Robust nuclear lamina-based cell classification of aging and senescent cells

被引:20
|
作者
Righolt, Christiaan H. [2 ,4 ]
van 't Hoff, Merel L. R. [1 ]
Vermolen, Bart J. [2 ,3 ]
Young, Ian T. [2 ]
Raz, Vered [1 ]
机构
[1] Leiden Univ, Dept Human Genet, Med Ctr, NL-2300 RA Leiden, Netherlands
[2] Delft Univ Technol, Fac Appl Phys, Quantitat Imaging Grp, Delft, Netherlands
[3] Univ Med Ctr Utrecht, Imaging Div, Utrecht, Netherlands
[4] Univ Manitoba, Manitoba Inst Cell Biol, Winnipeg, MB, Canada
来源
AGING-US | 2011年 / 3卷 / 12期
关键词
cell senescence; aging cells; apoptosis; nuclear lamina; image processing; TELOMERASE ACTIVITY; STEM-CELLS; AGGREGATION; LENGTH;
D O I
10.18632/aging.100414
中图分类号
Q2 [细胞生物学];
学科分类号
071009 ; 090102 ;
摘要
Changes in the shape of the nuclear lamina are exhibited in senescent cells, as well as in cells expressing mutations in lamina genes. To identify cells with defects in the nuclear lamina we developed an imaging method that quantifies the intensity and curvature of the nuclear lamina. We show that this method accurately describes changes in the nuclear lamina. Spatial changes in nuclear lamina coincide with redistribution of lamin A proteins and local reduction in protein mobility in senescent cell. We suggest that local accumulation of lamin A in the nuclear envelope leads to bending of the structure. A quantitative distinction of the nuclear lamina shape in cell populations was found between fresh and senescent cells, and between primary myoblasts from young and old donors. Moreover, with this method mutations in lamina genes were significantly distinct from cells with wild-type genes. We suggest that this method can be applied to identify abnormal cells during aging, in in vitro propagation, and in lamina disorders.
引用
收藏
页码:1192 / 1201
页数:10
相关论文
共 50 条
  • [31] Robust classification of cell cycle phase and biological feature extraction by image-based deep learning
    Nagao, Yukiko
    Sakamoto, Mika
    Chinen, Takumi
    Okada, Yasushi
    Takao, Daisuke
    MOLECULAR BIOLOGY OF THE CELL, 2020, 31 (13) : 1346 - 1354
  • [32] Microfluidic Capture Device for Simple, Cell Surface Marker-Based Quantification of Senescent CD8+ T Cells
    Choi, Yo-han
    Kim, Woo-Joong
    Lee, Dongwoo
    Jung, Bum-Joon
    Shin, Eui-Cheol
    Lee, Wonhee
    BIOCHIP JOURNAL, 2024, 18 (03) : 382 - 392
  • [33] PARACRINE EFFECTS OF YOUNG NEONATAL SKIN CELLS ON SENESCENT FIBROBLASTS: IMPLICATIONS FOR CELL-BASED THERAPEUTIC APPROACHES IN WOUND REPAIR
    Pratsinis, Harris
    Armatas, Andreas
    Dimozi, Anastasia
    Lefaki, Maria
    Vassiliu, Pantelis
    Kletsas, Dimitris
    WOUND REPAIR AND REGENERATION, 2012, 20 (05) : A106 - A106
  • [34] A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma Based on CT Images
    Yao, Ni
    Hu, Hang
    Chen, Kaicong
    Huang, Huan
    Zhao, Chen
    Guo, Yuan
    Li, Boya
    Nan, Jiaofen
    Li, Yanting
    Han, Chuang
    Zhu, Fubao
    Zhou, Weihua
    Tian, Li
    JOURNAL OF IMAGING INFORMATICS IN MEDICINE, 2024,
  • [35] ROBUST CELL-FREE DNA METHYLATION-BASED CLASSIFICATION OF PEDIATRIC BRAIN TUMOR LIQUID BIOPSIES
    Smith, Kyle S.
    Fischer, Tom T.
    Lin, Hong
    Senfter, Daniel
    Wedig, Tatjana
    Schwarz, Nathalie
    Stepien, Natalia
    Madlener, Sibylle
    Haberler, Christine
    Hulleman, Esther
    Atkinson, Natasha
    Dhanda, Sandeep K.
    Pfister, Stefan
    Upadhyaya, Santhosh
    Gajjar, Amar
    Robinson, Giles W.
    Haapasalo, Joonas
    Haapasalo, Hannu
    Nordfors, Kristiina
    Gojo, Johannes
    Maass, Kendra K.
    Pajtler, Kristian W.
    Northcott, Paul A.
    NEURO-ONCOLOGY, 2024, 26
  • [36] Senolytic elimination of senescent cells improved periodontal ligament stem cell-based bone regeneration partially through inhibiting YAP
    Jia, Linglu
    Xiao, Han
    Hao, Zhenghao
    Sun, Shaoqing
    Zhao, Wenxi
    Gong, Zikai
    Gu, Weiting
    Wen, Yong
    BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR CELL RESEARCH, 2025, 1872 (03):
  • [37] Polymer-Based Synthetic Dendritic Cells for Tailoring Robust and Multifunctional T Cell Responses
    Mandal, Subhra
    Hammink, Roel
    Tel, Jurjen
    Eksteen-Akeroyd, Zaskia H.
    Rowan, Alan E.
    Blank, Kerstin
    Figdor, Carl G.
    ACS CHEMICAL BIOLOGY, 2015, 10 (02) : 485 - 492
  • [38] Classification of renal cell carcinoma based on expression of VEGF and VEGF receptors in both tumor cells and endothelial cells
    Kluger, Harriet M.
    Siddiqui, Summar F.
    Angeletti, Cesar
    Sznol, Mario
    Kelly, William K.
    Molinaro, Annette M.
    Camp, Robert L.
    LABORATORY INVESTIGATION, 2008, 88 (09) : 962 - 972
  • [39] Machine learning based classification of cells into chronological stages using single-cell transcriptomics
    Singh, Sumeet Pal
    Janjuha, Sharan
    Chaudhuri, Samata
    Reinhardt, Susanne
    Kraenkel, Annekathrin
    Dietz, Sevina
    Eugster, Anne
    Bilgin, Halil
    Korkmaz, Selcuk
    Zararsiz, Gokmen
    Ninov, Nikolay
    Reid, John E.
    SCIENTIFIC REPORTS, 2018, 8
  • [40] Cell morphology-based classification of red blood cells using holographic imaging informatics
    Yi, Faliu
    Moon, Inkyu
    Javidi, Bahram
    BIOMEDICAL OPTICS EXPRESS, 2016, 7 (06): : 2385 - 2399