The high isomeric complexity of glycans makes them particularly difficult to analyze. While ultra-high-resolution ion mobility spectrometry (IMS) can offer rapid baseline separation of many glycan isomers, their unambiguous identification remains a challenging task. One approach to solving this problem is to identify mobility-separated isomers by measuring their highly resolved cryogenic vibrational spectra. To be able to apply this approach to complex mixtures at high throughput, we have recently developed a Hadamard transform multiplexed spectroscopic technique that allows measuring vibrational spectra of all species separated in both IMS and mass spectrometry dimensions in a single laser scan. In the current work, we further develop the multiplexing technique using ion traps incorporated directly into the IMS device based on structures for lossless ion manipulations (SLIM). We also show that multiplexed spectroscopy using perfect sequence matrices can outperform standard multiplexing using Simplex matrices. Lastly, we show that we can increase the measurement speed and throughput further by running multiple multiplexing schemes using several SLIM ion traps in combination with simultaneous spectroscopic measurements in the segmented cryogenic ion trap.
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http://dx.doi.org/10.1021/acs.analchem.3c01340 | DOI Listing |
Biosensors (Basel)
October 2024
Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
The autofluorescence of erythrocyte porphyrins has emerged as a potential method for multi-cancer early detection (MCED). With this method's dependence on research-grade spectrofluorometers, significant improvements in instrumentation are necessary to translate its potential into clinical practice, as with any promising medical technology. To fill this gap, in this paper, we present an automated ratio porphyrin analyzer for cancer screening (ARPA-CS), a low-cost, portable, and automated instrument for MCED via the ratio fluorometry of porphyrins.
View Article and Find Full Text PDFAnal Chem
October 2024
Department of Chemistry and Biochemistry and Bio5 Institute, University of Arizona, Tucson, Arizona 85721, United States.
Charge detection mass spectrometry (CD-MS) is a powerful technique for the analysis of large, heterogeneous biomolecules. By directly measuring the charge states of individual ions, CD-MS can measure the masses from spectra where conventional deconvolution approaches fail due to the lack of isotopic resolution or distinguishable charge states. However, CD-MS is inherently slow because hundreds or thousands of spectra need to be collected to produce adequate ion statistics.
View Article and Find Full Text PDFiScience
September 2024
Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR 999078, China.
J Am Soc Mass Spectrom
October 2024
Center for Innovative Technology, Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States.
Global discovery lipidomics can provide comprehensive chemical information toward understanding the intricacies of metabolic lipid disorders such as dyslipidemia; however, the isomeric complexity of lipid species remains an analytical challenge. Orthogonal separation strategies, such as ion mobility (IM), can be inserted into liquid chromatography-mass spectrometry (LC-MS) untargeted lipidomic workflows for additional isomer separation and high-confidence annotation, and the emergence of high-resolution ion mobility (HRIM) strategies provides marked improvements to the resolving power ( > 200) that can differentiate small structural differences characteristic of isomers. One such HRIM strategy, high-resolution demultiplexing (HRdm), utilizes multiplexed drift tube ion mobility spectrometry (DTIMS) with post-acquisition algorithmic deconvolution to access high IM resolutions while retaining the measurement precision inherent to the drift tube technique; however, HRdm has yet to be utilized in untargeted studies.
View Article and Find Full Text PDFNeural Netw
December 2024
School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China. Electronic address:
Vision Transformer have achieved impressive performance in image super-resolution. However, they suffer from low inference speed mainly because of the quadratic complexity of multi-head self-attention (MHSA), which is the key to learning long-range dependencies. On the contrary, most CNN-based methods neglect the important effect of global contextual information, resulting in inaccurate and blurring details.
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