imaris workflow  (Oxford Instruments)

Journal: Cerebellum (London, England)

Article Title: Innovative 3D-Image Analysis of Cerebellar Vascularization Highlights Angiogenic Gene Dysregulations in a Murine Model of Apnea of Prematurity

doi: 10.1007/s12311-026-02006-1

Figure Lengend Snippet: Images illustrating the Imaris workflow developed for the vascular network modeling. A–I: Sequential workflow steps allowing the analysis of the cerebellar vascular network of a P4 mouse cerebellum on the Imaris software. From a 3D lightsheet acquisition (A) , the cerebellum is delineated (B) and a mask is created (C) . Within that selected volume, the cerebellar vascularization is segmented (D) , which allows the network visualization (E) and the separation of a deep and a superficial network (F) . Then the threshold of seedpoints is defined (G) , and thanks to the artificial intelligence module (AI), Imaris is able to discriminate “true” (blue) and “false” (red) seedpoints (H) , and “true” (blue) and “false” (red) segments (I) . AI: artificial intelligence; Px: postnatal day x

Article Snippet: IH: intermittent hypoxia; N: normoxia; Px: postnatal day x Fig. 2 Images illustrating the Imaris workflow developed for the vascular network modeling.

Techniques: Software

Journal: Cerebellum (London, England)

Article Title: Innovative 3D-Image Analysis of Cerebellar Vascularization Highlights Angiogenic Gene Dysregulations in a Murine Model of Apnea of Prematurity

doi: 10.1007/s12311-026-02006-1

Figure Lengend Snippet: Images illustrating the Imaris workflow developed for the vascular network modeling. A–I: Sequential workflow steps allowing the analysis of the cerebellar vascular network of a P4 mouse cerebellum on the Imaris software. From a 3D lightsheet acquisition (A) , the cerebellum is delineated (B) and a mask is created (C) . Within that selected volume, the cerebellar vascularization is segmented (D) , which allows the network visualization (E) and the separation of a deep and a superficial network (F) . Then the threshold of seedpoints is defined (G) , and thanks to the artificial intelligence module (AI), Imaris is able to discriminate “true” (blue) and “false” (red) seedpoints (H) , and “true” (blue) and “false” (red) segments (I) . AI: artificial intelligence; Px: postnatal day x

Article Snippet: Moreover, to correlate putative modifications of gene expression with defects in vessel morphology, we developed an image analysis workflow using both Imaris and VesselVio software to visualize the cerebellar vascularization in 3D during postnatal development and obtain comparable quantitative parameters between normoxic and IH conditions.

Techniques: Software

Journal: STAR Protocols

Article Title: Protocol for 3D-guided sectioning and deep cell phenotyping via light sheet imaging and 2D spatial multiplexing

doi: 10.1016/j.xpro.2025.104296

Figure Lengend Snippet: 3D spatial and data analysis workflow 3D spatial analysis of 3D-IF stained and optically cleared samples with UltraMicroscope Blaze™ light sheet microscope (Step 16). Post processing (stitching) in case data was acquired with tile-scanning (Step 18). Surfaces generation of autofluorescence and target region with Imaris software (Oxford Instruments) and target plane definition with “Oblique Slicer” tool (Step 20 and 20).

Article Snippet: Continue the workflow in Imaris to create orientation marks for further analysis.

Techniques: Staining, Microscopy, Software

Journal: Scientific Reports

Article Title: Contribution of tissue clearing and 3D image analysis to in vitro modeling of human cortical development

doi: 10.1038/s41598-026-41741-7

Figure Lengend Snippet: Schematic overview of the workflow for 2D and 3D analysis of hiPSC-derived neurospheres. (A) Generation of neurospheres from hiPSC, followed by fixation and preparation for either 2D cryosectioning or 3D tissue clearing. (B) Cleared neurospheres are embedded in agarose for light-sheet fluorescence microscopy, producing a Z-stack of ~ 300–500 optical sections at 3 μm intervals, depending on spheroid size and maturation. (C) Quantification workflow in Imaris using the spot detection function to enumerate positive cells across the entire spheroid, providing precise volumetric data rather than estimates from a single section. Illustrations were created with BioRender.

Article Snippet: Fig. 1 Schematic overview of the workflow for 2D and 3D analysis of hiPSC-derived neurospheres. (A) Generation of neurospheres from hiPSC, followed by fixation and preparation for either 2D cryosectioning or 3D tissue clearing. (B) Cleared neurospheres are embedded in agarose for light-sheet fluorescence microscopy, producing a Z-stack of ~ 300–500 optical sections at 3 μm intervals, depending on spheroid size and maturation. (C) Quantification workflow in Imaris using the spot detection function to enumerate positive cells across the entire spheroid, providing precise volumetric data rather than estimates from a single section.

Techniques: Derivative Assay, Fluorescence, Microscopy