Chemistry teams up with data science to develop new investigative technique – W&M News

Kristin Wustholz’s lab has developed a knack for merging chemistry with other disciplines, and the lab’s latest work incorporates a strong data science component.

The developers of single-molecule spectroscopy were awarded the 2014 Nobel Prize in Chemistry. The technique has been implemented in laboratories around the world, and Wustholz believes his refinement of the process will expand it even further.

“Multicolor single-molecule imaging is very widely applied, primarily in biology — but also in materials science,” Wustholz said. “Often there is a general question like: What is the structure of this intracellular component?”

Grayson Hoy lines up the laser.

Wustholz is the Mansfield Associate Professor in the Department of Chemistry at William & Mary. Working with a group of student researchers and supported by a grant from the National Science Foundation, she developed a new and improved approach to single-molecule multicolor imaging. His group shared a proof-of-concept introduction, “Blink-Based Multiplexing: A Novel Approach to Differentiating Spectral Overlap Emitters,” in The Journal of Physical Chemistry Letters.

Wustholz’s co-authors on the paper are a group of college students, including Grace A. DeSalvo ’20, who stays on as an MS student. Undergraduate co-authors are Grayson R. Hoy ’23, Isabelle M. Kogan ’24, John Z. Li ’20, Elise T. Palmer ’22, Emilio Luz-Ricca ’23, and Paul Scemama de Gialluly ’22.

Blink-based multiplexing, or BBM, is an improved, data-rich variant of single-molecule multi-color imaging, which is based on the fluorescent emission of molecular components.

“Generally, if you have a sample that you want to image, you would stain it,” Wustholz said. “The stain adheres to different parts. Then you stick it on the microscope under a laser. But what you will see on a microscope image is not a clear image of the sample. All you will see are flashes of light as each of these molecules start emitting, then stop emitting, then emit again. A computer program puts all these flashes together to resolve the image.

Isabelle Kogan and Grace DeSalvo set up the microscope for BBM
Isabelle Kogan ’24 and Grace DeSalvo ’20 set up the microscope for BBM.

Wustholz explained that the problems with traditional multi-colored single-molecule imaging stem from the limited number of fluorescent probes that can be used.

“If you’re trying to do a multi-colored, super-resolved experiment, you’ll want to use three different colors,” she said. “So you have a choice of red, green and blue dyes. And it turns out that if you’re trying to make biological samples, there aren’t many dyes that work well together.

She then recounted various workarounds that labs use to circumvent the dyes’ mutual antipathy. Sometimes researchers use three different lasers. Other times, three different detectors. Or, she says, labs work out a sequential sampling scheme.

“Some institutions are able to purchase this gigantic instrument with 10 lasers and 10 detectors,” she said.

“The advantage here is that we’re getting rid of all that extra hardware,” Wustholz explained. “We only have a laser and a detector.”

Wustholz ticked other advantages, for example BBM not only requires less instrumentation, but also opens up a new palette of dyes.

Multiplexing occurs when searchers watch the emitter’s flashing patterns. She said the idea is to locate the molecule, which gives the resolution – then look at the pattern, to get the color of the molecule. The multiplexing translation process is a collaboration between humans and artificial intelligence, she added.

“My first instinct is to go with a human being; the human being knows the experience,” she said, noting that human beings first recognized that the blinking of molecules had meaning. “And so the human way is to take all the data, come up with stats, trying to make sure those stats are separable. The AI ​​way, the machine learning way, where you train the machine to differentiate between the two. They both work.

The lab called on Luz-Ricca and Scemama de Gialluly, two non-chemistry majors from the university’s data science program. “They really helped us with machine learning,” Wustholz said. “This is an exciting new part of the project that I’m excited to take forward.

“I have no experience in machine learning, so I relied heavily on students,” she added. “And, you know, when it came through peer review, that part flew with flying colors. So I really attribute that to them. They were guided by their own curiosity and intuition. I think combining basic sciences like chemistry with data science is probably the future, and where we are headed.

Joseph McClain, Research Writer

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