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Table Of Contents
An Introduction to Camera Terminology and Options
This page is designed to introduce terminology that is useful when choosing a scientific camera. It is split into four sections. Sensor Properties and Optical System discuss the various specifications and terms used to describe scientific cameras. These tabs explain how to use the specifications to choose the correct camera for your experiment, and also provide example calculations for our scientific camera product line. System Integration details features that allow Thorlabs' scientific cameras to be integrated with other laboratory instrumentation. Finally, Applications contains sample images and discussions of research enabled by our scientific cameras.
If you have any questions about cameras, or would like to discuss a particular application, please click the Contact Us button.
A camera sensor, such as a CCD or CMOS, converts incident light into an electrical signal for processing. This process is not perfect; for every photon that hits the sensor there will not necessarily be a corresponding electron produced by the sensor. Quantum Efficiency (QE) is the average probability that a photon will produce an electron, and it is expressed as a conversion percentage. A camera with a higher quantum efficiency imager will require fewer photons to produce a signal as compared to a camera with lower quantum efficiency imager.
Quantum efficiency is dependent on the properties of the material (e.g. silicon) upon which the imager is based. Since the wavelength response of the silicon is not uniform, QE is also a function of wavelength. QE plots are provided below so that the responsivity of different cameras can be compared at the wavelengths of the intended application. Note that the interline CCDs that are used in scientific cameras typically have a microlens array that spatially matches the pixel array. Photons that would otherwise land outside of the photosensitive pixel are redirected onto the pixel, maximizing the fill factor of the sensor. The QE plots in Figure 1 and the table below include the effects of the microlens arrays.
IR Blocking Filter
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Figure 1: An example of a QE curve for our monochrome 1.4 megapixel cameras; the QE curves for all of our scientific cameras are in the table below. The NIR Enhanced (Boost) Mode is available on our 1.4 megapixel cameras and can be selected via the software. The IR blocking filter should also be removed for maximum NIR sensitivity. For more information on Boost mode, please see the Imaging in the NIR section below.
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Figure 2: The IR blocking filter included with all of Thorlabs' scientific cameras except our UV-enhanced cameras. The filter can be removed; optionally it can be replaced with a user-supplied Ø1" (Ø25 mm) filter or another optic up to 4 mm thick. Removal instructions are provided in chapter 4 of the camera user's manual.
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Figure 3: Exploded view of a Thorlabs' scientific camera optical and mechanical components.
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Figure 5: The lens cap functions as a tool that can loosen the retaining ring holding the IR blocking filter in place. The filter can be replaced with any user-supplied Ø1" (25 mm) filter or other optic up to 4 mm thick. Removal instructions are provided in chapter 4 of the camera user's manual.
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Figure 4: The filter housing can be removed by completely unscrewing the C-mount adapter and lock nut with the wrench.
Imaging in the NIR
Imaging in the UV
Quantum Efficiency Summary
For a detailed review of camera noise, SNR, and the effects of sensor temperature, including sample calculations, please see our complete tutorial.
When purchasing a camera, it is important to consider the intended application, and its "light budget." For bright light situations, it is sufficient to choose a camera that has an adequately high quantum efficiency and then consider other factors, such as sensor format, frame rate, and interface. For low light situations, it is important to consider quantum efficiency as well as read noise and dark current, as described below.
Sources of Noise
If several images are taken of the same object under the same illumination, there will still be variation in the signal recorded by each pixel. This "noise" in a camera image is the aggregate spatial and temporal variation in the measured signal, assuming constant, uniform illumination. There are several components of noise:
Image quality (as represented by the Signal-to-Noise [SNR] ratio) is the ratio of:
Photon-derived signal electrons cannot be distinguished from the noise electrons that are generated during image formation, readout, and digitization. SNR is a convenient "figure of merit" to evaluate how well the signal electrons overcome the noise electrons in the system under a particular set of conditions. It provides a way to quantitatively compare the images, because a higher SNR usually correlates with an observable improvement in image quality. Complete details on calculating SNR can be found in the Camera Noise Tutorial.
Bright Light Imaging
Low Light Imaging
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Figure 6: Schematic showing the process for charge transfer and readout of an interline CCD.
This animation details how charge is accumulated on the pixels of an interline CCD, how those charges are shifted and then read out as the next exposure begins.
Our scientific CCD cameras are based on interline CCD sensors. This sensor architecture and readout is illustrated in Figure 6 as well as the animation to the lower right. An interline CCD may be visualized as a device that develops a 2D matrix of electronic charges on a horizontal and vertical (H x V) pixel array. Each pixel accumulates a charge that is proportional to the number of incident photons during an exposure period. After the exposure period, each element of the charge matrix is laterally shifted into an adjacent element that is shielded from light. Stored charges are clocked, or moved, vertically row-by-row into a horizontal shift register. Once a row of charges is loaded onto the horizontal shift register, charges are serially clocked out of the device and converted into voltages for the creation of an analog and/or digital display. An advantage of this architecture is that once the charges are shifted into the masked columns, the next exposure can immediately begin on the photosensitive pixels.
ThorCam, our camera control and image acquisition software, allows users to switch freely between combinations of readout parameters (clock rate, number of taps, binning, and region of interest) and select the combination that works best for the particular application. These readout options are described in the following sections.
Readout Clock Rate
Our scientific cameras operate at 20 or 40 MHz readout, which is the rate at which the pixels are clocked off of the shift register. The readout rate determines the maximum frame rate of the camera. Going from 20 MHz to 40 MHz will nominally double the frame rate of the camera, at the expense of slightly higher read noise, assuming all other parameters remain the same. There will be slightly higher read noise associated with the 40 MHz readout, but if there is plenty of light in the field of view, then the image is shot-noise limited, not read-noise limited. In shot-noise limited cases the 40 MHz readout mode results in a speed increase without any penalty.
Single Tap Readout
In conventional (single tap) readout mode, each row of charges is loaded onto the horizontal register and clocked out towards the sense node, at a rate determined by the readout clock rate of 20 or 40 MHz.
Dual Tap Readout
Quad Tap Readout
The summary table below the figures details the number of taps for USB 3.0 and Gigabit Ethernet models for each camera family. Each camera family webpage has a table with the maximum frame rate specifications for each readout rate and tap combination. The number of taps is user-selectable in the ThorCam software settings.
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Figure 7: Dual-Tap Camera Illustration
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Figure 8: Quad-Tap Camera Illustration
Multi-Tap Readout Operation Summary
Region of Interest (ROI)
Since changing the ROI affects the number of CCD rows read out (not columns), only the vertical ROI dimension affects the frame rate; changing the horizontal ROI dimension will have no effect on the frame rate. The resulting frame rate is not a linear function of the ROI size since there is some additional time involved to fast-scan the unwanted rows as well as to read out the desired ROI.
Binning and ROI modes can be combined to optimize readout and frame rate for the particular application.
In the following discussion, we will focus on calculating the field of view for an imaging system built using microscope objectives; for information about field of view when using machine vision camera lenses please see our Camera Lens Tutorial.
In a microscope system, it is important to know how the size of the camera sensor will affect the area of your sample that you are able to image at a given time. This is known as the imaging system's field of view. It is calculated by taking the sensor's dimensions in millimeters and dividing it by the imaging system's magnification. As an example, our 8 MP CCD sensor has an 18.13 mm x 13.60 mm array; at a 40X magnification, this corresponds to a 457 µm x 340 µm total area at the sample plane.
Choosing the imager size must also be balanced against the other parameters of the sensor. Generally, as sensor size increases, the maximum frame rate decreases.
Figure 9: An illustration of various camera format sizes, referenced to a Full 35 mm Frame. Please note that the sizes and aspect ratios shown are approximate and can vary for specific sensors.
Camera sensor sizes are given in terms of "format." The format designations are expressed in fractional inches, and represent the outer diameter of the video tube that has an imaging diagonal closest to the diagonal of the digital sensor chip. These sizes are not completely standardized, and therefore some variation exists between the exact sizes, and occasionally even aspect ratios, with the same format from different manufacturers. Figure 9 shows the approximate sensor size for several formats scaled to the standard 35 mm film frame.
The summary table below lists the format and sensor size for all of our scientific cameras in pixels and millimeters.
Imager Size Summary
It is a common misconception to refer to the number of pixels (H x V) as the resolution of a camera. More accurately, the resolution is the optical resolution: the ability to resolve small features. In the following discussion, we will focus on imaging systems which use microscope objectives; for information about building a system with machine vision camera lenses please see our Camera Lens Tutorial.
Calculating the Resolution of an Imaging System
where R is the distance between the two Airy disks, λ is the wavelength of light, and NA is the numerical aperture of the microscope's objective. For example, with a 0.75 NA objective, the resolution for 550 nm light will be (1.22*550 nm)/(2*0.75) = 0.45 µm.
In order to accurately represent the image on the discrete imaging array of the CCD or CMOS camera, it is necessary to apply the Nyquist criterion, which states that the smallest resolvable feature of the image needs to be sampled by the sensor at twice this rate; put another way, there needs to be (at least) two pixels to capture each optically resolvable feature. In the 0.75 NA, 40X objective example, this means that the pixel size needs to be less than or equal to 8.9 µm so that there are at least two pixels to accurately render the image of the smallest resolvable feature. This is the typically desired condition, with the system resolution being limited by the microscope optics and not the camera pixel size.
With some objectives, particularly those with lower magnification values, the smallest feature resolvable by the objective may be too small to be imaged by the camera. For example, with a 4X, 0.13 NA objective, the smallest feature resolvable by the objective at 550 nm is 2.58 µm long, which requires a camera pixel size of 5.2 µm. If one uses a camera with a pixel size of, for example, 5.5 µm, then the pixel size of the camera is the limiting factor in the resolution of this imaging system; the smallest sample feature that can be imaged is 2.75 µm. However, if a camera with a pixel size of 3.45 µm is used, then the optics-limited resolution of 2.58 µm is achievable.
The tables below give an overview of the minimum resolvable feature size for various objectives at 550 nm, and indicates which of our cameras can image that feature.
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Figure 10: A plot showing the minimum resolvable sample feature as a function of wavelength for several objective numerical apertures (solid lines). Also plotted are minimum resolvable sample features for several of our cameras (dotted lines), plotted for 60X as an example. For more details, please see the text.
Resolution and Wavelength
Figure 10 indicates an important detail of resolution in an imaging system. When wavelengths become shorter into the ultraviolet, the minimum size that the objective can image continues to decrease. Imaging these smaller features, however, will require smaller camera pixels. Where the solid line is below a dashed line, a feature that size cannot be imaged by the camera (the camera is the limiting element of the imaging system); conversely, when the solid line is above a dashed line, the feature will be able to be imaged by the camera (the objective is the limiting element in the imaging system).
Effects of Tube Lenses
Modern microscope objectives are infinity-corrected and therefore require a tube lens. The specified magnification of an objective is calculated by assuming a specific tube lens focal length. Each microscope manufacturer has adopted a different focal length for their tube lens, as shown by the table to the right. Hence, when combining optical elements from different manufacturers, or a lens with a different focal length in a custom imaging system, it is necessary to calculate an effective magnification for the objective, which is then used to calculate the magnification of the system.
The effective magnification of an objective is given by:.
Here, the Design Magnification is the magnification printed on the objective, fTube Lens in Microscope is the focal length of the tube lens in the microscope you are using, and fDesign Tube Lens of Objective is the tube lens focal length that the objective manufacturer used to calculate the Design Magnification. These focal lengths are given by the table to the right.
Note that Leica, Mitutoyo, Nikon, and Thorlabs use the same tube lens focal length; if combining elements from any of these manufacturers, no conversion is needed. Thorlabs offers several tube lenses, each with a 200 mm focal length; for general imaging applications, we recommend the TTL200, which features optimized color correction and high transmission at wavelengths below 480 nm.
Vignetting occurs when the optical image that is formed at the focal plane of the camera is smaller than the camera format. When this occurs, the area of the sensor is not completely exposed, causing a dark ring to appear around the borders of the image. The vignetting effect is illustrated in Figure 11, which were both captured with the same 4/3" format camera (8051M-GE). More detail about integrating machine vision lenses into an imaging system may be found in our Camera Lens Tutorial.
The effect is the same in microscopy, and a simple calculation can inform whether the (usually circular) image generated by an objective and tube lens will underfill the rectangular imager. In order to estimate the size of the image (in mm) at the focal plane of the camera, one can use the field number (FN) of the objective. Where given for an objective, it represents the diameter of the field of view (in millimeters) at the image plane (i.e., the camera sensor). If this dimension is greater than the diagonal of the imager, then vignetting is unlikely to be a problem. Please note that this calculation does not include the effects of the objective lens design; for example, it is possible that aberrations or slight vignetting as seen in Figure 11a will be present. A table of sensor diagonals for our scientific cameras is below.
If the magnification of the total microscope differs from that of the objective's specification, either due to a different focal length tube lens or magnifying camera tube, then the field size produced at the camera sensor will be a different size as well. This field size can be calculated by:
Effective FN = Design FN x (System Magnification / Design Objective Magnification).
Figure 11: The vignetting effect is illustrated in the two images above, which were both captured using the same 4/3" format camera. a: Using a 12 mm focal length, 4/3" format lens produces a full image with slight dimming around the edges. This minor example of vignetting is due to the lens design which has decreased transmission at the edge of the lens. b: A 2/3" format lens at the same focal length produces a prominent dark ring around the photo edge.
Thorlabs offers two interface options across our scientific camera product line: USB 3.0 and Gigabit Ethernet (GigE). Once other camera decisions, such as field of view and frame rates, have been made, for many of our camera types it is necessary to choose one of these interfaces. It is important to confirm that the computer system meets or exceeds the recommended requirements listed to the right; otherwise, dropped frames may result, particularly when streaming camera images directly to storage media.
USB 3.0 is a standard interface available on most new PCs, which means that typically no additional hardware is required, and therefore these cameras are not sold with any computer hardware. For users with PCs that do not have a USB 3.0 port, a PCIe card is sold separately below. USB 3.0 supports a speed up to 320 MB/s and cable lengths up to 3 m, however maximum speeds can be impacted by the host PC's chipset. Support for multiple cameras is possible using multiple USB 3.0 ports on the PC or a USB 3.0 hub.
GigE is ideal for situations requiring longer cable lengths, as well as for systems that require using multiple cameras with one computer. GigE supports a speed up to 100 MB/s and cable lengths up to 100 m. It also uses fairly inexpensive cables, but does require the use of a computer with a GigE card installed. Support for multiple cameras is easily achieved using a Gigabit Ethernet switch. However, the GigE card supplied with the camera is recognized as a public connection to the network; institutions with strict policies only allow registered devices and trusted connections. For any questions regarding using our GigE card at your institution, please contact your IT department.
Scientific Camera Interface Summary
Our scientific cameras have three externally triggered operating modes: streaming overlapped exposure, asynchronous triggered acquisition, and bulb exposure driven by an externally generated trigger pulse. The trigger modes operate independently of the readout (e.g., 20 or 40 MHz; binning) settings as well as gain and offset. Figures 12 through 14 show the timing diagrams for these trigger modes, assuming an active low external TTL trigger.
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Figure 12: Streaming overlapped exposure mode. When the external trigger goes low, the exposure begins, and continues for the software-selected exposure time, followed by the readout. This sequence then repeats at the set time interval. Subsequent external triggers are ignored until the camera operation is halted.
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Figure 13: Asynchronous triggered acquisition mode. When the external trigger signal goes low, an exposure begins for the preset time, and then the exposure is read out of the camera. During the readout time, the external trigger is ignored. Once a single readout is complete, the camera will begin the next exposure only when the external trigger signal goes low.
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Figure 14: Bulb exposure mode. The exposure begins when the external trigger signal goes low and ends when the external trigger signal goes high. Trigger signals during camera readout are ignored.
Figure 15: The ThorCam Camera Settings window. The red and blue highlighted regions indicate the trigger settings as described in the text.
External triggering enables these cameras to be easily integrated into systems that require the camera to be synchronized to external events. The Strobe Output goes high to indicate exposure; the strobe signal may be used in designing a system to synchronize external devices to the camera exposure. External triggering requires a connection to the auxiliary port of the camera. We offer the 8050-CAB1 auxiliary cable as an optional accessory. Two options are provided to "break out" individual signals. The TSI-IOBOB provides SMA connectors for each individual signal. Alternately, the TSI-IOBOB2 also provides the SMA connectors with the added functionality of a shield for Arduino boards that allows control of other peripheral equipment. More details on these three optional accessories are provided below.
Trigger settings are adjusted using the ThorCam software. Figure 15 shows the Camera Settings window, with the trigger settings highlighted with red and blue squares. Settings can be adjusted as follows:
In addition, the polarity of the trigger can be set to "On High" (exposure begins on the rising edge) or "On Low" (exposure begins on the falling edge) in the "HW Trigger Polarity" box (highlighted in red in Figure 15).
Equal Exposure Pulse (EEP) Mode
CMOS sensors often feature a rolling shutter, meaning that each row begins to acquire charge sequentially, rather than simultaneously as with the gloabal shutter on CCD sensors. If an external light source is triggered to turn on for the length of the exposure, this can lead to gradients across the image as different rows are illuminated for different lengths of time. The Equal Exposure Pulse (EEP) is an output signal available on our Quantalux® sCMOS camera's I/O connector. When selected in the ThorCam settings dialog, the strobe output signal is reconfigured to be active only after the CMOS sensor's rolling reset function has completed. The signal will remain active until the sensor's rolling readout function begins. This means that the signal is active only during the time when all of the sensor's pixels have been reset and are actively integrating. The resulting image will not show an exposure gradient typical of rolling reset sensors. Figure 5 shows an example of a strobe-driven exposure, where the strobe output is used to trigger an external light source; the resulting image shows a gradient as not all sensor rows are integrating charge for the same length of time when the light source is on. Figure 6 shows an example of an EEP exposure: the exposure time is lengthened, and the trigger output signal shifted to the time when all rows are integrating charge, yielding an image with equal illumination across the frame.
Please note that EEP will have no effect on images that are constantly illuminated. There are several conditions that must be met to use EEP mode; these are detailed in the User Guide.
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Figure 5: A timing example for an exposure using STROBE_OUT to trigger an external light source during exposure. A gradient is formed across the image since the sensor rows are not integrating charge for the same length of time the light source is on.
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Figure 6: A timing example for an exposure using EEP. The image is free of gradients, since the EEP signal triggers the light source only while all sensor rows are integrating charge.
Example: Camera Triggering Configuration using Thorlabs' Scientific Camera Accessories
Figure 16: A schematic showing a system using the TSI-IOBOB2 to facilitate system integration and control.
As an example of how camera triggering can be integrated into system control is shown in Figure 16. In the schematic, the camera is connected to the TSI-IOBOB2 break-out board / shield for Arduino using a 8050-CAB1 cable. The pins on the shield can be used to deliver signals to simultaneously control other peripheral devices, such as light sources, shutters, or motion control devices. Once the control program is written to the Arduino board, the USB connection to the host PC can be removed, allowing for a stand-alone system control platform; alternately, the USB connection can be left in place to allow for two-way communication between the Arduino and the PC. Configuring the external trigger mode is done using ThorCam as described above.
To download some of these images as high-resolution, 16-bit TIFF files, please click here. It may be necessary to use an alternative image viewer to view the 16-bit files. We recommend ImageJ, which is a free download.
The video to the right is an example of a multispectral image acquisition using a liquid crystal tunable filter (LCTF) in front of a monochrome camera. With a sample slide exposed to broadband light, the LCTF passes narrow bands of light that are transmitted from the sample. The monochromatic images are captured using a monochrome scientific camera, resulting in a datacube – a stack of spectrally separated two-dimensional images which can be used for quantitative analysis, such as finding ratios or thresholds and spectral unmixing.
In the example shown, a mature capsella bursa-pastoris embryo, also known as Shepherd's-Purse, is rapidly scanned across the 420 nm - 730 nm wavelength range using Thorlabs' KURIOS-WB1 Liquid Crystal Tunable Filter. The images are captured using our 1501M-GE Scientific Camera, which is connected, with the liquid crystal filter, to a Cerna® Series Microscope. The overall system magnification is 10X. The final stacked/recovered image is shown below.
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Final Stacked/Recovered Image
Thrombosis is the formation of a blood clot within a blood vessel that will impede the flow of blood in the circulatory system. The videos below are from experimental studies on the large-vessel thrombosis in Mice performed by Dr. Brian Cooley at the Medical College of Wisconsin. Three lasers (532 nm, 594 nm, and 650 nm) were expanded and then focused on a microsurgical field of an exposed surgical site in an anesthetized mouse. A custom 1.4 Megapixel Camera with integrated filter wheel were attached to a Leica Microscope to capture the low-light fluorescence emitted from the surgical site. See the videos below with their associated descriptions for further information.
In the video above, a gentle 30-second electrolytic injury is generated on the surface of a carotid artery in an atherogenic mouse (ApoE-null on a high-fat, “Western” diet), using a 100-micron-diameter iron wire (creating a free-radical injury). The site (arrowhead) and the vessel are imaged by time-lapse fluorescence-capture, low-light camera over 60 minutes (timer is shown in upper left corner – hours:minutes:seconds). Platelets were labeled with a green fluorophore (rhodamine 6G) and anti-fibrin antibodies with a red fluorophore (Alexa-647) and injected prior to electrolytic injury to identify the development of platelets and fibrin in the developing thrombus. Flow is from left to right; the artery is approximately 500 microns in diameter (bar at lower right, 350 microns).
Reference: Cooley BC. In vivo fluorescence imaging of large-vessel thrombosis in mice. Arterioscler Thromb Vasc Biol 31, 1351-1356, 2011. All animal studies were done under protocols approved by the Medical College of Wisconsin Institutional Animal Care and Use Committee.
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Example Setup for Simultaneous NIR/DIC and Fluorescence Imaging
Many life science imaging experiments require a cell sample to be tested and imaged under varying experimental conditions over a significant period of time. One common technique to monitor complex cell dynamics in these experiments uses fluorophores to identify relevant cells within a sample, while simultaneously using NIR or differential interference contrast (DIC) microscopy to probe individual cells. Registering the two microscopy images to monitor changing conditions can be a difficult and frustrating task.
Live overlay imaging allows both images comprising the composite to be updated in real-time versus other methods that use a static image with a real-time overlay. Overlays with static images require frequent updates of the static image due to drift in the system or sample, or due to repositioning of the sample. Live overlay imaging removes that dependency by providing live streaming in both channels.
Using the ThorCam overlay plug-in with the two-way camera microscope mount, users can generate real-time two-channel composite images with live streaming updates from both camera channels, eliminating the need for frequent updates of a static overlay image. This live imaging method is ideal for applications such as calcium ratio imaging and electrophysiology.
Simultaneous Fluorescence and DIC Imaging
The image sequence below shows mouse kidney cells imaged using a dichroic filter to separate the fluorescence and DIC signals into different cameras. These images are then combined into a two-channel composite live image with false color fluorescence by the ThorCam overlay plug-in.
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In the image above, the pipette is visible in the DIC image as two lines near the center of the frame.
Microaspiration Using a Micropipette
The image to the right shows a live, simultaneous overlay of fluorescence and DIC images. The experiment consists of a microaspiration technique using a micropipette to isolate a single neuron that expresses GFP. This neuron can then be used for PCR. This image was taken with our 1.4 Megapixel Cameras and our 2SCM1-DC Two-Camera Mount and shows the live overlay of fluorescence and DIC from the ThorCam plug-in. Image courtesy of Ain Chung, in collaboration with Dr. Andre Fenton at NYU and Dr. Juan Marcos Alarcon at The Robert F. Furchgott Center for Neural and Behavioral Science, Department of Pathology, SUNY Downstate Medical Center.
Simultaneous NIR Dodt Contrast and Epi Fluorescence imaging
The image to the right shows a live, simultaneous overlay of fluorescence and near-infrared Dodt contrast images of a 50 µm brain section from a CX3CR1-GFP mouse, which has been immunostained for PECAM-1 with Alexa-687 to highlight vasculature. The Dodt contrast uses a quarter annulus and a diffuser to create a gradient of light across the sample that can reveal the structure of thick samples. The image was taken with our Scientific Cameras and our 2SCM1-DC Two-Camera Mount. Sample courtesy of Dr. Andrew Chojnacki, Department of Physiology and Pharmacology, Live Cell Imaging Facility, Snyder Institute for Chronic Diseases, University of Calgary.