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Laboratory for Omics Technologies and big data for Personalized Medicine and Health

Contract number
075-10-2019-083, 075-10-2022-090
Time span of the project
Invited researcher

As of 01.11.2022

Number of staff members
scientific publications
Objects of intellectual property
General information

Name of the project: Next generation proteomics for enhancement of personalized medicine and healthcare

Goals and objectives

Project objective: Development of a modern, reliable, precise, rapid and affordable method for quantitative evaluation of human proteome and its implementation into clinical practices for objectivization of criteria in personalized medicine

The practical value of the study

Scientific results:

  • We have demonstrated that the new prm-PASEF method used in the next-generation mass spectrometer (MS) timsTOF Pro can be efficiently used for the targeted absolute quantitative proteomics with a high level of specificity and sensitivity. We have created and validated a method/toolkit for the quantitative detection of 125 proteins in a single blood plasma sample for use in the timsTOF Pro mass spectrometer with a significantly lower analysis time (5-6 times faster than the available standard method: 5-10 minutes compared to 60 minutes). Our researchers also demonstrated the capability of this MS device to detect more than 1000 peptides in one blood sample.
  • Our researchers have designed a method for the proteomic analysis of dried blood spots  using the timsTOF Pro mass spectrometer, which will make it easier to organize population screenings and personalized health checks in the population.
  • We have developed and tested a method for detecting SARS CoV-2 in buccal epithelial swabs relying on the mass-spectromeyty-based detection of the viral N-protein. The method allows to reliably detect the virus even in patients with a low viral load. This result has been published in the «Journal of Proteome Research» (Q1),  a patent for the invention has been obtained.
  • It has been demonstrated that a combination of technologies of absolute quantitative multi-omics and machine learning is a high-precision forecasting tool for assessing fatalities in COVID-19 patents by analyzing blood samples collected upon admission of the patient to hospital. We have identified 10 protein biomarkers found in blood plasma that have a high forecasting value in terms of COVID-19 fatalities. This result has been published in the Q1 journal «Molecular & Cellular Proteomics». 
  • Our researchers have determined a set of associated proteins for the development of a quantitative method for the study of the Alzheimer's pathogenesis and a potential diagnostic test for this disease that will be able to assess the risk of development of the Alzheimer’s within the following three years in patients with mild cognitive impairment (MCI). This results have been published in the «International Journal of Molecular Science» (Q1).
  • We have determined potential protein biomarkers of peripheral blood for the assessment of the influence of space flight factors on the human organism. 
  • Our researchers have conducted and published several studies confirming the feasibility of the introduction of mass-spectrometry-based quantitative proteomic technologies  into clinical practice, especially in the domain of precision oncology. 

Implemented results of research:

  • The mass-spectrometry-based method of SARS-CoV-2 detection  based on the viral protein has been successfully tested at the V. I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology of the Ministry of Health of Russia .
  • Medical centers of Moscow (the I. V Davydovskiy City Hospital of the Department of Health of the City of Moscow, the Research Institute of Pulmonology of the Federal Medical Biological Agency) are conducting tests of the technology for the forecasting of casualties in COVID-19 patients by analyzing blood plasma proteomes.
  • The technology for the assessment of the risk of development of the Alzheimer's  from an analysis of the blood plasma proteome has completed pilot testing at the  N. A. Alekseev Psychiatric Hospital No. 1 of the Department of Health of the City of Moscow. 

Education and career development:

  • We have created and launched two education courses for master’s degree and postgraduate students of Skoltech in the programs «Live Sciences» and «Computation systems and data analysis in science and technology»: «Omics Technologies» and «Post-genome technologies for high-precision medicine».
  • 4 master’s degree theses and two Candidate of Sceinces have been prepared and defended.
  • On the grounds of the Laboratory two training sessions devoted to quantitative proteomics have been organized for all undergraduate and postgraduate students of Skoltech.   

Organizational and structural changes:

  • On the grounds of the Laboratory a department for the chemical synthesis of peptides for proteomic technologies has been created.
  • We have created a subsidiary of Skoltech — «MRM Proteomics R» LLC — for the commercialization of our achievements. 


  • University of Sharjah (United Arab Emirates): collaborative projects in the domain of  high-precision oncology.
  • Bruker Daltonics (Germany), I. V Davydovskiy City Hospital of the Department of Health of the City of Moscow, Department of Cardiology of the Moscow State University of Medicine and Dentistry,  Institute for Biomedical Problems of the Russian Academy of Sciences, V. N. Orekhovich Institute of Biomedical Chemistry, Research Center of Psychic Health, V. I. Kulakov Research Center for Obstetrics, Gynecology and Perinatology of the Ministry of Health of Russia (Russia): joint research.

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kashirina dn, brzhozovskiy ag, pastushkova lk, kononikhin as, borchers ch, nikolaev en, larina im.
Semiquantitative Proteomic Research of Protein Plasma Profile of Volunteers in 21-Day Head-Down Bed Rest. Front Physiol. 2020 Jul 24;11:678. – Q1.
nikolaev en, indeykina mi, brzhozovskiy ag, bugrova ae, kononikhin as, starodubtseva nl, petrotchenko ev, kovalev gi, borchers ch, sukhikh gt
Mass-Spectrometric Detection of SARS-CoV-2 Virus in Scrapings of the Epithelium of the Nasopharynx of Infected Patients via Nucleocapsid N Protein. J Proteome Res. 2020 Nov 6;19(11):4393-4397. – Q1.
ibrahim s, lan c, chabot c, mitsa g, buchanan m, aguilar-mahecha a, elchebly m, poetz o, spatz a, basik m, batist g, zahedi rp, borchers ch.
Precise Quantitation of PTEN by Immuno-MRM: A Tool To Resolve the Breast Cancer Biomarker Controversy. Anal Chem. 2021 Aug 10;93(31):10816-10824. - Q1
laselva o, qureshi z, zeng zw, petrotchenko ev, ramjeesingh m, hamilton cm, huan lj, borchers ch, pomès r, young r, bear ce.
Identification of binding sites for ivacaftor on the cystic fibrosis transmembrane conductance regulator. iScience. 2021 May 15;24(6):102542. – Q1
froehlich bc, popp r, sobsey ca, ibrahim s, leblanc a, mohammed y, buchanan m, aguilar-mahecha a, pötz o, chen mx, spatz a, basik m, batist g, zahedi rp, borchers ch.
A multiplexed, automated immuno-matrix assisted laser desorption/ionization mass spectrometry assay for simultaneous and precise quantitation of PTEN and p110α in cell lines and tumor tissues. Analyst. 2021 Oct 25;146(21):6566-6575. – Q1
brzhozovskiy a, kononikhin a, bugrova ae, kovalev gi, schmit po, kruppa g, nikolaev en, borchers ch.
The Parallel Reaction Monitoring-Parallel Accumulation-Serial Fragmentation (prm-PASEF) Approach for Multiplexed Absolute Quantitation of Proteins in Human Plasma. Anal Chem. 2022 Feb 1;94(4):2016-2022. - Q1
pastushkova lk, goncharov in, koloteva mi, goncharova ag, kashirina dn, nosovsky am, glebova tm, kononikhin as, borchers ch, nikolaev en, larina im.
Characteristics of blood plasma proteome changes associated with the hemorrhagic purpura of cosmonauts on the first day after long-term space missions. Life Sci Space Res (Amst). 2022 May;33:7-12. - Q3.
zakharova nv, bugrova ae, indeykina mi, fedorova yb, kolykhalov iv, gavrilova si, nikolaev en, kononikhin as.
Proteomic Markers and Early Prediction of Alzheimer's Disease. Biochemistry (Mosc). 2022 Aug;87(8):762-776. - Q2.
richard vr, gaither c, popp r, chaplygina d, brzhozovskiy a, kononikhin a, mohammed y, zahedi rp, nikolaev en, borchers ch
Early Prediction of COVID-19 Patient Survival by Targeted Plasma Multi-Omics and Machine Learning. Mol Cell Proteomics. 2022 Aug 3;21(10):100277. - Q1.
kononikhin as, zakharova nv, semenov sd, bugrova ae, brzhozovskiy ag, indeykina mi, fedorova yb, kolykhalov iv, strelnikova pa, ikonnikova ay, gryadunov da, gavrilova si, nikolaev en.
Prognosis of Alzheimer's Disease Using Quantitative Mass Spectrometry of Human Blood Plasma Proteins and Machine Learning. Int J Mol Sci. 2022 Jul 18;23(14):7907. - Q1
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