imaris 10 1 0 3d image analysis software  (Oxford Instruments)

Journal: iScience

Article Title: Uterine collagen deposition fluctuates throughout the estrous cycle and provides a scaffold for gland visualization via the SHG-casting method

doi: 10.1016/j.isci.2025.113989

Figure Lengend Snippet: SHG intensity measurements in 3D scans reflect fluctuations in fibrillar collagen deposition in the mouse uterus across the estrous cycle (A–D) Representative 3D SHG scans of (A) proestrus, (B) estrus, (C) metestrus, and (D) diestrus uteri showing the myometrium and the endometrium. The location of the representative z stack slice (white dashed line) showing gland outlines is depicted later in discussion in (E–F). (E–H) Representative z stack images from 3D reconstruction showing variations in gland outlines in (E) proestrus, (F) estrus, (G) metestrus, and (H) diestrus scans. Arrowheads point to fibrillar collagen-gland cage, green star marks the uterine gland place that appears like a “hole” in SHG scans. (E’–H’) Fibrillar collagen cage enlarged ratio map showing the SHG intensity fold change of gland fibrillar collagen cage in relation to the endometrium in (E’) proestrus, (F’) estrus, (G’) metestrus, and (H’) diestrus scans. The entire image was divided by its mean gray value and is shown, scaled for changes from 1 to 50-fold. (I) Endometrial stroma SHG intensity measurements across the estrous cycle. Green cubes illustrate representative places from where SHG intensity measurements were taken. (J) SHG intensity fold-change measurements of differences in gland fibrillar collagen cages across the estrous cycle. AM, antimesometrial; En, endometrium; M, mesometrial; Myo; myometrium, SHG, second harmonic generation. Statistical analyses were performed with one-way ANOVA followed by the Tukey post hoc test. P values below 0.05 are reported in the graphs. The results are shown as individual data points with mean ± s.d., n = 3 uteri/estrous cycle phase. The data points in I represent the average of five ROI measurements per uterus. The data points in J represent one uterine gland z stack analyzed per uteri. Scale bars for (A–D) are 100 μm and for (E–H’) are 50 μm.

Article Snippet: To further test how the automated SHG-casting method could be used for gland morphology analysis, segmentation maps of the SHG scans ( A–8D and A–S2L) were analyzed using Imaris 3D image analysis software to assess whether it would be possible to isolate individual uterine glands.

Techniques:

Journal: iScience

Article Title: Uterine collagen deposition fluctuates throughout the estrous cycle and provides a scaffold for gland visualization via the SHG-casting method

doi: 10.1016/j.isci.2025.113989

Figure Lengend Snippet: Second harmonic generation and immunostaining reveal endometrial fibrillar collagen cages surrounding uterine glands (A–B’’) Representative images from z stack image show (A) uterine gland-specific FOXA2 staining (magenta), (A’) SHG signal (gold) from fibrillar collagen cages around glands, and (A’’) merged FOXA2 and SHG. 3D reconstructions of whole-mount uterus (B) stained with FOXA2 antibody depicting uterine glands, (B’) SHG signal depicting fibrillar collagens, and (B’’) merged FOXA2 and SHG signals. (C) Fibrillar collagen-rich stromal ECM has a more intense SHG signal at the gland cage. A basement membrane (white) underlies the epithelium in the glands and lumen. Because it is mainly composed of collagen IV, a non-fibrillar collagen, it is SHG-negative. Glandular epithelium is FOXA2 (nuclear stain, magenta) positive but has no fibrillar collagen content. Luminal epithelium (light blue) and lumen (dark gray) do not contain fibrillar collagen or express FOXA2, so they are negative for all of the listed markers. Scale bars (A–B’') are 100 μm. n = 3 independently stained pieces from 1 uterus.

Article Snippet: To further test how the automated SHG-casting method could be used for gland morphology analysis, segmentation maps of the SHG scans ( A–8D and A–S2L) were analyzed using Imaris 3D image analysis software to assess whether it would be possible to isolate individual uterine glands.

Techniques: Immunostaining, Staining, Membrane

Journal: iScience

Article Title: Uterine collagen deposition fluctuates throughout the estrous cycle and provides a scaffold for gland visualization via the SHG-casting method

doi: 10.1016/j.isci.2025.113989

Figure Lengend Snippet: Schematic diagram illustrating the SHG-casting method (A) Conventional casting method where a mold, containing the hollowed form of the object of interest, is filled. When the mold is removed, the cast of the object of interest is revealed. (B) The SHG-casting method is shown, where the fibrillar collagen-rich extracellular matrix, detected by SHG, serves as a mold. The negative spaces in the extracellular matrix, the result of uterine glands not expressing fibrillar collagen, are digitally filled, resulting in the recreation of the 3D form of a uterine gland. SHG, second harmonic generation.

Article Snippet: To further test how the automated SHG-casting method could be used for gland morphology analysis, segmentation maps of the SHG scans ( A–8D and A–S2L) were analyzed using Imaris 3D image analysis software to assess whether it would be possible to isolate individual uterine glands.

Techniques: Expressing

Journal: iScience

Article Title: Uterine collagen deposition fluctuates throughout the estrous cycle and provides a scaffold for gland visualization via the SHG-casting method

doi: 10.1016/j.isci.2025.113989

Figure Lengend Snippet: Fibrillar collagen detected by SHG provides a template for 3D gland reconstruction by the SHG-casting method (A–D) Representative 2D digital slice images of (A) fibrillar collagen detected by SHG (gold) forming a cage around the uterine gland and (B) uterine gland, identified by FOXA2 immunostaining (magenta). (C) Tracing of SHG outline (green) (D) aligns with FOXA2 staining (magenta). (E–H) 3D reconstruction of (E) SHG fibrillar collagen (gold), (F) FOXA2 staining of a uterine gland (magenta), (G) 3D reconstruction of gland tracing (green), and (H) merged uterine gland reconstructions of FOXA2 (magenta) and tracing (green). SHG; second harmonic generation. Scale bars are 50 μm. n = 2 manually traced scans.

Article Snippet: To further test how the automated SHG-casting method could be used for gland morphology analysis, segmentation maps of the SHG scans ( A–8D and A–S2L) were analyzed using Imaris 3D image analysis software to assess whether it would be possible to isolate individual uterine glands.

Techniques: Immunostaining, Staining

Journal: iScience

Article Title: Uterine collagen deposition fluctuates throughout the estrous cycle and provides a scaffold for gland visualization via the SHG-casting method

doi: 10.1016/j.isci.2025.113989

Figure Lengend Snippet: Deep learning-based segmentation model accurately recognizes and segments the fibrillar collagen cage (A) Schematic diagram shows the training and validation of machine learning trained model, featuring training to recognize the gland and lumen fibrillar collagen cages (SHG path), and to validate these segmentation and subsequent 3D reconstruction against that of FOXA2 uterine gland-specific whole-mount immunostaining (immunostaining path). (B and C) Representative images of 2D slices of (B, B’) SHG scan showing the negative imprints of uterine glands surrounded by fibrillar collagen cages in the x–y (top, B) and z (bottom, B’) planes, and the (C, C’) automatic segmentation of uterine gland imprints in the same regions in the x–y (top, C) and z (bottom, C’) planes. (D) The 3D model of uterine gland morphology resulting from the automatic segmentation of the complete SHG scan. (E) The Dice similarity coefficient (DSC), which compares the overlap of the reference 3D reconstruction to the SHG-casting segmentation model 3D reconstruction, is calculated by adding the overlap between the two methods together (lime green), divided by the total volume of the reference 3D model (yellow) with the volume from the SHG-casting segmentation model (green). The DSC is 93.8%. (F) Intersection over union (IoU) is a segmentation metric that measures the area overlap (intersection, bright green) between the SHG-casting segmentation model (green) and that of the reference (yellow), divided by the total area of both the model and reference (green) combined (union, bright green). The IoU score was 88.4%. (G) Hausdorff distance (HD) is a measure that assesses the worst case difference in distance between two sets of points in the model (green) and the reference (yellow). The HD is 5.5 voxels. Representative images to describe DSC, IoU, and HD were based on images from Pálsson et al. and Huynh.

Article Snippet: To further test how the automated SHG-casting method could be used for gland morphology analysis, segmentation maps of the SHG scans ( A–8D and A–S2L) were analyzed using Imaris 3D image analysis software to assess whether it would be possible to isolate individual uterine glands.

Techniques: Biomarker Discovery, Immunostaining

Journal: iScience

Article Title: Uterine collagen deposition fluctuates throughout the estrous cycle and provides a scaffold for gland visualization via the SHG-casting method

doi: 10.1016/j.isci.2025.113989

Figure Lengend Snippet: Comparison between uterine gland structure derived from FOXA2 immunostaining and SHG-casting method in combination with deep learning (DL) based (semantic) segmentation model (A) 2D digital slice of FOXA2 uterine gland staining and (B) 3D reconstruction of staining. (C) 2D segmentation map from SHG-casting DL-based segmentation model from the same digital slice shown in (A), followed by 3D reconstruction in (D). Scale bar for (A) is 50 μm and 100 μm in (B).

Article Snippet: To further test how the automated SHG-casting method could be used for gland morphology analysis, segmentation maps of the SHG scans ( A–8D and A–S2L) were analyzed using Imaris 3D image analysis software to assess whether it would be possible to isolate individual uterine glands.

Techniques: Comparison, Derivative Assay, Immunostaining, Staining

Journal: iScience

Article Title: Uterine collagen deposition fluctuates throughout the estrous cycle and provides a scaffold for gland visualization via the SHG-casting method

doi: 10.1016/j.isci.2025.113989

Figure Lengend Snippet: Comparison of workflow and time required for uterine gland 3D morphology reconstruction by whole-mount immunostaining and the automated SHG-casting method The estimated time for each step includes both hands-on work and passive waiting gaps. Scanning and analysis times are for one sample. (A) depicts the workflow of uterine gland 3D morphology reconstruction by whole-mount immunostaining, and (B) by the automated SHG-casting method. The initial steps involving specimen collection, fixation, and quenching of background signal are identical in both methods (A and B). The most significant time-related deviation comes at the immunostaining stage, taking up to 10 nights (A). Meanwhile, specimens processed by the SHG-casting method can already be chemically cleared for imaging (B). Critical to note that the SHG signal coming from fibrillar collagens is not sensitive to bleaching, enabling repetitive re-imaging of the specimens (B), whereas imaging a fluorophore associated with an antibody is bleaching the signal permanently (A). SHG; second harmonic generation. Uterus graphic is from BioRender: Savolainen, A. (2025) https://BioRender.com/jie1q29 .

Article Snippet: To further test how the automated SHG-casting method could be used for gland morphology analysis, segmentation maps of the SHG scans ( A–8D and A–S2L) were analyzed using Imaris 3D image analysis software to assess whether it would be possible to isolate individual uterine glands.

Techniques: Comparison, Immunostaining, Imaging

Journal: Journal of Translational Medicine

Article Title: Detection of extracellular amyloid beta aggregates by an Aducanumab-based synNotch receptor: an in vitro proof-of-concept study

doi: 10.1186/s12967-025-07324-2

Figure Lengend Snippet: Assessment of Aβ-targeting Adu-synNotch activation in an in vitro model of Aβ . (a) Expression cassette of EF1A-regulated Adu-synNotch and SNAP-tag. The VLC and VHC of Aducanumab is fused by a flexible G4S(3) linker to form an scFv, with an N-terminal human CD8α signal peptide (SP; white) for membrane localization. The SNAP-tag has an NLS to target the reporter to the nucleus. Adu-synNotch and SNAP-tag are separated by a T2A sequence for bicistronic expression. (b) Gal4UASmCMV-regulated CLIP-tag under the control of Adu-synNotch activation. The CLIP-tag contains an NLS for nuclear targeting. (c) Adu-synNotch regulating CLIP-tag expression in NIH 3T3 cells. (1) Adu-synNotch and SNAP-tag are constitutively expressed. SNAP expression in the nucleus is proportional to synNotch transcription. (2) Activation of Adu-synNotch by Aβ. (3) Adu-synNotch activation results in the expression of nuclear-targeted CLIP-tag. (4) Adu-synNotch activation results in colocalization of SNAP-tag and CLIP-tag, used to normalize Adu-synNotch activation to copy-number integration. (d) Schematic overview of the experiments. A day after plating cells, AβO (1−42) treatments are added for 48 h, after which cells are labeled with CLIP-tag and SNAP-tag substrates and processed for confocal image analyses. (e) 3D maximum intensity projection confocal images of clones expressing Adu-synNotch under the indicated treatment conditions. Yellow boxes are an expanded view of regions within the tile scans. DAPI for cell nuclei, SNAP-tag for transduced cells, and CLIP-tag for synNotch activation. DAPI + /SNAP − white arrowheads (untransduced), DAPI + /SNAP + /CLIP − yellow arrows (inactive Adu-synNotch), DAPI + /SNAP + /CLIP + white arrows (active Adu-synNotch). Scale bar: 200 μm tile scans; 40 μm expanded view. (f-i) Quantification of (f) the number of SNAP-positive cells, (g) mean SNAP and (h) CLIP as well as (i) CLIP to SNAP ratio. Voxel intensity is in arbitrary units (au). Statistics: (f , h , i) Independent t -test and (g) Welch’s t -test. Two-tails. Three independent experiments. * p <.05, ** p <.01. Error Bars 1x SEM

Article Snippet: For the quantification and statistical analyses of Adu-synNotch activation, we generated three-dimensional regions of interest (ROIs) based on the SNAP channel using the 3D image processing software IMARIS.

Techniques: Activation Assay, In Vitro, Expressing, Membrane, Sequencing, Control, Labeling, Clone Assay