Herrmann Anne, Taylor Arthur, Murray Patricia, Poptani Harish, Sée Violaine
1 Department of Biochemistry, University of Liverpool, Liverpool, United Kingdom.
2 Centre for Preclinical Imaging, Department of Cellular and Molecular Physiology, University of Liverpool, Liverpool, United Kingdom.
Mol Imaging. 2018 Jan-Dec;17:1536012118809585. doi: 10.1177/1536012118809585.
Metastasis is the most common cause of death for patients with cancer. To fully understand the steps involved in metastatic dissemination, in vivo models are required, of which murine ones are the most common. Therefore, preclinical imaging methods such as magnetic resonance imaging (MRI) have mainly been developed for small mammals and their potential to monitor cancer growth and metastasis in nonmammalian models is not fully harnessed. We have here used MRI to measure primary neuroblastoma tumor size and metastasis in a chick embryo model. We compared its sensitivity and accuracy to end-point fluorescence detection upon dissection. Human neuroblastoma cells labeled with green fluorescent protein (GFP) and micron-sized iron particles were implanted on the extraembryonic chorioallantoic membrane of the chick at E7. T RARE, T-weighted fast low angle shot (FLASH) as well as time-of-flight MR angiography imaging were applied at E14. Micron-sized iron particle labeling of neuroblastoma cells allowed in ovo observation of the primary tumor and tumor volume measurement noninvasively. Moreover, T weighted and FLASH imaging permitted the detection of small metastatic deposits in the chick embryo, thereby reinforcing the potential of this convenient, 3R compliant, in vivo model for cancer research.
转移是癌症患者最常见的死因。为了全面了解转移扩散所涉及的步骤,需要体内模型,其中小鼠模型最为常见。因此,诸如磁共振成像(MRI)等临床前成像方法主要是针对小型哺乳动物开发的,其在非哺乳动物模型中监测癌症生长和转移的潜力尚未得到充分利用。我们在此利用MRI测量鸡胚模型中原发性神经母细胞瘤的肿瘤大小和转移情况。我们将其与解剖时的终点荧光检测的灵敏度和准确性进行了比较。用绿色荧光蛋白(GFP)和微米级铁颗粒标记的人神经母细胞瘤细胞在E7期植入鸡的胚外绒毛尿囊膜。在E14期应用T RARE、T加权快速低角度激发(FLASH)以及飞行时间磁共振血管造影成像。神经母细胞瘤细胞的微米级铁颗粒标记使得在卵内无创观察原发性肿瘤并测量肿瘤体积成为可能。此外,T加权和FLASH成像能够检测鸡胚中的小转移灶,从而增强了这种方便、符合3R原则的体内癌症研究模型的潜力。