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Numerous imaging modules are utilized to study changes that occur during cellular processes. Besides qualitative (immunohistochemical) or semiquantitative (Western blot) approaches, direct quantitation method(s) for detecting and analyzing signal intensities for disease(s) biomarkers are lacking. Thus, there is a need to develop method(s) to quantitate specific signals and eliminate noise during live tissue imaging. An increase in reactive oxygen species (ROS) such as superoxide (O 2 • ⁻ ) radicals results in oxidative damage of biomolecules, which leads to oxidative stress. This can be detected by dihydroethidium staining in live tissue(s), which does not rely on fixation and helps prevent stress on tissues. However, the signal-to-noise ratio is reduced in live tissue staining. We employ the Drosophila eye model of Alzheimer's disease as a proof of concept to quantitate ROS in live tissue by adapting an unbiased method. The method presented here has a potential application for other live tissue fluorescent images.
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Reports
Unbiased automated quantitation of ROS signals
in live retinal neurons of Drosophila using
Fiji/ImageJ
Prajakta Deshpande,1 , Neha Gogia,1 , Anuradha Venkatakrishnan Chimata,1 & Amit Singh*,1,2,3,4,5
1Department of Biology, University of Dayton, Dayton, OH 45469, USA; 2PremedicalProgram, University of Dayton, Dayton, OH 45469, USA; 3Center for Tissue Regeneration
& Engineering at Dayton (TREND), University of Dayton, Dayton, OH 45469, USA; 4The Integrative Science & Engineering Center, University of Dayton, Dayton, OH 45469,
USA; 5Center for Genomic Advocacy (TCGA), Indiana State University, Terre Haute, IN, USA; *Author for correspondence: asingh1@udayton.edu; Authors contributed
equally
BioTechniques 71: 00–00 (August 2021) 10.2144/btn-2021-0006
First draft submitted: 19 January 2021; Accepted for publication: 7 July 2021; Published online: 5 August 2021
ABSTRACT
Numerous imaging modules are utilized to study changes that occur during cellular processes. Besides qualitative (immunohistochemical) or
semiquantitative (Western blot) approaches, direct quantitation method(s) for detecting and analyzing signal intensities for disease(s) biomark-
ers are lacking. Thus, there is a need to develop method(s) to quantitate specific signals and eliminate noise during live tissue imaging. An
increase in reactive oxygen species (ROS) such as superoxide (O2-) radicals results in oxidative damage of biomolecules, which leads to oxida-
tive stress. This can be detected by dihydroethidium staining in live tissue(s), which does not rely on fixation and helps prevent stress on tissues.
However, the signal-to-noise ratio is reduced in live tissue staining. We employ the Drosophila eye model of Alzheimer’s disease as a proof of
concept to quantitate ROS in live tissue by adapting an unbiased method. The method presented here has a potential application for other live
tissue fluorescent images.
METHOD SUMMARY
We present an imaging method and quantification strategy to assess oxidative stress using a fluorescent probe for quantitation of superoxide
(O2-), that is, reactive oxygen species during live cell imaging in the Drosophila eye model of Alzheimer’s disease. We attempted to reduce noise
in signal during live cell imaging by employing an unbiased automated quantitation strategy. Here, we employed the Fiji/ImageJ method to
perform an unbiased quantitation.
KEYWORDS:
Alzheimer’s disease automated quantitation confocal microscopy dihydroethidium Drosophila ImageJ live cell imaging
neurodegeneration oxidative stress reactive oxygen species
Fluorescent imaging is an important microscopy tool that helps to study several important questions in cell biology. Many new applica-
tions are being developed to address challenges pertaining to the specificity and sensitivity (i.e., reduced signal-to-noise ratio) of new
techniques. Specifically, fluorescent dyes and stains are repurposed to improve existing techniques and assays. Nonetheless, many
such assays cannot provide quantitative data because of reduced signal-to-noise ratio. Generally, immunohistochemistry (IHC) involves
fixing the tissue, followed by blocking the tissue to prevent nonspecific signals. However, in the case of live cell/tissue imaging, the
fluorescent probe used requires neither fixing nor blocking of the tissue. However, this results in an increase in the background noise.
ROS is a term used for an array of derivatives of molecular oxygen that serve as a reliable biomarkers for oxidative stress [1].ROS
are metabolic by-products that are normally produced during cellular respiration. Chemically, ROS comprise reactive O2molecules that
include superoxide (O2-), hydrogen peroxide (H2O2) molecules, hydroxyl radical (HO) and hydroxyl ion (OH-). Under normal conditions,
there is a balance between ROS levels and antioxidant(s) such as catalase and superoxide dismutase that neutralize ROS. Disruption of
this equilibrium in several pathological scenarios leads to increased ROS levels that cause damage to biomolecules and often lead to
cell death due to increased oxidative stress. ROS is randomly generated in tissues and exhibits low signal-to-noise ratio during live cell
imaging. Although ROS detection has been used as a qualitative assay, the quantitation of signals suffers consistency challenges due
to increased noise. Therefore, it is difficult to quantitate precisely and in an unbiased manner.
Increased oxidative stress due to higher levels of ROS during mitochondrial and/or electron transport chain dysfunction have emerged
as some of the main contributors of aging and diseases [2–4]. This dysfunction caused by increased ROS levels has been observed in
neurodegenerative diseases including Alzheimer’s disease (AD) [4–7]. Excessive ROS production in neurodegenerative diseases may
be due to aberrant activation of signaling pathways that results in progressive neuronal death [6–10]. We have previously shown that
the evolutionarily conserved Hippo pathway, known for growth regulation [11], is activated in AD and other neurodegenerative disor-
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ders [12–14]. In addition, Hippo signaling acts synergistically with another evolutionarily conserved c-Jun-amino-terminal-(NH2)-kinase
(JNK) signaling pathway and triggers cell death [12,13]. Oxidative stress also induces the MST1-FOXO signaling pathway and thereby
results in neuronal cell death [15]. Increased ROS levels lead to damage and oxidative modification of biomolecules in tissue and are
associated with AD pathology [16]. Therefore, it is important to study ROS levels during oxidative stress in neurodegenerative diseases
such as AD.
Studying levels of ROS generated is challenging due to its transient nature and high reactivity [17]. Several studies have used fluores-
cent dyes and chemiluminescent probes which interact with ROS in live cells or tissues [18,19]. One such fluorescent dye, dihydroethidium
(DHE), is readily permeable in live cells or tissues and normally exhibits blue fluorescence in the cytosol until it gets oxidized. In the pres-
ence of cellular O2-, DHE is oxidized to form 2-hydroxyethidium (2-OH-E+; excitation 500–530/emission 590–620 nm) and intercalates
to nucleic acid to emit a red fluorescent signal [20,21]. Thus, DHE staining helps assess ROS levels present in cells by using O2-as a sur-
rogate marker. As shown previously, DHE assay detects even minimal ROS in wild-type control, Canton-S, and alternatively, shows higher
ROS levels in the positive control [22,23]. However, the stability of DHE derived fluorescent intensity is transient [24]. Furthermore, DHE
also exhibits nonspecific oxidation by other sources of ROS to form ethidium (E+; excitation 480/emission 576 nm), but the wavelength
is different and can be distinguished.
Even with such qualitative assays, generation of ROS that triggers oxidative stress is not easy to quantitate because it is highly
unstable and requires live cell imaging. In live cell imaging, the samples are neither fixed nor blocked, which results in higher background
noise compared with fixed tissues. Harsher treatment(s) or stress during tissue dissection is also a concern that could increase stress-
induced ROS in tissues and may contribute to inaccurate results. DHE partially helps to overcome this challenge as it is readily permeable
in cells and tissues and is highly sensitive to superoxide anions. Even though efforts have been made to improve methods to study such
signals, better quantitation methods for studying such signals in stained tissues are needed. Furthermore, differences in methods across
labs, inconsistent handling of samples, nonuniform binding of dye, among other issues, lead to inaccurate and irreproducible results.
Other techniques such as semiquantitative traditional/quantitative Western blots can only study levels of protein expression that cannot
be performed spatially in real time [25]. Use of dye-based assays to detect ROS are highly popular but relatively qualitative. Therefore,
the best option is to use an unbiased automated quantitation approach.
Here, we present quantitation of DHE staining signals by adapting a Fiji/ImageJ method. We have used the Drosophila
melanogaster (aka the fruit fly) eye as a model to study and quantitate ROS, a salient feature of neurodegeneration. Our data is a proof
of concept that can be employed to any live imaging signals upon standardization of thresholds. D. melanogaster hasservedasan
established, versatile model organism to study human neurodegenerative diseases [13,14,26,27]. This organism’s faster reproduction
rate, inexpensive cost to rear and genetic conservation makes it useful to study human disease(s) [26–28]. This report discusses DHE
staining in live tissue, ROS (O2-) detection and automated quantitation using Fiji/ImageJ method [29]to detect and compare ROS levels
in the Drosophila eye model of AD.
Materials & methods
Stocks
All the fly stocks used in this study are listed in Flybase [30]. The stocks used were: GMR-hid; GMR-Gal4 (a gift from Dr Chun-Hong Chen),
GMR-Gal4 [31],UAS-Aβ42 [32,33],GMR-Gal4>UAS-Aβ42 (GMR>A42) [34]UAS-hpo [35]and UAS-hpoRNAi [36]. Canton-S was used as a
wild-type control in the study.
Genetic crosses
We employed Gal4/UAS system for targeted misexpression of the gene(s) of interest [37]. Glass Multiple Repeat (GMR) Gal4 driver was
used to misexpress human amyloid beta 42 transgene (UAS- Aβ42) in the differentiating retinal neurons (hereafter GMR>Aβ42) with
100% penetrance of phenotype [5,31,33]. We utilized ROS levels as a biomarker for stress in the dying neurons. Furthermore, we modulate
levels of hippo (hpo), a genetic modifier of GMR>Aβ42 mediated neurodegeneration in Drosophila eye model of Alzheimer’s disease [13],
by using UAS-hpo and UAS-hpoRNAi. We used the Canton-S and GMR-hid; GMR-Gal4 stocks as negative and positive controls, respectively.
All these cultures were maintained at 18, 25 and 29C to facilitate different levels of transgene expression [38,39].
DHE staining
The eye-antennal imaginal discs were dissected from the wandering third instar larva [10]in fresh cold 1×Schneider’s Drosophila medium
(Gibco, cat. no. 21720024, Thermo Fisher Scientific, MA, USA), which is commonly used for the imaginal disc growth and culture of
Drosophila S2 cells. It maintains physiological pH and reduces stress on tissues. The samples were incubated in DHE (Life Technologies
cat. no. D11347, CA, USA) dye solution (1:300 in 1×phosphate-buffered saline [PBS]) [22]for 5 min and washed thoroughly three times with
cold 1×PBS. The eye discs were then dissected and separated from the mouth hooks. The eye discs were imaged immediately on a Laser
Scanning Confocal microscope (Olympus Fluoview 3000, Shinjuku-ku, Tokyo, Japan) to keep the structure, function and physiological
state of the tissue intact. It is important to note that rearing of stocks at the same temperature, using freshly prepared chemicals or
reagents, consistent timing for processing tissues, quick mounting, and so forth can minimize batch-to-batch variation in the results.
We have listed some of the frequently faced issues and their potential solutions in Table 1 (stock solutions: DHE solution: 1 mg of DHE
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Table 1. Summary of potential challenges in dihydroethidium protocol and troubleshooting methods: problems and pos-
sible solutions.
Stages Problem(s) Probable reasons Troubleshooting
Dissection More ROS production in negative
control
Harsh handling of the tissue or injury could lead to
aberrant ROS production
Dissection should be done carefully without
damaging the tissue
Mounting Air bubble in the tissue i. Mount the tissue carefully without any air bubble,
push the air bubble aside
ii. Put the coverslip gently first by making 45angle
and then by gently placing it over the tissue
Imaging Tissue gets disintegrated This is a live staining with no tissue fixation.
Therefore, any damage or disintegration of tissue
can be due to improper chemical or longer incubation
i. 1×phosphate-buffered saline should be freshly
prepared
ii. Mounting should be done quickly
Imaging Faint or high signal It could be due to incorrect DHE concentration.
Alternatively, the confocal imaging settings may
require optimization
i. Optimize the dilution of the DHE dye
ii. Change the gain or high-voltage settings during
imaging
DHE: Dihydroethidium; ROS: Reactive oxygen species.
in 660 μlofDMSO5mM;10×PBS: NaCl: 80 g, KCl: 2 g, Na2HPO4: 6.1 g, KH2PO4: 2 g, dissolved in 1 l of water, pH 7.4).
Image analysis
Imaging was conducted by using fluorescent settings in laser scanning confocal microscope [40]. The parameters such as layouts,
objective lens (20×), aspect ratio, scanning speed, focus, color look-up table (LUT), high-voltage (HV), gain, offset, intensities of all the
lasers and photomultiplier tube (PMT) detectors were optimized and set to avoid excessive or weak signal. The Hi–Lo feature in the
software was used to assess the intensity saturation of the lasers hitting the sample. The Fiji/ImageJ freeware software was used for
automated quantitation [29]. It involves the image visualization in grayscale, selection of the region of interest (ROI) using the polygon
tool, filtering the background noise and using the interactive watershed algorithm to perform segmentation and to identify each signal
as an ROI. It was followed by particle analysis to get the specific parameters such as area, integrated density and the number of puncta.
For automated quantitation, we optimized the settings and found that the following settings were ideal across all experimental groups
used in the study. These settings include seed dynamics: 30; intensity threshold: 35; peak flooding: 50; and no splitting. For manual
quantitation, three independent blinded counts were performed and analyzed. All quantitation and analysis were performed with five eye
imaginal discs for each genotype analyzed. Statistical analysis was performed using Student’s t-test in Microsoft Excel, and graphs were
plotted using GraphPad Prism [41–43]. Statistical significance in each graph is shown by p-value: ***p <0.001; **p <0.01; *p <0.05.
Magnification of all eye imaginal discs is 20×.
Results & discussion
A salient feature of neurodegenerative diseases like AD is increased oxidative stress in response to increased ROS generation due to
A42 plaques accumulation, which in turn triggers neuronal cell death. This increased oxidative stress due to increased ROS levels
results in modification of biomolecules and other compounds in tissue, and is associated with AD pathology [16]. Therefore, detection of
ROS can serve as a reliable marker for neurodegeneration. However, ROS generation occurs dynamically and therefore can be detected
in live tissue(s). Thus, if ROS can be detected and quantitated precisely, it can serve as a biomarker for qualitative as well as quantitative
detection of neurodegeneration in live cells /tissues. Therefore, fluorescent probes to detect ROS can serve as an excellent tool.
The DHE staining involves the use of DHE as a fluorescent probe to detect ROS in live tissue. We used this assay to detect A42-
mediated neurodegeneration in retinal neurons using the Drosophila eye model [7,33]. We wanted to develop a reliable method to quan-
titate ROS using live cell imaging in developing Drosophila eye imaginal disc. The adult compound eye of Drosophila develops from an
eye-antennal imaginal disc housed inside the larva [26,38,44,45]. The DHE staining detects ROS and is observed as distinct fluorescent
puncta in biological tissues. Generally, there is no established or validated method to quantitate puncta apart from manual counting.
To achieve unbiased and consistent quantitation from such expression-based imaging studies using a fluorochrome, we have used the
Fiji/ImageJ software and a semiautomated quantitation method [29]. Using interactive H-watershed segmentation via Fiji/ImageJ is a
relatively faster approach to accurately analyze more datasets compared with other quantitation methods.
We first established the ideal settings for imaging (described in the ‘Materials & methods’ section), which should be consistent and
reproducible for all the experimental groups. We found that any change(s) in these settings for image acquisition from one batch to
another could result in dramatic changes in ROS quantitation as shown in Figure 1. Thus, it is crucial to have consistency in ROS quan-
titation in all the experimental conditions. To optimize the signal intensity during imaging, we used the HiLo feature on the confocal
microscopy to identify signal saturation (Figure 2A). We used three exposure settings (HV, photomultiplier tube voltage setting) to un-
derstand how these settings affect imaging and quantitation of ROS puncta. Low exposure (low HV setting), high exposure (high HV
setting, highly saturated signal: red saturation in image) and optimal exposure (optimal signal by using ideal HV setting such that there
was no saturation) were the three settings used (Figure 1A–C). For sensitivity comparison of the three levels of HV, we used the GMR-
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0
50
100
Number of ROS puncta
Low
150
Optimum
High
**
0
100
200
Area
Low
300
Optimum
High
ns ns
0
5000
10,000
Integrated density
Low
25,000
20,000
15,000
Optimum
High
ns ns
DHE
Low exposure
DHE
Optimum exposure
DHE
High exposure
GMR-hid; GMR-Gal4 GMR-hid; GMR-Gal4 GMR-hid; GMR-Gal4
Figure 1. Ideal imaging conditions are required to quantify reactive oxygen species. DHE, a fluorescent probe, is used to detect the ROS produced
inside the cell. (A–C) The third instar larval eye-antennal imaginal discs from GMR-hid; GMR-Gal4 larvae (n =5) were stained with DHE (grayscale) and
imaged under laser scanning confocal microscopy (Olympus Fluoview 3000) at (A) low, (B) optimal and (C) high exposure (high HV setting, highly
saturated signal: red saturation in image) conditions as per saturation levels. Note that the ROS are randomly distributed in the eye and are present as
distinct puncta. (D–F) Scatter plots of data from five discs for low, optimal and high settings (represented mean ±standard error of the mean). These
graphs represent (D) number of ROS puncta, (E) integrated density and (F) area across the settings. The p-values were calculated in a set of five (n =5)
using Student’s t-test. Magnification of all eye-antennal imaginal discs is 20×.
Statistical significance in each graph is shown by ***p <0.001; **p <0.01; *p <0.05 and ns.
DHE: Dihydroethidium; ns: Nonsignificant; ROS: Reactive oxygen species.
hid; GMR-Gal4 genotype that serves as a positive control for ROS [22]. As shown previously, misexpression of hid in GMR-hid;GMR-Gal4
genotype initiates the apoptotic process and subsequently activates JNK signaling via dronc activation. The study clearly reports that
hid induced apoptosis triggers ROS generation in tissues and is detectable for up to 24 h after apoptosis induction [22]. Therefore, we
used GMR-hid; GMR-Gal4, as a positive control, to optimize the quantitation and imaging settings. As expected, we observed an increas-
ing trend in the number of puncta (Figure 1D), integrated density (Figure 1E) and area (Figure 1F) across low, optimal and high voltage
groups. The number of ROS puncta in low and high groups were significantly lower and higher, respectively, than the ones in the optimum
setting (Figure 1D). In low exposure, weaker signals were lost and thus showed lower puncta number and intensity compared with the
actual signal. In higher exposure, the signals got saturated and led to the merging of puncta that were closer to each other. This not only
contributed to higher average area and intensity but also to significantly higher number of puncta due to additional artifacts. Moreover,
if the ROS levels were higher in any genotype, we observed that puncta appeared merged, and they become indistinct which potentially
made analyses inaccurate. For integrated density and area, average of all signals for each disc was used to plot the graph, and it showed
a nonsignificant increase in signals across groups (Figure 1E & F). Because ROS are observed as random puncta, it is suitable to quan-
titate the number and not the area or integrated density. Therefore, for any image with distinct puncta or aggregates, quantitation using
numbers is more reliable. Hence optimizing the HV setting and ideal saturation is an important step during imaging and a prerequisite
for accurate quantitation (Figure 1).
Using the representative images for all genotypes, we optimized the quantitation parameters to be used across experimental groups.
Following the methods described earlier [29], we adapted them to quantitate ROS in live tissue images. Because our aim was to reduce
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Selection of
area of interest
for quantitation
DHE staining,
setting up
confocal
parameters
lmaging
ROl selection
Gaussian blur filter
for
noise reduction
Sigma value = 1
lmage
preprocessing
Segmentation
lnteractive
watershed to
segregate signal
Area, lntensity
and Density of the
particle analysed
Particle
analysis
Data
analysis
Quantitation,
data analysis,
graphs preparation,
presentation
GMR-hid; GMR-Gal4 GMR-hid; GMR-Gal4 GMR-hid; GMR-Gal4 GMR-hid; GMR-Gal4 GMR-hid; GMR-Gal4
DHE DHE DHE DHE DHE
0
2000
4000
Area
lntegrated density
20 40
060
6000
A
B
C
D
E
F
Figure 2. Workflow diagram of key steps in automated quantitation for live cell imaging to observe reactive oxygen species using DHE staining. (A–F)
Image shows summary of steps for unbiased quantitation in Fiji/Image-J. It shows how the final ROIs are selected and analyzed using watershed and
particle analysis. (A) The workflow and analysis of one representative image. The tissue samples are first dissected and subjected to DHE staining
(grayscale) and are imaged under laser scanning confocal microscopy. (B) The live tissue images are then subjected to automated quantitation using
Fiji/ImageJ software where the first step is selection of ROI. (C) The image is then processed using appropriate filter to reduce background noise. (D)
The processed image is subjected to H-interactive watershed algorithms to segment each ROS signal for further analysis. (E) The parameters to
quantitate are set, and all segments are subjected to analysis to obtain results. (F) The data are analyzed, interpreted and subjected to further statistical
analysis and representation. Graphical representation in the form of scatter plot depicts the area (x-axis) against their respective intensities (y-axis).
DHE: Dihydroethidium; ROI: Region of interest.
noise and quantitate ROS accurately, after selecting the ROI (Figure 2B), we used filters to smoothen signals (Figure 2C) and H-interactive
watershed for segmentation (Figure 2D). Among the several filters such as mean, median, Gaussian blur, maxima and minima, for ex-
ample, we found that the Gaussian blur was most suitable for our analyses. The Gaussian blur is a low pass filter based on a Gaussian
matrix that is used to perform smoothening of the image [46]. For any out-of-image pixels, the Gaussian matrix is generated with higher
weightage to nearest edge and corner pixels than the noncorner pixels. For selecting pixels to be considered, the radius value corre-
sponding to the standard deviation, or sigma (), is entered in the software. A lower sigma radius corresponds to smaller filter size
that reduces noise seen as small nonspecific staining in the background, whereas a larger sigma radius corresponds to larger filter size
that takes into consideration a higher percentage of pixels; thus, blurs to smoothen larger structures and reduces its intensity too. The
mean/median filter calculates the mean/median values of all surrounding pixels. However, using a mean filter in an image with more
nonspecific background noise will introduce additional artifacts by spreading out the mean of some random extreme values. Alterna-
tively, the median filter eliminates extreme pixel values, leading to erroneous removal of smaller or uneven signals. Because we aimed
to simply reduce nonspecific background and avoid losing smaller ROS puncta, we only used the Gaussian blur.
Next, we used the interactive H-watershed for segmentation to identify and segregate each signal distinctly [47]. Watershed recon-
structs each image as an uneven 3D surface with heights and depths based on the maxima (white or pixel value = 255) and minima
(black or pixel value = 0) in the image. To generate these values, the H-maxima, intensity threshold and peak flooding (Figure 2D) need
to be optimized. We individually changed each of these three settings to optimize the parameters for quantitation. The H-maxima (seed
dynamics) value subtracts the selected value from all the individual pixels. The resultant maxima are then recognized as local maxima
and are segmented accordingly. We observed that a higher setting resulted in fewer local maxima, leading to the loss of signals and
information. The next parameter, intensity threshold, represents the minimum pixel value beyond which the signals are detected. Al-
though lower thresholds tend to detect more signals, they also include noise with high intensity and possibly show merged structures.
In contrast, slightly higher thresholds help in reducing noise and clearly demarcate ROS puncta. We optimized these parameters in all
experimental groups to avoid biased quantitation. Lastly, peak flooding is the percentage height of flooding allowed in each peak dur-
ing image processing. Lower peak flooding helped avoid nonspecific signals and thus eliminated noise during quantitation. Therefore,
proper selection of all these parameters is crucial to identify specific signals and not noise. Every particle in the preprocessed image was
assigned a number, and intensity was calculated by particle analysis (Figure 2E). This method eliminates the chances of manual error,
biological variation, and other concerns. A stepwise method was depicted schematically using a representative GMR-hid;GMR-Gal4eye
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Table 2. Comparison of manual and automated quantitation to demonstrate the lower variability and higher consistency with
automated quantification.
Genotype Number of ROS puncta
Automated quantification Manual: analyst 1 Manual: analyst 2 Manual: analyst 3
Canton-S 7 0 2 1
71466
11255
22575
31000
GMR-hid;GMR-Gal464847778
47 56 38 30
58 105 69 77
56 90 97 137
53 102 69 84
GMR>Aβ42 43 73 58 62
90 78 111 110
17 49 41 46
68 41 50 53
54 53 52 56
GMR>Aβ42+hpo 49 99 116 134
134 107 173 192
54 110 159 188
49 85 82 78
31 77 70 69
GMR>Aβ42+hpoRNAi 12 12 1 5
13 11 13 12
12846
11300
1 000
2000
disc to show the steps of automated quantitation (Figure 2A–F). These results were then analyzed in Microsoft Excel and plotted using
GraphPad prism (Figure 2F). The scatter plot shows the area of each particle on the x-axis against their respective intensities on y-axis.
The intensity of each particle is varied and ranges from 506 to 5632 (Figure 2F).
We further wanted to assess whether we could use this method to study changes in ROS levels in the Drosophila eye model of AD
(GMR>Aβ42). To do so, we employed this unbiased quantitation method to measure the ROS levels in AD. The GMR>Aβ42 flies show
elevated ROS signals. During stress-induced apoptosis, there is ROS generation leads to head involution defective (hid) induced cas-
pase activation and cell death [22]. Moreover, higher ROS levels increase oxidative stress, which results in Hippo pathway activation and
triggers neuronal cell death, as seen in neurodegenerative diseases [48]. Earlier we have shown that hippo (hpo) is a genetic modifier
of A42-mediated neurodegeneration [13]. Gain-of-function of hpo in GMR>Aβ42 background enhances A42-mediated neurodegen-
eration, whereas loss of function of hpo in GMR>Aβ42 background rescues the A42 phenotype [13]. To investigate changes in ROS
levels in the Drosophila eye model AD, we modulated hippo (hpo), a genetic modifier of A42-mediated neurodegeneration. GMR-hid;
GMR-Gal4 (Figure 3B) serves as a positive control with high ROS production [22], whereas Canton-S (Figure 3A) serves as a negative
control that has a minimal amount of ROS production [23].
As discussed previously, analyzing the ROS signals in live cell images with background noise is a challenge. To compare the efficacy
of either methods, we quantitated ROS puncta manually as well as by the H-Interactive Watershed algorithm. We tabulated our results
from automated and manual quantitation (Table 2). Automated quantitation showed that misexpression of UAS-hid in the GMR domain
of the eye (GMR-hid; GMR-Gal4) (Figure 3B), resulted in dramatic increase in ROS in the eye imaginal disc as compared to the wild-
type control, Canton-S (Figure 3A). Similarly, misexpression of UAS-Aβ42 in the GMR domain of the eye using GMR-Gal4 resulted in an
increase in ROS production in the eye imaginal disc (Figure 3C). GMR>Aβ42+hpo (Figure 3D) shows increased ROS puncta compared
with GMR>Aβ42;howeverGMR>Aβ42+hpoRNAi (Figure 3E) shows dramatic decrease in ROS levels compared with GMR>Aβ42.The
ROS signals in GMR>Aβ42+hpoRNAi were almost similar to wildtype and were nonsignificant. In contrast, blinded manual quantitation
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0
50
100
Automated quantification
number of ROS puncta
Canton-S
150
GMR-hid; GMR-Gal4
GMR>Aβ42
GMR>Aβ42, hpo
GMR>Aβ42, hpoRNAi
Canton-S GMR-hid; GMR-Gal4 GMR>Aβ42 GMR>Aβ42, hpo GMR>Aβ42, hpoRNAi
**
*
*
ns
ns
ns
0
50
100
Manual quantification
number of ROS puncta
Canton-S
150
GMR-hid; GMR-Gal4
GMR>Aβ42
GMR>Aβ42, hpo
GMR>Aβ42, hpoRNAi
*** ***
**
*
*
ns
DHE DHE DHE DHE DHE
DHE DHE DHE DHE DHE
Figure 3. Quantitation of reactive oxygen species production between controls and experimental treatments in the fly model of Alzheimer’s disease.
Panel shows the third instar larval eye-antennal imaginal discs of (A) wild-type Canton-S, (B) GMR-hid; GMR-Gal4, (C) GMR>Aβ42,(D)
GMR>Aβ42+hpo and (E) GMR>Aβ42+hpoRNAi were stained with DHE (red) and imaged under laser scanning confocal microscopy (Olympus Fluoview
3000) according to the optimal settings as per saturation levels. Note that the ROS are randomly distributed in the eye and are present as distinct
puncta. (A,B
,C
,D
&E
)Eye-antennal imaginal disc showing grayscale for DHE staining. (F) Scatter plot shows automated quantitation of the average
number of ROS signals from five discs for each genotype (n =5) (represented mean ±standard error of the mean). (G) Scatter plot shows average
manual quantitation of ROS signals from five discs of each genotype. Manual quantitation was performed by three people independently. The p-value
was calculated using Student’s t-test. Magnification of all the images is 20×.
Statistical significance in each graph is shown by p-value: ***p <0.001; **p <0.01; *p <0.05.
DHE: Dihydroethidium; ns: Nonsignificant; ROS: Reactive oxygen species.
of the number of ROS puncta was performed independently by three individuals and found variation in counting (Figure 3G). Hence,
automated quantitation provides more precise unbiased results compared with manual counting of the ROS puncta.
Observing and quantitating ROS production in real time has many challenges. Several recent reports have shown DHE as a more
reliable dye to mark ROS qualitatively. We observe batch-to-batch variation in ROS levels in biological tissues stained with DHE due to
lower signal-to-noise ratio in live tissue staining. To avoid further bias with manual quantitation of signal, we have applied an unbiased
quantitation method based on Gaussian filter and Interactive Watershed plugin in Fiji/ImageJ. We show that optimal imaging parameters
are imperative to get accurate results, and higher settings could lead to introduction of several artifacts. We have adapted the previously
described quantitation method to quantitate ROS in live tissue images of AD fly model [29]. Using the Drosophila eye model of AD, a
background with higher level ROS generation with respect to the wildtype control, we show greater precision and less variation in ROS
quantitation using our method. Furthermore, when using hpo, a genetic modifier of Aβ42 that can modulate ROS levels during its gain of
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Reports
function and loss of function, we observed significant quantitative differences in ROS levels compared with Aβ42 only background. In
gain of function, more ROS generation was observed compared with Aβ42 only, whereas in hpo loss of function, less ROS generation was
observed compared with Aβ42 only background. This suggests that this method is highly accurate and sensitive in detecting the changes
in level of ROS. This method can preferentially quantitate signals uniformly in all experimental groups without any bias because they are
processed similarly with the same parameters.
Broadly speaking, this method of quantitation can be used in any live tissue or cell images to quantitate fluorescent signals and thus
eliminate manual bias. It can be used for any fluorescent dye-based staining and other fluorochrome tagged fusion protein(s) localization
using real-time imaging. The insights obtained from this study can be implemented to reduce noise in other assays such as cell death
(TUNEL, acridine orange), cell proliferation (BrdU, phospho histone H3) and reporter assays (GFP, lacZ etc.) that show puncta-like signals.
In the future, there is a scope to modify and apply such freeware algorithms to study spatiotemporal gene expression in live and fixed
tissues.
Executive summary
Fluorescent dye-based method was used to detect cellular superoxide (O2•−) as a marker for reactive oxygen species (ROS) in the
Drosophila melanogaster model of Alzheimer’s disease (AD).
We compared manual and automated quantitation to check which method is more reliable and reproducible.
We have used genetic controls that serve as positive controls to optimize imaging parameters and to reduce bias.
With automated quantitation, it was clear that quantitation of ROS intensity and area are not as informative as quantitating the numbers of
cells.
With this method, we were able to quantitate and see an evident increase in ROS generation in a Drosophila eye model of
neurodegeneration.
Furthermore, modulation of Hippo pathway, a genetic modifier of A42-mediated neurodegeneration, resulted in the change in levels of
ROS generation, which can be quantitated by using Fiji/ImageJ.
We documented high quantitative variations in data by manual counting approach. However, the automated quantitation ensured data
integrity when the same parameters were used.
This automated quantitation method can be used to study ROS generation in fluorescent images of other live cells and tissues.
Authors contributions
P Deshpande, N Gogia and AV Chimata performed the experiments. P Deshpande, N Gogia and AV Chimata and A Singh were involved
in developing the concept, design, analysis, manuscript writing and editing and figures preparation.
Acknowledgments
The authors thank Bloomington Drosophila Stock Center (BDSC) for the Drosophila strains as well as A Singh and members of the Singh
lab for providing comments on the manuscript.
Financial & competing interests disclosure
Authors are supported by the University of Dayton Graduate program in biology. This work is supported by NIH1R15GM124654-01 from
the NIH, Schuellein Chair Endowment Fund and start-up support from the University of Dayton to A Singh. The authors have no other
relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject
matter or materials discussed in the manuscript apart from those disclosed.
No writing assistance was utilized in the production of this manuscript.
Open access
This work is licensed under the Attribution-NonCommercial-NoDerivatives 4.0 Unported License. To view a copy of this license, visit
http://creativecommons.org/licenses/by-nc-nd/4.0/
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Chapter
RNA is an important connecting link between DNA and proteins. Levels of RNA within a cell or a tissue serve as the unique genetic signatures, which can help in correlating gene expression to the resultant phenotype(s) during development and disease. Transcriptomics is the study of all RNAs expressed/available in cells or tissues that allow study of (1) differences in gene expression patterns among various cell types or organs, (2) identify novel messenger RNAs and transcripts, and (3) study epigenetic changes within the transcriptome. This knowledge can be applied to human disease(s) by developing disease markers or to study developmental landmarks using biomarkers. In this chapter, we have highlighted the history of transcriptomic and genetic engineering, the available transcriptomic techniques to study various types of RNA, their analysis using gene ontology tools, and finally the utility of genetic engineering tools and model organisms in transcriptomics research.
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