Proteomic Analysis of Chr 18 Proteins Using 2D Fractionation

Vavilov, Nikita E.; Zgoda, Victor G.; Tikhonova, Olga V.; Farafonova, T. E.; Shushkova, N. A.; Novikova, Svetlana E.; Yarygin, K N; Radko, Sergey P.; Ilgisonis, Ekaterina V.; Ponomarenko, Elena A.; Лисица, А. В.; Archakov, Alexander I.

doi:10.1021/acs.jproteome.0c00856

Cited by 7 publications

(9 citation statements)

References 26 publications

(49 reference statements)

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“…Transcriptomic profiling using the Illumina platform (RNASeq) was reported in two different studies by Poverennaya et al (2017) and by Vavilov et al (2020). Figure 1 shows the results obtained in 2017 and 2020 at different RPKM levels, demonstrating 90% correspondence; therefore, only the results obtained in 2020 were used for the comparative analysis among the three technologies in this study.…”

Section: Resultsmentioning

confidence: 98%

Genome of the Single Human Chromosome 18 as a “Gold Standard” for Its Transcriptome

et al. 2021

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The cutoff level applied in sequencing analysis varies according to the sequencing technology, sample type, and study purpose, which can largely affect the coverage and reliability of the data obtained. In this study, we aimed to determine the optimal combination of parameters for reliable RNA transcriptome data analysis. Toward this end, we compared the results obtained from different transcriptome analysis platforms (quantitative polymerase chain reaction, Illumina RNASeq, and Oxford Nanopore Technologies MinION) for the transcriptome encoded by human chromosome 18 (Chr 18) using the same sample types (HepG2 cells and liver tissue). A total of 275 protein-coding genes encoded by Chr 18 was taken as the gene set for evaluation. The combination of Illumina RNASeq and MinION nanopore technologies enabled the detection of at least one transcript for each protein-coding gene encoded by Chr 18. This combination also reduced the probability of false-positive detection of low-copy transcripts due to the simultaneous confirmation of the presence of a transcript by the two fundamentally different technologies: short reads essential for reliable detection (Illumina RNASeq) and long-read sequencing data (MinION). The combination of these technologies achieved complete coverage of all 275 protein-coding genes on Chr 18, identifying transcripts with non-zero expression levels. This approach can improve distinguishing the biological and technical reasons for the absence of mRNA detection for a given gene in transcriptomics.

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Section: Resultsmentioning

confidence: 98%

Genome of the Single Human Chromosome 18 as a “Gold Standard” for Its Transcriptome

et al. 2021

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“…Targeted MS analysis is more sensitive but suffers from limited multiplexity [3]. In this experiment, 265 proteins encoded by human chromosome 18 were tested with 60 protein identifications registered in the whole sample, which was twice as high as that obtained by the shotgun (panoramic) analysis (Table S4).…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Number of detected proteins as the function of the sensitivity of proteomic technology in human liver cells

Vavilov

Ilgisonis

Lisitsa

et al. 2021

Preprint

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The main goal of the Russian part of C-HPP is to detect and functionally annotate missing proteins (PE2-PE4) encoded by human chromosome 18. However, identifying such proteins in a complex biological mixture using mass spectrometry (MS)-based methods is difficult due to the insufficient sensitivity of proteomic analysis methods. In this study, we determined the proteomic technology sensitivity using a standard set of UPS1 proteins as an example. The results revealed that 100% of proteins in a mixture could only be identified at a concentration of at least 10−9 M. The decrease in concentration leads to protein losses associated with technology sensitivity, and no UPS1 protein is detected at a concentration of 10−13 M. Therefore, two-dimensional fractionation of samples was applied to improve sensitivity. The human liver tissue was examined by selected reaction monitoring and shotgun methods of MS analysis using one-dimensional and two-dimensional fractionation to identify the proteins encoded by human chromosome 18. A total of 134 proteins were identified. The overlap between proteomic and transcriptomic data in human liver tissue was ~50%. This weak convergence is due to the low sensitivity of proteomic technology compared to transcriptomic approaches. Data is available via ProteomeXchange with identifier PXD026997.

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“…The Russian contribution to the project is associated with the study of chromosome 18 proteins on three types of biological material: blood plasma [2], liver tissue, and the HepG2 cell line [3]. Human chromosome 18 encodes 264 genes, of which 255 have evidence at protein level (PE1) in neXtProt database.…”

Section: Introductionmentioning

confidence: 99%

“…There are several methods of protein and peptide fractionation; however, for our task, the most appropriate method is the chromatographic separation of peptides which collects fractions during reverse-phase chromatography under alkaline conditions. Such separation is well reproduced, which allows the creation of a reliable method for selected reaction monitoring (SRM) analysis of each peptide fraction [3].…”

Section: Introductionmentioning

confidence: 99%

Number of Detected Proteins as the Function of the Sensitivity of Proteomic Technology in Human Liver Cells

Vavilov

Ilgisonis

Lisitsa

et al. 2022

CPPS

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Aims: The main goal of the Russian part of C-HPP is to detect and functionally annotate missing proteins (PE2-PE4) encoded by human chromosome 18. To achieve this goal it is necessary to use the most sensitive methods of analysis. Background: However, identifying such proteins in a complex biological mixture using mass spectrometry (MS)-based methods is difficult due to the insufficient sensitivity of proteomic analysis methods. A possible solution to the problem is the pre-fractionation of a complex biological sample at the sample preparation stage Objective: The aims of this study are to measure the detection limit of SRM SIS analysis using a standard set of UPS1 proteins and find a way to enhance the sensitivity of the analysis and to detect proteins encoded by the human chromosome 18 in liver tissue samples and compare the data with transcriptomic analysis of the same samples. Methods: Mass spectrometry, data-dependent acquisition, selected reaction monitoring, high-performance liquid chromatography, data-dependent acquisition in combination with pre-fractionation by alkaline reversed-phase chromatography, selected reaction monitoring in combination with pre-fractionation by alkaline reversed-phase chromatography methods were used in this study. Results: The results revealed that 100% of UPS1 proteins in a mixture could only be identified at a concentration of at least 10-9 М. The decrease in concentration leads to protein losses associated with technology sensitivity, and no UPS1 protein is detected at a concentration of 10-13 М. Therefore, the two-dimensional fractionation of samples was applied to improve sensitivity. The human liver tissue was examined by selected reaction monitoring and shotgun methods of MS analysis using one-dimensional and two-dimensional fractionation to identify the proteins encoded by human chromosome 18. A total of 134 proteins were identified. The overlap between proteomic and transcriptomic data in human liver tissue was ~50%. Conclusion: The sample concentration technique is well suited for a standard UPS1 system that is not contaminated with a complex biological sample. However, it is not suitable for use with a complex biological protein mixture. Thus, it is necessary to develop more sophisticated fractionation systems for the detection of all low-copy proteins. This weak convergence is due to the low sensitivity of proteomic technology compared to transcriptomic approaches. Also, total mRNA was used to perform RNA-seq analysis, but not all detected mRNA molecules could be translated into proteins. This introduces additional uncertainty in the data; in the future, we plan to study only translated mRNA molecules-the translatome. Data is available via ProteomeXchange with identifier PXD026997.

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Proteomic Analysis of Chr 18 Proteins Using 2D Fractionation

Cited by 7 publications

References 26 publications

Genome of the Single Human Chromosome 18 as a “Gold Standard” for Its Transcriptome

Genome of the Single Human Chromosome 18 as a “Gold Standard” for Its Transcriptome

Number of detected proteins as the function of the sensitivity of proteomic technology in human liver cells

Number of Detected Proteins as the Function of the Sensitivity of Proteomic Technology in Human Liver Cells

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