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Evaluating Individual mRNA Molecules Detection Techniques in Microscope Images

Authors:

Abstract

Single molecule fluorescence in situ hybridization followed by microscopic image analysis is one of the prominent methods used to study gene expression on a single cell level. There are various microscopic image analysis methods, leading to differing mRNA spots being detected in images for the same experiment. We present a technique to evaluate different mRNA spots detection algorithms. It is based on image annotation by expert biologists and the receiver operating characteristics. The detection methods can be compared using parameters that withstand imprecise and imbalanced environments. The proposed evaluation procedure highlighted the difference between two microscopic image analysis methods that are frequently used. It can be applied to any image analysis method that seeks to find mRNA spots on a single cell level.
Evaluating Individual mRNA Molecules Detection
Techniques in Microscope Images
Rethabile Khutlang, Loretta Magagula, Musa Mhlanga
Gene Expression and Biophysics Group
Emerging Health Technologies
CSIR Biosciences
Pretoria, South Africa
rkhutlang@csir.co.za
Abstract—Single molecule fluorescence in situ hybridization
followed by microscopic image analysis is one of the prominent
methods used to study gene expression on a single cell level.
There are various microscopic image analysis methods, leading to
differing mRNA spots being detected in images for the same
experiment. We present a technique to evaluate different mRNA
spots detection algorithms. It is based on image annotation by
expert biologists and the receiver operating characteristics. The
detection methods can be compared using parameters that
withstand imprecise and imbalanced environments. The
proposed evaluation procedure highlighted the difference
between two microscopic image analysis methods that are
frequently used. It can be applied to any image analysis method
that seeks to find mRNA spots on a single cell level.
Keywords—sm-FISH; spot detection; receiver operating
characteristics; F-measure
I. BACKGROUND
Gene expression is studied more and more on the single cell
level [1]. One of the methods used to provide mRNA counts in
individual cells is single molecule fluorescence in situ
hybridization (sm-FSIH) followed by microscopic image
analysis [2].
Single molecule FISH is a microscopy-based assay that
allows for the visualization, detection and localization of
specific nucleic acid sequences in their native environment.
Since its origins, over 20 years ago [3], it has become a
powerful molecular tool for the detection of cytogenetic and
molecular genetic alterations. Applications of FISH have even
extended to clinical diagnosis chromosome analysis [4]. In a
molecular setting, FISH has revealed insights in transcriptional
dynamics [5, 6], mechanisms of RNA synthesis [2] and
transport [7] and intracellular distribution [8,9].
The first application of fluorescent in situ detection
involved the use of RNA probes directly labelled on the 3’end
with a fluorophore to bind specific DNA sequences [10]. The
labelling of probe sequences developed to use fluorophore-
coupled amino-allyl modified bases [11] and the use of
enzymatic incorporation of fluorophore-modified bases [12].
These advances in the technology allowed for the simple
chemical production of an array of low-noise probes. Attempts
to improve signal output of this assay came in the form of nick-
translated, biotinylated probes, which were indirectly detected
using fluorescently labelled streptavidin conjugates [13].
Currently, the standard FISH probe is produced by simple
esterification chemistry to couple fluorophore to a 3’amine-
modified base [14]. This method of probe preparation allows
for precise and direct detection with high signal-to-noise ratios,
improving the sensitivity of the assay.
Initially, RNA detection using FISH was constrained to use
of large oligonucleotide probes. This was problematic as large
probes could adhere to samples non-specifically resulting in
false positives as well as lead to high levels in background
noise. The use of reduced probe sizes lead to improved signal-
to-noise-ratio and sensitivity, allowing for the single-copy
detection of RNA entities and even parts of RNA [15, 16]. In
this variation of the assay, 5 oligonucleotides, each about 50
oligonucleotides long, were labelled with fluorophore moieties.
The hybridization of these probes to their mRNA targets
yielded each target to be visualized as a diffraction-limited
fluorescent “spot” [16]. However, the synthesis and
purification of a small number of heavily labelled probes came
with high difficulty and these probes tended to interact with
each other altering hybridization characteristics which lead to
severe quenching [17]. An improvement of the assay was
made by using a tandem array (12-48) of reliably and singly
labelled probes to accurately detect individual mRNA
molecules at high spatial-temporal resolution. This
advancement in the assay has lead to the simultaneous and
accurate detection of multiple targets using spectrally distinct
fluorophores within the same sample [18,19].
Post image acquisition, Femino et al. [16] used a
constrained deconvolution algorithm to quantitatively restore
out-of-focus light to its original points of origin. They could
then calibrate for the fluorescent output per molecule of probe.
In [19], calibration of fluorescent output per molecule of probe
was not performed, however for 48 probes per mRNA they
detected the same number of mRNA spots per image over a
broad range of thresholds, validating the choice of a threshold
parameter. Additionally, they avoided the difficulty in
synthesizing and purifying heavily labelled probes.
Raj et al. [19] used the Laplacian of Gaussian filter to
remove the non-uniform background and enhance particles.
The resulting image conserves spatial resolution of spots, so
does the wavelet transform based filtering as used in [20]. The
procedures are computationally less expensive than constrained
deconvolution algorithms; so is the procedure proposed by
Trcek et al. [21] spatial band-pass filtering and local
background subtraction to remove residual unevenness in the
image.
There are different thresholding techniques that are applied
to a filtered image to eventually find spots [16,19,20,21]. Raj
et al. [19] chose a threshold from a range of thresholds for
which the number of mRNAs detected varied the least. Trcek et
al. [21] used Gaussian mask fitting to find the centre and
intensity of each spot. In any case, the detected spots can be
analysed on a per cell basis if the cell marker is used in an
experiment.
We present an evaluation of individual mRNA molecules
detection techniques in microscope images. The evaluation
procedure is applied to two detection techniques. It is based on
the use of expert biologists as the gold standard in marking
spots in a microscope image. The evaluation procedure uses the
receiver operating characteristics analysis (ROC) and
performance evaluation metrics used in machine vision and
learning.
The organisation of this paper is as follows. The next
section outlines the method of evaluating detection techniques
(methods used to prepare mRNAs are in supplementary data).
Then detection techniques evaluation results are presented.
II. METHODS
A. Spots validation
Spots found in a z-stack image by an expert biologist
constituted the gold standard used in evaluating the
performance of a detection algorithm on that stack. Biologists
circled all mRNA spots they could find using a custom made
GUI. Hausdorff distance [22] was used to study intra- and
inter-observer variability in marking spots and compare that to
detection algorithms’ found spots; the modified Williams index
(MWI) [23] was obtained from the Hausdorff distances to
further compare algorithms’ spots boundaries to hand drawn
ones. The index is the ratio between the average computer-
observer agreement and the average observer-observer
agreement. For N observations, MWI is calculated leaving one
observation out at a time, for N-1 observations, resulting in N
estimates.
B. Detection techniques evaluation
The posterior probability of a detected spot was calculated
by finding the ratio of pixels found by both an algorithm and an
expert to pixels found by an expert; minus fraction of pixels
missed or over-segmented by an algorithm. Background pixels
were regarded as non-target objects. The ROC curves were
plotted using spots as the target class. The area under the ROC
curve (AUC) is used as an evaluation value integrating the
entire ROC. Sensitivity and specificity, typical two-class
detection performance evaluation measures, could be
established from the ROC curve at a chosen operation point.
Since the non-target class far exceeds the target class, the
posfrac-recall ROC [24,25] was used to evaluate detection
algorithms, as this is the imbalanced problem. The prior
probability of the positive class is significantly less than that of
the negative class, their ratio skew, was used to study what
fraction of non-target objects to include in the analysis. Typical
imprecise environment detection evaluation measures can then
be used to compare detection algorithms at one operating point:
posfrac fraction of positive detections (1),
posfrac =TP +FP
N
(1)
precision (2) the fraction of positive detections that are
actually correct and it is usually a meaningful parameter when
detecting rare events because it effectively estimates an overall
posterior probability [25],
precision =TP
TP +FP
(2)
recall and F-measure (3) the geometric mean of precision and
recall [25].
TP
denotes the test objects labeled as target and
are truly targets, while
FP
denotes false targets.
TP
r
- recall,
and
FP
r
are calculated by normalizing
TP
and
FP
by the
total number of positive and negative objects respectively,
N
is the sum of positive and negative objects.
TP
r
indicates
sensitivity while 1
FP
r
denotes specificity.
Fmeasure =2TPr
2TP
r
TP
r+FP
r+1
(3)
III. RESULTS
A. Spot validation
Spot validation was studied using a set of 10 z-stack
images. In each stack, the plane that showed spots the most
clearly was chosen. The similarity of spots marked by the two
expert biologists was studied on spots contours extracted using
the custom made GUI. The comparisons in Table I were made
using the Hausdorff distance. T11 and T12 represent the first
expert marking spots the first and second times, more than a
week apart, T2 represents the second expert. AL1 represents
spots detected using the image analysis procedure outlined in
[19], while AL2 represents spots found using wavelets-based
detector [20].
The first expert had the highest intra-observer variability,
4.5518. There was the highest dissimilarity in the ellipses
drawn around spots. The variability is further confirmed by the
standard deviation of the Hausdorff distances between the first
and second times the first expert marked the spots, it is the
highest. The second expert still had high intra-observer
variability, although it was not higher than inter-observer
variability. The standard deviation of inter-observer variability
is the second highest, elucidating the difference in marking
spots between the two experts.
The mean Hausdorff distances between first round of spot
marking by experts and automated detection procedures were
lower than those between and among experts; prompting a
TABLE I. COMPARISON OF SPOTS MARKED BY TWO EXPERTS AND
THOSE FOUND BY LOG PLUS THRESHOLDING AND WAVELETS BASED-
METHODS
T11&
T12
T21&
T22
T11&
T21
T11&
AL1
T21&
AL1
T11&
AL2
T21&
AL2
Mean
Std
4.5518
1.5064
4.4353
1.1813
4.5190
1.4060
4.2816
1.3628
4.1721
1.0184
4.2768
1.3247
4.1102
1.1138
TABLE II. COMPARISON OF SPOTS MARKED BY THE TWO EXPECTS THE
SECOND TIME AND THOSE FOUND BY LOG PLUS THRESHOLDING AND
WAVELETS BASED- METHODS
T12&AL1
T22&AL1
T12&AL2
T22&AL2
Mean
Std
4.5932
1.0878
4.1684
1.1322
4.5353
1.1336
4.1472
1.1209
suspicion than maybe experts marked spots differently the
second time, a week later. The Hausdorff distances between
both LoG-based and wavelets-based detections and experts the
second time they marked spots were calculated, Table II.
Instead of experts marking spots differently the second
time, Table II suggests that the first expert has higher
variability in marking spots than the second expert. This is
because variability between the second expert marking spots
the second time and automated detections is stable when
compared to that expert the first time and automated detections.
This observation suggests that the first expert is the source of
variability. The low Hausdorff distances between the first
expert the first time and automated detections imply that
though the first expect had the highest overall variability, the
first expert had high variability the second time they marked
spots.
Table II further shows that spot contours found by the
wavelets-based method agree better with experts than those
found using LoG-based method, as this was established in
Table I. Fig. 1 shows typical spots marked by the first expert
side by side with those detected by the two methods. If the first
expert had the highest variability in marking the spots, yet
visually that experts spots marking look consistent then it can
be concluded that the two experts marked spots similarly. Spots
detected by automated detections visually have contours that
differ from those of experts, however are acceptable as
Hausdorff distances for 10 stacks are comparable to those of
inter-expert.
The set of expert markings comprised four observations per
object; two experts marked spots twice. The value of the MWI
for the LoG based method was 1.0094; its 95% confidence
interval, assuming the standard normal distribution, was
(1.0070, 1.0118). The value of the MWI for the wavelets based
method was 1.0172; its 95% confidence interval was (1.0148,
1.0196). The upper limit of the confidence interval for both
methods is greater than one, indicating that the methods agree
with the experts at least as well as the experts agree with each
other.
Figure 1. A shows original image, B is spots marked by the first expert the
first time, C highlights those detected by the LoG-based method, D shows
spots marked by the expert the second time and lastly E is spots found using
wavelets-based method.
B. Detection techniques evaluation
Fig. 2 shows ROC plot for both methods using objects on a
z-stack level deemed the most in focus visually. Spots marked
by an expert constituted the gold standard. The AUC for the
LoG-based method was 0.7751, while that of the wavelets-
based method was 0.6070. The LoG-based method had a higher
AUC value; over a range of posterior probabilities cut-offs it
had better performance than the wavelets-based method.
For each method, at the operating point corresponding to
posterior probability threshold set at 0.5, Table III shows the
performance evaluated using parameters deemed suitable for
imprecise environment. Sensitivity versus specificity was
considered not informative enough, as the two classes were
imbalanced.
Even though the LoG-based method had the highest AUC
value, it is less precise than the wavelets-based method at the
operating point chosen. Precision, what fraction of detected
spots are actually spots should be an important measure in
evaluating detection algorithms as noise frequently increases
the false positive detections. The gain in precision came at the
loss in sensitivity recall. Sensitivity fell by 10% for an
increase in precision of 20.50%. The wavelets-based method
picks up a lot less non-spots, a quarter of those by LoG-based
method, objects at the expense of missing a few true positive
spots. This leads to the implication that maybe the normal ROC
is not suitable for this problem; the posfrac-recall ROC could
offer better performance evaluation.
TABLE III. PERFORMANCE EVALUATION OF THE LOG-BASED AND
WAVELETS-BASED METHODS THE IN THE IMPRECISE ENVIRONMENT
Precision
Recall
F-measure
Posfrac
LoG-based
0.6906
0.9600
0.6575
0.9720
Wavelets-
based
0.8958
0.8600
0.6324
0.8727
E
ABC
D
Figure 2. Example The ROC curves for the LoG-based and wavelets-based
methods.
Figures 3 and 4 show posfrac-recall ROC curves for the
LoG-based and wavelets-based methods respectively, for the
target prior probabilities = 0.5, 0.1 and 0.01. The prior
probability of the non-target class was varied by varying the
fraction of background pixels from the gold standard image
considered as the non-target objects.
The posfrac-recall curves indicate that the two methods
have similar performance with varying skew values. The
choice of skew, fraction of non-target objects to include in
evaluating a method, depends on the percentage of posfrac
deemed acceptable in detecting spots in an application. The
posfrac of both methods lowers with increasing skew for a set
sensitivity. However, precision is fixed as skew varies.
Figure 3. The posfrac-recall ROC curves for the LoG-based method.
TABLE IV. POSFRAC OF THE LOG- AND WAVELETS-BASED METHODS FOR
VARYING VALUES AT 80% SENSITIVITY AND THEIR AUC VALUES
LoG
Wavelets
AUC
Posfrac
AUC
Posfrac
0.5
0.6844
0.4800
0.6524
0.4350
0.1
0.9129
0.0960
0.8425
0.0870
0.01
0.9643
0.0096
0.8852
0.0087
Figure 4. The posfrac-recall ROC curves for the wavelets-based method.
According to posfrac-recall curves, the LoG-based method
has better overall sensitivity; but since it is less precise than the
wavelets-based method its posfrac is high due to high false
positives. Table 4 shows the posfrac of the two methods at 80%
sensitivity with varying skew; it also shows their AUC. At
80% sensitivity, the wavelets-based method has lower posfrac
for all skew values.
However, above maximum sensitivity of the wavelets-
based method, its posfrac significantly surpasses that of the
LoG-based method. That is confirmed by the AUC values
LoG-based method values are consistently higher than those of
the wavelets-based method. The choice of the skew value and
sensitivity at which to operate depends on the problem being
investigated. If a method that finds all the spots, even at an
expense of including background noise is desired, the high
posfrac value can be ignored.
When the spot detection algorithms performance evaluation
is treated as an imbalanced case problem, the posfrac-recall
curves can be used to help decide at what skew and sensitivity
different methods can be compared. This is appropriate because
the distribution of spots to be detected is not known a priori.
The methods are evaluated on a per stack basis, but the
evaluations can be conducted on a batch of stacks of images.
Spots can be detected in 3D or maximum projections of stacks,
the evaluation metrics proposed would still hold. The
0 0.2 0.4 0.6 0.8 1
0
0.2
0.4
0.6
0.8
1
1 ï Specificity
Sensitivity
ROC curve
LoGïbased
Waveletsï
based
0 0.2 0.4 0.6 0.8 1
0
0.2
0.4
0.6
0.8
1
Posfrac
Recall
Posfrac recall curve
0.01
0.1
0.5
0 0.2 0.4 0.6 0.8 1
0
0.2
0.4
0.6
0.8
1
Posfrac
Recall
Posfrac recall curve
0.01
0.1
0.5
evaluation metrics can be applied to other spot detection
algorithms, not just the two tested here.
IV. CONCLUSIONS
We have proposed a procedure to evaluate performance of
spot detection algorithms in microscope images. The procedure
depends on the marking of spots in images by an expert
biologist. The marked spots form a gold standard in
determining accuracy of an algorithm in imprecise and
imbalanced environment. This methodology was demonstrated
on two spot detection algorithms, the LoG-based and wavelets-
based methods. It was able to highlight the differences in
performance between the two methods. It can be applied on
other spot detection algorithms, provided that they seek to find
the entire diffraction-limited spot.
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V. SUPPLIMENTARY DATA
Methods and Materials
The eGFP gene sequence was found on PubMed and inserted
in 5’-3’ direction into the probe designer algorithm on
www.singlemoleculefish.com. The parameters set on the
algorithm were as follows:
Number of probes 48
Probe length 20 nucleotides
GC content 45%
No of GFP probes
Lyophilized probes (Biosearch Technologies) were
resuspended in 100 µl of TE (10mM Tris, 1 mM EDTA,
Sigma) buffer (pH 8) to a final concentration of 100 mM each
and stored at -20°C. Equal volumes of thawed probes were
aliquoted (10 mM each) and pooled together for each gene to a
final concentration of 480 mM for genes with 48 probes.
Initially, precipitation was carried out with 10% volume of 3M
Sodium Acetate (pH 5.2, Sigma) and 2.5X volume 100% cold
Ethanol (Minema) according to smFISH protocol by Batish et
al. (2011) Probes were precipitated overnight by incubation at
-20°C. Probes were then spun at 14 500 Xg, 4°C for 20 min.
The pellet was then resuspended in 200 µl 0.1 M Sodium
Bicarbonate (Sigma) or Sodium Tetraborate (Sigma).
Approximately 0.3 mg of ATTO-565 NHS-ester dye (ATTO-
TEC, Germany) was dissolved in 10 µl dimethyl sulphoxide
(DMSO, Sigma). Dissolved dye solution was added to 190 µl
of 0.1 M Sodium Bicarbonate (Sigma). The dye solution was
added to the probe solution and incubated overnight in the
dark at 37°C. Following conjugation reaction, the probes were
reprecipitated at -20°C overnight as previously described.
Probes were then spun at 14 500 Xg, 4°C for 20 min.
Supernatant which consisted of unconjugated dye was
discarded and conjugated probe pellet was rinsed twice with
70% Ethanol at 14 500 Xg, 4°C for 5 min. Supernatant was
discarded and pellet was allowed to air dry. Pellet was
resuspended in 200 µl of Buffer A (0.1 M Triethyl ammonium
(TEA, Sigma)). Conjugated probes were separated and
purified to enrich for dye-conjugated probes by reverse phase
HPLC on a C18 column. Buffer A is the aqueous phase
column which allows sample molecules to adhere to column
and Buffer B (Triethyl ammonium and 70% (v/v) acetonitrile
(Labscan) contains organic solvents in which oligonucleotides
are preferentially soluble. An optimized programme of 2 to
98% Buffer B over 20 min was used to purify probes.
Conjugated probes were detected at two wavelengths, 260 nm
for nucleic acid and corresponding wavelength for dye used
either 565 nm for ATTO-565. The appropriate fractions,
containing conjugated were collected and dried in a Centri-
Vac. Dried probes will were then re-precipitated overnight as
previously described. Probes were then spun down with the
same parameters as previously described. Probes were allowed
to air dry and were re-suspended in a small volume of TE
buffer (pH 8, Sigma). DNA concentrations were then
determined using a Nanodrop. Probes were then diluted to a
final concentration of 50ng and stored at -20°C until
hybridization steps.
Cell Culture
Transfections
HeLa cells were grown in DMEM (Dulbecco’s Modified
Eagles’s Medium, Gibco) with 10% FBS (Fetal Bovine
Serum, Gibco), 2 mM L-glutamine (Sigma Aldrich) and G418.
Cells were transfected with 1 µg JOMU WT and
LIpofectamine 2000 (Invitrogen) complexes and 1ml Opti-
MEM I Reduced Serum Medium (Gibco). Media was changed
to DMEM after 4 hours and cells were incubated at 37°C and
5% CO2 for 24 hr. Cells were passaged at 1:10 into fresh
growth medium containing kanamycin sulphate (Roche). After
cells had reached 90% confluency, cells were seeded in 12
well plates, each well containing an ethanol cleaned 15mm
coverslip. Approximately 1 X 105 cells were seeded in each
well in 1 ml of media. Cells were grown in a 37°C incubator
with 5% CO2 overnight. Cells were stimulated with 20ng/ml
TNF-α (Tumor Necrosis Factor Alpha, Sigma Aldrich) and
fixed after the following time points: 2hr, 2hr 30min and 3hr.
Cell Fixation
For fixation, culture medium was aspirated off wells and cells
were gently washed 2X with phosphate buffered saline (PBS,
Lonza). 1ml of paraformaldehyde (PFA, Sigma Adrich) was
added to cells and incubated in PFA for at least 10min. PFA
was aspirated off and cells were gently washed 2X with PBS.
Cells were then stored in 70% Ethanol (Minema) at 4°C in
parafilm sealed plates until hybridization experiments.
Probe hydrization and Imaging
Prior to hybridization, cells are gently washed 2X with PBS. A
volume of 50ng of a specific conjugated probe is then added to
hybridization buffer (50% (v/v) deionised formamide
(CalBiochem), 10% (w/v) dextran sulphate (Sigma), 300 mM
NaCl (Sigma), 20 mM NaH2PO4 (Sigma), 2 mM EDTA
(Sigma), 10 µl vanadyl ribonucleoside complex (Sigma), 250
ug/ml E. coli tRNA (Sigma). For each coverslip, 7 µl of
hybridization buffer containing 50ng of probe is used.
Coverslips are then inverted, cell side down, onto 7 µl of
hybridization buffer on parafilm coated glass. Hybridization
was then carried out in 37°C water bath in the dark overnight.
Coverslips were transferred into a 12 and 2X SSC (300 mM
NaCl, 0.3 M tri-sodium citrate, Ambion) at 37°C for 30min.
Wash step was repeated three times in fresh wash buffer. Then
0.125 µg DAPI (Invitrogen) was added 20 min into the final
wash step and incubated under the same conditions for 10 min.
Coverslips were then gently washed 2X in PBS and incubated
with equilibration buffer for 2-5min. Coverslips were then
mounted onto ethanol cleaned coverslips, using glox buffer
containing 3.7 X 10-3 mg/µl glucose oxidase (Sigma) and
164.38U/µl catalase (Sigma) as a mounting buffer. Cells were
imaged on a Nikon widefield TIRF microscope using a 100X
oil immersion objective under lamp illumination. Imaging was
done using mercury lamp illumination through the appropriate
filter sets at low camera gain in each of the fluorescent
channels using an Andor iXion897 camera. The DAPI nuclear
stain was visualized in the 405 channel at 10ms exposure time.
GFP was imaged in the 488 channel with 100ms exposure
time. eGFP mRNA (“spots”) were imaged in the 561nm
channel after 200ms exposure (imaging software, µManager).
JOMU WT Plasmid Map
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