Min Junhong, Vonesch Cédric, Kirshner Hagai, Carlini Lina, Olivier Nicolas, Holden Seamus, Manley Suliana, Ye Jong Chul, Unser Michael
Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.
1] Institute of Microengineering, EPFL, Switzerland [2].
Sci Rep. 2014 Apr 3;4:4577. doi: 10.1038/srep04577.
Super resolution microscopy such as STORM and (F)PALM is now a well known method for biological studies at the nanometer scale. However, conventional imaging schemes based on sparse activation of photo-switchable fluorescent probes have inherently slow temporal resolution which is a serious limitation when investigating live-cell dynamics. Here, we present an algorithm for high-density super-resolution microscopy which combines a sparsity-promoting formulation with a Taylor series approximation of the PSF. Our algorithm is designed to provide unbiased localization on continuous space and high recall rates for high-density imaging, and to have orders-of-magnitude shorter run times compared to previous high-density algorithms. We validated our algorithm on both simulated and experimental data, and demonstrated live-cell imaging with temporal resolution of 2.5 seconds by recovering fast ER dynamics.
诸如STORM和(F)PALM之类的超分辨率显微镜如今是用于纳米尺度生物学研究的一种广为人知的方法。然而,基于光开关荧光探针稀疏激活的传统成像方案在时间分辨率上固有地较慢,这在研究活细胞动力学时是一个严重的限制。在此,我们提出一种用于高密度超分辨率显微镜的算法,该算法将促进稀疏性的公式与点扩散函数(PSF)的泰勒级数近似相结合。我们的算法旨在在连续空间上提供无偏定位,并在高密度成像时具有高召回率,并且与先前的高密度算法相比,运行时间缩短了几个数量级。我们在模拟数据和实验数据上都验证了我们的算法,并通过恢复快速的内质网动态,展示了具有2.5秒时间分辨率的活细胞成像。