期刊论文详细信息
BMC Genomics
High-throughput phenotyping and genetic linkage of cortical bone microstructure in the mouse
Marco Stampanoni3  Ralph Müller1  Leah Rae Donahue2  Kevin S Mader3 
[1]Institute for Biomechanics, ETH Zurich, Zurich, 8093, Switzerland
[2]The Jackson Laboratory, Bar Harbor, 04609, ME, USA
[3]Swiss Light Source, Paul Scherrer Institut, WBBA 213, 5352, PSI, Villigen, Switzerland
关键词: cell alignment;    Cell distribution;    cell shape;    Cortical bone;    3D morphology;    Osteocyte lacunae;    Quantitative trait loci;    3D morphology;    Automated 3D imaging;    Phenotyping;   
Others  :  1219234
DOI  :  10.1186/s12864-015-1617-y
 received in 2014-10-09, accepted in 2015-05-05,  发布年份 2015
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【 摘 要 】

Background

Understanding cellular structure and organization, which plays an important role in biological systems ranging from mechanosensation to neural organization, is a complicated multifactorial problem depending on genetics, environmental factors, and stochastic processes. Isolating these factors necessitates the measurement and sensitive quantification of many samples in a reliable, high-throughput, unbiased manner. In this manuscript we present a pipelined approach using a fully automated framework based on Synchrotron-based X-ray Tomographic Microscopy (SRXTM) for performing a full 3D characterization of millions of substructures.

Results

We demonstrate the framework on a genetic study on the femur bones of in-bred mice. We measured 1300 femurs from a F2 cross experiment in mice without the growth hormone (which can confound many of the smaller structural differences between strains) and characterized more than 50 million osteocyte lacunae (cell-sized hollows in the bone). The results were then correlated with genetic markers in a process called quantitative trait localization (QTL). Our findings provide a mapping between regions of the genome (all 19 autosomes) and observable phenotypes which could explain between 8–40 % of the variance using between 2–10 loci for each trait. This map shows 4 areas of overlap with previous studies looking at bone strength and 3 areas not previously associated with bone.

Conclusions

The mapping of microstructural phenotypes provides a starting point for both structure-function and genetic studies on murine bone structure and the specific loci can be investigated in more detail to identify single gene candidates which can then be translated to human investigations. The flexible infrastructure offers a full spectrum of shape, distribution, and connectivity metrics for cellular networks and can be adapted to a wide variety of materials ranging from plant roots to lung tissue in studies requiring high sample counts and sensitive metrics such as the drug-gene interactions and high-throughput screening.

【 授权许可】

   
2015 Mader et al.

【 预 览 】
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【 参考文献 】
  • [1]Huang X, Feng Q, Qian Q, Zhao Q, Wang L, Wang A et al.. High-throughput genotyping by whole-genome resequencing. Genome Res. 2009; 19(6):1068-76.
  • [2]Wang Z, Clavijo CA, Roessl E, van Stevendaal U, Koehler T, Hauser N et al.. Image fusion scheme for differential phase contrast mammography. J Instrum. 2013; 8(07):07011.
  • [3]de Souza N. High-throughput phenotyping. Nat Methods. 2010; 7(1):36-6.
  • [4]Hellrung DJ, Rossi G, Link CJ. High-throughput fluorescent screening of transgenic animals: phenotyping and haplotyping. Cytometry. Part A : J Int Soc Anal Cytol. 2006; 69(10):1092-5.
  • [5]Pardo-Martin C, Allalou A, Medina J, Eimon PM, Wählby C, Fatih Yanik M. High-throughput hyperdimensional vertebrate phenotyping. Nat Commun. 2013; 4:1467.
  • [6]Tanabata T, Shibaya T, Hori K, Ebana K, Yano M. SmartGrain: high-throughput phenotyping software for measuring seed shape through image analysis. Plant Physiol. 2012; 160(4):1871-80.
  • [7]Team RC. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria; 2013.
  • [8]Jiao F, Chiu H, Jiao Y, de Rijk WG, Li X, Eckstein EC et al.. Quantitative trait loci for tibial bone strength in C57BL/6J and C3H/HeJ inbred strains of mice. J Genet. 2010; 89(1):21-7.
  • [9]Jiao Y, Chiu H, Fan Z, Jiao F, Eckstein EC, Beamer WG et al.. Quantitative trait loci that determine mouse tibial nanoindentation properties in an F2 population derived from C57BL/6J x C3H/HeJ. Calcif Tissue Int. 2007; 80(6):383-90.
  • [10]Ruffoni D, Kohler T, Voide R, Wirth AJ, Donahue LR, Müller R, et al. 2013; 55(1):216–1. doi:10.1016/j.bone.2013.02.015.
  • [11]Mader K, Marone F, Mikuljan G, Isenegger A, Stampanoni M. High-throughput, fully-automatic, synchrotron-based microscopy station at TOMCAT. J Synchrotron Radiat. 2011; 18(2):117-24.
  • [12]Blomfeldt R, Törnkvist H, Ponzer S, Söderqvist A, Tidermark J. Internal fixation versus hemiarthroplasty for displaced fractures of the femoral neck in elderly patients with severe cognitive impairment. J Bone Joint Surg. Br Vol. 2005; 87(4):523-9.
  • [13]Snyder SM SE, Snyder SM, Schneider E. Estimation of mechanical properties of cortical bone by computed tomography. J Orth Res. 1991; 9(3):422-31.
  • [14]Beamer WG, Shultz KL, Coombs HF, Horton LG, Donahue LR, Rosen CJ. Multiple quantitative trait loci for cortical and trabecular bone regulation map to mid-distal mouse chromosome 4 that shares linkage homology to human chromosome 1p36. J Bone Miner Res. 2011. doi:10.1002/jbmr.515.
  • [15]Bouxsein ML, Uchiyama T, Rosen CJ, Shultz KL, Donahue LR, Turner CH et al.. Mapping quantitative trait loci for vertebral trabecular bone volume fraction and microarchitecture in mice. J Bone Miner Res. 2004; 19(4):587-99.
  • [16]Kohler T, Stauber M, Donahue LR, Müller R, Rae L. Automated compartmental analysis for high-throughput skeletal phenotyping in femora of genetic mouse models. Bone. 2007; 41(4):659-7.
  • [17]Koller DL. Genome Screen for QTLs Contributing to Normal Variation in Bone Mineral Density and Osteoporosis. J Clinical Endocrinol Metab. 2000; 85(9):3116-120.
  • [18]Norris FC, Wong MD, Greene NDE, Scambler PJ, Weaver T, Weninger WJ et al.. A coming of age: advanced imaging technologies for characterising the developing mouse. Trends Genet. 2013; 29(12):700-711.
  • [19]Jansen RC, Stam P. High resolution of quantitative traits into multiple loci via interval mapping. Genetics. 1994; 136(4):1447-55.
  • [20]Topp CN, Iyer-Pascuzzi AS, Anderson JT, Lee CR, Zurek PR, Symonova O et al.. 3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture. Proc Nat Acad Sci USA. 2013; 110(18):1695-704.
  • [21]Raman P, Raman R, Newman B, Venkatraman R, Raman B, Robinson TE. Development and validation of automated 2D-3D bronchial airway matching to track changes in regional bronchial morphology using serial low-dose chest CT scans in children with chronic lung disease. J Digital Imaging. 2010; 23(6):744-54.
  • [22]Rosen CJ, Beamer WG, Donahue LR. Defining the genetics of osteoporosis: using the mouse to understand man. Osteoporos Int. 2001; 12(10):803-10.
  • [23]Elefteriou F, Yang X. Genetic mouse models for bone studies–Strengths and limitations. Bone. 2011; 49(6):1242-54.
  • [24]Mader KS, Schneider P, Müller R, Stampanoni M. A quantitative framework for the 3D characterization of the osteocyte lacunar system. Bone. 2013; 57(1):142-54.
  • [25]Volkman SK, Galecki AT, Burke DT, Miller RA, Goldstein SA. Quantitative trait loci that modulate femoral mechanical properties in a genetically heterogeneous mouse population. J Bone Miner Res. 2004; 19(9):1497-505.
  • [26]Devoto M, Falchi M. Genetic mapping of quantitative trait loci for disease-related phenotypes. Methods Mol Biol. 2012; 871:281-311.
  • [27]Schneider P, Stauber M, Voide R, Stampanoni M, Donahue LR, Müller R. Ultrastructural Properties in Cortical Bone Vary Greatly in Two Inbred Strains of Mice as Assessed by Synchrotron Light Based Micro- and Nano-CT. J Bone Miner Res. 2007; 22(10):1557-70.
  • [28]Hildebrand T, Ruegsegger P. A new method for the model-independent assessment of thickness in three-dimensional images. J Microsc. 1997; 185(1):67-75.
  • [29]Broman KW, Wu H, Sen S, Churchill GA. R/qtl: QTL mapping in experimental crosses. Bioinformatics (Oxford, England). 2003; 19(7):889-90.
  • [30]Broman KW, Sen S. A Guide to QTL Mapping with R/qtl: Springer; 2009. , Accessed 26/10/11. http://books. [http:/ / www.springer.com/ life+sciences/ systems+biology+and+bioinformatics/ book/ 978-0-387-92124-2] webcitegoogle.com/books?hl=enlr=id=tPoXT_dCguQC;oi=fndpg=PA1dq=A+Guide+to+QTL+Mapping+with+R/qtlots=8tscXutQz9sig=T2EQ2sI3bL8GxWuc1rxSFmHkKeI webcite
  • [31]Griffiths AJ, Miller JH, Suzuki DT, Lewontin RC, Gelbart WM. An Introduction to Genetic Analysis. W. H. Freeman, New York; 2000.
  • [32]Arends D, Prins P, Jansen RC, Broman KW. R/qtl: high-throughput multiple QTL mapping. Bioinformatics (Oxford, England). 2010; 26(23):2990-2.
  • [33]Wickham H. Ggplot2: Elegant Graphics for Data Analysis: Springer; 2009. http://had. co.nz/ggplot2/book webcite
  • [34]Wickham H. The Split-Apply-Combine Strategy for Data Analysis. J Stat Softw. 2011; 40(1):1-29.
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