mouse kidney tissue sections  (Thermo Fisher)

Journal: eLife

Article Title: Revealing architectural order with quantitative label-free imaging and deep learning

doi: 10.7554/eLife.55502

Figure Lengend Snippet: ( A ) Light path of the microscope. Volumes of polarization-resolved images are acquired by illuminating the specimen with light of diverse polarization states. Polarization states are controlled using a liquid-crystal universal polarizer. Isotropic material’s optical path length variations cause changes in the wavefront (i.e., phase) of light that is measurable through defocused intensity stack. Anisotropic material not only changes the wavefront, but also changes the polarization of light depending on the degree of optical anisotropy (retardance) and orientation of anisotropy. Intensity Z-stacks of an example specimen, mouse kidney tissue, under five illumination polarization states ( I RCP , I 0 , I 45 , I 90 , I 135 are shown. The intensity variations that encode the reconstructed physical properties of isotropic and anisotropic material are illustrated in the stack I 135 . These polarization-resolved stacks are used to reconstruct (Materials and methods) the specimen’s retardance, slow-axis orientation, and phase. Slow-axis orientation at given voxel reports the axis in the focal plane along which the material is the densest and is represent by a color according to the half-wheel shown in inset. ( B ) Multi-channel, 2.5D U-Net model is trained to predict fluorescent structures from label-free measurements. In this example 3D distribution of F-actin and nuclei are predicted. During training, pairs of label-free images and fluorescence images are supplied as inputs and targets, respectively, to the U-Net model. The model is optimized by minimizing the difference between the model prediction and the target. During inference, only label-free images are used as input to the trained model to predict fluorescence images.

Article Snippet: biological sample ( M. musculus ) , mouse kidney tissue section , Thermo-Fisher Scientific , Cat. # F24630 , .

Techniques: Microscopy, Fluorescence

Journal: eLife

Article Title: Revealing architectural order with quantitative label-free imaging and deep learning

doi: 10.7554/eLife.55502

Figure Lengend Snippet: Accuracy of prediction of F-actin in mouse kidney tissue as a function of input channels. Median values of the Pearson correlation ( r ) and structural similarity index (SSIM) between predicted and target volumes of F-actin. We evaluated combinations of brightfield (BF), phase (Φ), retardance (ρ), orientation x (ω x ), and orientation y (ω y ), as input. Model training conditions and computation of test metrics is described in Table 1 .

Article Snippet: biological sample ( M. musculus ) , mouse kidney tissue section , Thermo-Fisher Scientific , Cat. # F24630 , .

Techniques:

Journal: eLife

Article Title: Revealing architectural order with quantitative label-free imaging and deep learning

doi: 10.7554/eLife.55502

Figure Lengend Snippet: Accuracy of prediction of nuclei in mouse kidney tissue. Median values of the Pearson correlation ( r ) and structural similarity index (SSIM) between predicted and target volumes of nuclei. See Table 2 for description.

Article Snippet: biological sample ( M. musculus ) , mouse kidney tissue section , Thermo-Fisher Scientific , Cat. # F24630 , .

Techniques:

Journal: eLife

Article Title: Revealing architectural order with quantitative label-free imaging and deep learning

doi: 10.7554/eLife.55502

Figure Lengend Snippet:

Article Snippet: biological sample ( M. musculus ) , mouse kidney tissue section , Thermo-Fisher Scientific , Cat. # F24630 , .

Techniques: Software