Journal of Microscopy, Vol. 246, Pt 2 2012, pp. 113–123

doi: 10.1111/j.1365-2818.2012.03596.x

Received 28 September 2011; accepted 05 January 2012

Quantitative comparison between full-spectrum and filter-based
imaging in hyperspectral fluorescence microscopy
L . G A O ∗, N . H A G E N ∗ & T . S . T K A C Z Y K †∗ Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77005, U.S.A.

†Department of Bioengineering and Department of Electrical and Computer Engineering, Rice
University, 6100 Main Street, Houston, TX 77005, U.S.A.

Key words. Hyperspectral fluorescence microscopy, filterless illumination,
spectroscopy.

Summary
We implement a filterless illumination scheme on a
hyperspectral fluorescence microscope to achieve full-range
spectral imaging. The microscope employs polarisation
filtering, spatial filtering and spectral unmixing filtering to
replace the role of traditional filters. Quantitative comparisons
between full-spectrum and filter-based microscopy areprovided in the context of signal dynamic range and accuracyof measured fluorophores’ emission spectra. To show potential
applications, a five-colour cell immunofluorescence imaging
experiment is theoretically simulated. Simulation results
indicate that the use of proposed full-spectrum imaging
technique may result in three times improvement in signal
dynamic range compared to that can be achieved in the filterbased imaging.
Introduction
Fluorescence microscopy is used extensively to gain a deeper
understanding of varying cellular dynamics. A major impetus
towards the widespread application of this analytical tool
is the ongoing development of fluorescent probes, whichhave revolutionised studies of cell and tissue physiology inthe past decades (Belmont, 2001; Lichtman & Conchello,
2005; Spector et al., 2004). Taking full advantage of
their properties, however, can require modification of the
traditional fluorescence imaging set-up. Available fluorescentprobes exhibit different photo activities in the visible andnear infrared region with Stokes shifts (difference between
peak excitation and emission wavelengths) from a few toover a hundred nanometres (Peng et al., 2005). It can bechallenging to image multiple probes simultaneously because
Correspondence to: Tomasz Tkaczyk, 6100 Main Street, MS-142 Houston,TX, 77005, U.S.A. Tel: +713–348-3019; fax: +713.348.5877; e-mail:ttkaczyk@rice.edu

C 2012 The Authors
C 2012 Royal Microscopical Society
Journal of Microscopy 

the absorption spectrum of one fluorophore may overlap withthe emission spectrum of another fluorophore.
To acquire fluorescence images, conventional fluorescencemicroscopy usees exciter/dichroic/emitter filter sets toseparate fluorescent emission from excitation. The filter
combination of exciter/dichroic/emitter can reach a very highextinction ratio in damping the excitation background, thus
providing high contrast for fluorescent objects. However, formany commonly used fluorophores, the Stokes shifts are
small, for example, ∼20 nm for most dyes in the families ofAlexa Fluor and Cy (McNamara et al., 2006). To image these
fluorophores with filter sets, one faces an inevitable trade-off
between the excitation and emission spectra because they are
not allowed to overlap: one either sacrifices a large portion of
the excitation spectrum (blocking illumination photons andthus sacrificing measurement speed) or acquires a reduced
portion of the emission spectrum (blocking emission photons
and thus sacrificing signal-to-noise ratio) to accommodate agiven filter set.
To avoid the trade-off between emission and excitation
spectra in standard filtered imaging, polarisation filtering has
been adopted as an alternative approach to replace the roleof traditional filters (Lei & Yao, 2008). It is based on the factthat fluorescence emission often exhibits random polarisation
whereas most back-reflected or scattered excitation lightremains in the original polarisation state (Lakowicz, 2006).
However, polarisation filtering itself cannot achieve a highextinction ratio of excitation light, a fact which is ofparticular concern in weak light imaging experiments. To
further improve image contrast, recently a novel filterlessillumination scheme which employs spatial filtering andstructured illumination as additional filtering mechanisms
has been reported (Booth et al., 2010). Although the imaging performance of this method has been demonstrated
in fluorescence microscopy, its dependence on structured
illumination for background removal halves the throughput

114 L. GAO ET AL.

of a microscope because of the introduction of grid patternin the illumination path, and this approach falters if there isa strong nonfluorescent scattering signal from the in-focus
plane.
To overcome this limitation and explore the potential
application of this filterless illumination scheme, in thispaper we built a full-spectrum hyperspectral fluorescencemicroscope (HFM), which combines three excitation/emission
separation techniques: polarisation filtering, spatial filteringand spectral unmixing filtering. Compared to the previous
design (Booth et al., 2010), we used a spectral unmixing
filtering algorithm in background removal so that scattered
light can be isolated from the fluorescent emission even when
the two are co-located at the same plane. Because the full
spectrum is used at both the excitation side and emission
side, the difficulty of avoiding spectral overlap in filter-based
imaging can be eliminated. The acquisition of high-contrast
hyperspectral images of bovine pulmonary artery endothelial
cells and fluorescent beads prove the feasibility of this new
approach (Section 3.1 and Section 3.2). Although Booth didsuggest multispectral approaches would be good partners forfilterless illumination strategy (Booth et al., 2010), to the bestof our knowledge, it is the first time that this technique has been
implemented in a HFM to achieve full-range spectral imaging.
In addition, quantitative comparisons between fullspectrum imaging and filter-based imaging are implemented
in the context of signal dynamic range and accuracyof measured fluorophores’ emission spectra. To show theadvantage of full-spectrum HFM technique in multiplexed
imaging applications, we also simulate a five-colour cell
immunofluorescence imaging experiment under the twoillumination schemes (Section 3.3). The results indicate thatup to three times improvement in signal dynamic range canbe achieved if filter-based imaging is replaced by the proposed
full-spectrum imaging technique.
System description
Optical set-up
The full-spectrum HFM is implemented on a Zeiss Axio
Observer microscope with epi-illumination (Fig. 1). The
excitation source is a supercontinuum laser (Fianium SC400;
Fianium Ltd, Southampton, UK) with broadband spectrum(410–2100 nm). The excitation laser is linearly polarised(s component) by a Glan-Thompson prism (PN: GTH5M,
Thorlab, NJ, USA). A polarisation beam-splitter cube (Lambda
Research Optics, CA, USA; extinction ratio >500:1) ismounted on the microscope filter turret, reflecting theexcitation light towards the microscope objective. Because of
the spatially coherent nature of the supercontinuum laser,
the excitation light is focused onto a single point at the backpupil plane of the microscope objective, where the spatial filter
is located. The focus’ in-plane position is finely adjusted by

Fig. 1. Optical set-up for the full-spectrum HFM. PBS, polarisation beam
splitter, A: Microscope back illumination port, B: Microscope stage and C:
Microscope side image port.

a folding mirror until it reaches the conjugate position of
the spatial filter’s blocking part, so that most of the specularreflected excitation light can be rejected. The fluorescence lightis collected by the microscope objective, and filtered by thebeam-splitter cube with ∼50% transmission throughput (scomponent is rejected). Note that building the proposed system
requires laser safety operation procedures. Because the nearinfrared excitation light is blocked by a hot filter (inside themicroscope), the light seen through the microscope’s eyepieces
is in the visible light range. In addition, the laser power isoperated at 20% during alignment, a range that the crosspolarisation decreases effective irradiance as seen througheyepieces.
The full-spectrum (x, y, λ) datacubes are captured andprocessed by a snapshot hyperspectral imager – image
mapping spectrometer (IMS; Gao et al., 2009; Kester et al.,2011). The IMS is used here because it requires no scanning
to measure a (x, y, λ) datacube, a fact that maximises thethroughput of a microscope in spectral imaging applications(Kester et al., 2011). The (x, y, λ) datacube that the IMS
acquires is of size 350 × 350 × 48 (the first two numbers arefor spatial sampling; the third number is for spectral sampling).
When coupled to a microscope, the spatial resolution of the
IMS is determined by the N.A. of microscope objective and thespectral sampling is ∼4 nm across 200 nm spectral range.
Operation principle of full-spectrum HFM
The operating principle of the full-spectrum HFM is shownin Figure 2. At the sample plane, because the excitation isoften several orders of magnitude brighter than the fluorescentC 2012 The Authors
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115

Fig. 2. Operating principle of full-spectrum HFM.

emission, the fluorescent signal is buried in a high levelof excitation background. To remove excitation light, a
triple-step procedure is employed: first, the specular reflected
excitation light is removed by the spatial filtering. Then,the scattering background is reduced by the polarisation
filtering with a beam-splitter cube. Finally, a mixed 3D
datacube (x, y, λ), which contains fluorescent emission
and residual background is acquired by the IMS. With
the known reference spectra of each component, a linear
unmixing algorithm (Section 2.3) is implemented on thecaptured (x, y, λ) datacube, and discriminates the original
datacube into two subcubes based on their spectral difference –one with the residual scattering background and the otherwith fluorescent emissions. Consequently, the datacube offluorescence signals within the image have now been
separated from the background light and the image contrast
is improved.
Image contrast improvement algorithm by spectral unmixing
filtering

within the sample at spatial position (x, y). For a sample whose
reduced scattering coefficient can be assumed homogeneous
throughout the sample, we can write j (x, y, λ) =  j (λ)Asca (x, y)(2)

so that the total elastically scattered light is
Rsca (x, y, λ) = Asca (x, y)m j (λ)(3)

j =1

for m different spectral scattering components. In this case, thesystem’s spectral component matrix H can be expressed as
H = [R1 R2 ... Rn Rsca ](4)

Because the object’s datacube f (x, y, λ) can be experimentallymeasured by the IMS and theoretically equals themultiplication of the system’s spectral component matrix H
with the components’ concentration distributionAi (x, y), thetrue concentration can be estimated as
Âi (x, y) = H + f i (x, y) =K[H + ]ik f ki (x, y)

(5)

k=1

Although spatial filtering and polarisation filtering can block
most specular reflected excitation light, neither of themworks effectively when elastic scattering from the sample
becomes a source of background signal (Mourant et al., 1998).
To further improve the image contrast, we implemented aspectral unmixing filtering algorithm to isolate the elastic
scattering from the fluorescent emission based on their spectral
fingerprints (Ahmed et al., 2004).
Assuming there are n fluorophores in the sample, each has areference emission spectrum and concentration distribution in
the form of Ri (λ) and Ai (x, y) (i = 1, 2, 3, . . . , n), respectively.
At the same time, elastically scattered light will also produceja ‘background’ spectral signature Rsca (x, y, λ) given byj
Rsca(x, y, λ) = S(λ) j (x, y, λ)(1)

where S(λ) is the illumination source spectrum, and j (x, y, λ) is the scattering efficiency of the jth componentC 2012 The Authors
C 2012 Royal Microscopical Society, 246, 113–123
Journal of Microscopy in which H + is the Moore–Penrose pseudoinverse of H, andk is the index over the number of wavelength channels K in
the datacube. As long as H is well conditioned, then Âi (x, y)provides a reliable estimate for the concentration of the ith
chromophore within the image. Based on this solution, we
can remove the scattering from the original datacube byf ∗ (x, y, λ) = f (x, y, λ) − Rsca (λ) Ân+1 (x, y)(6)∗and obtain a filtered datacube f .
Imaging results and discussions
To demonstrate the spectral imaging performance of the
proposed full-spectrum HFM technique, two experiments were
designed and implemented – imaging of bovine pulmonary
artery endothelial cells (Section 3.1) and imaging of six-colour
fluorescent beads (Section 3.2). Quantitative comparison

116 L. GAO ET AL.

Fig. 3. Hyperspectral imaging of BPAE cells. Each image displays an
80 μm × 80 μm region of the sample (a) Color image captured by
the IMS (before spectral unmixing filtering),(b) Scattering background,(c) Fluorescence and (d) Reference color image at microscope’s side image
port.

between full-spectrum imaging and filter-based imaging was
made in the context of signal dynamic range and accuracy ofmeasured emission spectra. In addition, to show the potential
applications, a five-colour cell immunofluorescence imaging
experiment was also theoretically simulated in Section 3.3.
Full-spectrum hyperspectral imaging of bovine pulmonary artery
endothelial cells
A bovine pulmonary artery endothelial cell sample was imagedby the proposed full-spectrum HFM. The cellular F-actin waslabelled with dye Alexa Fluor 488 and cellular mitochondria

were labelled with dye MitoTracker Red. A Zeiss PlanApochromat 63×/NA = 1.40 objective was used to image
the sample on the microscope. The datacube was capturedby the IMS with 0.5 s exposure time (Fig. 3a). This datacube,
after filtering and pseudocolouring, is shown in Figure 3(c).
Spectral channels of captured datacube are presented in
Figure 4(a). To show the power of spectral unmixing filteringin this cellular imaging experiment, the same field of viewwas directly captured at the microscope’s side image port
by a colour camera [Infinity 2–1C (Lumeneva, Ottawa, ON,
Canada); Fig. 3d]. Comparing Figure 3(c) with Figure 3(d),most scattered background light is successfully removed
because of proposed solution.
For comparison with filter-based imaging, the sample wasalso imaged under the filter-based illumination with the same
exposure time. A Chroma multiband filter set 61 001 wasused to separate fluorescent emission from the excitation
light. The captured datacube is displayed as spectral channels
in Figure 4(b). Comparing Figure 4(a) with Figure 4(b),fluorescent signals are collected in all spectral channels in
the full-spectrum imaging mode; whereas in the filter-based
imaging mode, several spectral channels (e.g. 501–508 nm
and 554–585 nm) are dark because they are spectrally locatedat the excitation bands of the filter.
To further assess the effect of filters on the fluorophore
emission spectra, the normalised fluorophore emission spectrafrom full-spectrum imaging and filter-based imaging atcorresponding positions are also compared (Fig. 5a and b). Two
major spectral changes in the filter-based spectra have been
identified – termed ‘spectral gap’ and ‘peak shift’. The ‘spectral
gap’ is caused by the blocking effect of the filter’s excitation
bands in the emission collection range, which changes theshape of the fluorophores’ original emission spectra (indicatedFig. 4. Acquired (x, y, λ) datacube and datacube’s spectral channel images in (a) full-spectrum imaging and (b) filter-based imaging. Each spectral
channel image displays an 80 μm ×80 μm region of the sample.
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Fig. 5. Measured fluorophore emission spectra in full-spectrum imaging and filter-based imaging. The full-spectrum and filter-based spectrum shown
here have been normalised differently to match peak heights (a) Alexa Fluor 488 and (b) Mito Tracker Red.
Table 1. The excitation and emission maxima of fluorescent beads.
Bead label

Excitation maxima

Emission maxima

1
2
3
4
5
6

503 nm
511 nm541 nm545 nm
578 nm589 nm

511 nm524 nm
555 nm
565 nm605 nm613 nmby arrows in Fig. 5a). The ‘peak shift’ is caused by the
wavelength cut-off effect of emission filter. Because of thiseffect, the measured emission peaks of fluorophore Alexa
Fluor 488 and MitoTracker Red in filter-based imaging are
shifted 4 and 15 nm, respectively from their corresponding
locations under the full-spectrum illumination. The ‘spectral
gap’ and ‘peak shift’ may cause problems in quantitative
fluorescence imaging experiments, whose results are sensitive
to the emission’s profiles or absolute spectral peak position,e.g. the measurement of spectral response of fluorophore tochanges in pH (Ford et al., 2001) or temperature (Lakowicz
et al., 1983).

where there are no beads (Fig. 6). After spectral unmixing
filtering, the measured datacube is displayed as a colour
image in Figure 7(b). To show the power of spectral unmixing
filtering, the same field of view was directly captured at the
microscope’s side image port by a colour camera (Infinity 2–
1C; Fig. 7a). Comparing Figure 7(a) with Figure 7(b), the resultshows that the residual background has been reduced by the
proposed algorithm. In addition, a spectral feature map was
also acquired as a result of spectral unmixing and shown in
Figure 7(c). Note that the bead pairs 1 and 2, 3 and 4, 5 and 6,
which are hardly discriminated in the colour image (Fig. 7(b))
are now visually distinguishable in the spectral feature map(Fig. 7c).
For quantitative comparison, the same field of view was
also imaged by the IMS under filter-based illumination withthe same exposure time (0.2 s). A long-pass filter set (PN:11012v2, 515 nm LP, Chroma, VT, USA) was chosen to
separate the excitation from the emission light. By using thisfilter configuration, because most fluorescent beads’ emissions
fall in the emission filter’s transmission range, the photon
collection efficiency is maximised for all fluorescent beads;
however, at the excitation side, the red fluorescent beads are
not effectively excited because filter’s excitation band does not

Full-spectrum hyperspectral imaging of six-colour fluorescentbeads
A prepared mixture of different colour fluorescent beads (6 μm
diameter, Invitrogen (Grand Island, NY, U.S.A.) FocalCheckfluorescence microscope test slide #2) was placed on the
microscope stage and was imaged by a Zeiss Epiplan-Neofluar
40×/NA = 0.75 objective. The fluorescent beads’ excitationand emission maxima are shown in Table 1.
A field of view that contains all six-colour beads wasimaged by the IMS under full-spectrum illumination with
0.2 s exposure time. The bead emission reference spectra
were measured at the bead location, whereas the backgroundreference spectrum was measured by averaging an areaC 2012 The Authors
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Fig. 6. Background and beads’ fluorescent emission reference spectra.

118 L. GAO ET AL.

Fig. 7. Hyperspectral imaging of multicoluor fluorescent beads (a) Reference color image at microscope’s side image port, (b) Color image captured by
the ims after spectral unmixing filtering and (c) Spectral feature map(The beads are classified and pseudo-color labeled.

reach the dye’s peak absorption range. The polariser in theillumination optical path was removed to transmit the ‘full’laser power to the sample as it is not needed in a filter-based setup. In addition, because the fluorescent beads were proven tobe photostable (Johnson & Spence, 2011), no photobleachinginduced intensity decrease is assumed to be observed in thiscontrolled experiment.
We first measured the excitation spectral intensity at the
sample by placing the entrance port of an integrating sphere atthe plane of the sample, and used a fibre-coupled spectrometer(Ocean Optics USB 4000; Dunedin, FL, U.S.A.) to sample thelight inside the sphere. The result is shown in Figure 8. The
curve for the filter-based measurement thus gives the spectrumof the source multiplied by the transmission of the optics andthe excitation filter-dichroic beam-splitter pair
Sfilt (λ) = Ssrc (λ)Topt (λ)[Texc (λ)Tdic (λ)]ηfilt (λ) =Ssrc (λ)Topt (λ)[Tdic(λ)Temi (λ)]
Sfilt (λ)=
Ssrc (λ)
Ssrc (λ)

(9)


(λ)
Sfull (λ)
Ssrc (λ)Topt (λ)TPBS=
Ssrc (λ)
Ssrc (λ)(10)

andηfull (λ) =(7)whereas the curve for the full-spectrum measurement
replaces the filter-beam splitter pair with a polarising beam
splitterSfull (λ) = Sscr (λ)Topt (λ)TPBS (λ)

Up until the cut-off wavelength, the filter-based illumination
is approximately two times brighter than the full-spectrum
set-up but beyond the cut-off none of the light can be used for
fluorescence excitation.
Next we measured the system collection efficiency (orthroughput) for fluorescent emitted light under the two setups. Here we replaced the sample with a broadband lightsource and measure the resulting spectrum at the microscope’s
side image port. Dividing this by the spectrum measured
directly at the source gives the throughput value η, i.e.

(8)Fig. 8. Excitation spectra under filter-based and full-spectrum illumination.

Here Temi (λ) is the emission filter transmission, and primed
quantities indicate that the geometric path is different, eventhough the same optical component is being used, so thatthe transmission spectrum will be changed. The resulting
throughput spectra ηfilt (λ) and ηfull (λ)are shown in Figure 9.
Here we see that the full-spectrum set-up throughput isprimarily limited by the polarising beam splitter. The filterbased set-up thus has higher throughput for wavelengths

Fig. 9. System throughput for emission in filter-based and full-spectrum
imaging.
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Fig. 10. Beads’ emission spectra comparison in full-spectrum imaging and filter-based imaging (a) Full spectrum imaging, (b) Filter-based imaging, (c)Measured spectrum of bead 1, (d) Measured spectrum of bead 2 and (e) Measured spectrum of bead.

above the emission filter’s cut-on wavelengths, but that thesystem cannot collect light below λcut−on .
Next, the dynamic range of the IMS’s captured images wascompared under the two illumination schemes. Here we define
the dynamic range as the average of detected signals from afluorescent bead, divided by the average of detected signals
in the image background. The background signal level was
first measured in the filter-based imaging and full-spectrum
imaging, respectively. The result shows that the averagebackground signal is 6.3 counts in the full-spectrum imaging
and 3.4 counts in the filter-based imaging. Thus the averagebackground signal in the full-spectrum imaging is ∼1.88
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Journal of Microscopy times higher than that in the filter-based imaging. Then theemission spectra of bead 1, 2 and 3 (detectable under both
illumination schemes; Fig. 10a and b) were measured byaveraging over the pixels at the corresponding bead location(Fig. 10c–e). The ratio of total photons that are collected in fullspectrum imaging (area under the red curve in Fig. 10) to thatin filter-based imaging (area under the blue curve in Fig. 10)
is calculated and shown in Table 2. By taking the backgroundlevel difference into consideration, the ratio of signal dynamic
range in full-spectrum imaging to that in filter-based imaging
is also calculated (Table 2). These results indicate that fullspectrum imaging provides comparable dynamic range for120 L. GAO ET AL.
Table 2. Comparison between full-spectrum imaging and filter-based imaging on photon collection and signal dynamic range.
Bead
label

Collected photons N 1 (in counts)
in full-spectrum imaging

Collected photons N 2 (in counts)
in filter-based imaging

Photon collection
ratio (N 1 /N 2 )Signal dynamic range ratio(full spectrum/filter based)12
3

3.23 × 103
8.94 × 103
1.27 × 104

1.38 × 103
2.92 × 103
1.33 × 103

2.39
3.07
9.50

1.27
1.63
5.05

dyes, which are optimal in a specific filter-based imaging.
Although for those dyes which are not, full-spectrum imaging
provides considerable improvement.
Quantitative simulation for multiplexed imaging applications
The full-spectrum HFM technique we present here hasfeatures, which are important for researchers imaging
multiple fluorescent probes at once. One of the promising
applications lies in multiplexed imaging of cancer stem cells.
Laboratory experiments show that targeting the stem cellswith multiple contrast agents enables accurate characterisation of molecular phenotypes and cellular identification(Schieker et al., 2007; Hart & El-Deiry, 2008). To show theadvantage of full-spectrum HFM in this potential application,
we simulate the results for a five-colour immunofluorescenceimaging experiment, which was previously experimentally
implemented by filter-based imaging (Schieker et al., 2007).
For a fluorophore, under the full-spectrum illumination, thenumber of effective excitation photons is equal to the integral
over illumination excitation spectrum Sfull (λ) multiplied byfluorophore’s absorption spectrum B(λ)

Sfull (λ)B(λ)[11]
Nfull =
λwhereas under the filter-based illumination,

Nfilt =
Sfilt (λ)B(λ)

[12]

λ

The ratio of M = Nfull /Nfilt gives the amplitude scaling factor
to compare the collected fluorophore’s emission spectra under
the two illumination schemes.

At the collection side, the fluorophore’s emission spectra
are calculated as the fluorophore’s reference emission
spectrum S emi multiplied by system’s throughput η andamplitude scaling factor M. In full-spectrum imaging, this
gives
emi
Sfull
(λ) = S emi (λ)ηfull (λ)Nfull[13]

and in filter-based imaging, it is
emi
Sfilt
(λ) = S emi (λ)ηfilt (λ)Nfilt[14]

To validate these expressions, we first simulate results forthe fluorescent beads 1 and 2, which have been experimentallymeasured in Section 3.2. Results show that simulated spectra
match well with measured results (Fig. 11).
Next, a five-colour immunofluorescence hyperspectral
imaging experiment is simulated under two illumination
schemes. A human mesenchymal stem cell is assumed to
be prepared by the same immunofluorescence method as
described (Schieker et al., 2007): Cellular endoglin is labelledwith dye Texas Red, FCAM-1 is labelled with dye FITC,
collagen is labelled with dye Alexa 546, fibronectin is labelledwith dye Cy2 and F-actin is labelled with dye Alexa 633.
The triple-band emission filter (green, red and infrared, ASI)
which was previously used in the experiment (Schieker et al.,2007) is also used in the simulation. The simulated results
are shown in the Figure 12(a)–(e). By summing the areas
under the fluorophores’ emission spectra, the numbers of total
emission photons that are collected under the two illumination
schemes are compared. Assuming the background level in fullspectrum imaging remains ∼two times higher than that in
filter-based imaging as calculated in Section 3.2, results show

Fig. 11. Theoretical simulated results versus experimentally measured results in fluorescent beads imaging experiment (a) Bead 1 and (b) Bead 2.
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Fig. 12. Simulated results for fluorophores’ emission spectra in full-spectrum and filter-based imaging (a) Cy2, (b) FITC, (c) Alexa 546, (d) TexasRed and(e) Alexa 633.

that comparable to up to three times improvement in signal
dynamic range can be achieved by replacing the filter withthe proposed full-spectrum HFM technique in this multiplexed
imaging experiment. And because there is no trade-off
problem in the full-spectrum imaging mode, the number offluorescent probes that can be used in an experiment can beincreased.
Conclusions
In this paper, we modified a filterless illumination scheme(Booth et al., 2010) and implemented in the hyperspectral
fluorescence microscopy. The resulting system provides higher
signal levels and higher spectral accuracy than that canbe achieved by filter-based imaging in spectral imaging
applications. By eliminating the excitation filters on theillumination side, we can achieve a full-spectrum excitation
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without a trade-off between throughput and the numberof fluorescence channels excited. By replacing the dichroic
mirror and emission filter with a polarising beam splitter,
we can achieve a uniform throughput on the collection side,
regardless of the number of fluorophores being imaged. This
fact is especially important in hyperspectral imaging systems.
In addition, because the full-spectrum HFM technique shows
little wavelength-dependent throughput on the collectionside, fluorophores’ emission spectral profile can be accurately
measured – a crucial feature in quantitative fluorescenceimaging experiments.
An important feature is that, unlike the filter-based
approach, the full-spectrum method easily scales up forexperiments requiring a large number of fluorophores.
Experiments attempting to differentiate rare subpopulations ofcells, for example, have been steadily increasing the numberof fluorophores used to identify and characterise these rare

122 L. GAO ET AL.

subsets (De Rosa et al., 2001; De Rosa et al., 2003; Perfetto et al.,
2004; Schieker et al., 2007; Chattopadhyay et al., 2008; Hart
& El-Deiry, 2008; ). Exciting this many dyes properly, anddetecting their individual concentrations, can require several
excitation lasers and multiple filter combinations (Schieker
et al., 2007). The full-spectrum approach, however, can excite
all dyes whose absorption spectra lie within its spectral band,and can collect the complete emission spectra from the entire
set of fluorophores without modifying the instrument set-up
described in Sections 4 and 5.
In the proposed system configuration, a snapshot
hyperspectral imager IMS is used as the spectral detector.
The advantage of employing IMS in full-spectrum HFM is that
all wavelengths can be acquired within a single snapshot,a fact that dramatically increases system throughput (Gaoet al., 2010). However, the full-spectrum HFM technique
is not restricted to be used only with the IMS. It can also
be integrated with other traditional scanning-based widefield hyperspectral imagers, e.g. multiple filter cubes, liquid
crystal or acoustic tuneable filters (Morris et al., 1994),
which are commonly used in standard microscope setups. Using scanning-based hyperspectral imagers in fullspectrum HFM may reduce system’s overall cost (approximate
cost of hardware components of the presented IMS system
is $30k–$35k) but at the risk of sacrificing systemthroughput, especially in dynamic imaging applications(Gao et al., 2010).
Currently, the major limitations for the proposed fullspectrum HFM technique are its need for a spatiallycoherent source, and requirement that the sample haveweak elastic scattering, compared to its fluorescent emissions,after polarisation filtering and spatial filtering. Although
the scattering components can be further isolated by the
proposed datacube filtering algorithm, the effectiveness of thistechnique is limited by the noise in the captured spectra.
In addition, for the highly scattering sample, the shot noiseof scattering light is still contributing to the image signalto-noise ratio, a fact that degrades the image quality. And
as more fluorescent, scattering and absorption components
are added to the system’s spectral component matrix in
Eq. (1), H may become ill conditioned, leading to an unstable
solution for Eq. (2). In this case, the captured datacube is
no longer suitable for spectral filtering. However, note thatrecent developments in digital micromirror device (DMD)based illumination technology (OneLightSpectra, 2011)provide the ability to control the illumination intensities
at each wavelength. If the current incoherent light sourcein this DMD-based illumination technology is replaced
by a coherent supercontinuum laser source, an optimal
illumination spectral profile that minimises the condition
number of the spectral component matrix H might beproduced for imaging a given sample, a fact which can benefit
the proposed full-spectrum HFM technique in multiplexed
imaging experiments.

Acknowledgements
This work is supported by the National Institute of Healthunder Grant No. R21EB009186 and R21EB011598.
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