• Screening for toxins and poisonous compound using modern mass spectrometry and artificial intelligence

Screening for toxins, drugs, poisonous compounds is extremely important in many areas including homeland security, forensic science, food industry etc. Mass spectrometry combined to liquid or gas chromatography is the major instrument for such studies. However, important limitation of the classical approach is the insufficient size of the corresponding reference databases of LC-MS/MS fingerprints for standard compounds. The project is dedicated to the use of artificial intelligence for predicting physical and chemical properties of chemical compounds which can be used for identification.

• AI Histologist: Machine Learning for Cancer Tumor Resection Margin prediction in Imaging Mass Spectrometry

Project description:

Surgical resection, along with subsequent chemotherapy and radiation are the primary way of cancer caring. The correct choice of tumor resection margin has significant implications for patients having surgical resection: the choice between saving enough healthy tissue and removing all tumor cells is always intraoperative by specially trained histologist performing frozen tissue sections light microscopy analysis.

Despite widespread use, histological imaging has some significant disadvantages: it is subjective and directly depends on histologist experience [1], which requires a long time that prolongs the patient’s anesthesia period [2], and is limited with a certain number of sampling points available. For these reasons, this procedure might be unreliable for up to 30% of patients who underwent surgical resection. [3]

Mass spectrometry imaging (MSI) allows to directly determine the spatial distribution of ions with a high resolution by measuring the number of ions with a specific mass to charge ratio (m/z) for every microscopic area of the sample. In comparison to light microscopic analysis, the MSI approach is not limited to 2-3 dyes available for clinical histologists and might provide rich data of thousands of measured sample ions.Such data contain complete information about the ion distributions in the studied tissue samples and open up a wide field for its analysis and study using machine learning methods, opening a new way of defining a tumor resection margin.

Ultimate goals:

This project proposes a novel scheme of intraoperative prediction of a tumor resection border using a currently developing machine learning-based computational tool for intraoperative tumor resection margin prediction using tissue sections and obtained mass spectrometry imaging data.

Fig.1 Possible intraoperative procedure that includes proposed MSI step

After receiving the sample, it is proposed to place the sample in a mass spectrograph of sufficient resolution, according to the data from which it would be possible to identify the affected tissue cells with adequate reliability. After that, we can use this data to predict the optimal boundary along which the surgeon will remove the tumor using a designed deep learning model. Such technology will be a reliable help for histologists, providing them with an independent view from the outside, and will also increase the effectiveness of treatment and, possibly, will speed up the overall process.

In more details, in collaboration with Burdenko Research Institute, we will construct new datasets consist of mass spectrometry imaging data of tumor affected tissues and related histological ground truth references and use it for hypothesis testing and development of deep learning architectures and training algorithms, providing an efficient and reliable way of tumor margin resection prediction.

Existing solutions for intraoperative tumor margin prediction that based on MS technologies such as Smart Knife (iKnife) [4], desorption electrospray ionization (DESI) [5], Picosecond Infrared Laser (PIRL) [6], and MasSpec Pen [7] differ in invasiveness, speed, spatial resolution, and user skill requirements. However, all of them are still at the research/prototype and clinical testing stages, and the problem remains open and relevant.

Key references:

1. Van Den Brekel MW, Lodder WL, Stel HV, Bloemena E, Leemans CR, Van Der Waal I. Observer variation in the histopathologic assessment of extranodal tumor spread in lymph node metastases in the neck. Head Neck 34(6), 840–845 (2012).
2. Novis DA, Zarbo RJ. Interinstitutional comparison of frozen section turnaround time. A College of American Pathologists Q-Probes study of 32868 frozen sections in 700 hospitals. Arch. Pathol. Lab. Med. 121(6), 559–567 (1997).
3. Shen JG, et al. (2006) Intraoperative frozen section margin evaluation in gastric cancer of the cardia surgery. Hepatogastroenterology 53(72):976–978.
4. Alexander J, Gildea L, Balog J et al. A novel methodology for in vivo endoscopic phenotyping of colorectal cancer based on real-time analysis of the mucosal lipidome: a prospective observational study of the iKnife. Surg. Endosc. 31(3), 1361–1370 (2017)
5. Takats Z, Wiseman JM, Gologan B, Cooks RG. Mass spectrometry sampling under ambient conditions with desorption electrospray ionization. Science (New York, N.Y.) 306(5695), 471–473 (2004).
6. Woolman M, Ferry I, Kuzan-Fischer CM et al. Rapid determination of medulloblastoma subgroup affiliation with mass spectrometry using a handheld picosecond infrared laser desorption probe. Chem. Sci. 8(9), 6508–6519 (2017).
7. Zhang J, Rector J, Lin JQ et al. Nondestructive tissue analysis for ex vivo and in vivo cancer diagnosis using a handheld mass spectrometry system. Sci. Transl. Med. 9(406), (2017)

• Machine learning for OMICs technologies

Project Description:

OMICs study can be defined as a study of the totality of something. Proteomics, lipidomics and metabolomics operate with big molecular data that is typically collected from mass spectrometry analysis. Molecular data is organized as a table that contains molecular features, such as m/z (mass-to-charge ratio), retention times, and m/z for fragments of the molecule.Correct interpretation of this data requires unequivocal identification of molecular structures from mass-spectrometry data. The most common approach is to perform database search on this features. However, there is a lack of experimental database information, for example MZCloud database of fragment mass spectra covers about 10⁵ of molecules, while PubChem database of all synthesized molecules counts more than 10⁷, and the whole chemical space was estimated as 10⁶⁰. That is why computational approaches are desired to enhance identification process.

Ultimate goals:

The ultimate goal of the project is to produce both experimental and data processing workflow that allows discriminating all molecular species in complex biological samplesin mass spectrometry based OMICs studies.

Experimental part assumes adding new features that can be collected using mass spectrometry. In particular, we will implement previously developed approach based on isotope exchange reactions for identification of illicit drugs and environmental pollutants. For that purpose we will establish experimental conditions and design an ion source for on-fly isotope labelling.

Computational part will include development of the data processing approach for isotope exchange experiments to assign mass spectral results with certain parts of a molecule.

We will also use common features (retention times, fragment spectra) for identification. Deep learning will be used to predict these features from molecular structures and to assign fragmentation data with molecular structures.

 

Figure 1. A – General idea of the transfer learning approach. B – Illustration of the data augmentation method. C – Illustration of self-supervised learning on SMILES strings.

KeyReferences:

1. Kostyukevich Y, Zherebker A, Orlov A, Kovaleva O, Burykina T, Isotov B, et al. Hydrogen/Deuterium and O-16/O-18-Exchange Mass Spectrometry Boosting the Reliability of Compound Identification. Analytical Chemistry. 2020;92(10):6877-85. doi: 10.1021/acs.analchem.9b05379.
2. Kostyukevich Y, Acter T, Zherebker A, Ahmed A, Kim S, Nikolaev E. Hydrogen/deuterium exchange in mass spectrometry. Mass Spectrometry Reviews. 2018;37(6):811-53. doi: 10.1002/mas.21565.
3. Kostyukevich Y, Kononikhin A, Zherebker A, Popov I, Perminova I, Nikolaev E. Enumeration of non-labile oxygen atoms in dissolved organic matter by use of O-16/O-18 exchange and Fourier transform ion-cyclotron resonance mass spectrometry. Analytical and Bioanalytical Chemistry. 2014;406(26):6655-64. doi: 10.1007/s00216-014-8097-9.
4. Kostyukevich Y, Kononikhin A, Popov I, Nikolaev E. Simple Atmospheric Hydrogen/Deuterium Exchange Method for Enumeration of Labile Hydrogens by Electrospray Ionization Mass Spectrometry. Analytical Chemistry. 2013;85(11):5330-4. doi: 10.1021/ac4006606.
5. Osipenko S, Bashkirova I, Sosnin S, Kovaleva O, Fedorov M, Nikolaev E, et al. Machine learning to predict retention time of small molecules in nano-HPLC. Analytical and Bioanalytical Chemistry. 2020. doi: 10.1007/s00216-020-02905-0.

• Molecular organization of ultracomplex natural mixtures

Many  natural systems such as petroleum, humics substances, dissolved organic matter,  archeological, paleontological objects are ultracomplex mixtures which passed a long period of transformation in the environment. Such samples consists of more than 100,000 individual molecules, what makes it essential to use a modern Big Data processing tools for the investigation of such objects.

Students will be working with ultrahigh resolution mass spectra of natural samples in order to develop tools for the automatic comparison and classification of samples. One of the goals of the project will be understanding of the nature and composition of ancient resins used by ancient Egyptians for mummification.

References:

Y Kostyukevich, S Solovyov, A Kononikhin, I Popov, E Nikolaev The investigation of the bitumen from ancient Greek amphora using FT ICR MS, H/D exchange and novel spectrum reduction approach. Journal of Mass Spectrometry 51 (6), 430-436

Full list of publication is available here:

https://scholar.google.com/citations?user=wjXOn44AAAAJ&hl=ru

Selected publications:

1. Kostyukevich, Y. et al. Enumeration of labile hydrogens in natural organic matter by use of hydrogen/deuterium exchange Fourier transform ion cyclotron resonance mass spectrometry. Analytical chemistry85, 11007-11013 (2013).
2. Kostyukevich, Y., Kononikhin, A., Popov, I. & Nikolaev, E. Simple atmospheric hydrogen/deuterium exchange method for enumeration of labile hydrogens by electrospray ionization mass spectrometry. Analytical chemistry85, 5330-5334 (2013).
3. Kostyukevich, Y., Kononikhin, A., Popov, I. & Nikolaev, E. In-ESI source hydrogen/deuterium exchange of carbohydrate ions. Analytical chemistry86, 2595-2600 (2014).
4. Kostyukevich, Y., Kononikhin, A., Popov, I. & Nikolaev, E. Conformational changes of ubiquitin during electrospray ionization as determined by in‐ESI source H/D exchange combined with high‐resolution MS and ECD fragmentation. Journal of Mass Spectrometry49, 989-994 (2014).
5. Kostyukevich, Y. et al. Enumeration of non-labile oxygen atoms in dissolved organic matter by use of 16 O/18 O exchange and Fourier transform ion-cyclotron resonance mass spectrometry. Analytical and bioanalytical chemistry406, 6655-6664 (2014).
6. Perminova, I.V. et al. Molecular mapping of sorbent selectivities with respect to isolation of Arctic dissolved organic matter as measured by Fourier transform mass spectrometry. Environmental science & technology48, 7461-7468 (2014).
7. Kostyukevich, Y. et al. Supermetallization of peptides and proteins during electrospray ionization. Journal of mass spectrometry50, 1079-1087 (2015).
8. Kostyukevich, Y., Kononikhin, A., Popov, I. & Nikolaev, E. Conformations of cationized linear oligosaccharides revealed by FTMS combined with in‐ESI H/D exchange. Journal of Mass Spectrometry50, 1150-1156 (2015).
9. Zherebker, A.Y. et al. Synthesis of model humic substances: a mechanistic study using controllable H/D exchange and Fourier transform ion cyclotron resonance mass spectrometry. Analyst140, 4708-4719 (2015).
10. Kononikhin, A. et al. An untargeted approach for the analysis of the urine peptidome of women with preeclampsia. Journal of proteomics149, 38-43 (2016).
11. Kostyukevich, Y. et al. Supermetallization of peptides and proteins with tetravalent metal Th (IV). European journal of mass spectrometry22, 39-42 (2016).
12. Nikolaev, E.N., Kostyukevich, Y.I. & Vladimirov, G.N. Fourier transform ion cyclotron resonance (FT ICR) mass spectrometry: Theory and simulations. Mass spectrometry reviews35, 219-258 (2016).
13. Zherebker, A. et al. High desolvation temperature facilitates the ESI-source H/D exchange at non-labile sites of hydroxybenzoic acids and aromatic amino acids. Analyst141, 2426-2434 (2016).
14. Kostyukevich, Y., Efremov, D., Ionov, V., Kukaev, E. & Nikolaev, E. Remote detection of explosives using field asymmetric ion mobility spectrometer installed on multicopter. Journal of mass spectrometry52, 777-782 (2017).
15. Kostyukevich, Y. et al. CID fragmentation, H/D exchange and supermetallization of Barnase-Barstar complex. Scientific reports7, 6176 (2017).
16. Zherebker, A. et al. Enumeration of carboxyl groups carried on individual components of humic systems using deuteromethylation and Fourier transform mass spectrometry. Analytical and bioanalytical chemistry409, 2477-2488 (2017).
17. Kostyukevich, Y. etal. Hydrogen/deuteriumexchangeinmassspectrometry. Mass spectrometry reviews37, 811-853 (2018).
18. Kostyukevich, Y., Kononikhin, A., Popov, I. & Nikolaev, E. Analytical description of the H/D exchange kinetic of macromolecule. Analytical chemistry90, 5116-5121 (2018).
19. Kostyukevich, Y. & Nikolaev, E. Ion source multiplexing on a single mass spectrometer. Analytical chemistry90, 3576-3583 (2018).
20. Kostyukevich, Y., Zherebker, A., Vlaskin, M.S., Borisova, L. & Nikolaev, E. Microprobe for the Thermal Analysis of Crude Oil Coupled to Photoionization Fourier Transform Mass Spectrometry. Analytical chemistry90, 8756-8763 (2018).
21. Tsvetkov, V.B. et al. i-Clamp phenoxazine for the fine tuning of DNA i-motif stability. Nucleic acids research46, 2751-2764 (2018).
22. Kostyukevich, Y. et al. Proteomic and lipidomic analysis of mammoth bone by high-resolution tandem mass spectrometry coupled with liquid chromatography. European Journal of Mass Spectrometry24, 411-419 (2018).
23. Gridnev, I.D., Zherebker, A., Kostyukevich, Y. & Nikolaev, E. Methylene Group Transfer in Carbonyl Compounds Discovered in silico and Detected Experimentally. ChemPhysChem20, 361-365 (2019).
24. Kostyukevich, Y. et al. High-Resolution Mass Spectrometry Study of the Bio-Oil Samples Produced by Thermal Liquefaction of Microalgae in Different Solvents. Journal of The American Society for Mass Spectrometry, 1-10 (2019).
25. Kostyukevich, Y. et al. Speciation of structural fragments in crude oil by means of isotope exchange in near- critical water and Fourier Transform Mass Spectrometry. . Analytical and Bioanalytical Chemistry (2019).
26. Kostyukevich, Y., Kitova, A., Zherebker, A., Rukh, S. & Nikolaev, E. Investigation of the archeological remains using ultrahigh resolution mass spectrometry. European Journal of Mass Spectrometry (2019).

Research areas:

High resolution mass-spectrometry, metabolomics, imaging, natural compounds, gas phase ion chemistry, machine learning, structural proteomics, analysis of complex biochemical mixtures, supercomputer modeling of ion cloud behavior in accumulation and transportation of ions, novel approaches for structure elucidation, mass spectrometry in archeology and paleontology.  Research is supported by RFBR and RSF grants.