Last Updated on December 18, 2023

In part one of this series, we introduced the fundamental concepts of Fiber Photometry and discussed how this revolutionary technique is advancing the field of neuroscience. In part two, we will transition into discussing the inner workings of Fiber Photometry and how the process works on a technical level.

Part 2: How Does Fiber Photometry Work?

Understanding the Components

To better understand how Fiber Photometry allows researchers to record neural activity in real time, it is first important to break the typical Fiber Photometry system down into its individual components. From the hardware of the optical components, the surgical implantation of cannulae, and the software based data analysis, these must all come together in order to produce quality data. There are 4 key components for a working Fiber Photometry system, each of which will be discussed briefly below:

1. Light Source

The choice of light source is pivotal in ensuring accurate and reliable fiber photometry measurements. It must emit a precise wavelength of light that matches the excitation spectrum of the fluorescent indicator being used. Additionally, considerations for light intensity and stability are crucial. A stable and well-calibrated light source guarantees consistent excitation, which directly impacts the quality and reliability of the recorded signals. Advanced light sources may also offer modulation capabilities, enabling researchers to perform more sophisticated experiments by synchronizing light pulses with specific events or behaviors.

2. Optical Fiber

The optical fiber acts as the conduit through which the excitation light travels from the source to the target area within the brain. It must be carefully selected based on factors such as numerical aperture, core diameter, and material composition. A higher numerical aperture allows for better light-gathering efficiency, while a larger core diameter permits more flexibility in positioning the fiber. Additionally, specialized coatings can enhance the fiber’s durability and protect against potential damage during implantation or use. Researchers must also consider factors like numerical aperture matching with the collection optics and the compatibility with chosen light sources.

3. Photodetector

The photodetector is responsible for capturing the emitted fluorescent signals from the neural tissue. It must be highly sensitive to the specific wavelengths emitted by the fluorescent indicator. Additionally, factors like quantum efficiency, noise levels, and dynamic range are critical considerations. A high quantum efficiency ensures that a larger proportion of emitted photons are converted into electrical signals, enhancing the detector’s sensitivity. Low noise levels are crucial for detecting weak signals amidst background noise, while a wide dynamic range allows for accurate recording of both strong and weak signals without saturation or loss of resolution.

4. Fluorescent Indicator

Genetically encoded fluorescent indicators are the cornerstone of fiber photometry. These indicators are typically proteins engineered to fluoresce in response to changes in calcium ion or neurotransmitter concentrations, providing a direct readout of neuronal activity. Choosing the right indicator is a crucial decision, as it determines the sensitivity and specificity of the measurements. Factors to consider include the indicator’s excitation and emission spectra, its target affinity and kinetics, as well as potential interactions with other cellular processes (Shen et.al. 2020). Additionally, researchers must ensure that the chosen indicator is compatible with the chosen excitation wavelength and photodetector.

Expression of Genetic Sensors

Advances in genetic engineering have been hand in hand with advances in imaging techniques. As such, researchers are now able to express complex fluorescent protein structures in a highly efficient cell type specific manner, allowing for unprecedented specificity when collecting neural activity data. Achieving success in fiber photometry relies heavily on obtaining optimal expression of genetically encoded fluorescent indicators (GEFIs) within your selected animal model. This critical step determines the quality and reliability of the fluorescent signals that will be measured, ultimately influencing the accuracy of the neural activity data obtained. Now we will briefly discuss the two primary methods for achieving optimal GEFI expression: viral expression and transgenic mouse models.

Day-Cooney et al., (2023) J.Neurochemistry.

  • Viral Expression: One approach to achieve targeted GEFI expression involves viral expression, a technique that involves injecting a specially engineered virus carrying the genetic code for the desired GEFI directly into the brain. This method offers the advantage of precision, allowing researchers to target specific cell types or regions within the brain for GEFI expression. However, the effectiveness of viral expression hinges on a critical consideration: virus selection. Different types of viruses are commonly used in neuroscience research, each with specific characteristics that make them valuable tools for manipulating and studying neural circuits. Adeno-Associated Viruses (AAVs) are widely employed due to their efficiency in transducing neurons without causing significant damage. While lentiviruses are useful for studies requiring prolonged expression of genetic constructs as they can integrate their genetic material into the host genome. Additionally, Retrograde Rabies Viruses have gained significant attention in recent years (Osakada et al. 2011). These modified viruses are particularly valuable for tracing neuronal pathways. By introducing a genetically engineered rabies virus into a specific target area, researchers can trace its path backward to identify the upstream neurons that project to that region. This technique provides crucial insights into the connectivity and communication between different brain regions, offering a powerful tool for mapping neural circuits. Finally, achieving the desired level of expression requires careful consideration of virus dilution. Dilution significantly influences the balance between optimal GEFI expression and potential drawbacks such as background noise or over-expression, which can result in inaccurate data interpretation. Researchers must carefully fine-tune the concentration of the injected virus to strike the right balance between signal intensity and stability in order to collect reliable neural activity data.
  • Transgenic Mouse Models: Another effective strategy involves utilizing transgenic mouse models. These models are genetically engineered to express GEFIs uniformly across the entire brain or in specific brain regions. This approach offers versatility and convenience, as it eliminates the need for viral injections and allows researchers to explore neural activity across multiple brain areas simultaneously. However, it’s important to note that transgenic models may have variations in GEFI expression levels between different brain regions. Researchers must account for these potential differences when analyzing the data and drawing conclusions about neural circuitry and behavior. Furthermore, the choice of transgenic mouse model is a critical aspect of the experimental design. Different lines may have distinct patterns of GEFI expression, allowing researchers to select models that align with the specific brain regions or cell populations under investigation. Additionally, advancements in genetic engineering techniques have enabled the development of more sophisticated transgenic models, including those that allow for cell-type-specific expression of GEFIs. These models provide an even higher level of specificity, allowing researchers to target specific neuronal populations with precision.

Implanting the Optical Cannula

In fiber photometry, successful expression of GEFIs represents a critical step, laying the foundation for the subsequent implantation of an optical cannula. This step is of paramount importance as it provides access to the fluorescent signal emitting from specific regions of interest within the brain. Optical cannulas are instrumental in this process, capturing the emitted light and conveying it to a photodetector for subsequent analysis. Their compact design serves to minimize tissue disruption and damage during the implantation, preserving the neural tissue’s integrity.

The significance of mitigating tissue damage and inflammation during the implantation process is crucial in neuroscience research, particularly in techniques like fiber photometry. Inflammation and tissue trauma can trigger a cascade of unintended physiological responses, including immune activation and the release of signaling molecules that have the potential to impact neighboring cells and brain regions. This phenomenon introduces unwanted signals and noise into the recorded data, complicating the task of accurately interpreting the neural activity of interest. Therefore, attention to detail and precision in the implantation of optical cannulas are imperative to uphold the integrity of the experimental system. Things to consider are locating precise stereotaxic coordinates, using fine tip micropipettes for injection, slow and controlled placement of the injection needle and injection rate, controlled viral delivery using a pneumatic pump, minimizing total volume injected, and implementing a post-injection wait period. This diligence ensures minimal tissue damage ensuring that subsequent fiber photometry measurements yield precise, reliable insights into neural dynamics. Join us for part 3, where we will discuss novel approaches and recent innovations in the realm of Fiber Photometry.

If you missed Part 1 of this introduction to Fiber Photometry take a look here. Learn more about Amuza’s cutting-edge Wireless Fiber Photometry system, TeleFipho.

Learn more about Amuza’s cutting-edge Wireless Fiber Photometry system, TeleFipho.

References

Shen et al., (2020) ‘Engineering genetically encoded fluorescent indicators for imaging of neuronal activity: progress and prospects’, Neurosci. Res., 152, pp. 3-14. doi: 10.1016/j.neures.2020.01.01

Osakada et al., (2011) ‘New rabies virus variants for monitoring and manipulating activity and gene expression in defined neural circuits’, Neuron, Aug 25;71(4):617-31. doi: 10.1016/j.neuron.2011.07.005.

Day-Cooney et al., (2023) Genetically encoded fluorescent sensors for imaging neuronal dynamics in vivo. J. Neurochemistry, 164, 284–308. Available from: https://doi.org/10.1111/jnc.15608