Researchers at the University of Southern California (USC) report that they have developed a new tool to look more deeply and clearly into living things, a visual advantage that saves time and helps advance medical cures. It’s the sort of foundational science that can be used to develop better diagnostics and treatments, including detecting lung cancer or damage from pollutants, say the scientists, who add that the technology is versatile enough it could become a smartphone app for use in remote medicine, food safety or counterfeit currency detection.
The team, whose study (“Pre-processing visualization of hyperspectral fluorescent data with Spectrally Encoded Enhanced Representations”) appears in Nature Communications, explains that the technique focuses, literally, on the building blocks of biology. When biologists look deeply into a living thing (e.g., a cell, fish, person), it’s not always clear what’s going on. Cells and proteins are deeply intertwined across tissues, leaving lots of questions about the interactions between components. The first step to curing disease is seeing the problem clearly, and that has not always been easy.
To solve the problem, researchers have been relying on a technique called fluorescence hyperspectral imaging (fHSI). It’s a method that can differentiate colors across a spectrum, tag molecules so they can be followed, and produce vividly colored images of an organism’s insides. But the advantages that fHSI offers come with limitations. It doesn’t necessarily reveal the full-color spectrum. It requires lots of data, due to the complexity of biological systems, so it takes a long time to gather and process the images. Many time-consuming calculations are also involved, which is a big drawback because experiments work better when they can be done in real time.
To solve those problems, the USC researchers developed a new method called spectrally encoded enhanced representations (SEER). It provides greater clarity and works up to 67 times faster and at 2.7 times greater definition than present techniques. It relies on mathematical computations to parse the data faster. It can process vibrant fluorescent tags across the full spectrum of colors for more detail. And it uses much less computer memory storage, even more important with the explosion of big data research behind modern convergent bioscience research. According to the study, SEER is a “fast, intuitive, and mathematical way” to interpret images as they are collected and processed.
“Hyperspectral fluorescence imaging is gaining popularity for it enables multiplexing of spatiotemporal dynamics across scales for molecules, cells and tissues with multiple fluorescent labels. This is made possible by adding the dimension of wavelength to the dataset. The resulting datasets are high in information density and often require lengthy analyses to separate the overlapping fluorescent spectra. Understanding and visualizing these large multi-dimensional datasets during acquisition and pre-processing can be challenging,” write the investigators.
“Here we present Spectrally Encoded Enhanced Representations (SEER), an approach for improved and computationally efficient simultaneous color visualization of multiple spectral components of hyperspectral fluorescence images. Exploiting the mathematical properties of the phasor method, we transform the wavelength space into information-rich color maps for RGB display visualization. We present multiple biological fluorescent samples and highlight SEER’s enhancement of specific and subtle spectral differences, providing a fast, intuitive and mathematical way to interpret hyperspectral images during collection, pre-processing and analysis.”
“There are a number of scenarios where this after-the-fact analysis, while powerful, would be too late in experimental or medical decision-making,” said Francesco Cutrale, PhD, lead author of the study and research assistant professor of biomedical engineering at the USC Viterbi School of Engineering. “There is a gap between acquisition and analysis of the hyperspectral data, where scientists and doctors are unaware of the information contained in the experiment. SEER is designed to fill this gap.”
SEER’s first application will be in the medical and research field. The algorithm will be used for detecting early stages of lung disease and potential damage from pollutants in patients in a collaboration with doctors at Children’s Hospital Los Angeles. Also, scientists in the life sciences field have started adopting SEER in their experimental pipelines in an effort to further improve efficiency.
Improvements in imaging technologies can also reach the consumer level, so it’s likely that technologies such as fHSI and SEER could be installed on mobile phones to provide powerful visualization tools.