Identification of endoplasmic reticulum formation mechanism by multi-parametric, quantitative super-resolution imaging

Abstract

The endoplasmic reticulum (ER) is a highly dynamic membrane-bound organelle in eukaryotic cells which spreads throughout the whole cell and contacts and interacts with almost all organelles, yet quantitative approaches to assess ER reorganization are lacking. Herein we propose a multi-parametric, quantitative method combining pixel-wise orientation and waviness features and apply it to the time-dependent images of co-labeled ER and microtubule (MT) from U2OS cells acquired from two-dimensional structured illumination microscopy (2D SIM). Analysis results demonstrate that these morphological features are sensitive to ER reshaping and a combined use of them is a potential biomarker for ER formation. A new, to the best of our knowledge, mechanism of MT-associated ER formation, termed hooking, is identified based on distinct organizational alterations caused by interaction between ER and MT which are different from those of the other three mechanisms already known, validated by 100% discrimination accuracy in classifying four MT-associated ER formation mechanisms.

Mapping the 3D remodeling of the extracellular matrix in human hypertrophic scar by multi-parametric multiphoton imaging using endogenous contrast

“…The general process of MFM analysis is shown in Fig. 1 C. Based on the voxel-wise 3D orientation, directional variance and waviness, quantified in a truly 3D context, were calculated to measure the fiber alignment and bending degree [ [41] , [42] , [43] , [44] ], with both metrics ranging from 0 to 1. For directional variance, a lower variance value corresponded to more highly aligned fibers while a higher readout revealed more random fiber alignment.…”

“…Based on voxel-wise quantities, we were able to generate color-coded maps to visually display each metric. Algorithm details of these metrics mentioned above could be found in our previous study [ [40] , [41] , [42] , [43] , [44] ] and all the calculations were performed using MATLAB software.…”

“…The weighted vector summation algorithm proposed in our previous study enabled highly-accurate, truly 3D characterization of spatial orientation for fiber-like structures with a high sensitivity [ 40 ]. Based on orientation information, we further proposed optical metrics, named directional variance and waviness, respectively, as quantitative measures of alignment and bending degree of fibers, and applied them to mapping physiological and pathological functions of collagen fibers in articular cartilage, mammary glands, breast tissues, ovarian tissues and pancreas, in the context of diseases including osteoarthritis and cancer [ [41] , [42] , [43] , [44] ]. Besides, we developed an algorithm for the quantification of morphologically localized distribution of fiber-like structures, termed local coverage, which provided new insights into the spatial organization of fibrous tissues [ 45 ].…”