The Role of Kinetic Curves in Bioluminescence Imaging

Introduction

In bioluminescence imaging (BLI), numerous factors can influence the intensity of light emission, making it essential to establish a kinetic curve to ensure accuracy and consistency in experiments. But what exactly is a kinetic curve? Which variables can alter your signal? And how can you create one? Let’s explore these questions step-by-step.

Understanding Enzymatic Kinetic Curves

An enzymatic kinetic curve illustrates how the rate of an enzyme-driven reaction changes as specific components in the reaction vary. Most often, this variation is linked to different concentrations of the substrate.

The data are typically analyzed using the Michaelis–Menten equation, then plotted on a graph where the vertical (Y) axis shows reaction velocity, and the horizontal (X) axis reflects substrate concentration (Atkins Lab, n.d.).

Michaelis-Menten equation and general enzymatic kinetic curve are listed below:

• Vmax: is the maximum velocity of the system when the substrate is at maximum concentration.

• [S]: is substrate concentration.

• Km; is substrate concentration when the velocity of the enzyme reaction is at ½ of Vmax.

What is a kinetic curve in bioluminescence imaging?

A kinetic curve for luciferase activity in your experimental system helps pinpoint the moment when light output reaches its maximum and begins to level off. In essence, the luciferin–luciferase kinetic profile can be viewed as a “time–signal intensity curve” (Yang et al., 2016). By examining this data, researchers can identify the optimal imaging window following substrate administration.

In bioluminescence imaging, constructing a kinetic curve involves collecting a sequence of signal intensity readings over multiple time points. These readings are then plotted to show how emission strength changes with time after the substrate is injected.

Although the luciferin–luciferase process is enzymatic, the kinetic curve used in bioluminescence imaging differs from a traditional Michaelis–Menten plot. Instead of depicting reaction velocity against substrate concentration, the light emission kinetics graph displays relative light units (RLU) on the vertical axis and time, in minutes, on the horizontal axis.

In some image acquisition software, kinetic data may be displayed as the percentage of light intensity over time, with the Y-axis representing relative intensity levels.

Ultimately, kinetic curves in bioluminescence imaging serve as a visual guide for pinpointing the window after substrate administration when light output peaks and then stabilizes. This information helps researchers determine the most advantageous moment to capture images for reliable and consistent results.

Why Kinetic Curves Matter in Bioluminescence Imaging?

Numerous variables can alter the kinetics of a bioluminescent signal. Since every experiment has its own specific parameters—such as tumor site, size, application, and the model chosen—it is essential to determine the exact time point of peak signal to ensure both accuracy and reproducibility.

Key Factors That Influence Bioluminescent Output?

Bioluminescent emission can be affected by many different elements. The list below outlines common considerations, but don’t be discouraged—approaching them systematically and generating a new kinetic curve whenever conditions change will help maintain optimization, reproducibility, and overall success in BLI (Sim, Bibee, Wickline, & Sept, 2011; Sadikot & Blackwell, 2005).

· Tumor location – for example, light emission may take longer to appear if the tumor is located in the brain.

· Tumor size

· Cell lines used in the xenograft

· Luciferin administration method

· Animal model being studied

· Therapeutic treatment applied

· Tissue depth

· Type of substrate (including modified versions)

· Presence of scar tissue

· Tumor geometry

How to Create a Kinetic Curve for Bioluminescence Imaging

The process of generating a kinetic curve for bioluminescence imaging depends heavily on both the imaging system you use for data capture and the software used for analysis.

Some imaging platforms include built-in sequence programs that can automatically capture images over time, allowing you to later process the data with imaging analysis tools.

Below is a guide for producing a kinetic curve to monitor luciferase activity in your experimental model.

​Administering Luciferin

Inject luciferin using one of the following routes: intraperitoneal (IP), intravenous (IV), or subcutaneous (SQ).

If sedation is required before injection, keep in mind this may slightly delay the peak time of luciferase activity. Additionally, luciferin biodistribution can vary depending on the chosen administration route.

​For IP or SQ Injection

· If animals can be injected while awake, wait approximately three minutes after injection before sedating them with your preferred anesthesia method (gas or injectable).

· Refer to the Appendix for recommended anesthesia/analgesia agents and dosages for rats and mice.

· Place sedated animals into the imaging chamber and capture the first image about five minutes after luciferin administration.

​For IV Injection

· Immediately sedate the animal after injection (or ensure they are already under anesthesia).

· See the Appendix for suitable anesthesia/analgesia choices and dosages.

· Position the sedated animal in the imaging chamber and take the first image within two minutes of luciferin injection.

​Image Acquisition Schedule

· IP or SQ injections: Capture images every 5–10 minutes for a total duration of around 40–60 minutes.

· IV injections: Take images every 1–5 minutes for 20–30 minutes.

 (Note: Many injectable anesthetics last only 20–30 minutes; additional dosing may be required for full kinetic curve acquisition.)

​Selecting the Optimal Time Point

From the completed kinetic curve, determine the ideal imaging time for your model:

· For IP or SQ routes, the peak signal often occurs 10–20 minutes after injection.

· For IV routes, maximum activity is typically seen 2–5 minutes post-injection.

References

Atkins Lab. (n.d.). Michaelis-Menten Kinetics and Briggs-Haldane Kinetics. Retrieved September 24, 2020, from https://depts.washington.edu/wmatkins/kinetics/mic...

Baert, A. (Ed.). (1970, January 01). Time‐signal Intensity Curve (TIC). Retrieved September 24, 2020, from https://link.springer.com/referenceworkentry/10.10...

Burgos, J., Rosol, M., Moats, R., Khankaldyyan, V., Kohn, D., Nelson, M., & Laug, W. (2003, June). Time course of bioluminescent signal in orthotopic and heterotopic brain tumors in nude mice. Retrieved September 24, 2020, from https://www.ncbi.nlm.nih.gov/pubmed/12813886

Dai, Y., Chen, D., Wang, G., Yin, J., Zhan, Y., Wu, K., . . . Chen, X. (2020, February). Kinetic modeling and analysis of dynamic bioluminescence imaging of substrates administered by intraperitoneal injection. Retrieved September 24, 2020, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC70632...

Inoue, Y., Kiryu, S., Watanabe, M., Tojo, A., & Ohtomo, K. (2010). Timing of imaging after d-luciferin injection affects the longitudinal assessment of tumor growth using in vivo bioluminescence imaging. Retrieved September 24, 2020, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC29104...

Sadikot, R., & Blackwell, T. (2005). Bioluminescence imaging. Retrieved September 21, 2020, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC27133...

Sim, H., Bibee, K., Wickline, S., & Sept, D. (2011, February 1). Pharmacokinetic modeling of tumor bioluminescence implicates efflux, and not influx, as the bigger hurdle in cancer drug therapy. Retrieved September 24, 2020, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC30737...

Yang, S., Li, F., Chen, J., Zhang, G., Liao, Y., & Huang, T. (2016, April 7). Kinetic Curve Type Assessment for Classification of Breast Lesions Using Dynamic Contrast-Enhanced MR Imaging. Retrieved September 24, 2020, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC48244...

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