Journal: Nature Communications

Article Title: Automatic optimization of flat-field corrections by evaluation and enhancement (EVEN) in multimodal optical microscopy

doi: 10.1038/s41467-025-68150-0

Figure Lengend Snippet: A three-channel fluorescence microscopy measurement of stained HEK293 cells measured by Ph2 objective is automatically optimized by EVEN (prediction dataset 2, red: peroxisomal proteins (anti-GFP nanobody); green: TOMM20 protein; blue: peroxisomal proteins (eGFP)). a Raw multi-channel image. The inset shows the 2 × 2 tile section of the image used in this figure, with dashed white lines marking tile borders. Multiple corrections are obtained by applying BaSiC, CIDRE, Fourier methods, and then optimizing the multi-channel image with EVEN. EVEN selects CIDRE for the red and green channel, and Fourier for the blue channel. b Steps to analyse the measurements of stained cells: multi-channel images are converted to greyscale by summing the single channels (that contain signals from different components of the cytoplasm) and are analysed with automatic cells segmentation using Cellpose . The greyscale image is obtained for the raw measurement, the single-channel corrections and the EVEN optimization. c Intensity sum (along y) of the greyscale inset for each method. The black dashed line indicates the border between neighbouring tiles. The corrected images show higher intensities at the edges of the tiles and the enhancement of sample features. EVEN and CIDRE show the greatest intensity recovery between tiles. d Top row: multi-channel images obtained with single-method corrections and EVEN optimization; the white dashed boxes highlight two regions significantly improved by EVEN. Bottom row: Cellpose prediction on the greyscale sum of the three channels for each method. After correction of uneven illumination, Cellpose can outline a greater number of cells, especially at the borders of neighbouring tiles. White dashed boxes highlight three regions where EVEN optimization provides better identification of the cells compared to non-optimized images. Bottom labels show, for each image, the normalized EVEN score summed over three channels and the cell count in the zoomed region. While counts are not strictly correlated with segmentation performance, good correction of uneven illumination enhances downstream analysis and generally increases the number of detected cells. Further quantification is provided in Supplementary Fig. . Scale bar: 180 µm, size of a single tile.

Article Snippet: Additionally, immunolabeling was performed on TOMM20-protein with anti-Tomm20 rabbit polyclonal antibodies (proteintech, USA), dilution 1:200, and goat anti-rabbit IgG secondary antibodies labelled with Abberior STAR Orange (Abberior, Germany) at a dilution of 1:350.

Techniques: Fluorescence, Microscopy, Staining, Cell Counting

Journal: bioRxiv

Article Title: Oxa1L-Mediated Co-translational Protein Insertion Maintains Mitochondrial Function in Parkinson’s Disease Models

doi: 10.64898/2025.12.22.696118

Figure Lengend Snippet: A Confocal image showing mitochondrial localization of Flag-tagged Oxa1L truncation mutants in SH-SY5Y cells. Oxa1L truncations were detected by Flag-FITC, mitochondria by TOM20, and nuclei by DAPI. B Quantification of Manders’ colocalization coefficients between Oxa1L truncation mutants and TOM20, indicating intact mitochondrial targeting. C Cellular ATP levels in cells expressing wild-type (WT) Oxa1L or truncation mutants under MPP⁺ treatment. Loss of ATP maintenance in truncation mutants indicates that multiple Oxa1L regions are required for mitochondrial protection under stress conditions. (All bar graphs above represent mean ± SEM. ****p < 0.0001 ; ***p < 0.001 ; **p < 0.01 ; *p < 0.05 )

Article Snippet: Primary antibody incubation was carried out overnight at 4 °C using a mouse anti-Flag antibody (proteintech, 1:5000) and a rabbit anti-TOM20 antibody (proteintech, 1:200).

Techniques: Expressing