maria Pardo-Figuerez & Javier Gonzalez-Fernandez & Jaime Llera-Oyola & Estefania Fernández & Inmaculada Moreno & Andres , 2023. "Decoding the endometrial niche of Asherman’s Syndrome at single-cell resolution," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41656-1
    DOI: 10.1038/s41467-023-41656-1
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-41656-1
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-023-41656-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Harunobu Kagawa & Alok Javali & Heidar Heidari Khoei & Theresa Maria Sommer & Giovanni Sestini & Maria Novatchkova & Yvonne Scholte op Reimer & Gaël Castel & Alexandre Bruneau & Nina Maenhoudt & Jenna, 2022. "Human blastoids model blastocyst development and implantation," Nature, Nature, vol. 601(7894), pages 600-605, January.
    2. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    3. Suoqin Jin & Christian F. Guerrero-Juarez & Lihua Zhang & Ivan Chang & Raul Ramos & Chen-Hsiang Kuan & Peggy Myung & Maksim V. Plikus & Qing Nie, 2021. "Inference and analysis of cell-cell communication using CellChat," Nature Communications, Nature, vol. 12(1), pages 1-20, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Prabal Das & D. A. Sachindra & Kironmala Chanda, 2022. "Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6043-6071, December.
    2. Paulo Infante & Gonçalo Jacinto & Anabela Afonso & Leonor Rego & Pedro Nogueira & Marcelo Silva & Vitor Nogueira & José Saias & Paulo Quaresma & Daniel Santos & Patrícia Góis & Paulo Rebelo Manuel, 2023. "Factors That Influence the Type of Road Traffic Accidents: A Case Study in a District of Portugal," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    3. Yanchuan Li & Huamei Li & Cheng Peng & Ge Meng & Yijun Lu & Honglin Liu & Li Cui & Huan Zhou & Zhu Xu & Lingyun Sun & Lihong Liu & Qing Xiong & Beicheng Sun & Shiping Jiao, 2024. "Unraveling the spatial organization and development of human thymocytes through integration of spatial transcriptomics and single-cell multi-omics profiling," Nature Communications, Nature, vol. 15(1), pages 1-25, December.
    4. Ephrem Habyarimana & Faheem S Baloch, 2021. "Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-23, March.
    5. Alexander Wettstein & Gabriel Jenni & Ida Schneider & Fabienne Kühne & Martin grosse Holtforth & Roberto La Marca, 2023. "Predictors of Psychological Strain and Allostatic Load in Teachers: Examining the Long-Term Effects of Biopsychosocial Risk and Protective Factors Using a LASSO Regression Approach," IJERPH, MDPI, vol. 20(10), pages 1-20, May.
    6. Tang, Kayu & Parsons, David J. & Jude, Simon, 2019. "Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 24-36.
    7. Daifeng Xiang & Gangsheng Wang & Jing Tian & Wanyu Li, 2023. "Global patterns and edaphic-climatic controls of soil carbon decomposition kinetics predicted from incubation experiments," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    8. Joel Podgorski & Oliver Kracht & Luis Araguas-Araguas & Stefan Terzer-Wassmuth & Jodie Miller & Ralf Straub & Rolf Kipfer & Michael Berg, 2024. "Groundwater vulnerability to pollution in Africa’s Sahel region," Nature Sustainability, Nature, vol. 7(5), pages 558-567, May.
    9. Lichun Ma & Sophia Heinrich & Limin Wang & Friederike L. Keggenhoff & Subreen Khatib & Marshonna Forgues & Michael Kelly & Stephen M. Hewitt & Areeba Saif & Jonathan M. Hernandez & Donna Mabry & Roman, 2022. "Multiregional single-cell dissection of tumor and immune cells reveals stable lock-and-key features in liver cancer," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    10. Qingnan Liang & Yuefan Huang & Shan He & Ken Chen, 2023. "Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    11. Tranos, Emmanouil & Incera, Andre Carrascal & Willis, George, 2022. "Using the web to predict regional trade flows: data extraction, modelling, and validation," OSF Preprints 9bu5z, Center for Open Science.
    12. Štefan Lyócsa & Petra Vašaničová & Branka Hadji Misheva & Marko Dávid Vateha, 2022. "Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    13. Faith H. Brennan & Yang Li & Cankun Wang & Anjun Ma & Qi Guo & Yi Li & Nicole Pukos & Warren A. Campbell & Kristina G. Witcher & Zhen Guan & Kristina A. Kigerl & Jodie C. E. Hall & Jonathan P. Godbout, 2022. "Microglia coordinate cellular interactions during spinal cord repair in mice," Nature Communications, Nature, vol. 13(1), pages 1-20, December.
    14. Marcos Rodrigues & Fermín Alcasena & Pere Gelabert & Cristina Vega‐García, 2020. "Geospatial Modeling of Containment Probability for Escaped Wildfires in a Mediterranean Region," Risk Analysis, John Wiley & Sons, vol. 40(9), pages 1762-1779, September.
    15. Sandra Curras-Alonso & Juliette Soulier & Thomas Defard & Christian Weber & Sophie Heinrich & Hugo Laporte & Sophie Leboucher & Sonia Lameiras & Marie Dutreix & Vincent Favaudon & Florian Massip & Tho, 2023. "An interactive murine single-cell atlas of the lung responses to radiation injury," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    16. Giovanny Pillajo-Quijia & Blanca Arenas-Ramírez & Camino González-Fernández & Francisco Aparicio-Izquierdo, 2020. "Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods," Sustainability, MDPI, vol. 12(4), pages 1-28, February.
    17. Zander S. Venter & Adam Sadilek & Charlotte Stanton & David N. Barton & Kristin Aunan & Sourangsu Chowdhury & Aaron Schneider & Stefano Maria Iacus, 2021. "Mobility in Blue-Green Spaces Does Not Predict COVID-19 Transmission: A Global Analysis," IJERPH, MDPI, vol. 18(23), pages 1-12, November.
    18. Moujtaba Y. Kasmani & Paytsar Topchyan & Ashley K. Brown & Ryan J. Brown & Xiaopeng Wu & Yao Chen & Achia Khatun & Donia Alson & Yue Wu & Robert Burns & Chien-Wei Lin & Matthew R. Kudek & Jie Sun & We, 2023. "A spatial sequencing atlas of age-induced changes in the lung during influenza infection," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    19. G. Brooke Anderson & Keith W. Oleson & Bryan Jones & Roger D. Peng, 2018. "Classifying heatwaves: developing health-based models to predict high-mortality versus moderate United States heatwaves," Climatic Change, Springer, vol. 146(3), pages 439-453, February.
    20. Van Belle, Jente & Guns, Tias & Verbeke, Wouter, 2021. "Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains," European Journal of Operational Research, Elsevier, vol. 288(2), pages 466-479.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41656-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.