1
|
Kuzin AA, Sobolev DI, Eliferov VA, Stupnikova GS, Popov IA, Nikolaev EN, Pekov SI. Matrix-assisted laser desorption/ionization matrix incorporation evaluation algorithm for improved peak coverage and signal-to-noise ratio in mass spectrometry imaging. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2024; 38:e9830. [PMID: 38813850 DOI: 10.1002/rcm.9830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 05/08/2024] [Accepted: 05/08/2024] [Indexed: 05/31/2024]
Abstract
RATIONALE Despite decades of implementation, the selection of optimal sample preparation conditions for matrix-assisted laser desorption/ionization (MALDI) imaging is still ambiguous due to the lack of a universal and comprehensive evaluation methodology. Thus, numerous experiments with different matrix application conditions accompany a translation of the method to novel sample types and matrices. METHODS Mouse brain tissues were covered with 9-aminoacridine through sublimation, followed by recrystallization in vapors of 5% (v/v) methanol solution in water. The samples were analyzed by MALDI time-of-flight mass spectrometry, and the efficiency of lipid and small-molecule ionization was evaluated with different metrics. RESULTS We first investigate the dependency of matrix density and recrystallization conditions on the thickness of an analyte-empty matrix layer to roughly evaluate the laser shot number required to obtain an intense signal with minimal noise. Then, we introduce metrics for the analysis of small imaging datasets (small sample regions) of model samples based on median quantity of peaks in spectra (medQP) and weighted median signal-to-noise ratio (wmSNR). The evaluation of small regions and taking median values for metrics help overcome the sample heterogeneity and allow for the simultaneous comparison of different acquisition parameters. CONCLUSIONS Here, we propose a methodology based on gradual laser ablation of small regions of sample and further implementation of weighted signal-to-noise ratio to assess various matrix application conditions. The proposed approach helps reduce the number of test samples required to determine optimal sample preparation conditions and improve the overall quality of images.
Collapse
Affiliation(s)
- Andrey A Kuzin
- Laboratory for Molecular Medical Diagnostics, Moscow Institute of Physics and Technology, Dolgoprudny, Russian Federation
| | - Daniil I Sobolev
- Laboratory for Mass Spectrometry, Skolkovo Institute of Science and Technology, Moscow, Russian Federation
| | - Vasiliy A Eliferov
- Laboratory for Molecular Medical Diagnostics, Moscow Institute of Physics and Technology, Dolgoprudny, Russian Federation
| | - Galina S Stupnikova
- Laboratory for Molecular Medical Diagnostics, Moscow Institute of Physics and Technology, Dolgoprudny, Russian Federation
| | - Igor A Popov
- Laboratory for Molecular Medical Diagnostics, Moscow Institute of Physics and Technology, Dolgoprudny, Russian Federation
- Laboratory for Translational Medicine, Siberian State Medical University, Tomsk, Russian Federation
| | - Eugene N Nikolaev
- Laboratory for Mass Spectrometry, Skolkovo Institute of Science and Technology, Moscow, Russian Federation
| | - Stanislav I Pekov
- Laboratory for Mass Spectrometry, Skolkovo Institute of Science and Technology, Moscow, Russian Federation
- Laboratory for Translational Medicine, Siberian State Medical University, Tomsk, Russian Federation
- Department of Molecular and Biological Physics, Moscow Institute of Physics and Technology, Dolgoprudny, Russian Federation
| |
Collapse
|
2
|
Sorokin AA, Pekov SI, Zavorotnyuk DS, Shamraeva MM, Bormotov DS, Popov IA. Modern machine-learning applications in ambient ionization mass spectrometry. MASS SPECTROMETRY REVIEWS 2024. [PMID: 38671553 DOI: 10.1002/mas.21886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 03/29/2024] [Accepted: 04/05/2024] [Indexed: 04/28/2024]
Abstract
This article provides a comprehensive overview of the applications of methods of machine learning (ML) and artificial intelligence (AI) in ambient ionization mass spectrometry (AIMS). AIMS has emerged as a powerful analytical tool in recent years, allowing for rapid and sensitive analysis of various samples without the need for extensive sample preparation. The integration of ML/AI algorithms with AIMS has further expanded its capabilities, enabling enhanced data analysis. This review discusses ML/AI algorithms applicable to the AIMS data and highlights the key advancements and potential benefits of utilizing ML/AI in the field of mass spectrometry, with a focus on the AIMS community.
Collapse
Affiliation(s)
- Anatoly A Sorokin
- Laboratory of Molecular Medical Diagnostics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Stanislav I Pekov
- Mass Spectrometry Laboratory, Skolkovo Institute of Science and Technology, Moscow, Russia
- Translational Medicine Laboratory, Siberian State Medical University, Tomsk, Russia
- Department for Molecular and Biological Physics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Denis S Zavorotnyuk
- Laboratory of Molecular Medical Diagnostics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Mariya M Shamraeva
- Laboratory of Molecular Medical Diagnostics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Denis S Bormotov
- Laboratory of Molecular Medical Diagnostics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Igor A Popov
- Laboratory of Molecular Medical Diagnostics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
- Translational Medicine Laboratory, Siberian State Medical University, Tomsk, Russia
| |
Collapse
|
3
|
Shapley Value as a Quality Control for Mass Spectra of Human Glioblastoma Tissues. DATA 2023. [DOI: 10.3390/data8010021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
The automatic processing of high-dimensional mass spectrometry data is required for the clinical implementation of ambient ionization molecular profiling methods. However, complex algorithms required for the analysis of peak-rich spectra are sensitive to the quality of the input data. Therefore, an objective and quantitative indicator, insensitive to the conditions of the experiment, is currently in high demand for the automated treatment of mass spectrometric data. In this work, we demonstrate the utility of the Shapley value as an indicator of the quality of the individual mass spectrum in the classification task for human brain tumor tissue discrimination. The Shapley values are calculated on the training set of glioblastoma and nontumor pathological tissues spectra and used as feedback to create a random forest regression model to estimate the contributions for all spectra of each specimen. As a result, it is shown that the implementation of Shapley values significantly accelerates the data analysis of negative mode mass spectrometry data alongside simultaneous improving the regression models’ accuracy.
Collapse
|
4
|
Bormotov DS, Eliferov VA, Peregudova OV, Zavorotnyuk DS, Bocharov KV, Pekov SI, Sorokin AA, Nikolaev EN, Popov IA. Incorporation of a Disposable ESI Emitter into Inline Cartridge Extraction Mass Spectrometry Improves Throughput and Spectra Stability. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:119-122. [PMID: 36535019 DOI: 10.1021/jasms.2c00207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Rapid and reliable methods for detecting tumor margins are crucial for neuro-oncology. Several mass spectrometry-based methods have been recently proposed to address this problem. Inline Cartridge Extraction (ICE) demonstrates the potential for clinical application, based on ex-vivo analysis of dissected tissues, but requires time-consuming steps to avoid cross-contamination. In this work, a method of incorporating a disposable electrospray emitter into the ICE cartridge by PEEK sleeves melting is developed. It reduces total analysis time and improves throughput. The proposed setup also improves the robustness of the ICE molecular profiling as demonstrated with human glial tumor samples in that stability and reproducibility of the spectra were increased.
Collapse
Affiliation(s)
- Denis S Bormotov
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russian Federation
| | - Vasily A Eliferov
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russian Federation
| | - Olga V Peregudova
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russian Federation
| | - Denis S Zavorotnyuk
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russian Federation
| | - Konstantin V Bocharov
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Moscow 119334, Russian Federation
| | - Stanislav I Pekov
- Skolkovo Institute of Science and Technology, Moscow 121205, Russian Federation
- Siberian State Medical University, Tomsk 634050, Russian Federation
| | - Anatoly A Sorokin
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russian Federation
| | - Eugene N Nikolaev
- Skolkovo Institute of Science and Technology, Moscow 121205, Russian Federation
| | - Igor A Popov
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russian Federation
| |
Collapse
|
5
|
Pekov SI, Zhvansky ES, Eliferov VA, Sorokin AA, Ivanov DG, Nikolaev EN, Popov IA. Determination of Brain Tissue Samples Storage Conditions for Reproducible Intraoperative Lipid Profiling. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27082587. [PMID: 35458785 PMCID: PMC9029908 DOI: 10.3390/molecules27082587] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 04/11/2022] [Accepted: 04/12/2022] [Indexed: 11/16/2022]
Abstract
Ex-vivo molecular profiling has recently emerged as a promising method for intraoperative tissue identification, especially in neurosurgery. The short-term storage of resected samples at room temperature is proposed to have negligible influence on the lipid molecular profiles. However, a detailed investigation of short-term molecular profile stability is required to implement molecular profiling in a clinic. This study evaluates the effect of storage media, temperature, and washing solution to determine conditions that provide stable and reproducible molecular profiles, with the help of ambient ionization mass spectrometry using rat cerebral cortex as model brain tissue samples. Utilizing normal saline for sample storage and washing media shows a positive effect on the reproducibility of the spectra; however, the refrigeration shows a negligible effect on the spectral similarity. Thus, it was demonstrated that up to hour-long storage in normal saline, even at room temperature, ensures the acquisition of representative molecular profiles using ambient ionization mass spectrometry.
Collapse
Affiliation(s)
- Stanislav I. Pekov
- Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (E.S.Z.); (V.A.E.); (A.A.S.); (D.G.I.)
- Siberian State Medical University, 634050 Tomsk, Russia
- Correspondence: (S.I.P.); (E.N.N); (I.A.P.)
| | - Evgeny S. Zhvansky
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (E.S.Z.); (V.A.E.); (A.A.S.); (D.G.I.)
| | - Vasily A. Eliferov
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (E.S.Z.); (V.A.E.); (A.A.S.); (D.G.I.)
| | - Anatoly A. Sorokin
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (E.S.Z.); (V.A.E.); (A.A.S.); (D.G.I.)
- Department of Biochemistry and Systems Biology, Faculty of Health and Life Sciences, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 3BX, UK
| | - Daniil G. Ivanov
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (E.S.Z.); (V.A.E.); (A.A.S.); (D.G.I.)
| | - Eugene N. Nikolaev
- Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
- Correspondence: (S.I.P.); (E.N.N); (I.A.P.)
| | - Igor A. Popov
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (E.S.Z.); (V.A.E.); (A.A.S.); (D.G.I.)
- National Medical Research Center for Obstetrics, Gynecology and Perinatology Named after Academician V.I. Kulakov, 117997 Moscow, Russia
- Correspondence: (S.I.P.); (E.N.N); (I.A.P.)
| |
Collapse
|
6
|
Lipid Profiles of Human Brain Tumors Obtained by High-Resolution Negative Mode Ambient Mass Spectrometry. DATA 2021. [DOI: 10.3390/data6120132] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Alterations in cell metabolism, including changes in lipid composition occurring during malignancy, are well characterized for various tumor types. However, a significant part of studies that deal with brain tumors have been performed using cell cultures and animal models. Here, we present a dataset of 124 high-resolution negative ionization mode lipid profiles of human brain tumors resected during neurosurgery. The dataset is supplemented with 38 non-tumor pathological brain tissue samples resected during elective surgery. The change in lipid composition alterations of brain tumors enables the possibility of discriminating between malignant and healthy tissues with the implementation of ambient mass spectrometry. On the other hand, the collection of clinical samples allows the comparison of the metabolism alteration patterns in animal models or in vitro models with natural tumor samples ex vivo. The presented dataset is intended to be a data sample for bioinformaticians to test various data analysis techniques with ambient mass spectrometry profiles, or to be a source of clinically relevant data for lipidomic research in oncology.
Collapse
|
7
|
Eliferov VA, Zhvansky ES, Sorokin AA, Shurkhay VA, Bormotov DS, Pekov SI, Nikitin PV, Ryzhova MV, Kulikov EE, Potapov AA, Nikolaev EN, Popov IA. The Role of Lipids in the Classification of Astrocytoma and Glioblastoma Using Mass Spectrometry Tumor Profiling. BIOCHEMISTRY (MOSCOW), SUPPLEMENT SERIES B: BIOMEDICAL CHEMISTRY 2021. [DOI: 10.1134/s1990750821020025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
|
8
|
Pekov SI, Bormotov DS, Nikitin PV, Sorokin AA, Shurkhay VA, Eliferov VA, Zavorotnyuk DS, Potapov AA, Nikolaev EN, Popov IA. Rapid estimation of tumor cell percentage in brain tissue biopsy samples using inline cartridge extraction mass spectrometry. Anal Bioanal Chem 2021; 413:2913-2922. [PMID: 33751161 DOI: 10.1007/s00216-021-03220-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 01/28/2021] [Accepted: 02/03/2021] [Indexed: 10/21/2022]
Abstract
Tumor cell percentage (TCP) is an essential characteristic of biopsy samples that directly affects the sensitivity of molecular testing in clinical practice. Apart from clarifying diagnoses, rapid evaluation of TCP combined with various neuronavigation systems can be used to support decision making in neurosurgery. It is known that ambient mass spectrometry makes it possible to rapidly distinguish healthy from malignant tissues. In connection with this, here we demonstrate the possibility of using non-imaging ambient mass spectrometry to evaluate TCP in glial tumor tissues with a high degree of confidence. Molecular profiles of histologically annotated human glioblastoma tissue samples were obtained using the inline cartridge extraction ambient mass spectrometry approach. XGBoost regressors were trained to evaluate tumor cell percentage. Using cross-validation, it was estimated that the TCP was determined by the regressors with a precision of approximately 90% using only low-resolution data. This result demonstrates that ambient mass spectrometry provides an accurate method todetermine TCP in dissected tissues even without implementing mass spectrometry imaging. The application of such techniques offers the possibility to automate routine tissue screening and TCP evaluation to boost the throughput of pathology laboratories. Rapid estimation of tumor cell percentage during neurosurgery.
Collapse
Affiliation(s)
- Stanislav I Pekov
- Skolkovo Institute of Science and Technology, Skolkovo, Moscow region, 143026, Russian Federation.,Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russian Federation
| | - Denis S Bormotov
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russian Federation
| | - Pavel V Nikitin
- N.N. Burdenko National Scientific and Practical Center for Neurosurgery, Moscow, 125047, Russian Federation
| | - Anatoly A Sorokin
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russian Federation
| | - Vsevolod A Shurkhay
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russian Federation.,N.N. Burdenko National Scientific and Practical Center for Neurosurgery, Moscow, 125047, Russian Federation
| | - Vasiliy A Eliferov
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russian Federation
| | - Denis S Zavorotnyuk
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russian Federation
| | - Alexander A Potapov
- N.N. Burdenko National Scientific and Practical Center for Neurosurgery, Moscow, 125047, Russian Federation
| | - Eugene N Nikolaev
- Skolkovo Institute of Science and Technology, Skolkovo, Moscow region, 143026, Russian Federation
| | - Igor A Popov
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russian Federation.
| |
Collapse
|
9
|
Zhvansky E, Sorokin A, Shurkhay V, Zavorotnyuk D, Bormotov D, Pekov S, Potapov A, Nikolaev E, Popov I. Comparison of Dimensionality Reduction Methods in Mass Spectra of Astrocytoma and Glioblastoma Tissues. Mass Spectrom (Tokyo) 2021; 10:A0094. [PMID: 33747696 PMCID: PMC7953827 DOI: 10.5702/massspectrometry.a0094] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 01/21/2021] [Indexed: 11/24/2022] Open
Abstract
Recently developed methods of ambient ionization allow the collection of mass spectrometric datasets for biological and medical applications at an unprecedented pace. One of the areas that could employ such analysis is neurosurgery. The fast in situ identification of dissected tissues could assist the neurosurgery procedure. In this paper tumor tissues of astrocytoma and glioblastoma are compared. The vast majority of the data representation methods are hard to use, as the number of features is high and the amount of samples is limited. Furthermore, the ratio of features and samples number restricts the use of many machine learning methods. The number of features could be reduced through feature selection algorithms or dimensionality reduction methods. Different algorithms of dimensionality reduction are considered along with the traditional noise thresholding for the mass spectra. From our analysis, the Isomap algorithm appears to be the most effective dimensionality reduction algorithm for negative mode, whereas the positive mode could be processed with a simple noise reduction by a threshold. Also, negative and positive mode correspond to different sample properties: negative mode is responsible for the inner variability and the details of the sample, whereas positive mode describes measurement in general.
Collapse
Affiliation(s)
- Evgeny Zhvansky
- Moscow Institute of Physics and Technology, Dolgoprudny,
Moscow Region, Russian Federation
| | - Anatoly Sorokin
- Moscow Institute of Physics and Technology, Dolgoprudny,
Moscow Region, Russian Federation
- Institute of Cell Biophysics RAS, Pushchino, Russian
Federation
- Institute of Systems, Molecular and Integrative Biology,
University of Liverpool, Liverpool, UK
| | - Vsevolod Shurkhay
- Moscow Institute of Physics and Technology, Dolgoprudny,
Moscow Region, Russian Federation
- Federal State Autonomous Institution «N.N. Burdenko
National Scientific and Practical Center for Neurosurgery» of the Ministry of
Healthcare of the Russian Federation, Moscow, Russian Federation
| | - Denis Zavorotnyuk
- Moscow Institute of Physics and Technology, Dolgoprudny,
Moscow Region, Russian Federation
| | - Denis Bormotov
- Moscow Institute of Physics and Technology, Dolgoprudny,
Moscow Region, Russian Federation
| | - Stanislav Pekov
- N.N. Semenov Federal Research Center of Chemical Physics
Russian Academy of Sciences, Moscow, Russian Federation
| | - Alexander Potapov
- Federal State Autonomous Institution «N.N. Burdenko
National Scientific and Practical Center for Neurosurgery» of the Ministry of
Healthcare of the Russian Federation, Moscow, Russian Federation
| | - Evgeny Nikolaev
- Skolkovo Institute of Science and Technology, Moscow,
Russian Federation
| | - Igor Popov
- Moscow Institute of Physics and Technology, Dolgoprudny,
Moscow Region, Russian Federation
| |
Collapse
|
10
|
Eliferov VA, Zhvansky ES, Sorokin AA, Shurkhay VA, Bormotov DS, Pekov SI, Nikitin PV, Ryzhova MV, Kulikov EE, Potapov AA, Nikolaev EN, Popov IA. [The role of lipids in the classification of astrocytoma and glioblastoma using MS tumor profiling]. BIOMEDIT︠S︡INSKAI︠A︡ KHIMII︠A︡ 2020; 66:317-325. [PMID: 32893821 DOI: 10.18097/pbmc20206604317] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Express MS identification of biological tissues has become a much more accessible research method due to the application of direct specimen ionization at atmospheric pressure. In contrast to traditional methods of analysis employing GC-MS methods for determining the molecular composition of the analyzed objects it eliminates the influence of mutual ion suppression. Despite significant progress in the field of direct MS of biological tissues, the question of mass spectrometric profile attribution to a certain type of tissue still remains open. The use of modern machine learning methods and protocols (e.g., "random forests") enables us to trace possible relationships between the components of the sample MS profile and the result of brain tumor tissue classification (astrocytoma or glioblastoma). It has been shown that the most pronounced differences in the mass spectrometric profiles of these tumors are due to their lipid composition. Detection of statistically significant differences in lipid profiles of astrocytoma and glioblastoma may be used to perform an express test during surgery and inform the neurosurgeon what type of malignant tissue he is working with. The ability to accurately determine the boundaries of the neoplastic growth significantly improves the quality of both surgical intervention and postoperative rehabilitation, as well as the duration and quality of life of patients.
Collapse
Affiliation(s)
- V A Eliferov
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Russia
| | - E S Zhvansky
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Russia
| | - A A Sorokin
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Russia
| | - V A Shurkhay
- N.N. Burdenko National Medical Research Center of Neurosurgery, Moscow, Russia
| | - D S Bormotov
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Russia
| | - S I Pekov
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Russia
| | - P V Nikitin
- N.N. Burdenko National Medical Research Center of Neurosurgery, Moscow, Russia
| | - M V Ryzhova
- N.N. Burdenko National Medical Research Center of Neurosurgery, Moscow, Russia
| | - E E Kulikov
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Russia; Federal Research Center "Fundamentals of Biotechnology", RAS, Moscow, Russia
| | - A A Potapov
- N.N. Burdenko National Medical Research Center of Neurosurgery, Moscow, Russia
| | - E N Nikolaev
- Skolkovo Institute of Science and Technology, Moscow, Russia
| | - I A Popov
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Russia
| |
Collapse
|