Ordinary mass spectrometers blast analytes into ionized fragments. But mass spectrometers that incorporate resonator-based detectors don’t blast anything. They keep analytes intact, potentially simplifying the identification of molecules in a sample. However, the key word here is potentially. Resonator-based detectors, or nanoelectromechanical systems (NEMSs), are so small, so plagued by fabrication differences, and so hard to characterize, that their applications have been limited. For example, NEMSs have not been especially useful in proteomics, even though they have been reported to provide single-molecule mass measurements.
To get around the problems of tininess, variability, and resistance to characterization, scientists at the California Institute of Technology led by Michael L. Roukes, PhD, tried using fingerprint analysis, a pattern recognition tool perhaps best known for its applications in facial recognition and forensics. For Roukes and colleagues, the patterns of interest were in the frequency shifts of NEMS vibrational modes.
Basically, Roukes and colleagues developed a fingerprint approach that used machine learning technology to capture the vibrational behaviors of NEMS devices. Crucially, the approach was not limited to well-specified NEMS devices with readily determined vibrational modes. It was applicable to NEMS devices of arbitrary specification with complex vibrational modes.
The scientists presented their findings in Nature Communications, in an article titled, “Data-driven fingerprint nanoelectromechanical mass spectrometry.”
“[Accurate] knowledge of the NEMS device mode shapes … are rarely, if ever, measured (or verified) in practice,” the article’s authors wrote. “Advanced NEMS devices can have three-dimensional mode-shapes of nanometer size, which further complicates matters. [But our] fingerprint approach … circumvents this mode-shape requirement, thereby permitting the use of NEMS devices of arbitrary geometry and specification. The current requirement for calibration of the device mass is also eliminated.”
Essentially, the new technique opens the possibility of using a variety of devices for the measurement of mass and, therefore, the identification of proteins, and it could pave the way to the compilation of complete proteomes.
NEMS mass spectrometry, or NEMS-MS, is typically accomplished with a silicon device that you can think of as a tiny beam tethered on either end. When the beam is struck, it resonates like a guitar string and moves up and down with certain mode shapes occurring at different frequencies.
If a sample is placed on such a beam, the individual frequencies of the beam’s vibrational modes will change. “From these frequency changes, you can infer the mass of the sample,” said John E. Sader, PhD, the lead author of the new paper and a Caltech research professor of aerospace and applied physics. “But to do that, you need to know the shape of each mode. That’s at the core of all these measurements currently—you need to know how these devices vibrate. [And you] can’t just simply calculate the mode shapes and their frequencies using theory and assume that these hold during a measurement.”
A further complication is that the precise location at which a sample is dropped within a device affects the frequency measurements of the beam. In a simple beam device, if the sample is placed close to one of the tethered ends, the frequency will not change as much as if it were placed near the center, for example, where the vibrational amplitude is likely to be greater. But with devices roughly a single micron by a single micron in size, it is not possible to visualize the exact placement of a sample.
Using their fingerprint method, the researchers would randomly place a single particle on the NEMS device under ultrahigh vacuum and at ultralow temperature. In real time, they would measure how the frequencies of several device modes change with that placement. This allowed them to construct a high-dimensional vector representing those changes in frequency, with one vector dimension for each mode. By doing this repeatedly for particles placed in a variety of random locations, they built a library of vectors for the device that is used to train the machine learning software.
It turned out that each vector is something of a fingerprint. It has an identifying shape—or direction—that changes uniquely depending on where a particle lands.
“If I take a particle with an unknown mass and place it anywhere on the NEMS device—I don’t know where it has landed; in fact, I don’t really care—and measure the frequencies of the vibrational modes, it will give me a vector that points in a specific direction,” Sader explained. “If I then compare it to all the vectors in the database and find the one which is most parallel to it, that comparison will give me the unknown particle mass. It’s simply the magnitude ratio of the two vectors.”
The Caltech team theoretically analyzed phononic crystal NEMS devices developed in the laboratory of their colleague, Stanford physicist Amir H. Safavi-Naeni, PhD, for this study. These advanced NEMS devices effectively trap vibrations so that at certain frequencies they continue to “ring” for a long while, giving researchers plenty of time to gather quality measurements. The fingerprint method enables MS measurements with these state-of-the-art devices. In preparation, the team used alternate devices to benchmark their fingerprint method. This included measuring the mass of individual particles of GroEL, a molecular chaperone protein that helps with proper protein folding in the cell.
Roukes noted that for large protein complexes and membrane proteins such as GroEL, standard methods of MS are problematic for several reasons. First, those methods provide the total mass and charge, and those measurements do not uniquely identify a single species. For such large complexes, there would be many possible candidates. “You need to disambiguate that in some way,” Roukes said. “The preeminent method of disambiguation at this point is taking the puzzle and chopping it up into fragments that are between 3 and 20 amino acids long.” Then, he said, you would use pattern recognition to identify the mother molecule from all the daughter fragments. “But you no longer have a unique identifier of what the configuration or conformation of the original thing was because you destroyed it in the process of chopping it up.”
The new fingerprint technique, Roukes noted, “is heading toward an alternative called native single-molecule MS, where you look at large proteins and protein complexes, one by one, in their native form without chopping them up.”
