# Elucidating nanostructural organisation and photonic properties of butterfly
wing scales using hyperspectral microscopy

Anna-Lee Jessop1,†, Primož Pirih2,†, Limin Wang2,
Nipam H. Patel3,4, Peta L. Clode5,6,
Gerd E. Schröder-Turk1,7,∗, and Bodo D. Wilts2,∗

##  1 Abstract

Biophotonic nanostructures in butterfly wing scales remain fascinating
examples of biological functional materials, with intriguing open questions in
regards to formation and evolutionary function. One particularly interesting
butterfly species, Erora opisena (Lycaenidae: Theclinae), develops wing scales
that contain three-dimensional photonic crystals that closely resemble a
single gyroid geometry. Unlike most other gyroid forming butterflies, E.
opisena develops discrete gyroid crystallites with a pronounced size gradient
hinting at a developmental sequence frozen in time. Here, we use a
hyperspectral (wavelength-resolved) microscopy technique to investigate the
ultrastructural organisation of these gyroid crystallites in dry, adult wing
scales. We show that reflectance corresponds to crystallite size, where larger
crystallites reflect green wavelengths more intensely; this relationship could
be used to infer size from the optical signal. We further successfully resolve
the red-shifted reflectance signal from wing scales immersed in refractive
index oils with varying refractive index, including values similar to water or
cytosol. Such photonic crystals with lower refractive index contrast may be
similar to the hypothesized nanostructural forms in the developing butterfly
scales. The ability to resolve these fainter signals hints at the potential of
this facile light microscopy method for in vivo analysis of nanostructure
formation in developing butterflies.

††1School of Mathematics, Statistics, Chemistry and Physics, Murdoch
University, Australia.††2Chemistry and Physics of Materials, University of
Salzburg, Austria.††3Marine Biological Laboratory, University of Chicago,
USA.††4Centre for Microscopy, Characterisation, and Analysis, University of
Western Australia, Australia.††5School of Biological Sciences, University of
Western Australia, Australia.††6Research School of Physics, The Australian
National University, Australia.††$\dagger$ Contributed equally.††*Authors for
correspondence: G.E. Schröder-Turk<EMAIL_ADDRESS>B.D.
Wilts<EMAIL_ADDRESS>
##  2 Introduction

Some of the most visually stunning displays in nature are created by
structural colours. This phenomenon arises as a result of the interference of
light with periodic nanostructures and is known to occur across almost all
kingdoms of life including animals [1, 2, 3, 4], plants [5, 6, 7], bacteria
[8], and protists [9, 10] (for reviews, see [11, 12, 13, 14]). Among those,
butterfly wings are arguably the most well recognized example of structural
colour in nature. Butterflies employ a diverse set of nanostructural
morphologies within their wing scales that behave as photonic structures [4,
13]. The single gyroid crystals found in numerous green butterflies [15, 16,
17, 18, 19, 20] are one of the more intriguing nanostructural morphologies,
comprising a highly ordered, chiral and sponge-like network with cubic crystal
symmetry [21] (Figure 1). In butterflies, these gyroid photonic crystals
reflect green light that likely functions in camouflage, as an aposematic
signal, and in mate choice. Biophotonic single gyroid structures have also
been reported in weevils [22] and birds [23] with slightly shorter and longer
reflectance wavelengths compared to the butterflies, respectively.

The development of butterfly nanostructures occurs during pupation. Each wing
scale develops from a single epithelial cell that secretes a chitinous cuticle
that follows the template of the cell’s plasma membrane [24, 25]. Based on
Ghiradella’s seminal ultramiscroscopic studies [26, 27, 28], the prevailing
hypothesis of gyroid formation in butterflies is that the wing scale cell’s
smooth endoplasmic reticulum adopts a gyroid morphology and acts as a template
to the nascent chitin polymer [16, 27, 18, 29, 30], while the whole scale cell
is still living and containing cytosol. In parts due to the inability to
observe nanostructure growth in butterflies in vivo several aspects of this
formation or growth process remain open or speculative.

The optical properties of single gyroid photonic crystals are well understood,
through band structure analyses [31, 32, 33, 34] and numerical reflectance
calculations using finite-difference time-domain methods [35, 31, 34], plane-
wave expansions [36, 32, 16], or scattering matrix methods [37]. Single gyroid
optical properties have also been analysed through experiments on custom
fabricated 3D gyroid geometries on the centimeter scale [38, 39] or
nano/micrometer scale [40, 41, 42], see [43] for a review. The optical
observations in gyroid biophotonic crystals in butterflies [16, 34, 44, 19,
20] or replicas thereof [45] are largely explained as photonic crystal
effects, with or without pigmentary absorption. It is well known that the
immersion of dielectric photonic crystals in media with refractive indices
different to that of air (n=1) changes the color of their reflectance, used in
sensor applications [46, 47, 48]; the changes in coloration of butterfly wings
immersed in alcohol or other liquids is a popular demonstration experiment
[49]. If indeed a chitin gyroid structure was present in the pupae during
development, it would be immersed within the cytosol ($1.36<n<1.39$, [50])
leading to a change in reflected color compared to the dry state. Indeed, the
transition of some green butterflies from a bronze color state just after
emerging to a bright green coloration is likely to be due to this effect [51].

The focus of this study is on the green wing scales of the Neotropical
lycaenid butterfly, Erora opisena (formerly Thecla opisena; Figure 1). These
wing scales contain single gyroid nanostructures that, remarkably, form
disjoint crystallites with a pronounced size gradient along the scale [18] (a
similar crystallite structure was observed in Mitoura grynea [27]). Gyroid
crystallites towards the tip of the scale are larger and gradually decrease in
size towards the base of the scale. Wilts et al. [18] hypothesised that this
size gradient may be a developmental sequence frozen in time and that the
formation of the crystallites could be through a growth or extrusion process.
If this were the case, one would expect the gyroid photonic crystals to begin
developing initially as small crystallites towards the tip of the wing scale,
growing larger over time. In this sense, the optical properties of the smaller
gyroid crystallites found towards the base of the adult wing scales may be
analogous to those of crystallites at an early stage of development.

Figure 1: The green wings of E. opisena originate from wing scales containing
photonic gyroid nanostructures. (A) Photograph of E. opisena, reproduced with
permission from P. Brodkin. (B) Light microscopy image of the wings of E.
opisena. (C) Scanning electron microscopy image of a single green scale
showing the ridges, crossribs, and discrete gyroid crystallites within the
scale lumen beneath. (D) Gyroid crystallites imaged from the underside of the
scale with the lower lamina removed. Scale bars: B) 100 $\mu$m, C) 10 $\mu$m,
D) 1 $\mu$m.

Here, we use hyperspectral microscopy (HSM), and auxiliary scanning electron
microscopy, X-ray tomography, and optical modelling, to explore the
relationship between gyroid crystallite size and reflectance and the effect of
immersing wing scales in refractive index-matching oils with refractive
indices similar to cytosol. Hyperspectral microscopy is a microscopy technique
that provides spectral data for each image pixel [52]. This differs from
conventional light microscopy that only records in three colour channels (red,
blue, and green) and from multispectral imaging that records in three to
fifteen colour channels. Hyperspectral microscopy, on the other hand, can
record in many hundreds of colour channels, depending on the HSM set up (here,
we record from 30 channels). Hyperspectral microscopy also differs from the
more conventional microspectrophotometry (MSP) that also records a full
spectrum but can only record from a single location at a time, whereas HSM
allows spectra to be recorded from many locations simultaneously. This
advantage enables efficient reflectance measurements to be collected across a
large area, such as an entire wing scale. As such, HSM is a method that
combines the spatial resolution of light microscopy with high spectral
resolution, as an alternative to MSP.

Hyperspectral microscopy, and hyperspectral imaging more broadly, have been
applied across many different fields (see the reviews in [53, 54]) providing a
method for measuring spectra across small spatial scales. To date, HSM has
already been used to elucidate colour generation in adult butterfly wings
[55], for species discrimination in Drosophila [56], coating quality control
[57], in vivo measurements of human irises [58], for angular-dependent
reflectance of iridescent butterflies [59], for determination of screening
pigments in the eye [60, 61], and to measure defects in self-assembled
cellulose nanocrystals [62].

While HSM, like all conventional light microscopy methods, cannot resolve the
biophotonic nanostructures, HSM can resolve the optical reflections the
structure creates. This article demonstrates that this method is sufficiently
sensitive and accurate to resolve finer ultrastructural details of the
photonic signal of butterfly wing scales. This includes resolving signals in
situations (namely in immersion experiments) that mimic the situation one may
find in a developing wing scale, hence pointing at the potential of HSM for
future applications in imaging biophotonic nanostructural development in vivo.

##  3 Results

Erora opisena is a small Lycaenid butterfly with an overall size of $\approx$2
cm, native to the Northern Neotropics [18]. The ventral sides of both wings of
the butterfly are vivid green, with the hindwings containing small red and
white patches (Figure 1A). The green colour arises from wing scales that
contain single gyroid photonic nanostructures within the scale lumen (see
Figure 1 of [18] and Figure 1B–D). Single scales show a diminishing gradient
of green colouration from the scale tip to middle, whereas the lower half is
red-coloured and no photonic nanostructure is present (Figure 2A). When
attached to the wing, the wing scales partially overlap and the reddish parts
are mostly hidden (Figure 1B).

Observing the scale through microscope objectives with increasing numerical
apertures (10$\times$, NA=0.30; 20$\times$, NA=0.60; 50$\times$, NA=0.95), the
green colour of crystallites changes in hue, desaturates, and increases in
intensity and uniformity (Figure 2B). A similar NA-dependent effect, which is
due to illuminating the sample and collecting the light from ever larger
spatial angles, has been previously observed in the gyroid structures of the
butterfly, Callophrys rubi [63]. Using HSM and MSP, we measure reflectance
(spectra) as a measure of the relative reflectance to a mirror standard, see
Methods section.

We used the green, red, and white scales of E. opisena to compare the spectra
obtained with HSM and MSP methods (see Figure 2B and Figure S1). HSM was
performed between 420–720 nm with a wavelength resolution of 10 nm (see
Methods), while MSP was performed between 380–760 nm. To directly compare the
two methods, the same locations and approximately equal measurement areas were
used (see Figure 2A). The MSP measurement areas were $\approx$ 320, 78, 14
$\mu m^{2}$ for the 10$\times$, 20$\times$, 50$\times$ objectives,
respectively. The corresponding HSM square measurement area (100$\times$100
camera pixels) could in principle be further reduced to 10$\times$10 or less
pixels, achieving diffraction-limited measurement area ¡1 $\mu m$ with a 50
$\times$ objective.

The reflectance curves obtained with HSM (dots) and MSP (lines) were closely
matching for green, red, and white E. opisena wing scales (Figure 2B; Figure
S1). The peak wavelength for green scales measured by HSM with 10$\times$,
20$\times$, and 50$\times$ objectives occurred at 533 nm, 523 nm, and 523 nm,
respectively, differing by less than 1% from MSP measurements (528 nm, 521 nm,
and 523 nm, respectively). Reflectance amplitudes also agreed well, differing
on average over wavelengths by 16%, 20%, and 23%, respectively. For the red
and white scales, the MSP and HSM reflectance measurements differed by 4% and
1%, respectively (Figure S1).

Figure 2: Reflectance spectra from small patches of a single green wing scale
from E. opisena measured using hyperspectral microscopy (HSM) and
microspectrophotometry (MSP). (A) Light microscopy images of a single wing
scale taken with 10$\times$ (NA=0.30), 20$\times$ (0.60) and 50$\times$ (0.95)
objectives, showing locations of measurement areas producing the results shown
in (B). The colour of crystallites changes with the numerical aperture of
objectives. (B) Reflectance spectra from each location in (A) measured using
HSM (dots) and MSP (lines). The scale bars in (A) are 10 $\mu m$, the circles’
sizes represent the measurement area for MSP and the square sizes represent
the measurement area for HSM (10000 camera pixels).

###  3.1 Crystallite size determines reflectance amplitude

The lumen of E. opisena green scales contains discrete gyroid crystallites
that exhibit a pronounced size gradient with smaller crystallites towards the
middle and larger crystallites at the tip [18] (Figure 3A–F). HSM enabled us
to efficiently investigate whether there is a relationship between crystallite
size and reflectance. For this, we measured the average reflectance across 13
regions of interest (200 $\times$ 800 pixels) beginning from the base of the
scale (where no photonic nanostructure is present) and ending at the tip of
the scale (where the largest crystallites are located) (Figure 3A–C). The
reflectance at 520 nm increased from the base ($\approx 0.20$) to the tip
($\approx 0.55$) of the scale. The opposite trend was observed at 600 and 680
nm, where the reflectance was higher towards the base of the scale.
Reflectance at 440 nm remained approximately constant across the scale length.

Segmentation of individual crystallites was based on the SEM image (Figure
3E). This allowed identification of the exact locations of the structures
without the uncertainty caused by the optical effects of the structures. We
further analysed only the crystallites that appeared sharp in the HSM image
stack. In total, 50 crystallites were segmented, ranging in size from 0.9
$\mu$m2 to 28 $\mu$m2, with smaller crystallites displayed in blue and larger
crystallites displayed in yellow (Figure 3E–F). The mean reflectance at 520 nm
was calculated for each crystallite (dots in Figure 3G, the colours consistent
with Figure 3E–F). The mean reflectance of crystallites increased with their
size and ranged from $\approx$0.16 for the smaller crystallites to
$\approx$0.58 for the larger crystallites (Figure 3G).

The measured trend is supported by optical modelling of idealised chitin
gyroid structures. We modelled the reflectance of idealised chitin gyroid
structures with thicknesses varying between 0.66 $\mu$m and 1.65 $\mu$m, and
areas varying between 1.96 $\mu$m2 and 25 $\mu$m2. A positive correlation was
found between reflectance and crystallite area (grey shading in Figure 3G),
however, increasing only the area does not fully explain the experimental
results. The modelling suggests that both crystallite area and thickness
determine the absolute reflectance of the crystallites. This hints that in the
green scales of E. opisena, the larger crystallites at the tip of the scale
could be $\approx$ 50% thicker than the smallest crystallites towards the
scale base, based on the readings 1.3–1.5 $\mu m$ and 0.8–1.1 $\mu m$ for
large and small crystallites, respectively. To further investigate this, we
measured the thickness and area of gyroid crystallites from an X-ray tomogram
of a different E. opisena wing scale (Figure S3). Crystallites that covered a
larger area were indeed also thicker, with the largest measured crystallites
(area$\approx$40 $\mu m^{2}$) having a thickness of $\approx$2.5 $\mu m$ and
the smallest measured crystallites (area$\approx$3 $\mu m^{2}$) having a
thickness of $\approx$0.5 $\mu m$ (Figure S3). These data are consistent with
thickness estimates that can be extracted from the reflectance and optical
modelling data presented in Figure 3G.

Figure 3: Reflectance increases with crystallite size across a single wing
scale. (A–C) Microscopy images of a single green E. opisena wing scale showing
the locations of 13 regions of interest that were used to measure relative
reflectance from the base to the tip of the scale. (A) Colour image taken
under white light illumination. (B) Monochromatic image at 520 nm. (C)
Scanning electron microscopy (SEM) on-view image of the same scale. The
crystallites are visible below the array of ridges and cross-ribs. (D) Average
reflectance across 13 regions of interest measured using hyperspectral
microscopy (HSM). Coloured points show the average reflectance under 440 nm
(blue), 520 nm (green), 600 nm (orange), and 680 nm (red) light. (E,F) The
locations of segmented crystallites from a single scale superposed on the SEM
image (E) and the monochromatic light microscopy image (under 520 nm
illumination). (G) The average reflectance of 520 nm light across each
segmented crystallite versus the area of each crystallite. The colours of each
point correspond to the colours of each segmented crystallite in panel E and
F. Grey shading shows the theoretical reflectance obtained from FDTD
simulations of single gyroid geometries with a refractive index equal to 1.56,
a lattice parameter of 330 nm, a solid volume fraction of 0.3, areas between
1.96 $\mu$m2 and 25 $\mu$m2 and thicknesses between 0.66 $\mu$m and 1.65
$\mu$m.

###  3.2 Reduced refractive index contrast causes dimmed, red-shifted
reflectance

Photonic crystals in mature insect scales are usually composed of a network of
dry, chitinous cuticle and a network of air [15, 16, 1, 18], resulting in a
refractive index contrast between the two networks of $\approx$1.55 [64]. The
situation is quite different in the growing scales, where the refractive index
contrast is much lower, since one network is cytosol with a refractive index
$1.36<n<1.39$ [50] and the other is likely hydrated chitin, $n<1.55$. To mimic
these conditions, we can replace the air in the network with a fluid of known
refractive index. We therefore used HSM to measure the reflectance of isolated
wing scales immersed in different refractive index oils.

When immersed in a medium with a refractive index greater than 1.30, single
green scales appear predominantly red (Figure 4A–B). HSM measurements across
ten crystallites from each scale show a red shift of $\approx$90 nm in the
mean peak reflectance wavelength between scales measured in air (mean peak
wavelength = 526 nm) and scales measured in an immersion oil of refractive
index 1.30 (mean peak wavelength = 616 nm; Figure 4B–C). Increasing the
refractive index further red-shifted the peak to mean peak wavelengths of 651
nm, 664 nm, and 678 nm for oil with refractive indices of 1.40, 1.45, and
1.50, respectively (Figure 4B–C). Using FDTD simulations, we modelled the
reflectance of three different idealised gyroid crystals of chitin in
different refractive index environments (grey dashes and black line and points
in Figure 4C). The three models were gyroid crystals with lattice parameter
equal to 320 nm, 330 nm, and 340 nm, with solid volume fractions of 0.2, 0.3,
and 0.4, respectively. The model comprising the gyroid crystal with lattice
parameter 330 nm, produced reflectance values that most closely matched our
experimental data, but the simulated peak reflectance wavelengths are slightly
(¡10 nm) red-shifted in comparison with the measurement. All simulations
however, supported the observed trend.

Figure 4: Change in reflectance spectra of E. opisena wing scales upon
immersion in refractive index matching oils. (A) Light microscopy images of a
green wing scale in air (n = 1.00, upper panel) and immersed in oil with
refractive index n = 1.40 (lower panel). (B) Reflectance curves of ten
crystallites (mean$\pm$SD) from a single green wing scale in air (green) and
from scales immersed in oils with refractive indices of 1.30 (orange), 1.40
(light red), 1.45 (medium red), and 1.50 (dark red). Measurements for each
crystallite were averaged from areas containing 1600 camera pixels. (C) The
peak reflectance wavelength (mean$\pm$SD) for each immersion experiment
(coloured points, mean$\pm$SD) along with the simulated peak reflectance
wavelengths determined for theoretical immersion refractive indices of 1
through to 1.50 (black and grey lines). FDTD simulations were conducted on a
single gyroid geometry within a range of values for the lattice parameter $a$
and volume fraction $\phi$. The combination of $a=330nm$ and $\phi=0.3$, which
provides a close agreement with the measured data, fits with estimates from
electron microscopy (black points and line). The combinations $a=340,\phi=0.4$
and $a=320nm,\phi=0.2$ are provided for reference (grey dashed lines). The
refractive index used for the chitin phase is 1.56.

##  4 Discussion

The discrete and variably sized gyroid crystallites of E. opisena offer an
opportunity to gain insights into the formation of these remarkable structures
[18]. In line with the findings from ref. [18], we have shown that the
crystallite areas are largest towards the tip ($\approx$40 $\mu$m2) of the
scale and smallest towards the base ($\approx$3 $\mu$m2), using both SEM
(Figure 3) and X-ray tomography (Figure S3). Using HSM, we have also shown
that a positive correlation exists between the crystallite area and the
intensity of reflected green wavelengths (Figure 3G). Full-wave optical
modeling further supported these observations, but also hinted that, in
addition to the crystallite area, the thickness of the crystallites plays a
role (Figure 3G).

An analysis of X-ray tomography data showed that larger crystallites (in terms
of area) were also thicker and both the area and thickness values obtained
from the tomography data corresponded to our model parameters. By combining
our tomography measurements with the modelling results, we show that it is
possible to infer the thickness of each crystallite from their reflectance,
and suggest that the larger crystallites at the scale tip are also thicker.
Further investigations, such as a correlative tomography and spectroscopy
study, would help to clarify this observation and enable more accurate
predictions of crystallite sizes based on reflectance.

###  4.1 Elucidating biophotonic nanostructure development using immersion
experiments

Our current understanding of biophotonic nanostructural development in
butterflies is mostly based on studies using either fixed pupal wing tissue
from discrete time points, or from adult wing scales [26, 27]. Electron
microscopy studies of fixed pupal wing tissues have offered insights into some
of the more complex nanostructural formations such as the formation of lamella
ridges and the growth of single gyroid photonic crystals [26, 27, 16]. More
recently, studies using confocal microscopy have uncovered details of the
cellular dynamics of scale formation including the role of F-actin in the
formation of scale fingers and ridges [65, 66], in the spacing of ridges [67],
and in the formation of the honeycomb lattice found in the upper lamina of
some Papilionid butterfly species [68].

Many details of the developmental processes underlying nanostructure formation
in butterflies remain elusive and can only be uncovered through continuous,
spatio-temporally resolved in vivo investigations of living scale cells.
However, imaging nanostructural features in vivo remains an unsolved
challenge. Constrained by Abbé’s diffraction limit, the spatial resolution of
light-microscopy is limited to hundreds of nanometers, a resolution too low to
directly observe the development of photonic nanostructural features. In the
last two decades, several super-resolution microscopy techniques utilising
fluorescent probes have emerged, enabling in vivo imaging with resolutions as
low as 50 nm (for a review, see [69]). While these techniques are promising,
non-toxic fluorescent probes are scarce and expensive, and the strong
illumination required by these techniques can easily disturb delicate
subcellular processes, particularly when imaging must be performed over a span
of several hours or days. Recently, a label-free imaging technique using
optical speckle-correlation reflection phase microscopy achieved a resolution
high enough to resolve some of the larger nanostructural changes, showing
ridge and lamellae formation in vivo [70], however the technique is not able
to achieve the resolution needed to resolve the smaller gyroid nanostructures.

An alternative approach, suggested by this paper, is to take advantage of the
fact that these nanostructures behave as photonic structures. As this paper
has shown, it is possible to infer information about crystal size, and
refractive index contrast from the optical signals that are created by
photonic nanostructures. Importantly, our results suggest that HSM may be a
convenient method for studying the small optical signals one would expect to
occur during the formation of biological photonic crystals in vivo. While MSP
can provide similar spectral information, the HSM technique that we have
applied in this study allows accurate measurements of spectra at the spatial
resolution limit of optical microscopy while also efficiently providing
spatial information (Figure 2). For in vivo imaging, this spatial information
will be a necessity as keeping measurement consistency may be difficult during
the inevitable movements caused by larval growth and HSM enables spatial
registration of an image series.

Specifically, by immersing adult wing scales in refractive index-matching oils
with similar refractive index to cytosol ($1.36<n<1.39$, [50]), we attempted
to mimic the refractive index conditions that are likely to exist in a
developing gyroid nanostructure. A reduction in the refractive index contrast
between the chitin nanostructure and its surrounding medium causes a red-shift
and a reduction of the reflectance (Figure 4). We additionally simulated this
scenario using optical modelling on three different idealized gyroid
nanostructures with varying lattice parameters and solid volume fractions. Our
simulations of a gyroid nanostructure with lattice parameter equal to 330 nm
and a solid volume fraction equal to 0.3, resulted in a near perfect match to
our experimental data (Figure 4C). Lattice parameter and solid volume fraction
are not easily obtained with high accuracy from the SEM images, but the best
fitting values from our optical models correspond to those values reported
previously from a related species [17, 18].

Whether the red-shift also occurs in the developing butterfly remains to be
seen, but these experimental simulations demonstrate the capacity of HSM to
locally and accurately measure small reflectance signals. These results on
mature wing scale nanostructures highlight that HSM has the appropriate
spatial, temporal, and wavelength resolution to study the optical signals
expected in developing butterfly scales.

###  4.2 Hyperspectral microscopy as a tool to obtain spatially resolved
reflectance

The HSM technique implemented here uses a monochromatic camera and a liquid
crystal tunable filter. These two devices can be easily added to most standard
optical microscopes (Figure 1). Tunable filters have been used previously in a
variety of contexts, for example, to rapidly resolve variations in
electroluminescence of light-emitting diodes [71], to obtain simultaneous low-
resolution spectrophotometry of multiple stars [72], for fine arts diagnostics
[73], in fluorescence microscopy [74], and to analyze reflectance of butterfly
wings [59]. Their ease of use and relatively low cost makes them an attractive
option for an in vivo experimental set up.

Comparing HSM to MSP techniques, several advantages and limitations become
apparent: HSM offers enhanced spatial resolution, allowing access to smaller
measurement spots and providing statistical power that is difficult and
laborious to obtain with MSP (Figure 3). The superior spatial resolution
brings about a trade-off with limited spectral range, reduced spectral
resolution and slower acquisition rate. The spectral resolution is generally
not a problem, as reflectance spectra of photonic structures are usually quite
smooth. Particularly for UV-VIS-NIR spectral analysis and rapid data
acquisition, wavelength range extension, increase in response speed and
reduction of the sidebands (Figure 5B) would be welcome. The wavelength range
of HSM can be matched to the transmission limit of microscope optics by using
a more traditional combination of a broadband (XBO) source, and a
monochromator or a gradient bandpass filter, but this also brings about the
necessity of focus control due to chromatic aberrations ([60, 61]). Off-the-
shelf multichannel LED sources based on dichroic mirrors, or multi-emitter
LEDs with adequate spatial homogenisation are usable if the wavelength
resolution is not of prime importance. Narrower spectra can be achieved by
combining the LEDs via a diffraction grating [75]. Compared to these
solutions, the liquid crystal filter technology is attractive because it is
lightweight, transportable and can be easily installed both in the
illumination and the observation path.

In conclusion, the HSM technique described in this study enables the
simultaneous recording of spectra across space in dynamic systems such as a
developing butterfly, where temporal, spatial, and spectral information must
be correlated. If the optical signal can be reliably measured in vivo during
wing scale development, hyperspectral microscopy may facilitate indirect
measurements of structure formation and add new perspectives on processes
involved in the growth of these intricate and intriguing natural optical
elements.

##  5 Methods

###  5.1 Samples

Erora opisena (Druce, 1912; Lepidoptera: Lycaenidae: Theclinae: Eumaeini)
butterflies were caught in Chiapas, Mexico and purchased from The Bugmaniac
(www.thebugmaniac.com). Individual wing scales were gently lifted off the
wings using a cotton swab and placed onto a glass microscope slide or an
aluminium stub covered with black aluminium foil (BKF12, T205, Thorlabs) or
copper tape. Whole wings displayed in Figure 1 were imaged using a Leica M205
C digital microscope. Scanning electron microscopy images shown in Figure 1
were imaged using a FEI Verios XHR scanning electron microscope after wing
scales were sputter coated with platinum. The electron beam acceleration
voltage was set to 5 kV and an in-lens secondary electron detector (Everhardt
Thornley Detector) was used to image the scale at a working distance of
approximately 5.0 mm.

###  5.2 Microspectrophotometry and hyperspectral microscopy

A modified Zeiss Axioscope 5 optical microscope was used to perform both
microspectrophotometry (MSP) and hyperspectral microscopy (HSM) measurements.
Microscope objectives included Zeiss EC Epiplan-Apochromat objectives with a
magnification of 50$\times$ (NA = 0.95), 20$\times$ (NA = 0.60), and
10$\times$ (NA = 0.30) for the measurements of Figures 2–3 and a multi-
immersion objective Zeiss 25$\times$ (NA = 0.80) LD LCI Plan-Apochromat for
the measurements in oils presented in Figure 4.

For MSP, illumination was provided by a halogen light source (OSL2, Thorlabs
Inc.) passed through a collimating lens (COP4-A, Thorlabs). Relative
reflectance spectra were collected via a microscope sideport below the tube
lens, with the light path consisting of a mirror, a focusing quartz lens and a
200 $\mu m$ quartz fibre (FC-UVIR200, Avantes) attached to a spectrometer
(AvaSpec-ULS2048XL-EVO, Avantes). The measurement spot diameter of 4, 10, or
20 $\mu m$ using the 50$\times$, 20$\times$ and 10$\times$ objectives,
respectively, was set to the centre of the image using a mirror in the object
plane. An aluminium mirror (PF10-03-F01, Thorlabs) served as the reference.

For HSM, illumination was provided by a Zeiss Axioscope 5 microscope
collimated white LED source or a halogen light source (OSL2, Thorlabs) that
was passed through a collimating lens (OSL2COL, Thorlabs) and a shortpass
filter (FESH0750, Thorlabs). The collimated light was passed through a tunable
liquid crystal bandpass filter (Kurios, Thorlabs Inc.), and a quartz
depolariser to counteract any polarisation effect of the liquid crystal filter
(DPU-25, Thorlabs; a schematic diagram is shown in Figure 5A). The tunable
bandpass filter was controlled over the serial interface by custom software
written in MATLAB 2023a (The MathWorks Inc., USA). The bandwidth was set to
deliver a restricted spectrum of light with a full-width at half-maximum
(FWHM) of approximately 18 nm (Figure 5B). Samples were illuminated with
wavelengths between 430–720 nm in 10 nm steps. For each illumination
wavelength, an image was captured with a 20 MP monochromatic camera with pixel
size 2.4 $\mu m$ (BFS-U3-200S6M-C, Teledyne FLIR) mounted to the camera port
of the microscope. Because the illumination intensity (Figure 5B) and the
camera sensitivity was not equal across all wavelengths, a calibration run was
initially conducted on a reference aluminium mirror (PF10-03-F01, Thorlabs)
and the exposure times and gain values of the camera were adjusted such that
the average pixel intensity of each image was approximately equal. This
ensured that pixels were neither over nor underexposed for any one wavelength.
Subsequently, the sample was measured using the calibrated exposure and gain
values and the reflectance at each wavelength was calculated by averaging the
pixels in the area of interest, subtracting the dark background from the
sample data, and then dividing the sample data by the reference mirror to
obtain relative reflectance.

RGB images were taken on the same microscope using a 20 MP colour camera (The
Imaging Source, DFK 38UX304) from different focal planes. Focus stacked images
were then created using Helicon Focus Pro (Helicon Soft Ltd, Ukraine). All
data was further analyzed using custom scripts written in MATLAB 2023a (The
MathWorks Inc., USA).

Figure 5: Hyperspectral microscopy set up and spectral output. (A) Schematic
of the hyperspectral microscope consisting of a light source, a liquid crystal
tunable bandpass filter, a monochromatic camera, and an optical microscope
(see Methods for details). (B) Intensity of incident broadband light after
filtering with the liquid crystal filter for centre wavelengths of 450–700 nm
in 50 nm steps measured using a spectrometer.

###  5.3 Correlation between reflectance and gyroid crystallite size

To investigate the effect of crystallite size on reflectance we measured the
average reflectance of regions of interest from scale base to tip and the
reflectance of individual gyroid crystallites of differing sizes. A single
scale was placed onto black foil attached to an aluminium stub and HSM was
performed using the 50 $\times$ objective. Subsequently, the same scale was
sputter-coated with a thin layer ($\approx$5–8 nm thick) of gold using a
Cressington Sputter Coater 108 auto (120 s, 40 mA, background pressure 0.08
mbar) and imaged with a Zeiss Ultra Plus 55 scanning electron microscope
(SEM). The electron beam acceleration voltage was set to 5 kV and an in-lens
secondary electron detector was used to image the scale at a working distance
of approximately 5.5 mm. SEM images were acquired at high magnifications
across different sections of the scale and subsequently stitched together
using Adobe Photoshop 2023. HSM and SEM data were then manually superposed.
The regions of interest displayed in Figure 3A–C each contain 160,000 camera
pixels that were averaged to obtain reflectance spectra for each region. The
regions of interest shown in Figure 3E–F were defined from the SEM image using
the Image Segmenter App in MATLAB 2023a.

To gain an understanding of how crystallite area (as viewed in the SEM image)
relates to the thickness of the crystallite, we measured the areas and
thicknesses of 25 crystallites from two X-ray tomograms of a green E. opisena
wing scale. Details of the X-ray tomography data collection can be found in
[18], and the tomography data sets are accessible at: 10.5061/dryad.w0vt4b912.
Briefly, an isolated wing scale was imaged using a Zeiss Xradia 810 Ultra
X-ray microscope producing two tomography datasets with voxel sizes of 64 nm,
one dataset of the base of the wing scale and a second of the tip of the wing
scale. The datasets were visualized and stitched together in Dragonfly
(v2022.1.0.1249, Object Research Systems, Montreal, Canada) and subsequent
analyses were conducted on the stitched data. Area was measured manually on
slices of the dataset using the free-hand drawing tool in Dragonfly (Figure
S3), thickness was measured on two perpendicular slices using the measurement
tool and the average of the two values was used as the crystallite thickness.

###  5.4 Immersion oil experiments

Single green wing scales were immersed in refractive index matching oils
(Series A, Cargille Laboratories Inc.) with nominal refractive indices of
1.30, 1.40, 1.45, and 1.50 at 589.3 nm at 25∘C. The mean reflectance of each
scale immersed in the particular oil was obtained by averaging HSM
measurements from ten randomly selected gyroid crystallites. Each measurement
area was a square containing 100 camera pixels.

###  5.5 Finite-difference time-domain modelling

Light scattering by the gyroid nanostructures was simulated with the three-
dimensional finite-difference time-domain (FDTD) method, using Lumerical 8.29
(Lumerical Solutions, Vancouver, Canada). The gyroid nanostructures of E.
opisena were approximated by simulating voxelised single gyroid geometries in
Houdini FX 19.5 (Side Effects Software Inc, Toronto, Canada) or via an
idealized single gyroid network approximated by triply periodic minimal
surface model from its level-set equation [76]. Three gyroid geometries were
simulated with lattice parameters of 320 nm, 330 nm, and 340 nm, and solid
volume fractions of 0.2, 0.3, and 0.4, respectively. Gyroid geometries were
set up in a rectangular simulation box with the two lateral directions,
corresponding to [110] and [100], having periodic boundary conditions and the
third (vertical) direction corresponding to the incident light direction was
also a [110] symmetry direction having a perfectly matched layer (PML)
boundary condition. The gyroid geometry that most closely matched our
experimental data consisted of repeating unit cells with lattice parameter 330
nm and a solid volume fraction 0.3, resulting in the peak reflectance
wavelength of 525 nm, approximately equal to that of our experimental
reflectance measurements. The gyroid consisted of cuticular chitin with a
refractive index of 1.56 [64], and we varied the refractive index of the
surrounding medium between 1.00 and 1.55. Simulations of area and thickness
dependence used PML boundary conditions on all boundaries of the simulation
box that was adjusted in size to fully fit the gyroid geometry. Light, with
wavelengths of 400-800 nm, was incident in normal direction onto the structure
along the [110] direction.

##  6 Acknowledgements

The authors acknowledge the facilities of Microscopy Australia at the Centre
for Microscopy, Characterisation and Analysis, The University of Western
Australia, a facility funded by the University, State and Commonwealth
Governments. We thank Dr. Monte Masarei for assistance with image
presentation, Dr. Alexandra Suvorova for assistance with SEM training and
sample preparation, Dr. Volker Framenau for providing access to the Leica
microscope, and Dr. Gregor Zickler for SEM support. We thank Dr. Neil Gale
(Aberystwyth Butterfly House) for pointing out reference [51]. We thank Dr.
Stephen Kelly (Zeiss), Dr. Benjamin Apeleo-Zubiri and Prof. Erdmann Spiecker
(Erlangen) for the tomography data sets used in Fig. S4 in the supplementary
material.

This work was supported by the Australian Research Council (ARC) through the
Discovery Project DP200102593. This work was supported by a Research Grant
from HFSP, Ref.-No: RGP0034/2021 (to B.D.W.).

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