Novel Way of Collecting Highly Detailed Information About Organic Tissues

An innovative technology created by USC Michelson Center for Convergent Bioscience researchers proposes a new way of acquiring and arranging highly comprehensive information about organic tissues in record time.

Video Credit: University of Southern California.

The approaches could be used to quickly process tissue samples in cancer care or detect bacteria in food processing facilities in the future.

Tissues transmit signals, or intrinsic fields, that, while observable, are low and difficult to distinguish. The method, described in two studies published in Nature Methods and Cell Reports Methods, employs a complicated mathematical procedure to increase the quality of the signals before separating them.

According to Francesco Cutrale, Co-Principal Investigator and Research Assistant Professor at the USC Viterbi School of Engineering, the new technique is similar to how a streaming service provides various levels of compression to make sure their video is consistent regardless of a user’s internet connection.

Based on how fast your connection is, the streamer will send the video with different levels of compression that is then recomposed optimally for your device. We’re doing something similar: We’re taking very large, very complex data and moving it into a space where it is compressed. We can then look at very large data sets—associated by similarity into an enormous histogram—and analyze this data in record time and with very high sensitivity.”

Francesco Cutrale, Co-Principal Investigator and Research Assistant Professor, Viterbi School of Engineering, University of Southern California

A Window into the Complexity of Cells and Organic Tissue

The algorithm, which was published earlier this year in Nature Methods, continues the recent development of fluorescence-based high-content imaging technologies. Fluorescence has permitted the detection and characterization of individual molecules due to its great contrast and specificity, as well as its versatility.

However, these newer techniques are ineffective for imaging living, or in vivo, samples due to their low sensitivity and risk of specimen destruction.

The research group demonstrated how the technology, known as Hybrid Unmixing, may be used to cleanly and rapidly evaluate live organic tissue. The method employs linear unmixing, a technique for evaluating distinct components inside a specimen indicated by chemical compounds known as fluorophores.

Researchers then employed hyperspectral phasors to depict these components, which uses the entire color spectrum rather than just red, blue, and green. As a result, Hybrid Unmixing enables simultaneous imaging of bright and dim labeled components within organic tissue, even under low-light conditions.

The technology will provide more accurate insights into the complexity of biological systems by allowing concurrent investigation of the cellular activities and cellular metabolism of these tagged components.

There’s a push in the research space for understanding complex biological systems. While researchers typically examine only two or three labels at once, the truth is that there are more than just a few factors interacting within cells. The challenge is that these signals often appear very similar, making them difficult to distinguish. In our paper, we have successfully identified and separated up to 14 different signals. This breakthrough will provide researchers with a more comprehensive understanding of the activity inside cellular and biological systems.”

Francesco Cutrale, Co-Principal Investigator and Research Assistant Professor, Viterbi School of Engineering, University of Southern California

According to Cutrale, the algorithm serves as the cornerstone for several industrial applications.

“We work in the life sciences, but it’s easy to imagine numerous applications to evaluate the quality of fruits, the presence of pesticides or how to optimize production in many other fields,” Cutrale notes.

SHy-Cam Offers Low-Cost, High-Quality Imaging Tool

In the study published in Cell Reports Methods, the researchers reveal a technology designed to collect this type of information, dubbed SHy-Cam, the acronym for Single-shot Hyperspectral Phasor Camera. To mitigate label overlap, standard tissue imaging techniques employ color channels across the spectrum. This approach slows down imaging and, if subjected to too much light, can harm the samples.

The scientists were able to utilize the novel method with the SHy-Cam to obtain spectrum information rapidly and efficiently in a camera that can be manufactured with readily available optical components.

The new equipment presented in the research can acquire 30 data sets per second and has a photon efficiency of more than 80%. According to the researchers, this makes it a potent tool for multi-color in-vivo imaging.

How do you produce a two-dimensional picture with a 2D sensor? You take a picture. Our challenge is how to capture a 3D data set with a 2D sensor. A typical color sensor acquires three colors—red, blue, and green—or it receives everything through its grayscale sensors. In our case, we need to request 42 channels of information—that’s not common, nor is it efficient. We designed in this paper a new approach that can obtain an encoded version of the spectral information with a single image.”

Francesco Cutrale, Co-Principal Investigator and Research Assistant Professor, Viterbi School of Engineering, University of Southern California

Cutrale stated that they accomplished this by the use of light. The scientists employed light to modify the data and complete the calculations before compressing it onto the sensor. Using this method, the team demonstrated how it can receive the whole spectrum as well as the image’s dimensions.

“We’ve captured the X- and Y-axes of the image—its height and width—and also the spectral information on the wavelength-axis, all together in a single image with a standard camera. That’s quite a powerful approach. We obtained efficiencies in this hardware approach which are in some cases up to eight times faster than existing instrumentation. In other words, eight times more light reaches the camera sensor in this compressed fashion,” Francesco Cutrale concludes.