## Abstract

Super-resolution fluorescence imaging based on single-molecule localization
relies critically on the availability of efficient processing algorithms to
distinguish, identify, and localize emissions of single fluorophores. In
multiple current applications, such as three-dimensional, time-resolved or
cluster imaging, high densities of fluorophore emissions are common. Here, we
provide an analytic tool to test the performance and quality of localization
microscopy algorithms and demonstrate that common algorithms encounter
difficulties for samples with high fluorophore density. We demonstrate that, for
typical single-molecule localization microscopy methods such as
*d*STORM and the commonly used rapi*d*STORM
scheme, computational precision limits the acceptable density of concurrently
active fluorophores to 0.6 per square micrometer and that the number of
successfully localized fluorophores per frame is limited to 0.2 per square
micrometer.

© 2011 OSA

## 1. Introduction

In recent years, subdiffraction-resolution far-field fluorescence microscopy methods attracted considerable interest because they allow the noninvasive observation of cellular processes with almost molecular resolution [1–8].

Single-molecule based localization microscopy methods such as photoactivated
localization microscopy (PALM, [3]), fluorescence photoactivation localization microscopy (FPALM,
[4]), stochastic optical
reconstruction microscopy (STORM, [5]), and direct stochastic optical reconstruction microscopy
(*d*STORM, [4,
8]) are very promising because
they are comparably simple, require only moderate irradiation intensities which
makes them ideally suited for live-cell imaging [8–10], and can be implemented in standard wide-field fluorescence
microscopes. In localization microscopy target structures are labeled with
fluorophores that can be stochastically switched between a fluorescent on state and
a non-fluorescent off state upon irradiation with light of appropriate wavelength.
Ideally, only a small subset of fluorophores is active at any time of the experiment
generating isolated images of the point spread function (PSF), so-called
*spots*, on the detector that are well resolvable within the
optical resolution limit. A model function is approximated to the PSF [11–14] to determine the nanometer-precise fluorophore position
called *localization*. A cycle of activation, detection, and
photobleaching or transfer to a reversible off state, respectively, is repeated
several thousand times to collect a sufficient number of localizations (typically
several ten thousands to millions of localizations) to reconstruct a super-resolved
image.

When considering the resolution of localization microscopy, two quantities have to be distinguished: The optical resolution, i.e. the shortest distance at which two point emitters can be distinguished, and the structural resolution, i.e. the finest resolvable level of detail in a continuous structure. Contrary to many classical microscopy methods, the structural resolution is potentially much lower than the optical resolution due to the nonlinear computational processing.

Achieving high structural resolution with localization microscopy has three prerequisites: The fluorophore density must be sufficiently high according to the Nyquist-Shannon sampling theorem [15], the imaging speed must surpass the sample dynamics, and the density of generated spots must be small enough to be optically resolvable to avoid imaging artifacts and false localizations [9, 16, 17]. While these prerequisites are easily fulfilled as long as only extended filaments, e.g. microtubulin and actin, are imaged, super-resolution imaging of complex and densely labeled structures necessitates the use of photoswitchable fluorophores with highly stable off states and appropriately set photoswitching rates [16, 17]. Three-dimensional and dynamic super-resolution imaging with high spatiotemporal resolution [5, 8, 9, 18, 19] or single turnover counting for spatially resolved observation of catalysis [20, 21] are even more challenging. In these cases, precise photophysical control of the number of active fluorophores is difficult and high spot densities are often unavoidable.

While spot density issues have been researched for localization microscopy of fluorescent particles [22], only recently [23] an algorithm was implemented for single-molecule super-resolution data analysis based on simultaneously fitting the data of overlapping PSF with multiple kernels to increase the allowed number of simultaneously active fluorophores. Unfortunately, current algorithms used for single-molecule localization microscopy mainly focus on spatial localization precision and computational speed and rely on well-separated input spots [24–26]. The lack of efficient algorithms results, in our opinion, primarily from the difficulties to check the performance of the algorithm on experimental data obtained from densely labeled samples. While it has been shown previously [24] that stochastic simulations can generate data sets sufficiently close to experimental localization microscopy data, direct stochastic analysis of suchlike simulations is difficult because the density of active fluorophores prevents direct mapping of active fluorophores and localizations (Fig. 1a).

Here, we introduce a method to evaluate the performance of localization microscopy
imaging algorithms on samples with high fluorophore density using simulated
fluorophore lattices. Our method is capable of quantifying the performance of a
localization microscopy algorithm with three standard characteristics:
*stochastic precision*, *recall*, and
*spatial precision*. The stochastic precision is defined as the
quotient of true positive localizations to all localizations found, and the recall
as the quotient of the number of true positive localizations to the total number of
spots that should have been localized [27]. The spatial precision gives the uncertainty in fluorophore
localization, that is, the spatial difference between the exact position of an
emitting fluorophore and the determined localization. (Stochastic precision and
recall are often referred to as false positive/negative rate or miss/hit
probability. Spatial precision is also known as localization precision, precision or
optical resolution, renamed here to avoid confusion with stochastic precision.) To
demonstrate its performance, we applied our method to the
rapi*d*STORM algorithm [24], an algorithm in the important class of Gaussian PSF
least-squares fitters to determine its performance on typical
*d*STORM data.

## 2. Material and methods

We measured the impact of high spot densities on algorithmic localization performance
by simulating fluorophores located on a dense lattice cycling reversibly between a
fluorescent on and a non-fluorescent off state. While this method can generate
signals sufficiently close to experimental data, straightforward comparison of the
simulation ground truth, i.e. the known position of simulated emissions, and the
computational result, i.e. the position of algorithmically recognized localization
positions, has proven difficult under dense-spot conditions since different
simulated fluorophores can cause any given localization. Our methodical solution to
this problem is an analysis based on a localization histogram from a large number of
analyzed frames with all lattice points averaged into a mean lattice interval. In
other terms, we *(i)* simulated a long localization microscopy image
stack, *(ii)* employed the algorithm under scrutiny to find
localizations in the stack, *(iii)* subtracted the position of the
nearest fluorophore from each localization, regardless of this fluorophore’s
state, *(iv)* fitted the histogram of these localization offsets with
a sum of Gaussian functions, and *(v)* extracted the observables of
interest from the obtained parameters.

#### 2.1. Input data simulation

We modelled localization microscopy input data as given in Eq. 1.
*PSF* (*f, p*) models the point spread
function for a simulated fluorophore *f* at the pixel
*p*, i.e. the probability that a photon emitted by the
fluorophore *f* (assumed to be point-like) is detected in the
pixel *p*. The photon rate *N*
_{P} gives
the number of photons emitted per time unit while residing in the on state, and
this rate was assumed to be constant.
*t _{on}*(

*f,t*) denotes the time the fluorophore

*f*resided in the on state during a time period

*t*. The product of these three quantities, assumed to be the number of photons detected by one pixel of a simulated CCD camera for one image and one fluorophore, was varied by Poisson statistics (denoted by Pois). No additional camera properties were considered because modern scientific CCD cameras come very close to linear response [28]. The contributions were summed over a set of fluorophores

*F*and additive background noise was modelled by randomly choosing a value

*G*out of a set of likely background noise values

_{r}*G*.

*G* was generated by selecting all pixels further than 10 pixels
from all localizations from a real *d*STORM acquisition. As for
other random numbers in this article, the randomness was drawn from the GSL
Mersenne Twister implementation [29, 30].

*F* was generated by placing one fluorophore on each junction of a
40 nm rectangular lattice. Fluorophore behavior was modeled as a time-continuous
Markov process between a dark and a bright state with lifetimes of
*τ*
_{off} and
*τ*
_{on}, respectively. In other terms, the
time a fluorophore spends in each state follows an exponential distribution with
mean *τ*
_{off} and
*τ*
_{on}, respectively, and each bright state
phase is followed by a dark state phase and vice versa. The simulated point
spread function was computed by assuming perfect focusing and sample planarity,
i.e. an optical point spread function equal to the Besselian of first kind and
first order *J*
_{1}, scaled by a factor
*κ* to match common experimental numerical apertures,
and integrated over the camera pixel size numerically using 87-point
Gauss-Kronrod [31]
integration (Eq. 2). x⃗_{f} denotes the subpixel-precise
fluorophore position in this equation, and *α* a scale
factor chosen such that *PSF* (*f, p*) =
1. While this formulation assumes point-like fluorophores, extension to
two-dimensional or three-dimensional objects is straightforward.

The average lifetime of the on-state was chosen to three times the simulated
acquisition time for a single image, but not synchronized with the simulated
acquisition time intervals, to produce simulated images with a broad spectrum of
spots with different photon counts. This mainly serves to emulate the broad
distribution of spot photon counts experienced in real TIRF experiments. We
simulated fluorophores spaced on a 40 nm lattice with a detection pixel raster
of 85 nm, causing many different detection raster/fluorophore lattice
orientations to occur. (The raster and lattice were chosen with a small lowest
common multiple to ensure computational tractability. This way, only a limited
number of PSFs had to be computed.) The Besselian PSFs was scaled with
*κ* = 1.37, equivalent to a spot full width
at half maximum (FWHM) of 370 nm.

#### 2.2. Simulation parameters

Three parameters were varied to identify influences in spot detection rate: The density of spots on the camera, the signal-to-noise ratio and the sampling raster width. The spot density was varied by prolonging the simulated lifetime of the dark state while keeping the on state lifetime and fluorophore density constant; the signal-to-noise ratio was varied by changing the simulated photon emission rate of fluorophores while keeping the background noise constant; and the sampling raster width was simulated by changing the simulated pixel raster while keeping the PSF size constant.

By default, we used physical parameters similar to typical
*d*STORM experiments: integration time 0.1 s,
*τ*
_{on} = 0.3 s,
*N*
_{P} = 10 kHz and a pixel size
ρ_{P} of 0.24 PSF FWHM. Each photon was counted as 16 A/D
counts in the linear part of the camera response.

Typical images generated with these parameters are shown in Fig. 1 and in the boxes in Fig. 4.

#### 2.3. Algorithmic molecule localization

We processed the simulated images using our previously published
rapi*d*STORM algorithm [24]. The rapi*d*STORM
algorithm is optimized for comparably noisy input images as commonly recorded in
widefield single-molecule localization microscopy, and processes input images in
two steps. In the first step, likely positions of bright-state fluorophores are
pre-selected by applying a suitable smoothing algorithm, such as a
*Spalttiefpass* filter, to the input images and selecting the
local maxima of the resulting image. In the second step, a Gaussian function
with fixed covariance matrix (i.e., the widths
*σ _{x}* and

*σ*and the X-Y-correlation are either estimated manually or automatically prior to the fitting) is fitted to the pixels around the strongest local maximums. By thresholding the amplitude of the fitting Gaussian, a distinction is made between random background noise and a real fluorophore emission. Strong local maxima are fitted in decreasing intensity until a predefined number of successive maxima were fitted with an amplitude below the threshold. We used an amplitude threshold of 180 times the noise standard deviation and filtered spots containing emissions from multiple fluorophores (

_{y}*multi-spots*) by fitting the data from the first spot with a sum of two Gaussian kernels [32]. The start positions of the centers were chosen 1 pixel apart along a line connecting the one-kernel center and the highest residues. The start amplitudes were set to half of the one-kernel amplitude. All localizations were tagged with the quotient of the sum of squared residues of the two-kernel and the one-kernel fit, termed their suspectedness. If the two centers found in two-kernel analysis differed by more than a threshold

*θ*

_{dist}, the two-kernel fit was instead discarded and the suspectedness set to 0. After computing all results, we discarded localizations that surpassed a suspectedness threshold of

*θ*

_{fishy}.

Three parameters were varied in the data processing: First, we used two different
spot finding methods (*Spalttiefpass* and Gaussian smoothing, see
[24]) to check
whether the used spot detection influences the results. Second, we changed the
distance threshold *θ*
_{dist} and the
suspectedness threshold *θ*
_{fishy} to increase
the probability to generate at least one reasonably good set of parameters.

By default, we used a *Spalttiefpass* smoother,
*θ*
_{dist}= 0.5 μm and
*θ*
_{fishy} = 0.1.

To control our measurements with an independently implemented algorithm and to show the easy integration of our method with other algorithms, we also computed the images generated with default photophysical parameters using QuickPALM [33] version 1.1 with its default settings.

#### 2.4. Statistical characterization of localizations

The spot density was characterized by calculating the area within a circle with a diameter of one PSF FWHM. The average density of simulated fluorophores, the average fraction of the acquisition time each fluorophore spent in the on state and the average density of spots was calculated to determine the average number of spots per area within a circle of one FWHM diameter by linear arithmetics. We did not correct for integration time.

We analyzed the localization distribution generated by the
rapi*d*STORM algorithm by histogramming the offsets of
localizations relative to the known fluorophore positions at the lattice points,
excluding a border of 5 pixels resulting in a two-dimensional point distribution
representing a mean lattice interval. We fitted the histogram with a sum of a 5
by 5 lattice of symmetrical, identical two-dimensional Gaussian functions
centered at the theoretical lattice points with a width of
*σ* plus a constant offset *B* to the
data, resulting in the model function given by Eq. 4. The histogram was fitted
with the Levenberg-Marquardt maximum likelihood estimator published by Laurence
et al [34].

The width of the Gaussian functions directly gives the spatial precision. The localizations explained by the Gaussian functions give the number of true positive localizations. On the other hand, the number of localizations explained by the shift gives the number of unspecific localizations (false positives), which include erroneously fitted background noise and localizations stemming from multi-spots. Both of these sources of false localizations can be expected to produce localizations with a very broad distribution, thus appearing identically distributed on the mean lattice interval. The total number of spots that should have been detected was determined from the number of spots in the simulation that contained enough photons above the background threshold. From these values, we computed stochastic precision and recall accordingly.

Due to multiple stochastical simplifications made, we computed each stochastical simulation 5 times with random number generation seeds 41–45 to gain information about data point validity. We performed these computations on an Intel(R) Core(TM) i5 CPU 650 clocked at 3.20 GHz with four cores, using close to a week of computer time for the whole set of data found on our website. The program code and additional scripts we used for generating and evaluating data sets can be found on our website.

## 3. Results and discussion

The most researched characteristic for super-resolution microscopy is its spatial
precision, i.e. for localization microscopy the stochastical uncertainty in each
localization. By applying our evaluation method, we found the spatial precision of
rapi*d*STORM to be very stable and decreasing only by a few
nanometers for spot densities up to 5 spots per μm^{2} (Fig. 2), corresponding to an average distance
to the nearest neighbouring molecule of 0.7 FWHM of a PSF. The deterioration of
spatial precision for higher spot densities cannot be accurately determined due to
the very small stochastic precision. The decrease in spatial precision is consistent
across a wide range of parameter variations, including photon count rate, pixel
size, smoothing algorithm, and multi-spot search thresholds.

The impact of stochastic precision and recall was investigated in two steps, first
fixing optimal parameters for multi-spot analysis and then analyzing variations
along the remaining parameters. The stochastic precision-recall diagram (Fig. 3), a parametric diagram which shows the
trajectory in precision-recall-space caused by variation of the spot search
threshold, was used to identify critical points in multi-spot search parameters.
Readers unfamiliar with this kind of diagram should note that the points are not a
function of stochastic precision, but rather of
*θ*
_{fishy}, and that both axes in the diagram
show measured quantities. In general, points close to the upper right corner of a
precision-recall diagram are considered optimal, and curves will tend to run from
top left (good recall, i.e. many true positives, and bad precision, i.e. many false
positives) to bottom right (few true positives and few false positives), with curves
running from bottom left to top right indicating suboptimal parameters (e.g. for
very low *θ*
_{fishy} both stochastic precision and
recall decrease, showing *θ*
_{fishy} = 0 to
be an absolutely inferior choice compared to
*θ*
_{fishy} = 0.01). The consistent shape
of the curve demonstrates that the combined optimum of stochastic precision and
recall consistently occurs at a residual quotient
*θ*
_{fishy} = 0.1 and a high distance
threshold *θ*
_{dist} should be selected. Therefore,
we fixed these settings to *θ*
_{fishy} = 0.1
and *θ*
_{dist} = 0.5 μm in the
following analysis. It should be noted that the overall low values of stochastic
precision and recall stem primarily from the high default spot density of 0.64 spots
μm^{−2} (1.74 PSF FWHMs), which was chosen to cause many
multi-spot events and thus test the effectivity of multi-spot search.

#### 3.1. Stochastical precision and recall

Despite applying multi-spot analysis, we found that the
rapi*d*STORM algorithm encounters problems with accurate spot
identification when the number of simultaneously active fluorophores increases.
Both recall and stochastic precision show a distinct decrease with increasing
spot density *ρ*
_{S} (Figs. 4a and b). In other words, the number of false
positive localizations increases in the super-resolved reconstructed image with
increasing *ρ*
_{S}. An exponential decay in the
recall emerges for all curves, even though the actual values differ by up to a
factor of two. Since the number of true localizations is given by the camera
area multiplied by the spot density and the recall rate, the density of true localizations scales with ${\rho}_{\text{S}}\text{exp}\left(-\frac{{\rho}_{\text{S}}}{k}\right)$ with *k* being
algorithm-dependent (e.g. 0.6
μm^{–}^{2} for default
settings) and has a maximum at *ρ*
_{S} =
*k* (Fig. 4c). We will
refer to the density of true localizations per frame as throughput. Using
default settings, the maximum occurs at 0.6 μm^{−2} (1.8
PSF FWHMs) and allows a throughput of 0.17 localizations per frame and
μm^{2}(Fig. 4c),
corresponding to a mean nearest-neighbour distance of correctly identified
localizations of 3.4 PSF FWHMs. Consistently, algorithmic alternative parameters
that employ less smoothing in the preprocessing stage offer better throughput.
Note, however, that the maximum throughput is offset by a considerable amount of
false localizations, i.e. noise or artifacts impair the reconstructed image.

This tradeoff can be analyzed and visualized by using a stochastic precision-throughput diagram (Fig. 5), i.e. a diagram analogous to a stochastic precision-recall diagram with the ordinate scaled by the spot density. When plotting throughput versus the stochastic precision, the slope curves along which points differ by spot density, the slope in the stochastic precision-throughput diagram characterizes both the existence and the sharpness of the tradeoff between stochastic precision and throughput. Positive slopes in the diagram show that stochastic precision and throughput can be optimized concurrently when changing spot density and negative slopes close to 0 or −∞ show that stochastic precision or throughput, respectively, can be gained at little cost.

#### 3.2. Influence on different types of localization microscopy

The interdependence of throughput and stochastic precision is influenced by
experimental constraints that can be divided into three categories:
(*i*) the fluorophore-limited case defined by unfavorable
photostability, including experiments with photoactivatable fluorescent proteins
(e.g. PALM and FPALM, [3, 4]) and necessitating maximum recall
rates, (*ii*) the ratio-limited case defined by the
photoswitching rates or lifetimes of the on and off state (e.g. STORM and
*d*STORM, [16]), and (*iii*) the throughput-limited case
defined by short acquisition times caused by experimental instability or the
need to acquire many localizations in a short time (e.g. dynamic
super-resolution imaging in living cells, [8, 9]).
This need is exemplified by the low spot densities (0.01 – 0.03
μm^{–}^{2}) used
recently by Frost et al. [35].

For the fluorophore-limited case, low spot densities should be adjusted to
guarantee good recall because in most cases each fluorophore only produces few
spots before photobleaching. The density-stochastic precision and density-recall
diagram (Figs. 4a and b) demonstrate that
both recall and stochastic precision approach 100% for very low spot
densities highlighting the stability of the rapi*d*STORM
algorithm against background noise [24].

The ratio-limited case occurs when reversible photoswitching allows repeated
detection of a fluorophore, making recall less relevant. Here, stochastic
precision is the relevant factor and mainly limited by the photoswitching rates,
i.e. the ratio of the lifetime of the off and on state, $r=\frac{{\tau}_{\text{off}}}{{\tau}_{\text{on}}}$ [16, 17]. Thus, the
ratio determines the acceptable fluorophore and spot density, and finally the
achievable structural resolution [16]. Without multi-spot analysis the stochastic precision
decreases exponentially with increasing spot density. On the other hand,
applying multi-spot analysis the fraction of true positive localizations
increases considerably enabling a stochastic precision of
*>* 80% for spot densities of 0.6
μm^{−2} or, in other words, more than 80% of
all localizations composing the reconstructed image are accurate localizations
and contain sample information (Fig. 4a).
The exponential behavior is also shown by the QuickPALM algorithm, indicating a
broad applicability of a simple exponential decay model for stochastic precision
and recall.

In the throughput-limited case when the acquisition speed is the crucial
parameter, the stochastic precision-throughput diagram (Fig. 5) is most useful. By plotting the product of
spot density and recall against the stochastic precision several optimal zones
can be identified. Already with default settings the rapi*d*STORM
algorithm allows a throughput of almost 0.2 localizations per frame and
μm^{2} at a stochastic precision of nearly 80% which
can be further optimized by changing the pixel size and other algorithmic input
parameters (Fig. 5).

These results show that effort is necessary to optimize the performance of the
rapi*d*STORM algorithm and probably other, similar algorithms
for localization microscopy. While a full inquiry into the causes of failure is
outside the scope of this article, several studies [9,22]
indicate that higher performance under high spot density conditions should be
possible. However, these studies did not provide a false positive rate. It has
to be pointed out that the investigation of recall or localization precision
alone is not adequate to judge the quality of algorithmic processing. Since the
distribution of false positives is both different from that of true positives
and can be expected to be highly dependent on the spatial configuration of
simulated fluorophores, localization precision, and stochastical precision must
be measured independently. At the same time, both recall and stochastical
precision are necessary for a meaningful stochastical analysis. In other words,
performance at high spot density can only be compared with previously published
results (reporting e.g. high recall values also for higher spot densities
[9, 22]) and new algorithms evaluated only if all
three parameters stochastic precision, recall, and localization precision are
considered, underlining the importance of the proposed measurement method.

#### 3.3. Information throughput and necessary acquisition time

The maximal throughput can be used to predict the minimal acquisition time
necessary to achieve a desired experimental Nyquist-Shannon-limited spatial
image resolution. To resolve a structure with a structural resolution of 20 nm,
i.e. reliably detect irregularities of at least 20 nm in size, one data point
has to be recorded every 10 nm and therefore up to 10,000 fluorophores
μm^{−2} are necessary. At the maximum throughput we
measured, at least 50,000 images have to be acquired to reconstruct a
super-resolved image containing a sufficient number of true localizations. For
example, at a frame rate of 100 Hz the acquisition time sums up to ~ 8 minutes
and prevents dynamic super-resolution imaging of highly dynamic samples.
However, this number should be interpreted with two caveats: firstly, less
complex structures such as filaments or small multi-protein complexes require
much lower labeling density and allow us to perform super-resolution imaging in
much shorter acquisition times [36]. Secondly, we ignored the effects of localization
redundancy, i.e. more than one localization per fluorophore, and localization
precision, both of which necessitate even longer acquisition times for
resolution.

The throughput limit we found also implies an information limit for localization microscopy applicable to all localization microscopy applications, surpassing structural resolution considerations. Since the number of true localizations determined per time unit is limited and each localization is determined with an inherent uncertainty given by the localization precision, localization microscopy must be limited by the Shannon-Hartley theorem [15], with the localization throughput representing the bandwidth of a classical communication channel and the acquisition area size and the localization precision determining the signal-to-noise ratio. Thereby, our results give an information throughput measured in bits per image for localization microscopy algorithms; if the results of the experiment can be easily treated in information theory terms, this result allows an estimation of the necessary acquisition time.

#### 3.4. Applicability

We consider our simulation-derived results to be applicable quantitatively to
real *d*STORM measurements since we adapted realistic noise
measurements and photophysical parameters from *d*STORM
measurements. The largest incongruence with reality is the distribution of
localization amplitudes, i.e. the estimated number of photons per spot: while
computations on real measurements show a broad distribution of localization
amplitudes, the simulations generate many localizations with small variation
around the photon rate times the integration time. This incongruence might be
due to different excitation intensities of of differently located fluorophores
induced e.g. by total internal reflection. However, with the simulated spots
already showing significant variation towards low amplitudes due to the chosen
simulated integration time and the combination of many spots probably
overshadowing the photon statistics of each single spot, we deem the variance in
spot strength in our simulations sufficient and did not try to enhance the model
to match a spot strength histogram of real data more closely.

While the above results have been obtained with simulation parameters typical for
the *d*STORM method and the rapi*d*STORM
algorithm, the proposed method for measuring dense-spot performance is easily
applicable to all current localization microscopy methods and algorithms. We
provide evaluation software on our website and can provide the stochastically
generated input image stacks on request. We expect our results to hold
quantitatively for many current localization microscopy algorithms, which
generally follow the the same pattern as rapi*d*STORM of
denoising, identifying spot positions by maximum search or thresholding, and
non-linearly fitting functional approximations of the PSF to these positions.
However, it should be stressed that photophysical parameters were assigned to
match typical *d*STORM data and were measured on
rapi*d*STORM, and thus our numerical results can not be
applied directly to other localization microscopy algorithms that operate with
different premises, under different conditions (e.g. molecule brightness and
background noise) or with different computational approaches. For these
algorithms, we suggest using the demonstrated method of simulated fluorophore
lattices to obtain their statistical properties, which is eased by the supplied
software and, if photophysical parameters allow, our generated input image
stacks. In general, our method should be considered as a practical proof and
testing procedure for localization microscopy algorithms, and does not indicate
the limits of localization microscopy.

## 4. Conclusions and outlook

Our results demonstrate that we have a powerful method for automatic and reliable
testing of localization microscopy algorithms under high spot density conditions.
The method relies on computing localization precision, stochastic precision and
recall from the parameters of a sum of Gaussian functions fitted to an average
raster interval histogram. The usefulness of stochastic precision recall, spot
density recall, spot density precision, and stochastic precision throughput diagrams
has been demonstrated by identifying the best algorithmic parameters and by
predicting ideal physical parameters for algorithmic performance. We determined
optimal spot densities on the basis of the rapi*d*STORM algorithm
demonstrating that fluorophore-limited experiments (PALM, FPALM) should be performed
well below 0.5 spots per μm^{2}, ratio-limited experiments with
reversibly photoswitchable fluorophores (STORM, *d*STORM) at 0.6
spots per μm^{2}, and throughput-limited dynamic super-resolution
imaging experiments are limited to 0.2 achieved localizations per frame and
μm^{2}. Our results highlight the complex interrelation of spot
density, photophysical fluorophore parameters, and acquisition speed expressed as
throughput (spots per frame and μm^{2}). They demonstrate that high
labeling densities are prone to generate false and artificial localizations unless
experimental parameters such as photoswitching and acquisition rates are set
appropriately and that very long acquisition times are necessary when localization
throughput is limited.

Our quantitative characterization of a localization microscopy algorithm is an important step towards a refined understanding of the resolution and quantification capability of single-molecule based localization microscopy methods. Furthermore, we expect the proposed lattice histogram method used to evaluate evaluating dense-spot performance to be very useful in designing and testing algorithms that extend the capabilities of standard localization microscopy methods.

## Acknowledgment

This work was supported by the Biophotonics and the Systems Biology Initiative (FORSYS) of the German Ministry of Research and Education (BMBF). This publication was funded by the German Research Foundation (DFG) in the funding programme Open Access Publishing.

## References and links

**1. **S. W. Hell, “Far-Field Optical
Nanoscopy,” Science **316**, 1153–1158
(2007). [CrossRef] [PubMed]

**2. **P. Kner, B. B. Chhun, E. R. Griffis, L. Winoto, and M. G. L. Gustafsson, “Super-resolution video microscopy of
live cells by structured illumination,” Nat.
Methods **6**, 339–342
(2009). [CrossRef] [PubMed]

**3. **E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging Intracellular Fluorescent
Proteins at Nanometer Resolution,”
*Science*313, 1642–1645
(2006). [CrossRef] [PubMed]

**4. **S. T. Hess, T. P. Girirajan, and M. D. Mason, “Ultra-high resolution imaging by
fluorescence photoactivation localization
microscopy,” Biophys. J. **91**, 4258–4272
(2006). [CrossRef] [PubMed]

**5. **B. Huang, W. Wang, M. Bates, and X. Zhuang, “Three-dimensional super-resolution
imaging by stochastic optical reconstruction
microscopy,” Science **319**, 810–813
(2008). [CrossRef] [PubMed]

**6. **M. Heilemann, S. van de Linde, M. Sch¨ttpelz, R. Kasper, B. Seefeldt, A. Mukherjee, P. Tinnefeld, and M. Sauer, “Subdiffraction-resolution fluorescence
imaging with conventional fluorescent probes,”
Angew. Chem. Int. Ed. **47**, 6172–6176
(2008). [CrossRef]

**7. **J. Vogelsang, T. Cordes, C. Forthmann, C. Steinhauer, and P. Tinnefeld, “Controlling the fluorescence of
ordinary oxazine dyes for single-molecule switching and superresolution
microscopy,” Proc. Nat. Acad. Sci.
U.S.A. **106**, 8107–8112
(2009). [CrossRef]

**8. **R. Wombacher, M. Heidbreder, S. van de Linde, M. P. Sheetz, M. Heilemann, V. W. Cornish, and M. Sauer, “Live-cell super-resolution imaging with
trimethoprim conjugates,” Nat.
Methods **7**, 717–719
(2010). [CrossRef] [PubMed]

**9. **H. Shroff, C. G. Galbraith, J. A. Galbraith, and E. Betzig, “Live-cell photoactivated localization
microscopy of nanoscale adhesion dynamics,”
Nat. Methods **5**, 417–423
(2008). [CrossRef] [PubMed]

**10. **T. Klein, A. Löschberger, S. Proppert, S. Wolter, S. van de Linde, and M. Sauer, “Live-cell dstorm with snap-tag fusion
proteins,” Nat. Methods **8**, 7–9
(2011). [CrossRef]

**11. **N. Bobroff, “Position measurement with a resolution
and noise-limited instrument,” Rev. Sci.
Instrum. **57**, 1152–1157
(1986). [CrossRef]

**12. **M. K. Cheezum, W. F. Walker, and W. H. Guilford, “Quantitative comparison of algorithms
for tracking single fluorescent particles,”
*Biophys. J.*81, 2378–2388
(2001). [CrossRef] [PubMed]

**13. **R. E. Thompson, D. R. Larson, and W. W. Webb, “Precise Nanometer Localization Analysis
for Individual Fluorescent Probes,” Biophys.
J. **82**, 2775–2783
(2002). [CrossRef] [PubMed]

**14. **K. I. Mortensen, L. S. Churchman, J. A. Spudich, and H. Flyvbjerg, “Optimized localization analysis for
single-molecule tracking and super-resolution
microscopy,” Nat. Methods **7**, 377–381
(2010). [CrossRef] [PubMed]

**15. **C. Shannon, “Communication in the Presence of Noise
(reprinted),” Proc. IEEE **72**, 1192–1201
(1984). [CrossRef]

**16. **S. van de Linde, S. Wolter, M. Heilemann, and M. Sauer, “The effect of photoswitching kinetics
and labeling densities on super-resolution fluorescence
imaging,” J. Biotechnol. **149**, 260–266
(2010). [CrossRef] [PubMed]

**17. **T. Cordes, M. Strackharn, S. W. Stahl, W. Summerer, C. Steinhauer, C. Forthmann, E. M. Puchner, J. Vogel-sang, H. E. Gaub, and P. Tinnefeld, “Resolving single-molecule assembled
patterns with superresolution blink-microscopy,”
*Nano Lett.*10, 645–651
(2010). [CrossRef]

**18. **M. F. Juette, T. J. Gould, M. D. Lessard, M. J. Mlodzianoski, B. S. Nagpure, B. T. Bennett, S. T. Hess, and J. Bewersdorf, “Three-dimensional sub-100 nm resolution
fluorescence microscopy of thick samples,”
Nat. Methods **5**, 527–529
(2008). [CrossRef] [PubMed]

**19. **S. Manley, J. M. Gillette, G. H. Patterson, H. Shroff, H. F. Hess, E. Betzig, and J. Lippincott-Schwartz, “High-density mapping of single-molecule
trajectories with photoactivated localization
microscopy,” *Nat. Methods*5, 155–157
(2008). [CrossRef] [PubMed]

**20. **M. B. J. Roeffaers, B. F. Sels, H. Uji-i, F. C. De Schryver, P. A. Jacobs, D. E. De Vos, and J. Hofkens, “Spatially resolved observation of
crystal-face-dependent catalysis by single turnover
counting,” Nature **439**, 572–575
(2006). [CrossRef] [PubMed]

**21. **M. B. J. Roeffaers, G. De Cremer, J. Libeert, R. Ameloot, P. Dedecker, A.-J. Bons, M. Bückins, J. A. Martens, B. F. Sels, D. E. De Vos, and J. Hofkens, “Super-resolution reactivity mapping of
nanostructured catalyst particles,” Angew.
Chem. Int. Ed. **48**, 9285–9289
(2009). [CrossRef]

**22. **H. Bornfleth, K. Sätzler, R. Eils, and C. Cremer, “High-precision distance measurements
and volume-conserving segmentation of objects near and below the resolution
limit in three-dimensional confocal fluorescence
microscopy,” Journal of Microscopy **189**, 118–136
(1998). [CrossRef]

**23. **S. Holden, S. Uphoff, and A. Kapanidis, “Crowded-field photometry increases
maximum super-resolution imaging density by an order of
magnitude,” Nat. Methods
(2010). Manuscript submitted. [PubMed]

**24. **S. Wolter, M. Schüttpelz, M. Tscherepanow, S. van de Linde, M. Heilemann, and M. Sauer, “Real-time computation of
subdiffraction-resolution fluorescence images,”
J. Microsc. **237**, 12–22
(2010). [CrossRef] [PubMed]

**25. **J. Tang, J. Akerboom, A. Vaziri, L. L. Looger, and C. V. Shank, “Near-isotropic 3D optical nanoscopy
with photon-limited chromophores,” Proc.
Nat. Acad. Sci. U.S.A. **107**, 10068–10073
(2010). [CrossRef]

**26. **T. Quan, P. Li, F. Long, S. Zeng, Q. Luo, P. N. Hedde, G. U. Nienhaus, and Z.-L. Huang, “Ultra-fast, high-precision image
analysis for localization-based super resolution
microscopy,” Opt. Express **18**, 11867–11876
(2010). [CrossRef] [PubMed]

**27. **D. A. Grossman and O. Frieder, Information Retrieval: Algorithms and
Heuristics, The Kluwer International Series of Information
Retrieval (Springer, Box P.O. 17, 3300 AA Dordrecht, The Netherlands, 2004), 2nd
ed.

**28. **A. TechnologyiXon camera manual (Andor Technology, 7 Millennium Way,
Springvale Business Park, Belfast, BT12 7AL, NORTHERN IRELAND, 2008).

**29. **M. Galassi, J. Davies, J. Theiler, B. Gough, G. Jungman, M. Booth, and F. Rossi, *Gnu Scientific Library: Reference Manual*
(Network Theory Ltd.,
2003).

**30. **M. Matsumoto and T. Nishimura, “Mersenne twister: a 623-dimensionally
equidistributed uniform pseudo-random number
generator,” ACM Trans. Model. Comput.
Simul. **8**, 3–30
(1998). [CrossRef]

**31. **S. Ehrich, “Error bounds for Gauss–Kronrod
quadrature formulae,” Math. Comp. **62**, 295–304
(1994). [CrossRef]

**32. **D. M. Thomann, “Algorithms for detection and tracking
of objects with super-resolution in 3d fluorescence
microscopy,” Ph.D. thesis, ETH Zürich
(2003).

**33. **R. Henriques, M. Lelek, E. F. Fornasiero, F. Valtorta, C. Zimmer, and M. M. Mhlanga, “QuickPALM: 3D real-time photoactivation
nanoscopy image processing in ImageJ,” Nat.
Methods **7**, 339–340
(2010). [CrossRef] [PubMed]

**34. **T. A. Laurence and B. A. Chromy, “Efficient maximum likelihood estimator
fitting of histograms,” Nat Meth **7**, 338–339
(2010). [CrossRef]

**35. **N. A. Frost, H. Shroff, H. Kong, E. Betzig, and T. A. Blanpied, “Single-molecule discrimination of
discrete perisynaptic and distributed sites of actin filament assembly
within dendritic spines,” Neuron **67**, 86 – 99
(2010). [CrossRef] [PubMed]

**36. **U. Endesfelder, S. van de Linde, S. Wolter, M. Sauer, and M. Heilemann, “Subdiffraction-resolution fluorescence
microscopy of myosin-actin motility,” Phys.
Chem. Chem. Phys. **11**, 836–840
(2010).