after 30 minutes' storage in dark & supply voltage 1 x 10^6.

**Table 1.** Typical characteristics of detectors used in single molecule fluorescence experiments.

**Figure 2.** Schematic diagram of Photodetectors. A) Schematic for PMT illustrating amplification of incident light by dynodes. B) Schematic for APD where p = p-layer, i = i-layer, and n = n-layer are indicated. Impact ionization and electron multiplication is illustrated. C) Schematic for CCD sensor chip where amp. (triangle) = amplifier, Vert. = vertical registration, and Horiz. = horizontal registration D) schematic for EMCCD sensor chip E) schematic for ICCD sensor chip where M.C.P = micro-channel plate F) schematic diagram for CMOS sensor chip where the amplifier (triangle) and associated digital processing is incorporated into each photodiode G) relative scale for single photon counting suitability for the various photodetectors with APD being the most suited and CCD the least. Absolute suitability will depend not only on detector type and characteristics but also instrument design.

rial (e.g. GaAs, or GaAsP) have maximal sensitivity at 400 nm, providing good spectral response in the UV and blue regions but a rapid drop-off in response in the green and red region of the visible spectrum. PMTS have large gain and low noise but suffer from low QE (20–40%) compared to other technologies (e.g. APDs). Importantly, single photons can be detected with PMTs, but discrimination of single versus multiple photons is difficult. Avalanche photodiodes have better QE and better sensitivity in the green and red region of the visible spectrum compared to PMTs [64].

#### **5.3. Avalanche photodiodes (APDs)**

Photographic film dominated many aspects of microscopic imaging until the mid-20th century. Photodetectors are devices that record changes in light intensities (photons) and then create an electrical or optical output. Modern photodetector technologies began with the invention of photomultiplier tubes (PMTs) in the 1930s [55–57] followed by the invention of avalanche photodiodes (APDs) in the 1960s [58]. Advances in semiconductor materials and integrated circuit technologies led to creation of focal plane array (FPA) detectors, such as charge-coupled devices (CCDs) in the 1970s, and to the present day scientific grade complementary metal oxide semiconductors (sCMOS) [59–62]. Different detector technologies have advantages/disadvantages, and there are several criteria used to evaluate them such as quan-

This section provides an overview of basic photodetector technologies with an emphasis on detectors commonly used in SRM & FFTs (e.g. APDs, CCD cameras, sCMOS). For an in-depth technical explanation of photodetectors the reader is referred to the textbook Optical Systems

The working principle of PMT is based on the photoelectric effect. PMTs are composed of vacuum tubes consisting of a cathode, multiple dynodes, and an anode (**Figure 2**). Incident photons are absorbed by the cathode ejecting primary electrons (~3 eV) that are accelerated by an electrostatic field toward a series of dynodes. Additional numbers of electrons are ejected (5–10 electrons) because each subsequent dynode is held at a more positive voltage potential (~100 eV), leading to an amplification of the signal. The electron current is then detected by an external electrical circuit. PMTs usually have 10–14 dynodes with a cathode-to-anode voltage gap of ~1 kV and current gain

tum efficiency (QE), and dark current levels. Cathodes made of multi-alkali semiconductor mate-

**Dark Count (e−/pixel/sec)**

R10699 PMT 20 2# 0 1.3 x 107

Hybrid detector 10 to 40 0–500 0 100,000 CCD 40 to 95 0.0002 to 0.001 8 to 12 4 EMCCD >90 0.0001 to 0.07 40 to 65 10,000 sCMOS 55 to 80 0.01 to 2 5 to 25 1

H33D Gen I\* 4.5 <1\*\* 0 10,000,000 H33D Gen IIB\* 30.9 <15\*\* 0 1,000,000

**Table 1.** Typical characteristics of detectors used in single molecule fluorescence experiments.

. The composition of the photocathode determines the PMTs spectral response, quan-

75 3.0 to 9.2 5 to 20 105

**Readout Noise (e- pixels/rms)**

**Detector Gain**

to 10<sup>6</sup>

tum efficiency (QE), readout noise, dark current levels, and SNR (**Table 1**).

Engineering by Kasunic [63].

of 10<sup>6</sup>

mode)

#

to 10<sup>8</sup>

silicon APD (Geiger

\*not commercially available. \*\*kHz across total surface.

**5.2. Photomultiplier tubes (PMTs)**

268 Photon Counting - Fundamentals and Applications

**Detector Type QE @ 600 nm** 

**(%)**

after 30 minutes' storage in dark & supply voltage 1 x 10^6.

Avalanche photodiodes (APDs) can be thought of as solid-state versions of PMTs with the exception that there is no photocathode and thus utilize primary photoelectrons more efficiently than PMTs. APDs are composed of three semiconductor layers called p-layer, i-layer, and n-layer (a.k.a *p-i-n*, **Figure 2**). The n-layer has extra electrons: whereas the p-layer is electron poor and has "holes". APDs are like *p-i-n* photodetectors except there is an extra p-layer between the i-layer and n-layer to create a *p-i-p-n* orientation where the second p-layer is much thicker than the other layers. A negative voltage (reverse bias) is applied across the junction, and absorption of a photon creates an electron–hole pair that is accelerated through the thicker p-layer (acts as gain layer) where conductive electrons impact non-conductive electrons thus making them conductive. This impact ionization can cascade as occurs with PMTs, leading to a large amplification of electrons called an avalanche. Average gain for APDs is 100 times, which is too low for single-photon detection, but operation of the APD in Geiger mode above breakdown voltage allows for single photon avalanche detection (SPAD) [65, 66]. The i-layer in APDs allows for better photon absorption, shorter photoelectron diffusion time, lower capacitance, and faster response. Researchers have wanted the sensitivity of APDs combined with the gain and dynamic range of PMTs. This motivation led to the development of hybrid photodetectors.

element is composed of a metal oxide semiconductor (MOS) capacitor consisting of surface gate electrode, aluminum or polysilicon, deposited on top of charge carrying channels that are insulated by silicon dioxide (**Figure 2**). Incident photons "strike" individual MOS elements generating electron–hole pairs, leading to an accumulation of charge in the potential well below the MOS. Application of an external voltage controls the movement and release of the built-up charge in each photosensitive element or pixel. The architecture of the electrodes required for the charge readout acts as a shift register for charge transfer, and there are several different register types employed in CCDs [63, 71]. The three main CCD types are full-frame, frame-transfer, and interline transfer architectures. Full-frame CCDs utilize the entire sensor array for light collection providing maximum efficiency. However, a mechanical shutter is needed to stop exposure and allow transfer of the built-up charge limiting image acquisition rates. Frame-transfer CCDs have half of the photosensitive surface covered by an opaque mask that acts as a photoelectron storage space. Charge is transferred to the masked area allowing the next exposure to commence while the first is being processed. This setup allows for faster frame rates but half the sensor is not available for image acquisition meaning

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Interline CCDs have alternating rows of pixels that are masked with an opaque material (e.g. aluminum) thus allowing acquisition and charge transfer to occur simultaneously. The charge in the unmasked rows is transferred to the masked row allowing for a second round of exposure during readout of the previous first exposure. This dramatically speeds up acquisition rates at the expense of reduced sensor surface. The addition of micro-lenses to the interline CCD design focuses more light onto the pixels increasing efficiency, from 50–75%, of collected light [72, 73]. A second added benefit of using micro-lenses is that it extends the spectral sensitivity of the CCD into the blue and UV light range that is ideal for imaging with GFP. One of the most effective strategies to increase QE is back illumination of the sensor where the wiring is behind the photocathode layer leading to less light scattering and up to 90% QE [74]. For all three architectures (full-frame, frame-rate, interline) the charge readout is fed into a CCD output amplifier, and then an analogue-to-digital converter (ADC). The stored charge in each sensor pixel is linearly proportional to the light absorbed up to the full well capacity (FWC). The FWC determines the maximum signal a pixel can record and is a major factor affecting a CCDs dynamic range. Traditionally, CCDs were composed of square sensor pixels arranged in a rectangular pattern with a 4:3 aspect ratio. Common CCD image sensors range in size from 6 to 16 mm (diagonal measurement). Many current scientific grade CCD cameras employ square sensor arrays to better match the microscope field of view (**Table 2**). Addition of an electron multiplication register between the shift register and output amplifier can increase the signal from the image sensor, and are called electron multiplication chargecoupled device (EMCCD). This modification of the CCD improves the SNR and increases the QE to 95%, or greater in most cases (**Figure 2**). EMCCD cameras have replaced CCD cameras for many imaging applications, including SRM (**Table 3**). Drawbacks to the electron multiplication are gain instability, performance decay with age, and potentially increased dark current [75]. Finally, an intensifying screen can be put in front of the CCD sensor (ICCD) to increase sensitivity to single-photon detection [76]. The intensifying screen is composed of a photocathode, micro-channel plate (MCP), and a phosphor screen (**Figure 2**). The photons

a larger chip is needed compared to a full-frame, thus adding to cost.

#### **5.4. Hybrid photodetectors**

Hybrid photodetectors are a combination of PMT-APD technologies and were developed in the 1990s for high energy physics experiments [67, 68]. Hybrid detectors have a photocathode, electron multiplication component, and output terminal housed inside a vacuum tube. The difference from PMTs is the electron multiplication is performed by a silicon avalanche diode (AD), instead of dynodes. The silicon diode contains a thin p-layer facing the photocathode followed by a thicker middle silicon layer and finally a p-n junction that is attached to the bias terminal. Photoelectrons ejected from the cathode are accelerated toward the AD by a very large voltage difference compared to PMTs (~8 kV). The electrons are multiplied in the AD first through electron bombardment and then by avalanche gain. Total gain can be greater than 100 times that is considerably lower than PMTs, but hybrid detectors have other benefits that make-up for the low gain. Hybrid detectors have better SNR compared to PMTs because the first gain step can be up to 1000 times (one photoelectron yields 1000 secondary electrons). This higher SNR allows for discrimination between one photon and multiple photons. The response time is faster for hybrid detectors compared to PMTs, and there is virtually no after-pulsing (false detection of photon). Hybrid detectors are well suited for fluorescence applications where after-pulsing can cause artifacts such as in time correlated single photon counting (TCSPC) and FCS. Hybrid detectors outperform SPADs and PMTs for FCS and other single-molecule fluorescence experiments [69]. Unfortunately, these point-like detectors discussed above are inefficient for imaging large regions. However, arrays of photodetectors are inherently suited to large field imaging. One popular array photodetector is the charged coupled device (CCD).

#### **5.5. Charged coupled devices (CCDs)**

Charge-coupled device (CCD) cameras have completely replaced photographic film cameras for scientific experiments, and are routinely used for microscopic imaging. A CCD is an array of photosensitive elements attached to a silica semiconductor substrate [62, 70]. Each element is composed of a metal oxide semiconductor (MOS) capacitor consisting of surface gate electrode, aluminum or polysilicon, deposited on top of charge carrying channels that are insulated by silicon dioxide (**Figure 2**). Incident photons "strike" individual MOS elements generating electron–hole pairs, leading to an accumulation of charge in the potential well below the MOS. Application of an external voltage controls the movement and release of the built-up charge in each photosensitive element or pixel. The architecture of the electrodes required for the charge readout acts as a shift register for charge transfer, and there are several different register types employed in CCDs [63, 71]. The three main CCD types are full-frame, frame-transfer, and interline transfer architectures. Full-frame CCDs utilize the entire sensor array for light collection providing maximum efficiency. However, a mechanical shutter is needed to stop exposure and allow transfer of the built-up charge limiting image acquisition rates. Frame-transfer CCDs have half of the photosensitive surface covered by an opaque mask that acts as a photoelectron storage space. Charge is transferred to the masked area allowing the next exposure to commence while the first is being processed. This setup allows for faster frame rates but half the sensor is not available for image acquisition meaning a larger chip is needed compared to a full-frame, thus adding to cost.

and n-layer (a.k.a *p-i-n*, **Figure 2**). The n-layer has extra electrons: whereas the p-layer is electron poor and has "holes". APDs are like *p-i-n* photodetectors except there is an extra p-layer between the i-layer and n-layer to create a *p-i-p-n* orientation where the second p-layer is much thicker than the other layers. A negative voltage (reverse bias) is applied across the junction, and absorption of a photon creates an electron–hole pair that is accelerated through the thicker p-layer (acts as gain layer) where conductive electrons impact non-conductive electrons thus making them conductive. This impact ionization can cascade as occurs with PMTs, leading to a large amplification of electrons called an avalanche. Average gain for APDs is 100 times, which is too low for single-photon detection, but operation of the APD in Geiger mode above breakdown voltage allows for single photon avalanche detection (SPAD) [65, 66]. The i-layer in APDs allows for better photon absorption, shorter photoelectron diffusion time, lower capacitance, and faster response. Researchers have wanted the sensitivity of APDs combined with the gain and dynamic range of PMTs. This motivation led to the development

Hybrid photodetectors are a combination of PMT-APD technologies and were developed in the 1990s for high energy physics experiments [67, 68]. Hybrid detectors have a photocathode, electron multiplication component, and output terminal housed inside a vacuum tube. The difference from PMTs is the electron multiplication is performed by a silicon avalanche diode (AD), instead of dynodes. The silicon diode contains a thin p-layer facing the photocathode followed by a thicker middle silicon layer and finally a p-n junction that is attached to the bias terminal. Photoelectrons ejected from the cathode are accelerated toward the AD by a very large voltage difference compared to PMTs (~8 kV). The electrons are multiplied in the AD first through electron bombardment and then by avalanche gain. Total gain can be greater than 100 times that is considerably lower than PMTs, but hybrid detectors have other benefits that make-up for the low gain. Hybrid detectors have better SNR compared to PMTs because the first gain step can be up to 1000 times (one photoelectron yields 1000 secondary electrons). This higher SNR allows for discrimination between one photon and multiple photons. The response time is faster for hybrid detectors compared to PMTs, and there is virtually no after-pulsing (false detection of photon). Hybrid detectors are well suited for fluorescence applications where after-pulsing can cause artifacts such as in time correlated single photon counting (TCSPC) and FCS. Hybrid detectors outperform SPADs and PMTs for FCS and other single-molecule fluorescence experiments [69]. Unfortunately, these point-like detectors discussed above are inefficient for imaging large regions. However, arrays of photodetectors are inherently suited to large field imaging. One popular array photodetector is the charged

Charge-coupled device (CCD) cameras have completely replaced photographic film cameras for scientific experiments, and are routinely used for microscopic imaging. A CCD is an array of photosensitive elements attached to a silica semiconductor substrate [62, 70]. Each

of hybrid photodetectors.

270 Photon Counting - Fundamentals and Applications

**5.4. Hybrid photodetectors**

coupled device (CCD).

**5.5. Charged coupled devices (CCDs)**

Interline CCDs have alternating rows of pixels that are masked with an opaque material (e.g. aluminum) thus allowing acquisition and charge transfer to occur simultaneously. The charge in the unmasked rows is transferred to the masked row allowing for a second round of exposure during readout of the previous first exposure. This dramatically speeds up acquisition rates at the expense of reduced sensor surface. The addition of micro-lenses to the interline CCD design focuses more light onto the pixels increasing efficiency, from 50–75%, of collected light [72, 73]. A second added benefit of using micro-lenses is that it extends the spectral sensitivity of the CCD into the blue and UV light range that is ideal for imaging with GFP. One of the most effective strategies to increase QE is back illumination of the sensor where the wiring is behind the photocathode layer leading to less light scattering and up to 90% QE [74].

For all three architectures (full-frame, frame-rate, interline) the charge readout is fed into a CCD output amplifier, and then an analogue-to-digital converter (ADC). The stored charge in each sensor pixel is linearly proportional to the light absorbed up to the full well capacity (FWC). The FWC determines the maximum signal a pixel can record and is a major factor affecting a CCDs dynamic range. Traditionally, CCDs were composed of square sensor pixels arranged in a rectangular pattern with a 4:3 aspect ratio. Common CCD image sensors range in size from 6 to 16 mm (diagonal measurement). Many current scientific grade CCD cameras employ square sensor arrays to better match the microscope field of view (**Table 2**). Addition of an electron multiplication register between the shift register and output amplifier can increase the signal from the image sensor, and are called electron multiplication chargecoupled device (EMCCD). This modification of the CCD improves the SNR and increases the QE to 95%, or greater in most cases (**Figure 2**). EMCCD cameras have replaced CCD cameras for many imaging applications, including SRM (**Table 3**). Drawbacks to the electron multiplication are gain instability, performance decay with age, and potentially increased dark current [75]. Finally, an intensifying screen can be put in front of the CCD sensor (ICCD) to increase sensitivity to single-photon detection [76]. The intensifying screen is composed of a photocathode, micro-channel plate (MCP), and a phosphor screen (**Figure 2**). The photons strike the photocathode generating photoelectrons that are amplified by the MCP, a plate with angled tubes that creates a "shower" of electrons like dynodes in PMT. Secondary electrons from the MCP strike the phosphor screen creating photons that are read by the CCD sensor. Importantly, ICCD sensors provide gate-ability (100's of picosecond temporal resolution) in addition to enhanced sensitivity over EMCCDs [77]. A drawback to ICCD and EMCCD cameras are their cost (~\$30,000–40,000), but advances in semi-conductor fabrication have led to smaller and more cost-effective photodetectors, such as complementary metal oxide semiconductors (CMOS).

#### **5.6. Complementary metal oxide semiconductors (CMOS)**

CMOS sensors, like CCDs, are arrays of photosensitive pixels, but are smaller in size due to advanced manufacturing techniques [78]. Specifically, the readout and processing circuitry are miniaturized, and incorporated into each pixel creating an "active-pixel sensor" (APS). The miniaturized transistors are fabricated using a complementary MOS (CMOS) technology (**Figure 2**). This miniaturization comes with a price, most notably higher noise compared to CCDs and the APS takes up pixel area affecting light absorption. An advantage of CMOS is that each pixel can be read out randomly and no electrons are lost by charge transfer across a row. CMOS chips consume less power and are more suitable for low-price products like cell phone cameras. The lower performance of early generation CMOS sensors, compared to CCDs, prohibited their use for scientific applications. Recently, higher performance CMOS sensors have been fabricated and are called scientific grade CMOS (sCMOS) [79]. These new sCMOS sensors were introduced in 2010 and were marketed as low noise, high QE (55–70%), and fast frame rate (>100 fps) cameras (**Table 4**). There is improved image resolution due to the smaller pixel size of sCMOS sensors compared to EMCCDs. However, distortions can occur in images due to rolling shutters that are used (i.e. each row captured at different time). The sCMOS camera noise is not Gaussian distributed and the improved resolution is at the expense of decreased sensitivity compared to EMCCDs [80]. Which camera technology (EMCCD, or sCMOS) is best suited for low-light microscopy experiments?

A 2012 application note by the camera manufacturer ANDOR found no performance difference between the tested EMCCD (ANDOR iXon3) and sCMOS (ANDOR Neo) cameras using a spinning disk confocal setup where the emission light from the sample was equally split between the two cameras [81]. However, the authors are quite right to raise the important caveat that the samples imaged contained near-perfect labeling, and were fluorescently very bright and stable. These artificial conditions are near ideal and do not reflect the typical 3D spinning disk imaging experiments. Typical signals are much lower and on the order of 0–20 photons/pixel [82]. The authors agree with other researchers in the field that there exists a SNR cross-over point where the sCMOS will outperform the EMCCD [83]. In this experiment, the cross-over point was somewhere between 40 and 100 photons/pixel. A second study found the cross-over point to be greater than 4 photons/pixel or 180 photons/pixel when comparing the ANDOR iXon DU897BV to the Hamamatsu ORCA-Flash 4.0, or ORCA-Flash 2.8 sCMOS cameras, respectively [83]. Image artifacts (streaking lines) were seen at specific illumination

> **Dark Current (e−/pixel/sec)**

Zyla 4.2 Plus Andor >80 0.019\* 1.1 @ 216 MHz 6.5 x 6.5 2048 x

Photometrics 72 0.01\* 1.3 6.5 x 6.5 2048 x

Qimaging 55 0.5 1.9 6.5 x 6.5 1920 x

Princeton Sci. 95 1.9 (−10 C) 1.5 11 x 11 1200 x

**Readout Noise (e- pixels/rms)**

**Resolution (um-pixels)** **Imaging array**

2048

1080

1200

2048

**Dark Current (e−/pixel/sec)**

Princeton Sci. ~95 0.002 26 @ 10 MHz\* 10 x 10 1024 x

Evolve 128 Photometrics >92 0.0069 46 @ 10 MHz 24 x 24 128 x 128 Evolve 512 Photometrics >95 0.003 65 @ 10 MHz\* 16 x 16 512 x 512

iXON Ultra 888 Andor >95 0.00011\*\* 40 @ 10 MHz\* 13 x 13 1024 x

**Readout Noise (e- pixels/rms)**

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**Resolution (um-pixels)**

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**Imaging array**

273

1024

1024

intensities for the sCMOS but not the EMCCD cameras.

**Table 4.** Examples of commercially available sCMOS cameras.

**600 nm (%)**

**Model Manufacturer QE @** 

Prime sCMOS

Optimos sCMOS

KURO sCMOS

\*water cooled.

**Table 3.** Examples of commerically available EMCCD cameras.

**Model Manufacturer QE @** 

ProEM-HS:1KBX3

\*with EM gain <1. \*\*at max cooling.

**600 nm (%)**


**Table 2.** Examples of commercially available CCD cameras.


**Table 3.** Examples of commerically available EMCCD cameras.

strike the photocathode generating photoelectrons that are amplified by the MCP, a plate with angled tubes that creates a "shower" of electrons like dynodes in PMT. Secondary electrons from the MCP strike the phosphor screen creating photons that are read by the CCD sensor. Importantly, ICCD sensors provide gate-ability (100's of picosecond temporal resolution) in addition to enhanced sensitivity over EMCCDs [77]. A drawback to ICCD and EMCCD cameras are their cost (~\$30,000–40,000), but advances in semi-conductor fabrication have led to smaller and more cost-effective photodetectors, such as complementary metal

CMOS sensors, like CCDs, are arrays of photosensitive pixels, but are smaller in size due to advanced manufacturing techniques [78]. Specifically, the readout and processing circuitry are miniaturized, and incorporated into each pixel creating an "active-pixel sensor" (APS). The miniaturized transistors are fabricated using a complementary MOS (CMOS) technology (**Figure 2**). This miniaturization comes with a price, most notably higher noise compared to CCDs and the APS takes up pixel area affecting light absorption. An advantage of CMOS is that each pixel can be read out randomly and no electrons are lost by charge transfer across a row. CMOS chips consume less power and are more suitable for low-price products like cell phone cameras. The lower performance of early generation CMOS sensors, compared to CCDs, prohibited their use for scientific applications. Recently, higher performance CMOS sensors have been fabricated and are called scientific grade CMOS (sCMOS) [79]. These new sCMOS sensors were introduced in 2010 and were marketed as low noise, high QE (55–70%), and fast frame rate (>100 fps) cameras (**Table 4**). There is improved image resolution due to the smaller pixel size of sCMOS sensors compared to EMCCDs. However, distortions can occur in images due to rolling shutters that are used (i.e. each row captured at different time). The sCMOS camera noise is not Gaussian distributed and the improved resolution is at the expense of decreased sensitivity compared to EMCCDs [80]. Which camera technology

oxide semiconductors (CMOS).

272 Photon Counting - Fundamentals and Applications

**Model Manufacturer QE @** 

**Table 2.** Examples of commercially available CCD cameras.

Cool-SNAP DYNO

SOPHIA 2048B

**5.6. Complementary metal oxide semiconductors (CMOS)**

(EMCCD, or sCMOS) is best suited for low-light microscopy experiments?

**Dark Current (e−/pixel/sec)**

Retiga R1 Qimaging 75 0.001 <5.5 6.45 x 6.45 1360 x

Clara Andor >40 0.0003 5 @ 10 MHz\* 6.45 x 6.45 1392 x

Photometrics 75 0.0006 5.2 4.54 x 4.54 1940 x

Princeton Sci. >95 0.00025 22 @ 4 MHz 15 x 15 2048 x

**Readout Noise (e- pixels/rms)**

**Resolution (um-pixels)** **Imaging Array**

1460

1024

2048

1040

**600 nm (%)**

A 2012 application note by the camera manufacturer ANDOR found no performance difference between the tested EMCCD (ANDOR iXon3) and sCMOS (ANDOR Neo) cameras using a spinning disk confocal setup where the emission light from the sample was equally split between the two cameras [81]. However, the authors are quite right to raise the important caveat that the samples imaged contained near-perfect labeling, and were fluorescently very bright and stable. These artificial conditions are near ideal and do not reflect the typical 3D spinning disk imaging experiments. Typical signals are much lower and on the order of 0–20 photons/pixel [82]. The authors agree with other researchers in the field that there exists a SNR cross-over point where the sCMOS will outperform the EMCCD [83]. In this experiment, the cross-over point was somewhere between 40 and 100 photons/pixel. A second study found the cross-over point to be greater than 4 photons/pixel or 180 photons/pixel when comparing the ANDOR iXon DU897BV to the Hamamatsu ORCA-Flash 4.0, or ORCA-Flash 2.8 sCMOS cameras, respectively [83]. Image artifacts (streaking lines) were seen at specific illumination intensities for the sCMOS but not the EMCCD cameras.


**Table 4.** Examples of commercially available sCMOS cameras.

The brightness of the fluorescent sample is highly dependent on the microscope setup. Therefore, the SNR cross-over point must be determined empirically for each experimental situation (typical ranges observed are ~4–200 photon/pixel). EMCCD cameras have inherent excess noise due to the amplification process that contributes to about 50% of the total noise. This prevents manufacture of a shot-noise limited EMCCD detector [83]. In contrast, sCMOS camera performance (increased QE and reduced noise) can be improved through hardware and software optimization [78]. It is predicted that sCMOS would be the camera of choice when greater than 50 photons/pixel is reached [84]. Currently, EMCCDs are better suited to measure small fluorescence changes in live cells with high spatial resolution compared to sCMOS [85]. In addition, EMCCDs superior imaging capability for low light samples outweigh the benefits of sCMOS for spinning disk confocal microscopy at this time [81]. This hotly debated comparison between EMCCD and sCMOS camera performance is not expected to slow down soon. In fact, implementation of in-camera signal-processing algorithms are being introduced to enhance both EMCCD and sCMOS camera technologies (see Section 5.7), and could re-ignite the debate.

have a QE of 40–60% [93, 94]. Recently, a parallelization of RE-SPADs has been fabricated and tested [94]. A new type of SPAD architecture has been implemented to enhance the electrical isolation between individual SPAD elements to reduce crosstalk [94]. These RE-SPAD array detectors are still in their infancy and further characterization of detector parameters (temporal resolution, dark count rate, after-pulsing, etc.) is necessary before commercialization and

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SPADs have sub-nanosecond time resolution but are inefficient imaging detectors because scanning is required for image formation. In contrast, wide-field detectors that have picosecond response, such as time-gated ICCDs, are photon-inefficient due to the lens coupling of the intensifier screen to the CCD sensor. Recently, several research groups have developed single-photon wide-field detectors with high temporal and spatial resolutions thus attempting to combine the best attributes of both SPADs and ICCDs [86, 90, 95]. One such detector called H33D (pronounced "HEED") is composed of S20 multi-alkali cathode, triple MCP stack, and cross-delay line anode [95]. A front-end field programmable gate array (FGPA) is used for time-stamping and synchronization of photon events. The H33D detector has 18% QE at 400 nm diminishing to 3% at 630 nm. Temporal resolution was 100 picosecond FWHM using a red diode laser. Fluorescence lifetimes of dyes in solution, colloidal quantum dots and quantum dot labeled receptors on the surface of cells have been measured using this high temporal and spatial single photon detector [95–97]. Several other groups have fabricated

There are multiple sources of noise (dark, read, shot) that affect the SNR collected by the detector. Cooling of the detector sensor can reduce dark noise and read noise can be minimized through thoughtful electronic design and sensor performance optimization. Photon shot noise is an intrinsic property of light and has a Poisson statistical distribution. Shot noise varies with the square root of the signal (shot noise = √signal) and thus increasing light levels leads to improving SNR. However, the low light levels encountered with SRM and FFTs measurements can lead to shot noise dominating the signal. Increased exposure time, frame averaging, and increased excitation light intensity have been used to circumvent this low SNR problem. A fourth method that is more compatible with SRM is the use of de-noising algorithms to dynamically analysis the acquired image and filter out the noise. Spatial and temporal filtering of images and video has been extensively studied [99–104]. One very popular spatial filter is the averaging-based non-local means (NLM) filter [99]. It is important to choose an appropriate algorithm and parameter settings that do not introduce artifacts (e.g.

Manufacturers are starting to add real-time de-noising algorithms to the firmware of their cameras for enhanced SNR and improved performance. The Prime™ family of cameras from Photometrics employs an algorithm developed at INRIA (NLM with patch based refinement) and optimized at the Curie Institute [101]. This algorithm called Prime-Enhance allows up to an 8-fold decreased exposure time while retaining a high SNR (due to reduction of photon shot noise effects) thus leading to reduced photo-toxicity in live cell experiments [101, 105]. The Prime-Enhance algorithm is also purported to not introduce common processing artifacts

large-area photon counting detectors with a similar architecture [98].

mass production are a reality.

aliasing, blurring or ringing).

such as aliasing, blurring or ringing.

#### **5.7. Next generation photodetectors**

Technological advances have brought the performance of EMCCD and CMOS cameras closer to point-like detectors such as APDs and Hybrid detectors. The sensitivity and readout noise are still generally better for point-like detectors, but these types of detectors cannot "count" photon numbers unless external electronics and software are used to bin the photons (~1 nano-sec to 10 msec). Importantly, there are inherent throughput limitations for point-like detectors in contrast to array detectors. One solution around this problem is parallelization of the point-like detector (i.e. an array of point-like detectors). Factors that must be considered for parallelization include: parallel excitation, parallel detection, excitation and detection alignment, and data processing [86]. Each light source must be sufficiently separate to prevent crosstalk during multiple-spot excitation and the spot separation must be a few diameters apart as a general rule of thumb [87]. An eight pixel custom linear SPAD array and a 32 x 32 CMOS SPAD array were used recently to perform parallel FCS measurements on a fluorescent dye in solution with quasi-diffraction limited spots [88–90]. Custom liquid crystal light modulators, or micro-lens are required to direct and separate the multiple PSFs. These results with highly-parallel arrays are encouraging but these parallelized detectors have lower sensitivity and larger dark counts thus leading to higher background counts for FCS measurements compared to individual detectors [90]. Recently, a frame summing/filtering scheme called "smart-aggregation" was introduced to increase SPAD array performance [91]. This approach promises to "push" SPAD camera performance beyond EMCCD and CMOS cameras, but significant technological advances are still required.

Bright photo-stable dyes that emit in the red and near-infrared range of the visible spectrum are commonly used in imaging studies involving animals. This is due to their favorable excitation using two-photon light sources and reduced scattering of emitted light in thick animal tissues [92]. The wide use of these dyes has led to the development of red enhanced SPAD (RE-SPAD). Traditional SPADs have a QE of 15% at 800 nm wavelengths but newer RE-SPADs have a QE of 40–60% [93, 94]. Recently, a parallelization of RE-SPADs has been fabricated and tested [94]. A new type of SPAD architecture has been implemented to enhance the electrical isolation between individual SPAD elements to reduce crosstalk [94]. These RE-SPAD array detectors are still in their infancy and further characterization of detector parameters (temporal resolution, dark count rate, after-pulsing, etc.) is necessary before commercialization and mass production are a reality.

The brightness of the fluorescent sample is highly dependent on the microscope setup. Therefore, the SNR cross-over point must be determined empirically for each experimental situation (typical ranges observed are ~4–200 photon/pixel). EMCCD cameras have inherent excess noise due to the amplification process that contributes to about 50% of the total noise. This prevents manufacture of a shot-noise limited EMCCD detector [83]. In contrast, sCMOS camera performance (increased QE and reduced noise) can be improved through hardware and software optimization [78]. It is predicted that sCMOS would be the camera of choice when greater than 50 photons/pixel is reached [84]. Currently, EMCCDs are better suited to measure small fluorescence changes in live cells with high spatial resolution compared to sCMOS [85]. In addition, EMCCDs superior imaging capability for low light samples outweigh the benefits of sCMOS for spinning disk confocal microscopy at this time [81]. This hotly debated comparison between EMCCD and sCMOS camera performance is not expected to slow down soon. In fact, implementation of in-camera signal-processing algorithms are being introduced to enhance both EMCCD and sCMOS camera technologies (see Section 5.7),

Technological advances have brought the performance of EMCCD and CMOS cameras closer to point-like detectors such as APDs and Hybrid detectors. The sensitivity and readout noise are still generally better for point-like detectors, but these types of detectors cannot "count" photon numbers unless external electronics and software are used to bin the photons (~1 nano-sec to 10 msec). Importantly, there are inherent throughput limitations for point-like detectors in contrast to array detectors. One solution around this problem is parallelization of the point-like detector (i.e. an array of point-like detectors). Factors that must be considered for parallelization include: parallel excitation, parallel detection, excitation and detection alignment, and data processing [86]. Each light source must be sufficiently separate to prevent crosstalk during multiple-spot excitation and the spot separation must be a few diameters apart as a general rule of thumb [87]. An eight pixel custom linear SPAD array and a 32 x 32 CMOS SPAD array were used recently to perform parallel FCS measurements on a fluorescent dye in solution with quasi-diffraction limited spots [88–90]. Custom liquid crystal light modulators, or micro-lens are required to direct and separate the multiple PSFs. These results with highly-parallel arrays are encouraging but these parallelized detectors have lower sensitivity and larger dark counts thus leading to higher background counts for FCS measurements compared to individual detectors [90]. Recently, a frame summing/filtering scheme called "smart-aggregation" was introduced to increase SPAD array performance [91]. This approach promises to "push" SPAD camera performance beyond EMCCD and CMOS cam-

Bright photo-stable dyes that emit in the red and near-infrared range of the visible spectrum are commonly used in imaging studies involving animals. This is due to their favorable excitation using two-photon light sources and reduced scattering of emitted light in thick animal tissues [92]. The wide use of these dyes has led to the development of red enhanced SPAD (RE-SPAD). Traditional SPADs have a QE of 15% at 800 nm wavelengths but newer RE-SPADs

and could re-ignite the debate.

274 Photon Counting - Fundamentals and Applications

**5.7. Next generation photodetectors**

eras, but significant technological advances are still required.

SPADs have sub-nanosecond time resolution but are inefficient imaging detectors because scanning is required for image formation. In contrast, wide-field detectors that have picosecond response, such as time-gated ICCDs, are photon-inefficient due to the lens coupling of the intensifier screen to the CCD sensor. Recently, several research groups have developed single-photon wide-field detectors with high temporal and spatial resolutions thus attempting to combine the best attributes of both SPADs and ICCDs [86, 90, 95]. One such detector called H33D (pronounced "HEED") is composed of S20 multi-alkali cathode, triple MCP stack, and cross-delay line anode [95]. A front-end field programmable gate array (FGPA) is used for time-stamping and synchronization of photon events. The H33D detector has 18% QE at 400 nm diminishing to 3% at 630 nm. Temporal resolution was 100 picosecond FWHM using a red diode laser. Fluorescence lifetimes of dyes in solution, colloidal quantum dots and quantum dot labeled receptors on the surface of cells have been measured using this high temporal and spatial single photon detector [95–97]. Several other groups have fabricated large-area photon counting detectors with a similar architecture [98].

There are multiple sources of noise (dark, read, shot) that affect the SNR collected by the detector. Cooling of the detector sensor can reduce dark noise and read noise can be minimized through thoughtful electronic design and sensor performance optimization. Photon shot noise is an intrinsic property of light and has a Poisson statistical distribution. Shot noise varies with the square root of the signal (shot noise = √signal) and thus increasing light levels leads to improving SNR. However, the low light levels encountered with SRM and FFTs measurements can lead to shot noise dominating the signal. Increased exposure time, frame averaging, and increased excitation light intensity have been used to circumvent this low SNR problem. A fourth method that is more compatible with SRM is the use of de-noising algorithms to dynamically analysis the acquired image and filter out the noise. Spatial and temporal filtering of images and video has been extensively studied [99–104]. One very popular spatial filter is the averaging-based non-local means (NLM) filter [99]. It is important to choose an appropriate algorithm and parameter settings that do not introduce artifacts (e.g. aliasing, blurring or ringing).

Manufacturers are starting to add real-time de-noising algorithms to the firmware of their cameras for enhanced SNR and improved performance. The Prime™ family of cameras from Photometrics employs an algorithm developed at INRIA (NLM with patch based refinement) and optimized at the Curie Institute [101]. This algorithm called Prime-Enhance allows up to an 8-fold decreased exposure time while retaining a high SNR (due to reduction of photon shot noise effects) thus leading to reduced photo-toxicity in live cell experiments [101, 105]. The Prime-Enhance algorithm is also purported to not introduce common processing artifacts such as aliasing, blurring or ringing.

## **6. Examples of photodetector used in SRM & FFTs**

A popular photodetector for probe-based SRM is EMCCD cameras. These camera-types have been used to study membrane protein dynamics in plant cells, assembly of HIV virus particles, and viral protein receptor interactions just to name a few applications [106–109]. New sCMOS cameras are inherently faster than EMCCDs, allow high-throughput capabilities, and are starting to be used for some SRM applications [83, 98]. Currently, EMCCD cameras are employed for rapid high resolution live cell imaging [85]. While, sCMOS cameras are employed for slower cellular dynamic studies, or fixed cell super-resolution imaging [98]. For fluorescence fluctuation studies, APD and hybrid detectors are commonly employed because of their sensitivity, efficiency, and faster response compared to PMTs. Usually APDs are used for FCS and PCH measurements of fluorescently-labeled molecules in tissue culture cells. For example, oligomerization (protein–protein association) was shown to be important for the trafficking of a membrane receptor (p75) to the apical surface of epithelial cells [49]. These studies used APDs to make FCS, PCH, and N&B measurements of wildtype and mutant receptors. The wildtype receptor formed higher-order oligomers in the Golgi membrane and dimers at the plasma membrane (**Figure 3**). In contrast, the mutant proteins that could not traffic to the api-

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Hybrid detectors have been employed to measure the formation of lipid rafts in a cell model of Fabry's disease [110]. In Fabry's disease, lysosomal function is disrupted due to reduced activity of a specific enzyme (alpha-galactosidase A) that leads to accumulation of neutral glycosphingolipids such as globotriaosylceramide (Gb3). N&B analysis was performed on wildtype and alpha-galactosidase deficient cells, which act as a model for Fabry's disease. Antibodyinduced clustering of a model lipid raft protein was increased in the mutant compared to control cells [110]. These results suggested that accumulated Gb3 may alter lipid raft protein interactions in membranes of alpha-galactosidase deficient cells. These two examples are just a snippet of the many experiments that have employed a variety of low-light photodetectors.

In the past seventy-five years, advances in micro-circuitry and semiconductor materials have led to dramatic advances in photodetector QE, sensitivity, and resolution. EMCCD and sCMOS cameras are the detectors of choice for probe-based SRM. In contrast, APD and hybrid detectors are becoming more common for use in PSF-engineered SRM and FFTs. Manufacturing attempts to combine the best attributes of point-like detectors with array detectors (CMOS and SPAD arrays) has been met with moderate success. These parallelized photodetectors are still in their early stages and are not routinely used. Shot noise and read noise are a problem especially as pixel densities have increased in array detectors. De-noising algorithms are being used to combat shot noise and increase SNR for low-light applications. Finally, A detector suitable for SRM and single molecule fluorescence experiments must have high sensitivity, high temporal resolution, and low readout noise. The researcher should compare the SNR, dark count, read noise, and frame rate to determine which detector type best fits their experimental needs. No single photodetector technology is suitable for all techniques and some researchers choose to outfit their microscopes with multiple camera technologies (e.g. EMCCD and sCMOS) to allow greatest flexibility when imaging. Advances in hardware and software promises to enhance detector technologies and

push the boundaries of single-molecule detection even further in the coming years.

& Sciences Dean's Office at Western Carolina University.

I apologize to authors whose research I was unable to cite due to page constraints. My sincere thanks to Dr. Greg Adkison and Dr. Haibing Teng for proof-reading the manuscript. The author is grateful for start-up funds from the Department of Biology and the College of Arts

cal membrane did not form higher-order oligomers at the Golgi (**Figure 3**).

**7. Summary and concluding remarks**

**Acknowledgements**

**Figure 3.** N&B analysis reveals spatially heterogeneous clustering of the p75 receptor at the trans-Golgi network. A) *Left:* Fluorescence images of MDCK cells expressing wildtype p75 receptor and apical sorting mutant (Δ193/C257A/ G266I) at the trans-Golgi network (TGN). Cells co-expressing TGN marker GalT-mcherry. *Right:* Molecular brightness maps of inserts with scale equal to normalized brightness (× EGFP per pixel). B) Normalized brightness values (B values) for wildtype and mutant p75 in non-TGN, peripheral-TGN (TGN peri.), and central-TGN (TGB cent.) asterisk, p < 0.05 unpaired T-test. Figure reproduced with permission from *Mol. Biol. Cell* 24(12), (Jun 15, 2013) doi: 10.1091/mbc. E13-02-0078.

oligomerization (protein–protein association) was shown to be important for the trafficking of a membrane receptor (p75) to the apical surface of epithelial cells [49]. These studies used APDs to make FCS, PCH, and N&B measurements of wildtype and mutant receptors. The wildtype receptor formed higher-order oligomers in the Golgi membrane and dimers at the plasma membrane (**Figure 3**). In contrast, the mutant proteins that could not traffic to the apical membrane did not form higher-order oligomers at the Golgi (**Figure 3**).

Hybrid detectors have been employed to measure the formation of lipid rafts in a cell model of Fabry's disease [110]. In Fabry's disease, lysosomal function is disrupted due to reduced activity of a specific enzyme (alpha-galactosidase A) that leads to accumulation of neutral glycosphingolipids such as globotriaosylceramide (Gb3). N&B analysis was performed on wildtype and alpha-galactosidase deficient cells, which act as a model for Fabry's disease. Antibodyinduced clustering of a model lipid raft protein was increased in the mutant compared to control cells [110]. These results suggested that accumulated Gb3 may alter lipid raft protein interactions in membranes of alpha-galactosidase deficient cells. These two examples are just a snippet of the many experiments that have employed a variety of low-light photodetectors.

## **7. Summary and concluding remarks**

**6. Examples of photodetector used in SRM & FFTs**

276 Photon Counting - Fundamentals and Applications

A popular photodetector for probe-based SRM is EMCCD cameras. These camera-types have been used to study membrane protein dynamics in plant cells, assembly of HIV virus particles, and viral protein receptor interactions just to name a few applications [106–109]. New sCMOS cameras are inherently faster than EMCCDs, allow high-throughput capabilities, and are starting to be used for some SRM applications [83, 98]. Currently, EMCCD cameras are employed for rapid high resolution live cell imaging [85]. While, sCMOS cameras are employed for slower cellular dynamic studies, or fixed cell super-resolution imaging [98]. For fluorescence fluctuation studies, APD and hybrid detectors are commonly employed because of their sensitivity, efficiency, and faster response compared to PMTs. Usually APDs are used for FCS and PCH measurements of fluorescently-labeled molecules in tissue culture cells. For example,

**Figure 3.** N&B analysis reveals spatially heterogeneous clustering of the p75 receptor at the trans-Golgi network. A) *Left:* Fluorescence images of MDCK cells expressing wildtype p75 receptor and apical sorting mutant (Δ193/C257A/ G266I) at the trans-Golgi network (TGN). Cells co-expressing TGN marker GalT-mcherry. *Right:* Molecular brightness maps of inserts with scale equal to normalized brightness (× EGFP per pixel). B) Normalized brightness values (B values) for wildtype and mutant p75 in non-TGN, peripheral-TGN (TGN peri.), and central-TGN (TGB cent.) asterisk, p < 0.05 unpaired T-test. Figure reproduced with permission from *Mol. Biol. Cell* 24(12), (Jun 15, 2013) doi: 10.1091/mbc.

E13-02-0078.

In the past seventy-five years, advances in micro-circuitry and semiconductor materials have led to dramatic advances in photodetector QE, sensitivity, and resolution. EMCCD and sCMOS cameras are the detectors of choice for probe-based SRM. In contrast, APD and hybrid detectors are becoming more common for use in PSF-engineered SRM and FFTs. Manufacturing attempts to combine the best attributes of point-like detectors with array detectors (CMOS and SPAD arrays) has been met with moderate success. These parallelized photodetectors are still in their early stages and are not routinely used. Shot noise and read noise are a problem especially as pixel densities have increased in array detectors. De-noising algorithms are being used to combat shot noise and increase SNR for low-light applications. Finally, A detector suitable for SRM and single molecule fluorescence experiments must have high sensitivity, high temporal resolution, and low readout noise. The researcher should compare the SNR, dark count, read noise, and frame rate to determine which detector type best fits their experimental needs. No single photodetector technology is suitable for all techniques and some researchers choose to outfit their microscopes with multiple camera technologies (e.g. EMCCD and sCMOS) to allow greatest flexibility when imaging. Advances in hardware and software promises to enhance detector technologies and push the boundaries of single-molecule detection even further in the coming years.

## **Acknowledgements**

I apologize to authors whose research I was unable to cite due to page constraints. My sincere thanks to Dr. Greg Adkison and Dr. Haibing Teng for proof-reading the manuscript. The author is grateful for start-up funds from the Department of Biology and the College of Arts & Sciences Dean's Office at Western Carolina University.

## **Author details**

Robert T. Youker

Address all correspondence to: rtyouker@wcu.edu

Department of Biology, Western Carolina University, Cullowhee, North Carolina, USA

## **References**

[1] Chudakov DM, Matz MV, Lukyanov S, et al. Fluorescent proteins and their applications in imaging living cells and tissues. Physiological Reviews. 2010;**90**:1103-1163

[14] Benda A, Aitken H, Davies DS, et al. STED imaging of tau filaments in Alzheimer's dis-

Detectors for Super-Resolution & Single-Molecule Fluorescence Microscopies

http://dx.doi.org/10.5772/intechopen.71943

279

[15] Ishigaki M, Iketani M, Sugaya M, et al. STED super-resolution imaging of mitochondria

[16] Hell SW, Kroug M. Ground-state-depletion fluorscence microscopy: A concept for breaking the diffraction resolution limit. Applied Physics B: Lasers and Optics. 1995;**60**:495-497

[17] Halpern AR, Howard MD, Vaughan JC. Point by point: An introductory guide to sample preparation for single-molecule, super-resolution fluorescence microscopy: Sample preparation for single-molecule super-resolution fluorescence microscopy. In: Mahal L, Romesberg F, Shah K, et al., editors. Current Protocols in Chemical Biology. Hoboken, NJ,

[18] Hell SW, Stelzer EH, Lindek S, et al. Confocal microscopy with an increased detection

[19] Heintzmann R, Cremer CG. Laterally modulated excitation microscopy: improvement of resolution by using a diffraction grating. In: Bigio IJ, Schneckenburger H, Slavik J,

[20] Gustafsson MGL, Agard DA, Sedat JW. Sevenfold improvement of axial resolution in 3D wide-field microscopy using two objective lenses. In: Wilson T, Cogswell CJ (eds),

[21] Ward EN, Pal R. Image scanning microscopy: An overview. Journal of Microscopy. 2017;**266**:

[22] Rust MJ, Bates M, Zhuang X. Sub-diffraction-limit imaging by stochastic optical recon-

[23] Betzig E, Patterson GH, Sougrat R, et al. Imaging intracellular fluorescent proteins at

[24] Dempsey GT, Vaughan JC, Chen KH, et al. Evaluation of fluorophores for optimal performance in localization-based super-resolution imaging. Nature Methods. 2011;**8**:

[25] Winter FR, Loidolt M, Westphal V, et al. Multicolour nanoscopy of fixed and living cells with a single STED beam and hyperspectral detection. Scientific Reports. 2017;**7**:46492

[26] Shroff H, Galbraith CG, Galbraith JA, et al. Dual-color superresolution imaging of genetically expressed probes within individual adhesion complexes. Proceedings of the

[27] Juette MF, Gould TJ, Lessard MD, et al. Three-dimensional sub–100 nm resolution fluo-

[28] Huang B, Wang W, Bates M, et al. Three-dimensional super-resolution imaging by sto-

rescence microscopy of thick samples. Nature Methods. 2008;**5**:527-529

chastic optical reconstruction microscopy. Science. 2008;**319**:810-813

struction microscopy (STORM). Nature Methods. 2006;**3**:793-796

Nanometer resolution. Science. 2006;**313**:1642-1645

National Academy of Sciences. 2007;**104**:20308-20313

aperture: Type-B 4Pi confocal microscopy. Optics Letters. 1994;**19**:222

ease cortical grey matter. Journal of Structural Biology. 2016;**195**:345-352

labeled with TMRM in living cells. Mitochondrion. 2016;**28**:79-87

USA: John Wiley & Sons, Inc. pp. 103-120

et al. (eds), pp. 185-196

pp. 147-156

221-228

1027-1036


[14] Benda A, Aitken H, Davies DS, et al. STED imaging of tau filaments in Alzheimer's disease cortical grey matter. Journal of Structural Biology. 2016;**195**:345-352

**Author details**

278 Photon Counting - Fundamentals and Applications

Robert T. Youker

**References**

Magazine. 1874;**47**:81-93

of Biochemistry. 2009;**78**:993-1016

Nature Methods. 2007;**4**:915-918

scopy. Elsevier, pp. 199-212

2441-2450

Address all correspondence to: rtyouker@wcu.edu

Department of Biology, Western Carolina University, Cullowhee, North Carolina, USA

in imaging living cells and tissues. Physiological Reviews. 2010;**90**:1103-1163

Journal of the Royal Microscopical Society. 1903;**23**:474-482

microscopy. The Journal of Cell Biology. 2010;**190**:165-175

cation beyond the diffraction limit. Science. 1992;**257**:189-195

A historical perspective. Optical Nanoscopy. 2012;**1**:8

[1] Chudakov DM, Matz MV, Lukyanov S, et al. Fluorescent proteins and their applications

[2] Rayleigh L. On the theory of optical images, with special reference to the microscope.

[3] Rayleigh L XII. On the manufacture and theory of diffraction-gratings. Philosophical

[4] Schermelleh L, Heintzmann R, Leonhardt H. A guide to super-resolution fluorescence

[5] Lauterbach MA. Finding, defining and breaking the diffraction barrier in microscopy –

[6] Huang B, Bates M, Zhuang X. Super-Resolution Fluorescence Microscopy. Annual Review

[7] Betzig E, Trautman JK. Near-field optics: Microscopy, spectroscopy, and surface modifi-

[8] Hwang J, Tamm LK, Böhm C, et al. Nanoscale complexity of phospholipid monolayers investigated by near-field scanning optical microscopy. Science. 1995;**270**:610-614

[9] Willig KI, Harke B, Medda R, et al. STED microscopy with continuous wave beams.

[10] Hell SW, Wichmann J. Breaking the diffraction resolution limit by stimulated emission: Stimulated-emission-depletion fluorescence microscopy. Optics Letters. 1994;**19**:780-782

[11] Thorley JA, Pike J, Rappoport JZ. Super-resolution Microscopy. In: Fluorescence Micro-

[12] Beater S, Holzmeister P, Pibiri E, et al. Choosing dyes for cw-STED nanoscopy using selfassembled nanorulers. Physical Chemistry Chemical Physics. 2014;**16**:6990-6996

[13] Steshenko O, Andrade DM, Honigmann A, et al. Reorganization of lipid diffusion by myelin basic protein as revealed by STED Nanoscopy. Biophysical Journal. 2016;**110**:


[29] Wegel E, Göhler A, Lagerholm BC, et al. Imaging cellular structures in super-resolution with SIM, STED and Localisation Microscopy: A practical comparison. Scientific Reports; 6. Epub ahead of print July 2016. DOI: 10.1038/srep27290

[44] Hillesheim LN, Müller JD. The dual-color photon counting histogram with non-ideal

Detectors for Super-Resolution & Single-Molecule Fluorescence Microscopies

http://dx.doi.org/10.5772/intechopen.71943

281

[45] Chen Y, Müller JD, Ruan Q, et al. Molecular brightness characterization of EGFP in vivo by fluorescence fluctuation spectroscopy. Biophysical Journal. 2002;**82**:133-144

[46] Macdonald P, Johnson J, Smith E, et al. Brightness analysis. In: Methods in Enzymology.

[47] Dross N, Spriet C, Zwerger M, et al. Mapping eGFP oligomer mobility in living cell

[48] Digman MA, Dalal R, Horwitz AF, et al. Mapping the number of molecules and brightness in the laser scanning microscope. Biophysical Journal. 2008;**94**:2320-2332

[49] Youker RT, Bruns JR, Costa SA, et al. Multiple motifs regulate apical sorting of p75 via a mechanism that involves dimerization and higher-order oligomerization. Molecular

[50] James NG, Digman MA, Gratton E, et al. Number and brightness analysis of LRRK2

[51] Ross JA, Digman MA, Wang L, et al. Oligomerization state of Dynamin 2 in cell membranes using TIRF and number and brightness analysis. Biophysical Journal.

[52] Adu-Gyamfi E, Digman MA, Gratton E, et al. Investigation of Ebola VP40 assembly and Oligomerization in live cells using number and brightness analysis. Biophysical Journal.

[53] Jiang F, Doudna JA. CRISPR–Cas9 structures and mechanisms. Annual Review of

[54] Dobell C. A Collection of Writings by the Father of Protozoology and Bacteriology, Antony Van Leeuwenhoek and his 'Little Animals'. New York: Dover Publications; 1960

[55] Lubsandorzhiev BK. On the history of photomultiplier tube invention. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers,

[56] Bay Z. Electron multiplier as an electron counting device. Nature. 1938;**141**:1011-1011

[57] Zworykin VK, Morton GA, Malter L. The secondary emission multiplier-a new elec-

[58] Goetzberger A, McDonald B, Haitz RH, et al. Avalanche effects in silicon p—n junctions. II. Structurally perfect junctions. Journal of Applied Physics. 1963;**34**:1591-1600

[59] Boyle WS, Smith GE. Charge coupled semiconductor devices. Bell System Technical

Detectors and Associated Equipment. 2006;**567**:236-238

tronic device. Proceedings of the IRE. 1936;**24**:351-375

Oligomerization in live cells. Biophysical Journal. 2012;**102**:L41-L43

Photodetectors. Biophysical Journal. 2005;**89**:3491-3507

Elsevier; 2013. pp. 71-98

2011;**100**:L15-L17

2012;**102**:2517-2525

Biophysics. 2017;**46**:505-529

Journal. 1970;**49**:587-593

nuclei. PLoS One. 2009;**4**:e5041

Biology of the Cell. 2013;**24**:1996-2007


[44] Hillesheim LN, Müller JD. The dual-color photon counting histogram with non-ideal Photodetectors. Biophysical Journal. 2005;**89**:3491-3507

[29] Wegel E, Göhler A, Lagerholm BC, et al. Imaging cellular structures in super-resolution with SIM, STED and Localisation Microscopy: A practical comparison. Scientific Reports;

[30] Schmidt R, Wurm CA, Jakobs S, et al. Spherical nanosized focal spot unravels the inte-

[31] Shao L, Isaac B, Uzawa S, et al. I5S: Wide-field light microscopy with 100-nm-scale reso-

[32] Shtengel G, Galbraith JA, Galbraith CG, et al. Interferometric fluorescent super-resolution microscopy resolves 3D cellular ultrastructure. Proceedings of the National

[33] Youker RT, Teng H. Measuring protein dynamics in live cells: Protocols and practical considerations for fluorescence fluctuation microscopy. Journal of Biomedical Optics.

[34] Weidemann T, Mücksch J, Schwille P. Fluorescence fluctuation microscopy: A diversified arsenal of methods to investigate molecular dynamics inside cells. Current Opinion

[35] Elson EL, Webb WW. Concentration correlation spectroscopy: A new biophysical probe based on occupation number fluctuations. Annual Review of Biophysics and Bioengin-

[36] Magde D, Elson EL, Webb WW. Fluorescence correlation spectroscopy. II. An experi-

[37] Eigen M, Rigler R. Sorting single molecules: Application to diagnostics and evolutionary biotechnology. Proceedings of the National Academy of Sciences. 1994;**91**:5740-5747

[38] Schwille P, Meyer-Almes FJ, Rigler R. Dual-color fluorescence cross-correlation spectroscopy for multicomponent diffusional analysis in solution. Biophysical Journal. 1997;

[39] Digman MA, Sengupta P, Wiseman PW, et al. Fluctuation correlation spectroscopy with a laser-scanning microscope: Exploiting the hidden time structure. Biophysical Journal.

[40] Digman MA, Gratton E. Analysis of diffusion and binding in cells using the RICS

[41] Rossow MJ, Sasaki JM, Digman MA, et al. Raster image correlation spectroscopy in live

[42] Digman MA, Stakic M, Gratton E. Raster image correlation spectroscopy and number and brightness analysis. In: Methods in Enzymology. Elsevier; 2013. pp. 121-144 [43] Schwille P, Haupts U, Maiti S, et al. Molecular dynamics in living cells observed by fluorescence correlation spectroscopy with one- and two-photon excitation. Biophysical

approach. Microscopy Research and Technique. 2009;**72**:323-332

6. Epub ahead of print July 2016. DOI: 10.1038/srep27290

lution in three dimensions. Biophysical Journal. 2008;**94**:4971-4983

rior of cells. Nature Methods. 2008;**5**:539-544

280 Photon Counting - Fundamentals and Applications

Academy of Sciences. 2009;**106**:3125-3130

in Structural Biology. 2014;**28**:69-76

mental realization. Biopolymers. 1974;**13**:29-61

cells. Nature Protocols. 2010;**5**:1761-1774

Journal. 1999;**77**:2251-2265

2014;**19**:90801

eering. 1975;**4**:311-334

**72**:1878-1886

2005;**88**:L33-L36


[60] Amelio GF, Tompsett MF, Smith GE. Experimental verification of the charge coupled device concept. Bell System Technical Journal. 1970;**49**:593-600

[76] Schühle UDO. Intensified solid state sensor cameras (chapter 25). In: Observing Photons

Detectors for Super-Resolution & Single-Molecule Fluorescence Microscopies

http://dx.doi.org/10.5772/intechopen.71943

283

[77] Dussault D, Hoess P. Noise performance comparison of ICCD with CCD and EMCCD

[78] Bigas M, Cabruja E, Forest J, et al. Review of CMOS image sensors. Microelectronics

[79] Fowler B, Liu C, Mims S, et al. A 5.5Mpixel 100 frames/sec wide dynamic range low noise CMOS image sensor for scientific applications. In: Bodegom E, Nguyen V (eds),

[80] Jonkman J, Brown CM. Any way you slice it: A comparison of confocal microscopy tech-

[81] EMCCD vs sCMOS - Cameras For Spinning Disk Confocal Microscopy. Application Note, ANDOR Technology http://www.andor.com/learning-academy/emccd-vs-scmoscameras-for-spinning-disk-confocal-microscopy-application-note (accessed 15 August

[82] Pawley JB. Handbook of Biological Confocal Microscopy. Springer-Verlag US: Boston,

[83] Long F, Zeng S, Huang Z-L. Localization-based super-resolution microscopy with an sCMOS camera part II: Experimental methodology for comparing sCMOS with EMCCD

[84] Joubert JR, Sharma DK. Emccd vs. scmos for microscopic imaging. Photonics Spectra.

[85] Beier HT, Ibey BL. Experimental comparison of the high-speed imaging performance of an EM-CCD and sCMOS camera in a dynamic live-cell imaging test case. PLoS One.

[86] Michalet X, Colyer RA, Scalia G, et al. Development of new photon-counting detectors for single-molecule fluorescence microscopy. Philosophical Transactions of the Royal

[87] Michalet X, Colyer RA, Scalia G, et al. High-throughput single-molecule fluorescence spectroscopy using parallel detection. In: Razeghi M, Sudharsanan R, Brown GJ (eds),

[88] Colyer RA, Scalia G, Rech I, et al. High-throughput FCS using an LCOS spatial light modulator and an 8 × 1 SPAD array. Biomedical Optics Express. 2010;**1**:1408

[89] Colyer RA, Scalia G, Villa FA, et al. Ultra high-throughput single molecule spectroscopy with a 1024 pixel SPAD. In: Enderlein J, Gryczynski ZK, Erdmann R (eds), p. 790503 [90] Michalet X, Colyer RA, Scalia G, et al. New photon-counting detectors for single-molecule fluorescence spectroscopy and imaging. In: Itzler MA, Campbell JC (eds), p. 803316

in Space. Noordwijk, the Netherlands: ISSI; 2010

Journal. 2006;**37**:433-451

p. 753607

2017)

MA; 2006

2011:46-50

2014;**9**:e84614

p. 76082D

cameras. Optics Express. 2012;**20**:17741

Society B. 2012;**368**:20120035-20120035

cameras. In: Dereniak EL, Sampson RE, Johnson CB (eds), p. 195

niques. Journal of Biomolecular Techniques. 2015 jbt.15-2602-003


[76] Schühle UDO. Intensified solid state sensor cameras (chapter 25). In: Observing Photons in Space. Noordwijk, the Netherlands: ISSI; 2010

[60] Amelio GF, Tompsett MF, Smith GE. Experimental verification of the charge coupled

[61] Wanlass F, Sah C. Nanowatt logic using field-effect metal-oxide semiconductor triodes.

[62] Mendis S, Kemeny SE, Fossum ER. CMOS active pixel image sensor. IEEE Transactions

[64] Lawrence WG, Varadi G, Entine G, et al. A comparison of avalanche photodiode and photomultiplier tube detectors for flow cytometry. In: Farkas DL, Nicolau DV, Leif RC

[65] Michalet X, Ingargiola A, Colyer RA, et al. Silicon photon-counting avalanche diodes for single-molecule fluorescence spectroscopy. IEEE Journal of Selected Topics in Quantum

[66] Dautet H, Deschamps P, Dion B, et al. Photon counting techniques with silicon ava-

[67] Suyama M, Kawai Y, Kimura S, et al. A compact hybrid photodetector (HPD). IEEE

[68] Suyama M, Hirano K, Kawai Y, et al. A hybrid photodetector (HPD) with a III-V photo-

[69] Michalet X, Cheng A, Antelman J, et al. Hybrid photodetector for single-molecule spectroscopy and microscopy. In: Enderlein J, Gryczynski ZK, Erdmann R (eds), p. 68620F

[70] Nakamura J, editor. Image Sensors and Signal Processing for Digital Still Cameras. Boca

[72] Spring KR, Fellers TJ, Davidson MW. Introduction to Charge-Coupled Devices (CCDs) https://www.microscopyu.com/digital-imaging/introduction-to-charge-coupled-

[73] Gale MT. Active alignment of replicated microlens arrays on a charge-coupled device

[74] Swain PK, Cheskis D. Back-illuminated image sensors come to the forefront. Photonics Spectra. 2008 https://www.photonics.com/Article.aspx?AID=34685 (2008, accessed 10

[75] Zhang L, Neves L, Lundeen JS, Walmsley IA. A characterization of the single-photon sensitivity of an electron multiplying charge-coupled device. Journal of Physics B:

[71] Burke B, Jorden P, Vu P. CCD technology. Experimental Astronomy. 2006;**19**:69-102

[63] Kasunic K. Optical systems engineering. New York: McGraw-Hill Professional, 2011

device concept. Bell System Technical Journal. 1970;**49**:593-600

Institute of Electrical and Electronics Engineers. pp. 32-33

lanche photodiodes. Applied Optics. 1993;**32**:3894

Transactions on Nuclear Science. 1997;**44**:985-989

cathode. IEEE Transactions on Nuclear Science. 1998;**45**:572-575

on Electron Devices. 1994;**41**:452-453

282 Photon Counting - Fundamentals and Applications

(eds), p. 68590M

Electronics. 2014;**20**:248-267

Raton, FL: Taylor & Francis; 2006

devices-ccds (accessed 10 September 2017)

imager. Optical Engineering. 1997;**36**:1510

Atomic, Molecular and Optical Physics. 2009;**42**:114011

September 2017)


[91] Gyongy I, Davies A, Dutton NAW, et al. Smart-aggregation imaging for single molecule localisation with SPAD cameras. Scientific Reports; 6. Epub ahead of print December 2016. DOI: 10.1038/srep37349

[105] PrimeEnhance: 2D Active Image Denoising. Technical Note Rev A4-02032017, Photometrics. https://www.photometrics.com/resources/technotes/pdfs/PrimeEnhance-

Detectors for Super-Resolution & Single-Molecule Fluorescence Microscopies

http://dx.doi.org/10.5772/intechopen.71943

285

[106] Dirk BS, Heit B, Dikeakos JD. Visualizing interactions between HIV-1 Nef and host cellular proteins using ground-state depletion microscopy. AIDS Research and Human

[107] Muranyi W, Malkusch S, Müller B, et al. Super-resolution microscopy reveals specific recruitment of HIV-1 envelope proteins to viral assembly sites dependent on the enve-

[108] Wang X, Li X, Deng X, et al. Single-molecule fluorescence imaging to quantify membrane protein dynamics and oligomerization in living plant cells. Nature Protocols.

[109] Dirk B, Van Nynatten L, Dikeakos J. Where in the cell are you? Probing HIV-1 host

[110] Labilloy A, Youker RT, Bruns JR, et al. Altered dynamics of a lipid raft associated protein in a kidney model of Fabry disease. Molecular Genetics and Metabolism. 2014;**111**:

interactions through advanced imaging techniques. Virus. 2016;**8**:288

TechNote.pdf (2017, accessed 28 August 2017)

lope C-terminal tail. PLoS Pathogens. 2013;**9**:e1003198

Retroviruses. 2015;**31**:671-672

2015;**10**:2054-2063

184-192


[105] PrimeEnhance: 2D Active Image Denoising. Technical Note Rev A4-02032017, Photometrics. https://www.photometrics.com/resources/technotes/pdfs/PrimeEnhance-TechNote.pdf (2017, accessed 28 August 2017)

[91] Gyongy I, Davies A, Dutton NAW, et al. Smart-aggregation imaging for single molecule localisation with SPAD cameras. Scientific Reports; 6. Epub ahead of print December

[92] Perry SW, Burke RM, Brown EB. Two-photon and second harmonic microscopy in clinical and translational cancer research. Annals of Biomedical Engineering. 2012;**40**:277-291

[93] Gulinatti A, Rech I, Panzeri F, et al. New silicon SPAD technology for enhanced redsensitivity, high-resolution timing and system integration. Journal of Modern Optics.

[94] Gulinatti A, Ceccarelli F, Rech I, et al. Silicon technologies for arrays of Single Photon

[95] Michalet X, Siegmund OHW, Vallerga JV, et al. Photon-counting H33D detector for biological fluorescence imaging. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 2006;**567**:

[96] Pinaud F, King D, Moore H-P, et al. Bioactivation and cell targeting of semiconductor CdSe/ZnS Nanocrystals with Phytochelatin-related peptides. Journal of the American

[97] Dahan M, Laurence T, Pinaud F, et al. Time-gated biological imaging by use of colloidal

[98] Ma H, Fu R, Xu J, et al. A simple and cost-effective setup for super-resolution localization microscopy. Scientific Reports; 7. Epub ahead of print December 2017. DOI:

[99] Buades A, Coll B, Morel JM. A review of image Denoising algorithms, with a new one.

[100] Park SW. Image denoising filter based on patch-based difference refinement. Optical

[101] Boulanger J, Kervrann C, Bouthemy P, et al. Patch-based nonlocal functional for Denoising fluorescence microscopy image sequences. IEEE Transactions on Medical Imaging. 2010;**29**:

[102] Boulanger J, Kervrann C, Bouthemy P. Space-time adaptation for patch-based image sequence restoration. IEEE Transactions on Pattern Analysis and Machine Intelligence.

[103] Buades A, Coll B, Morel J-M. Nonlocal image and movie Denoising. International Jour-

[104] Dabov K, Foi A, Katkovnik V, et al. Image Denoising by sparse 3-D transform-domain collaborative filtering. IEEE Transactions on Image Processing. 2007;**16**:2080-2095

Avalanche Diodes. In: Itzler MA, Campbell JC (eds), p. 98580A

2016. DOI: 10.1038/srep37349

284 Photon Counting - Fundamentals and Applications

Chemical Society. 2004;**126**:6115-6123

10.1038/s41598-017-01606-6

Engineering. 2012;**51**:67007

nal of Computer Vision. 2008;**76**:123-139

442-454

2007;**29**:1096-1102

quantum dots. Optics Letters. 2001;**26**:825-827

Multiscale Modeling and Simulation. 2005;**4**:490-530

2012;**59**:1489-1499

133


## *Edited by Nikolay Britun and Anton Nikiforov*

Photon counting is a unified name for the techniques using single-photon detection for accumulative measurements of the light flux, normally occurring under extremely low-light conditions. Nowadays, this approach can be applied to the wide variety of the radiation wavelengths, starting from X-ray and deep ultraviolet transitions and ending with far-infrared part of the spectrum. As a special tribute to the photon counting, the studies of cosmic microwave background radiation in astronomy, the experiments with muon detection, and the large-scale fundamental experiments on the nature of matter should be noted. The book provides readers with an overview on the fundamentals and state-of-the-art applications of photon counting technique in the applied science and everyday life.

Photo by Tee\_Photolive / iStock

Photon Counting - Fundamentals and Applications

Photon Counting

Fundamentals and Applications

*Edited by Nikolay Britun and Anton Nikiforov*