Unified representation of high- and low-resolution spectra to facilitate application of mass spectrometric techniques in clinical practice

Zhvansky, Evgeny; Sorokin, Anatoly; Pekov, Stanislav I.; Indeykina, Maria I.; Ivanov, Daniil G.; Shurkhay, Vsevolod; Eliferov, Vasiliy A.; Zavorotnyuk, Denis S.; Levin, Nikita; Bocharov, K. V.; Ткаченко, С. И.; Belenikin, M. S.; Потапов, А. А.; Nikolaev, Eugene; Popov, Igor

doi:10.1016/j.clinms.2019.03.004

Cited by 17 publications

(18 citation statements)

References 18 publications

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“…Spectra were processed with the algorithm similar to those described previously. 24,25 Mass spectra were binned with binning width 0.01 m/z, and then spectra were convoluted with Gaussian (FWHM equals 0.4 m/z for high resolution and 0.2 m/z for low resolution). Spectra of each measurement were filtered by a moving median filter.…”

Section: Processingmentioning

confidence: 99%

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Assessment of variation of inline cartridge extraction mass spectra

et al. 2020

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Recently, mass-spectrometry methods show its utility in tumor boundary location.The effect of differences between research and clinical protocols such as low-and high-resolution measurements and sample storage have to be understood and taken into account to transfer methods from bench to bedside. In this study, we demonstrate a simple way to compare mass spectra obtained by different experimental protocols, assess its quality, and check for the presence of outliers and batch effect in the dataset. We compare the mass spectra of both fresh and frozen-thawed astrocytic brain tumor samples obtained with the inline cartridge extraction prior to electrospray ionization. Our results reveal the importance of both positive and negative ion mode mass spectrometry for getting reliable information about sample diversity. We show that positive mode highlights the difference between protocols of mass spectra measurement, such as fresh and frozen-thawed samples, whereas negative mode better characterizes the histological difference between samples. We also show how the use of similarity spectrum matrix helps to identify the proper choice of the measurement parameters, so data collection would be kept reliable, and analysis would be correct and meaningful. K E Y W O R D Sbrain tumor, data conversion, inline cartridge extraction, low-and high-resolution comparison, spectra stability and reproducibility | INTRODUCTIONFast tissue profiling methods for mass spectrometers allowed developing methods of rapid analysis of biological substances. [1][2][3][4][5][6][7] There are a lot of attempts to incorporate mass spectrometry into the clinical routine for surgery assistance purposes. [8][9][10][11][12] Mass spectra of complex mixtures of biological molecules, even those whose mass is up to 1000 Da, is still rather a difficult task, because of the enormous diversity of molecules contained in biological tissues. 13,14 Despite the common practice of LC-MS/MS to be used as an instrument for accurate identification of complex mixture components, it could not be used as a routine technique for rapid analyses. 15 Therefore, rapid analyses

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Section: Processingmentioning

confidence: 99%

“…The high correlation between HR frozen and LR frozen means that low-and highresolution spectra are correctly represented in the vector space. Thus, high-resolution spectra could be used as low-resolution spectra 24 2, number in the sequence is 1). The left panel demonstrates spectra for the same three selected samples in each run.…”

Section: Ssms Inmentioning

confidence: 99%

Assessment of variation of inline cartridge extraction mass spectra

et al. 2020

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“…One half was analyzed in the clinic immediately after resection, and the other half was analyzed in a laboratory after the freeze-thaw cycle. The obtained spectra were similar (median CS of 0.907), and after the unification procedure 37 , high- and low-resolution data could be considered almost identical, which allows for the use of the same classifiers for the analysis of intra- and postoperative samples. The unification procedure provides the possibility to use high-resolution data for low-resolution classifier training and makes ICE suitable for postoperative tissue identification if, for example, high-resolution mass spectrometry profiling is required for diagnosis refinement.…”

Section: Resultsmentioning

confidence: 79%

Inline cartridge extraction for rapid brain tumor tissue identification by molecular profiling

Pekov

Eliferov

Sorokin

et al. 2019

Sci Rep

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The development of perspective diagnostic techniques in medicine requires efficient high-throughput biological sample analysis methods. Here, we present an inline cartridge extraction that facilitates the screening rate of mass spectrometry shotgun lipidomic analysis of tissue samples. We illustrate the method by its application to tumor tissue identification in neurosurgery. In perspective, this high-performance method provides new possibilities for the investigation of cancer pathogenesis and metabolic disorders.

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“…AI may provide a bridge for that gap, through the development of deep learning algorithms and the incorporation of both high‐ and low‐resolution mass spectra for use in clinical practice. [ 161 ] On a similar note, different ionization techniques used in research are also a hurdle for clinical translation. AI can also be used to provide cross‐platform normalization.…”

Section: Integration Of Ai and High Resolution Mass Spectrometry For mentioning

confidence: 99%

Increasing Complexity to Simplify Clinical Care: High Resolution Mass Spectrometry as an Enabler of AI Guided Clinical and Therapeutic Monitoring

Yee

Drum

2020

Advanced Therapeutics

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Clinical care increasingly relies on individualized, highly multidimensional data for patient phenotyping. This review focusses on two technologies which are contributing to this trend and, in their mutual overlap, may provide a novel and powerful contribution to future clinical care. Artificial intelligence (AI), in particular the technique of deep learning, is steadily making inroads to clinical care and has demonstrated the ability to extract clinically relevant features from complex datasets. Likewise, mass spectrometry (MS), in particular high-resolution MS, is gaining the ability to provide highly multidimensional datasets from patient samples with the quality necessary for clinical decision making. The convergence of these trends has resulted in the nascent application of deep learning analytics to high resolution mass spectra. Due to the large volumes of content provided by high resolution mass spectra and the ability of multilayer neural networks to extract subtle features from complex datasets, future integration of their mutual application is identified as a research area of high priority for clinical care. Factors are identified that have contributed to historical progress for each technology and recommendations are provided to accelerate the mutual application of deep learning and mass spectrometry techniques to patient care.

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Unified representation of high- and low-resolution spectra to facilitate application of mass spectrometric techniques in clinical practice

Cited by 17 publications

References 18 publications

Assessment of variation of inline cartridge extraction mass spectra

Assessment of variation of inline cartridge extraction mass spectra

Inline cartridge extraction for rapid brain tumor tissue identification by molecular profiling

Increasing Complexity to Simplify Clinical Care: High Resolution Mass Spectrometry as an Enabler of AI Guided Clinical and Therapeutic Monitoring

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