A Novel Approach to Deep-Tissue Metabolic Imaging

In a study published in Science Advances, researchers from the Massachusetts Institute of Technology (MIT) introduced a new approach that more than doubles the typical depth limit of metabolic imaging.

Metabolic imaging is a noninvasive technique that uses laser light to analyze living cells. It helps scientists and doctors track disease progression and monitor treatment responses. However, when light interacts with biological tissue, it scatters, limiting how deep it can penetrate and reducing the image quality.

The new technique increases the imaging depth and speeds up the process, resulting in images that are both richer and more detailed.

What sets this technology apart is that it eliminates the need for tissue preprocessing, such as cutting or dyeing. Instead, a specialized laser is directed deep into the tissue, causing specific chemicals within the cells to release light. This method allows for a more accurate, realistic depiction of tissue structure and function without altering the tissue itself.

The researchers achieved this by adapting the laser light to better penetrate deep tissues. By using a newly developed fiber shaper, they can adjust the color and pulses of the laser, bending the light to reduce scattering and enhance the signal as it travels deeper. This breakthrough enables them to capture more detailed images from much deeper within living tissue.

With its increased penetration depth, faster speeds, and higher resolution, this technique holds great potential for complex imaging applications, including cancer research, tissue engineering, drug discovery, and immune response studies.

This work shows a significant improvement in terms of depth penetration for label-free metabolic imaging. It opens new avenues for studying and exploring metabolic dynamics deep in living biosystems.

Sixian You, Study Senior Author and Assistant Professor, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology

Kunzan Liu, an EECS graduate student, was the lead author of the study. Fan Wang, a professor of Brain and Cognitive Sciences; Roger Kamm, the Cecil and Ida Green Distinguished Professor of Biological and Mechanical Engineering; Linda Griffith, the School of Engineering Professor of Teaching Innovation in the Department of Biological Engineering; Honghao Cao, an EECS graduate student; Tong Qiu, a postdoc at MIT; and other MIT colleagues also contributed to the research.

Laser-Focused

This new technology falls under label-free imaging, meaning no staining is needed before capturing images. Staining can provide contrast, helping clinical scientists better visualize cell nuclei and proteins. However, it often requires slicing the sample, which can damage the tissue and make it difficult to study dynamic processes in living cells.

In label-free imaging, lasers are used to highlight specific molecules within cells, causing them to emit light in different colors. This reveals various molecular contents and cellular structures. However, creating the right laser light with specific wavelengths and high-quality pulses for deep-tissue imaging has been a significant challenge.

To overcome this, the researchers developed a new approach. They combined a multimode fiber—a type of optical fiber that can carry a lot of power—with a small device called a "fiber shaper." This shaper allows them to precisely adjust the light transmission by modifying the shape of the fiber. Bending the fiber alters the laser's color and intensity.

Building on previous work, the team refined the first version of the fiber shaper to enable more detailed multimodal metabolic imaging.

We want to channel all this energy into the colors we need with the pulse properties we require. This gives us higher generation efficiency and a clearer image, even deep within tissues.

Honghao Cao, EECS Graduate Student, Massachusetts Institute of Technology

After developing the controlled mechanism, the team built an imaging platform that used the powerful laser source to generate longer wavelengths of light, which are essential for deeper penetration into biological tissues.

We believe this technology has the potential to significantly advance biological research. By making it affordable and accessible to biology labs, we hope to empower scientists with a powerful tool for discovery.

Kunzan Liu, Study Lead Author and EECS Graduate Student, Massachusetts Institute of Technology

Dynamic Applications

When the researchers tested their imaging system, they found that the light could penetrate more than 700 micrometers into a biological sample, a significant improvement over previous techniques, which could only reach around 200 micrometers.

“With this new type of deep imaging, we want to look at biological samples and see something we have never seen before,” Liu added.

This deeper imaging capability allowed them to observe cells at various levels within a living system, offering valuable insights into metabolic changes that occur at different depths. Additionally, the faster imaging speed enabled them to gather more accurate data on how a cell’s metabolism affects its movement, both in terms of pace and direction.

This innovative imaging technology has great potential for advancing research on organoids—lab-grown cells that replicate the structure and function of organs. Researchers in the Kamm and Griffith labs are leading efforts to develop brain and endometrial organoids that grow like real organs, providing a way to study disease and treatment responses.

Traditionally, monitoring internal processes in organoids has been challenging without cutting or staining the tissue, which could destroy the sample. However, with this new imaging method, researchers can noninvasively track the metabolic conditions within a living organoid as it develops.

Looking ahead, the researchers plan to improve the resolution even further, with a focus on biomedical applications. They are also developing low-noise laser sources, which could enable deeper imaging with less light exposure. In addition, they are working on algorithms that can process images and reconstruct detailed 3D models of biological samples.

Ultimately, they hope this technology will allow biologists to monitor drug responses in real time, aiding in the development of new treatments.

You added, “By enabling multimodal metabolic imaging that reaches deeper into tissues, we’re providing scientists with an unprecedented ability to observe nontransparent biological systems in their natural state. We are excited to collaborate with clinicians, biologists, and bioengineers to push the boundaries of this technology and turn these insights into real-world medical breakthroughs.”

“This work is exciting because it uses innovative feedback methods to image cell metabolism deeper in tissues compared to current techniques. These technologies also provide fast imaging speeds, which was used to uncover unique metabolic dynamics of immune cell motility within blood vessels. I expect that these imaging tools will be instrumental for discovering links between cell function and metabolism within dynamic living systems,” added Melissa Skala, an investigator at the Morgridge Institute for Research who was not involved with this study.

Irene Georgakoudi, a professor of biomedical engineering at Tufts University who was also not involved with this study, concluded, “Being able to acquire high resolution multi-photon images relying on NAD(P)H autofluorescence contrast faster and deeper into tissues opens the door to the study of a wide range of important problems. Imaging living tissues as fast as possible whenever you assess metabolic function is always a huge advantage in terms of ensuring the physiological relevance of the data, sampling a meaningful tissue volume, or monitoring fast changes. For applications in cancer diagnosis or in neuroscience, imaging deeper — and faster — enables us to consider a richer set of problems and interactions that haven’t been studied in living tissues before.”

This research is supported in part by MIT startup funding, a National Science Foundation CAREER Award, an MIT Irwin Jacobs and Joan Klein Presidential Fellowship, and an MIT Kailath Fellowship.

Journal Reference:

Liu, K. et. al. (2024) Deep and dynamic metabolic and structural imaging in living tissues. Science Advances. doi.org/10.1126/sciadv.adp2438