DOI: 10.5061/dryad.hqbzkh1rk
title: Whole mount 3D imaged adipose tissue demonstrating sympathetic neurons and blood vessels during cold exposure and thermoneutrality

description: WAT, primarily comprising lipid-storing adipocytes, also contains various
 non-adipocyte cell types, such as neurons, immune cells or endothelial
 cells. Recent advancements in whole-tissue imaging suggest that the degree
 of sympathetic innervation and vasculature is highly heterogeneous within
 WAT depots. Here, we present a protocol for whole-mount WAT clearing,
 overcoming the constraints associated with antibody-methanol sensitivity,
 broadening the spectrum of applicable antibodies for spatial analysis of
 many different cell types. Additionally, we present TiNeQuant (Tissue
 Network Quantifier) a Fiji tool for automated 3D quantification of neuron-
 or vascular network density, freely available at
 https://github.com/SchweigerLab/TiNeQuant. As this easy-to-use tool is not
 limited to WAT, it simplifies future efforts across various fields
 studying neuronal or vascular alterations in numerous pathologies.
description: # Whole mount 3D imaged adipose tissue demonstrating sympathetic neurons
 and blood vessels during cold exposure and thermoneutrality
 [https://doi.org/10.5061/dryad.hqbzkh1rk](https://doi.org/10.5061/dryad.hqbzkh1rk) **`TH_CD31_1R` through `TH_CD31_6R` LIF files**. The dataset contains image files obtained on a Leica DMi8 CS DLS confocal laser scanning microscope. Fluorescence labels were excited with an argon laser (488nm) and a helium neon laser (633nm), respectively. Whole iWAT samples were scanned using photomultiplier tube-detectors, using a HC FLUOTAR L 25x objective (NA 0.95, WD 2.4 mm, water) at 1024 x1024 pixel resolution. Z-stacks were bidirectionally recorded across the full tissue depth of approximately 2mm with 15µm step size. **Linear Regression evaluation graph pad file.** Linear regression analysis of manual neuron length quantification plotted against automated neuron length quantification by our pipeline. **Multiple Annotator Test graph pad file.** Accuracy was tested by comparing the results obtained from 6 independent manual annotators with the results of the automated quantification by our pipeline. **Vessel density graph pad file**. C57BL/6J mice where either single-housed at 30°C or at 4°C for 7 days. Mice were sacrificed, iWAT excised, labeled for CD31 and cleared. Quantification of iWAT vascular density across approximately 850 regions and three biological replicates per group. A Mann-Whitney U test was performed to analyze the non-normally distributed data. **Neuron density graph pad file.** C57BL/6J mice where either single-housed at 30°C or at 4°C for 7 days. Mice were sacrificed, iWAT excised, labeled for Tyrosin hydroxylase and cleared. Quantification of iWAT vascular density across approximately 850 regions and three biological replicates per group. A Mann-Whitney U test was performed to analyze the non-normally distributed data. **Neuron density Heatmap graph pad file.** Heatmap showing measured neuron density (neuron length per regional lobe volume) of the same sample. The image analysis pipeline was implemented in Fiji (NIH, Bethesda, MA, USA). The following plugins were used within the pipeline: Bio-formats importer was used for data import, Labkit was used for image segmentation, Skeletonize3D was used for 3D skeletonization.  Heatmap generation was executed in Python 3.11, utilizing the data analysis library Pandas 1.5.3 for data preparation and GraphPad Prism 9.0.0 for visualizing the heatmap. 3D volume rendering was performed using Blender (Foundation). Statistical analysis was performed in GraphPad Prism 9.0.0. To compare neuron and vascular density, 876 and 803 regions from three biological replicates per group were analyzed. ROUT outlier detection (Q=0.2%) was performed to remove statistical outliers within the dataset. Normal distribution was tested using a Shapiro-Wilk test. Subsequently a Mann-Whitney U test was performed to analyze the non-normally distributed data.  Analyzing clearness of samples using Adipoclear (**`Clearness_Adipoclear_TH.tif`**) or our new methanol (**`Clearness_MeOH_TH.tif`**) and THF (**`Clearness_THF_TH`**) protocol. Tif files from Analyzing labeling quality using Adipoclear (**`Labeling_quality_AC`**) or our new methanol (**`Labeling quality_MeOH`**) and THF (**`Labeling_quality_THF`**) protocol. Tif files from Analyzing labeling stability using our Methanol or THF protocol at day 0, day, 7, and day 14 after labeling. (**`Labeling_stability_MeOH` and `labeling_stability_THF`, respectively**) Testing methanol sensitivity of antibodies for labeling is shown in the **`MeOH_sensitivity_AC/MeOH/THF_ gain_850`** tif files, respectively Network density examples for neurons and vessels at 4°C and 30°C are shown in tif files: **`Network_density_examples_neurons` and `network density_examples_vessels`,** respectively Adipocyte diameters after clearing have been determined. Raw data and calculation is depicetd in the graph pad file: **`Adipocyte_diameter.pzfx`** Raw image files of Tissue morphology of uncleared (preclearing) and cleared samples is shown in the files: **`Tissue_morpholology_pre_clearing`** and **`Tissue_morphology_post clearing`**. The first number in the filename indicates the biological replicate, the second the selected region.