Bumblebee husbandry, sampling and sample preparation

Five bumblebee colonies (Bombus terrestris) were obtained from a commercial company (Koppert Biological Systems, the Netherlands). Upon arrival, colonies (each containing a queen and a mean of 95.4 workers (range = 71–127)) were transferred to wooden nest boxes within 24 hours. All colonies were then provided with ad libitum pollen and sucrose solution (40/60% sucrose/water). To understand the standing variation in brain morphology and to consider the precision of our measurements, we sacrificed individuals for brain scanning that were all of the same age – four days old. These early-adults had eclosed from their pupal case after the colonies had spent at least 21 days (approx. time taken for a worker to be reared from egg to adult67) under the same laboratory setting. Age matched individuals were sacrificed because adult maturation, changes in social environment and increased experience can all have effects on brain morphology36,61. We monitored colonies twice per day and marked any newly eclosed individuals using numbered Opalith tags (Christian Graze KG, Germany), which consequently allowed us to track the age of each individual throughout the sampling period.

Young bees were used for brain scanning to limit any potential changes in brain structure/size during adult development associated with allocation to different tasks in the colony. Bees were sacrificed by removing an individual from the colony using forceps and then swiftly decapitating the live individual using a disposable surgery scalpel (Supplementary Fig. 1a). Once cut, the head was fully submerged in a 70/30% ethanol/de-ionised water solution in a 1.5 ml centrifuge tube and stored at 5 °C until the staining process was undertaken.

The head case was prepared for staining by removing the front part (just below the antennae; see Supplementary Fig. 1a) with a scalpel under 10× magnification (using a stereomicroscope) ensuring that we did not cut into the brain. The head was then fully submerged in the staining solution in one well as part of a multi-well cell plate. We measured thorax width for each decapitated individual, just above the tegula (see Supplementary Fig. 1b), using a set of digital callipers (accuracy = 10 μm) as this has been shown to be strong estimator of body mass in bumblebees68. Thorax width measurements were taken twice per individual and the average taken. We sacrificed 34 bees for staining (n = 6 from one colony for the staining optimization test; n = 28 from five colonies for brain 3D reconstruction).

Selecting staining conditions

Measures of contrast enhancement to view staining success have been used for imaging animal soft tissue in previous studies69 although each have employed differing stains, techniques and preparation. In order to develop an accurate and repeatable protocol for visualising bumblebee brain structures, we first focused on establishing a staining procedure. There are a number of widely used tissue stains including uranyl acetate (UA)70,71, iodine (I)69,70,72, phosphotungstic acid (PTA)6,69,73 and osmium tetroxide52,71. For our investigation we examined the effectiveness of: i) 1% I solution (1 mg/ml concentration in 70/30% ethanol/water solution); ii) saturated (0.9–1%) aqueous solution of UA; iii) 0.5% PTA solution (0.5 mg/ml concentration in 70/30% ethanol/water solution). Osmium tetroxide was excluded due to its high toxicity and cost69,74.

For each optimization test we prepared two heads per stain. Each of the six heads were individually scanned on days 1, 3, 6, 7, 8 and 9 to observe how well the stain penetrated the brain (see Supplementary Fig. 2). Uranyl acetate – the brain scans indicated little staining across all nine days. As differentiation of brain regions could not be seen or was very difficult to detect we concluded this to be a poor staining method for soft brain tissue CT scanning. Iodine – this permeated the brain more extensively than UA and was quicker to stain tissue than PTA as it only required around 1–3 days. However, the contrast threshold for iodine was not as good, tissues were not as readily identifiable and edges were less defined compared to PTA stained samples. Moreover, after just six days iodine began to bleed causing very blurry edges and then poor tissue differentiation. PTA showed the highest level of contrast enhancement after seven days of staining of all stains and in comparison to Iodine we found no evidence of stain bleeding, even after nine days. Additionally, when comparing identical phases of perfusion for each stain, PTA produced superior definition of brain regions compared to Iodine and UA. Therefore, we decided to use seven-day exposure to PTA stain based on the achievable image quality (see Supplementary Fig. 3 and 4). Scans for all staining assays were performed using the following settings: 80–90 kV at 100–110 μA, gain 6 dB, with no noise reduction and no beam hardening corrections and was balanced by increased projections to 6284 and exposure time of 354 ms to provide a suitable noise to signal ratio. The relative performance of the stains we assessed could vary under different scanner settings, therefore, additional testing of these settings could be further optimized for each stain. For example small decreases to the voltage to align with the excitation edges for tungsten and to increase the current to increase flux.

Scanning of specimens

Micro-CT scans were carried out at the Imaging and Analysis Centre, London Natural History Museum, using a Nikon Metrology HMX ST 225 system (Nikon Metrology, Tring, UK), with cone beam projection system, four megapixel detector panel, maximum voltage output of 225 kV for the reflection target and a maximum current output of 2000 μA (Supplementary Fig. 5). The focal spot size is 5 μm and the exposure ranges from 0.25–5.6 frames per second. Reconstructed data were visualised in VG Studio Max 2.1 in which samples were rotated and re-sliced along the orientation plane that gave the optimum view for segmentation (Fig. 1a; mean slices per brain = 377, range = 294–580; see Supplementary Video 1). We micro-CT scanned the seven-day PTA stained brains by scanning two heads per scan run by inserting two heads inside a plastic straw, held firmly in place by rigid foam at each end and separated by tissue paper, before being placed in the scanner. Due to the number of scans required we needed to scan over a seven day period, therefore to ensure all bee brains were scanned at a standardised number of days after the first day of staining, we respectively staggered the staining start date.

From the 28 brains that were prepared and PTA stained for micro-CT scanning and of the possible 308 separate structures of interest (left and right MB lobes, MB calyces, ALs, Mes and Los and the CB), we obtained images to allow full and detailed 3D reconstruction of 297 (96.4%) of these structures. In this study we used the 19 brains (17 workers and 2 males) that provided us with reconstructions of all the brain structures of interest per individual (see Supplementary Table 8).

Image segmentation and geometric morphometric analyses

SPIERS 2.20 was run on a standard laptop computer (Samsung Series 3 Notebook 1TB HDD NP3530EC, Intel® Core™ i5-3210M CPU @ 2.50 GHz, RAM 6.00 GB, Intel® HD Graphics 3000, 64-bit Operating System). SPIERS is a custom software suite made up of three independent programs: SPIERS edit, SPIERS view and SPIERS align. SPIERS was chosen ahead of other potential software based on two key and advantageous features: i) financial cost – SPIERS is freely available; ii) system requirements – SPIERS has relatively low system requirements and will run on most standard desktop and laptop operating systems. Segmentation was carried out using the SPIERS edit program (see Supplementary Step-by-Step Guide).

For each sample, segmentation of five of the key composite structures of the brain (MBs; ALs; Mes, Los and the CB) was performed, with each MB being segmented as calyces and lobes separately. Each individual scan was opened in SPIERS edit in the form of an 8-bit greyscale bitmap (BMP) image stack. The sequence of slices could be viewed in either greyscale or threshold form (a black and white, binary visualization of the image) with a simple interchange between the two representations. For a single scan slice showing the brain region of interest, thresholding was performed by adjusting the Base and Top values representing the range of shades to be assigned on the grey-value scale covered where 0 = black and 255 = white. The optimum threshold being that which achieved the best ratio of white, active ‘on’ pixels comprising the structure of interest and black, inactive ‘off’ pixels for the surrounding tissues. To implement our ‘tracing method’ we outlined the following criteria for our threshold: i) it would separate all ‘background’ material from the bee head tissue, ii) it would retain the identifying features of each brain structure of interest (MBs, ALs, Mes, Los and CB), iii) and it would separate each structure of interest from the tissue directly surrounding it. This was achieved by overlaying the greyscale and threshold views, adjusting the base level until the binary image fulfilled our criteria - to effectively trace the structures of interest. For the ‘pixel intensity histogram’ method, we based the threshold point on the base value corresponding with the top of the second peak on the working image histogram. This second peak value was applied as the constant determinant of the threshold for the second method. The optimum threshold (for each structure) was independently determined for 15 slices, at 10 slice intervals, across one brain scan to account for slight changes in second peak values across slices. This interval spacing was selected to provide a robust subsample, as each structure is typically visible in 150 scan slices (this slice interval was adjusted for those smaller structures visible across fewer slices). The average of the ranges established for each of our 15 slices was taken to obtain a single range to apply and achieve an optimum threshold, across all scan slices for each individual scan (Fig. 1b).

Working in this ‘threshold view’, a central slice - of those in which the structure of interest was visible - was selected as a starting point. To filter down to the specific brain structure of interest we drew a looped spline around the object(s) to be followed across multiple slices and used a series of landmarks (nodes) to refine the loop to match the shape of the object as accurately as possible. We copied the fitted spline to the next slice (either five slices up or down from the starting slice) and adjusted it to fit the structure outline in the new slice (using the same number of nodes). Spline adjustment was performed on every fifth slice for the region in which the structure of interest was visible. We then interpolated the splines between each start and end slice over the five slice intervals, this automated the process of positioning nodes to define a curve on each intervening slice to create an accurate transition between curve shapes for every first to fifth slice in sequence. This produced a framework around the outline of the structure of interest across all relevant slices, which was then used as the defining foundation to create a ‘mask’, or in other words isolating the complete structure across all slices to be used to form a complete 3D image. The mask could then be exported as an independent object for reconstruction in SPIERS view rendering software, allowing 3D viewing and manipulation of the structure.

Following image reconstruction, we applied an automated processing feature in SPIERS view (called ‘island removal’ combined with ‘smoothing’) to reduce the amount of artefactual extraneous tissue and smooth the mask to depict a more accurate representation of the structure (Figs 2 and 3; see Supplementary Step-by-Step Guide for details). In SPIERS edit we then calculated the volume of the brain structure using a tool to calculate the number of voxels making-up each independent object (each segmented structure), which we could then multiply by the known voxel volume.