Mar 20, 2026
Immunofluorescence Staining of Free-Floating Brain Sections
- Aanishaa Jhaldiyal1,
- M. Natalie Davis1,
- Aanishaa Jhaldiyal2
- 1University of Alabama at Birmingham;
- 2UAB
Protocol Citation: Aanishaa Jhaldiyal, M. Natalie Davis, Aanishaa Jhaldiyal 2026. Immunofluorescence Staining of Free-Floating Brain Sections. protocols.io https://dx.doi.org/10.17504/protocols.io.dm6gp1oy8gzp/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: January 26, 2026
Last Modified: March 20, 2026
Protocol Integer ID: 241585
Keywords: floating brain section, surface rendering, surface detail parameter, immunofluorescence staining, brain section, immunofluorescence staining of free, local background fluorescence, surface, diffuse signal at spatial scale, surface parameter, surfaces module, minimum spatial scale of intensity variation, minimum spatial scale, smoother surface, spatial scale, producing smoother surface, image analysis
Funders Acknowledgements:
National Institutes of Health
Grant ID: RF1AG059405
National Institutes of Health
Grant ID: R01AG061785
National Institutes of Health
Grant ID: P30AG086401
National Institutes of Health
Grant ID: T32HD071866
National Institutes of Health
Grant ID: T32NS061788
Abstract
Three-dimensional reconstruction and image analysis were performed using Imaris (Bitplane). Surface rendering was conducted using the Surfaces module, with parameters optimized empirically to maximize biological specificity while minimizing non-specific background signal. In Imaris, the surface detail parameter defines the minimum spatial scale of intensity variations used to generate surfaces, with larger values producing smoother surfaces by excluding fine, small-scale fluctuations. The background subtraction diameter defines the diameter of the largest spherical structure used to estimate and remove local background fluorescence, thereby suppressing diffuse signal at spatial scales larger than the specified value. All surface parameters were applied uniformly across samples and experimental groups.
Attachments
Guidelines
Confocal images were acquired using a Zeiss LSM 800 microscope equipped with a 63× objective, using a 1.1× digital zoom. Images were collected at a voxel size of 0.094 μm × 0.094 μm × 0.3 μm.
Materials
- Phosphate-buffered saline (PBS)
- PBST (PBS containing 0.1% Tween-20)
- Low-pH citrate buffer (pH 6.0)
- 0.1% SDS
- Blocking buffer (0.5% fish gelatin, 10% goat serum, 3% bovine serum albumin (BSA), 0.1% Triton X-100)
- Primary antibodies: BIN1 (Proteintech, 1:200), parvalbumin (PV, 1:1000), NeuN (1:1000)
- Fluorescent secondary antibodies
- DAPI (1:1000)
- TrueVIEW® Autofluorescence Quenching Solution (Vector Laboratories, SP-8400-15)
- ProLong™ Glass Antifade Mountant (Thermo Fisher Scientific)
Troubleshooting
Surface Generation Parameters
For BIN1, surface rendering was optimized to detect discrete BIN1 puncta, which are typically submicron in size and spatially restricted. Empirical testing indicated that smaller surface detail values increased inclusion of diffuse background signal, including low-level fluorescence detected in Nestin-Cre–positive cells, which are not expected to express BIN1. Therefore, a larger surface detail parameter (0.6 μm) was selected to restrict detection to puncta consistent with the expected size of BIN1 signal and to suppress smaller, non-specific intensity fluctuations. In addition, a background subtraction diameter of 3 μm was applied to remove local diffuse cytoplasmic background while preserving compact BIN1 puncta.
For NeuN and parvalbumin (PV) channels, surface rendering parameters were optimized to specifically segment the neuronal soma and nucleus, rather than fine neuritic processes or diffuse neuropil signal. The expected diameter of neuronal nuclei and soma ranges up to approximately 10–12 μm, and therefore a background subtraction diameter of 12 μm was selected to match the physical scale of these structures. A smaller surface detail parameter (0.5 μm) preserved soma-localized signal while effectively excluding surrounding background fluorescence.
Masking Strategy for Cell-Type–Specific BIN1 Visualization
To visualize BIN1 signal within defined neuronal populations, a masking approach was used to restrict BIN1 fluorescence to the three-dimensional volume of NeuN- or PV-positive neurons.
Masking was performed in Imaris using the following steps:
Generate NeuN or PV surfaces
Surpass View → Add New Surfaces
Select NeuN or PV channel
Apply surface detail and background subtraction parameters as described above
Complete surface creation
Create masked BIN1 channel
Surpass View → Add New Channel → Mask Channe
Input channel: BIN1
Mask source: NeuN or PV surface
Voxels outside surface: Set to 0
Voxels inside surface: Retain original intensity
Generate masked BIN1 channel
Visualize BIN1 within NeuN or PV neurons
Use the masked BIN1 channel for visualization
Generate BIN1 surfaces from the masked channel using the same BIN1 parameters (surface detail 0.6 μm; background subtraction 3 μm)
This approach ensured that BIN1 visualization was restricted to signal located within the three-dimensional boundaries of NeuN- or PV-positive neurons, enabling accurate assessment of cell-type–specific BIN1 localization.
Visualization Strategy
Due to the high density of NeuN-positive neurons, rendering all detected cells within a single field resulted in excessive surface overlap and reduced visualization clarity. Therefore, a representative subset of NeuN-positive neurons was selected for three-dimensional visualization.
An analogous 3D rendering and masking approach was used for PV-positive neurons, in which PV-rendered surfaces defined a PV-specific bounding box, and BIN1 fluorescence was visualized within the PV-defined volume, enabling cell-type–specific assessment of BIN1 localization in PV interneurons.
Rendered surfaces and masked channels were used for three-dimensional visualization.