Bruker Ultima Investigator高品质双光子成像系统

多光子显微镜采用在生物组织中穿透性强的近红外/红外激光去激发样品中的荧光发光基团，进行荧光成像。该技术光毒性低，成像深度高，因此适用于厚的活体组织（如脑片、完整器官）甚至活体生物标本的成像及功能研究。2013年Bruker收购Prairie Technology公司，该公司于1996年开始生产双光子显微镜，是最早推出商业化双光子的厂家之一，在欧美市场的占有率极高。目前，Bruker的双光子产品主要有专注于高品质活体成像的Investigators系列以及专注于进行活体功能性研究的Ultima系列。20多年的技术沉淀，使得Bruker的双光子产品在仪器性能、使用便利性以及仪器应用拓展性方面都展现出无与伦比的优势。

（1）三种成像扫描模式：常规的检流振镜扫描（Galvo Scanning），龙卷风扫描（Spiral Scanning）和快速振镜扫描（Resonant Scanning）；

（2）有多种旋转物镜可供选择(单轴手动，多轴手动旋转、多轴电动)，进行离轴成像，可以从不同角度对实验样品进行成像；

（3）可升级移动显微镜平台，结合旋转物镜，无需样品移动，使其保持在自然生理状态下，即可找到感兴趣的成像视野或进行多视野图像采集

Streamlined Multiphoton Imaging

Best starting place for in vivo studies

As the most streamlined model of Bruker's Ultima family of multiphoton microscopes, Ultima Investigator™ features a base system specifically optimized for in vivo studies and is designed for add-on flexibility with a host of specialized options. Ultima Investigator's high-resolution, high-speed, high-sensitivity deep imaging provides the ultimate value for smaller labs and additional imaging bandwidth in larger labs.

Intravital Imaging with Behavioral Paradigms

Intravital imaging using functional indicators provides the opportunity to measure neural activity in awake, behaving specimens. Ultima 2Pplus provides the imaging speed to capture activity of neural networks in single or multiple focal planes.

Video courtesy of Michael Goard, MIT

Oncology

Multiphoton intravital imaging provides a high resolution method for imaging tumor implants. Tumor progression and response to pharmaceuticals as well as immune cell response, can be measured through longitudinal imaging sessions. Ultima 2Pplus can be configured to image the collagen fibers.

IntraVital Tumor Progression

High-Resolution Anatomic Mapping of Neural Networks

The Atlas Imaging module in Bruker's Prairie View software simplifies setup of 2-D and 3-D automated montages. X,Y boundaries are easily setup through a thumbnail display. Tiles not containing areas of interest can be turned off, and the Z range for 3-D montages can be variable, allowing more efficient collection of data.

Multifield montage of neural network; Courtesy Datta SR, Harvard University.