matlab-based automated scoring algorithm  (MathWorks Inc)

Journal: Nature Communications

Article Title: Myeloid lineage C3 induces reactive gliosis and neuronal stress during CNS inflammation

doi: 10.1038/s41467-025-58708-3

Figure Lengend Snippet: a Diagram of experimental paradigm showing how eyes were either evaluated for synaptic pathology (left) via cross-sections or retinal ganglion cell (RGC) loss via flat mounts (right) on EAE d16 mice. b Representative image of flat-mounted retina with 12 regions selected for Brn3a + RGC quantification. Scale bar in full retina image = 500 μm, in high magnification counting fields scale bar = 20 μm. c Quantification of Brn3a + RGCs at peak EAE. N = 4 per experimental group. d Quantification of the integrated density of markers Syn1 (left) and PSD95 (right) in the inner plexiform layer of CFA vs GFAP-Cre animals at peak EAE. Signal intensity values are normalized to CFA control, with AU representing “arbitrary units”. N = 4 CFA, 10 GFAP-Cre − and 7 GFAP-Cre + mice. e Representative immunofluorescent staining of Syn1 (red) and PSD95 (green) in the inner and outer plexiform layers across GFAP-Cre experimental groups. Scale bar = 50 um. f Same as in e but evaluating synaptic markers in the inner plexiform layer of LysM-Cre + and LysM-Cre − mice. Values are normalized to the CFA only animals utilized in Fig. 5d. N = 4 CFA, 6 LysM-Cre − and 8 LysM-Cre + mice. g Representative immunofluorescent staining of Syn1 and PSD95 in LysM-Cre − vs LysM-Cre + mice. Scale bar = 50 um. All bar graphs presented as mean +/- SEM, with one-way ANOVAs adjusted for multiple comparisons. Source data are provided as a source data file. Figure 5a created in BioRender. Smith, M. (2025) https://BioRender.com/j21s335 .

Article Snippet: Our MATLAB-based semi-automated RGC counting algorithm was used to determine RGC number in each mouse .

Techniques: Control, Staining

Journal: Frontiers in Pharmacology

Article Title: An Automated Quantification Tool for Angiogenic Sprouting From Endothelial Spheroids

doi: 10.3389/fphar.2022.883083

Figure Lengend Snippet: Schematic demonstrating setup of spheroid assay for angiogenesis sprouting. (A) Human umbilical vein endothelial cells (HUVEC) are fluorescently dyed with CellTracker Green CMFDA (5 μm) and seeded as hanging drops for 24 h. Spheroids are collected, resuspended in a fibrinogen/methylcellulose solution, and mixed with thrombin in a 96-well plate to embed spheroids within the generated fibrin gel. Medium containing pro- or anti-angiogenic factors is added 1 h later to initiate sprouting. Angiogenic sprouting is imaged 24 h later using a real-time, fluorescence microscopy plate reader. (B) Representative images of embedded spheroids in a single well and a close-up of sprouting from a single spheroid. Images were acquired using phase contrast and fluorescence microscopy. Scale bar of whole-well image = 2000 μm.

Article Snippet: We subsequently developed a Matlab-based automated algorithm to quantify angiogenic sprouting from HUVEC spheroids imaged using fluorescence microscopy ( ).

Techniques: Generated, Fluorescence, Microscopy

Journal: Frontiers in Pharmacology

Article Title: An Automated Quantification Tool for Angiogenic Sprouting From Endothelial Spheroids

doi: 10.3389/fphar.2022.883083

Figure Lengend Snippet: Quantification method of angiogenic sprouting from HUVEC spheroids. (A) Images demonstrating the automatic generation of masks and skeletons used to quantify total area, sprout area, and cumulative sprout length from each spheroid. The original image (1) is intensity adjusted using adaptive histogram equalization (2), and segmented using a combination of Sobel segmentation, convolution, and adaptive thresholding to generate a spheroid mask (3), as described before . The image was then reduced to an initial skeleton using a skeletonization method (4). After the spheroid center (5) and sprout area (6) were identified, the center was subtracted from the sprout skeleton to exclude extensions found in the spheroid center (7). Small extensions were then removed to generate the final sprout skeleton (8). (B) Images demonstrating the counting of individual sprouts as migrated or attached. Each sprout in the skeleton is assessed as to whether it intersects with the skeleton of the center. If a sprout intersects with the center, it is considered “attached”; otherwise, it is considered “migrated”. (C) View of output csv file from automated algorithm.

Article Snippet: We subsequently developed a Matlab-based automated algorithm to quantify angiogenic sprouting from HUVEC spheroids imaged using fluorescence microscopy ( ).

Techniques: