Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
Similar, but not the same: multiomics comparison of human valve interstitial cells and osteoblast osteogenic differentiation expanded with an estimation of data-dependent and data-independent PASEF proteomics. / Лобов, Арсений; Кучур , Полина; Боярская, Надежда Владимировна; Переплетчикова, Дарья ; Тараскин, Иван ; Ивашкин, Андрей ; Костина, Дарья ; Хворова, Ирина Александровна; Успенский, Владимир Евгеньевич; Репкин, Егор Алексеевич; Денисов, Евгений ; Геращенко , Татьяна ; Тихилов, Рашид; Божкова, Светлана; Карелкин, Виталий ; Wang, Chunli ; Xu, Kang ; Малашичева, Анна Борисовна.
в: GigaScience, Том 14, giae110, 06.01.2025.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
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TY - JOUR
T1 - Similar, but not the same: multiomics comparison of human valve interstitial cells and osteoblast osteogenic differentiation expanded with an estimation of data-dependent and data-independent PASEF proteomics.
AU - Лобов, Арсений
AU - Кучур , Полина
AU - Боярская, Надежда Владимировна
AU - Переплетчикова, Дарья
AU - Тараскин, Иван
AU - Ивашкин, Андрей
AU - Костина, Дарья
AU - Хворова, Ирина Александровна
AU - Успенский, Владимир Евгеньевич
AU - Репкин, Егор Алексеевич
AU - Денисов, Евгений
AU - Геращенко , Татьяна
AU - Тихилов, Рашид
AU - Божкова, Светлана
AU - Карелкин, Виталий
AU - Wang, Chunli
AU - Xu, Kang
AU - Малашичева, Анна Борисовна
PY - 2025/1/6
Y1 - 2025/1/6
N2 - Osteogenic differentiation is crucial in normal bone formation and pathological calcification, such as calcific aortic valve disease (CAVD). Understanding the proteomic and transcriptomic landscapes underlying this differentiation can unveil potential therapeutic targets for CAVD. In this study, we employed RNA sequencing transcriptomics and proteomics on a timsTOF Pro platform to explore the multiomics profiles of valve interstitial cells (VICs) and osteoblasts during osteogenic differentiation. For proteomics, we utilized 3 data acquisition/analysis techniques: data-dependent acquisition (DDA)-parallel accumulation serial fragmentation (PASEF) and data-independent acquisition (DIA)-PASEF with a classic library-based (DIA) and machine learning-based library-free search (DIA-ML). Using RNA sequencing data as a biological reference, we compared these 3 analytical techniques in the context of actual biological experiments. We use this comprehensive dataset to reveal distinct proteomic and transcriptomic profiles between VICs and osteoblasts, highlighting specific biological processes in their osteogenic differentiation pathways. The study identified potential therapeutic targets specific for VICs osteogenic differentiation in CAVD, including the MAOA and ERK1/2 pathway. From a technical perspective, we found that DIA-based methods demonstrate even higher superiority against DDA for more sophisticated human primary cell cultures than it was shown before on HeLa samples. While the classic library-based DIA approach has proved to be a gold standard for shotgun proteomics research, the DIA-ML offers significant advantages with a relatively minor compromise in data reliability, making it the method of choice for routine proteomics.
AB - Osteogenic differentiation is crucial in normal bone formation and pathological calcification, such as calcific aortic valve disease (CAVD). Understanding the proteomic and transcriptomic landscapes underlying this differentiation can unveil potential therapeutic targets for CAVD. In this study, we employed RNA sequencing transcriptomics and proteomics on a timsTOF Pro platform to explore the multiomics profiles of valve interstitial cells (VICs) and osteoblasts during osteogenic differentiation. For proteomics, we utilized 3 data acquisition/analysis techniques: data-dependent acquisition (DDA)-parallel accumulation serial fragmentation (PASEF) and data-independent acquisition (DIA)-PASEF with a classic library-based (DIA) and machine learning-based library-free search (DIA-ML). Using RNA sequencing data as a biological reference, we compared these 3 analytical techniques in the context of actual biological experiments. We use this comprehensive dataset to reveal distinct proteomic and transcriptomic profiles between VICs and osteoblasts, highlighting specific biological processes in their osteogenic differentiation pathways. The study identified potential therapeutic targets specific for VICs osteogenic differentiation in CAVD, including the MAOA and ERK1/2 pathway. From a technical perspective, we found that DIA-based methods demonstrate even higher superiority against DDA for more sophisticated human primary cell cultures than it was shown before on HeLa samples. While the classic library-based DIA approach has proved to be a gold standard for shotgun proteomics research, the DIA-ML offers significant advantages with a relatively minor compromise in data reliability, making it the method of choice for routine proteomics.
KW - Aortic Valve Stenosis/metabolism
KW - Aortic Valve/cytology
KW - Calcinosis/metabolism
KW - Cell Differentiation
KW - Cells, Cultured
KW - Gene Expression Profiling/methods
KW - Humans
KW - Multiomics
KW - Osteoblasts/metabolism
KW - Osteogenesis/genetics
KW - Proteome/metabolism
KW - Proteomics/methods
KW - Transcriptome
U2 - 10.1093/gigascience/giae110
DO - 10.1093/gigascience/giae110
M3 - Article
C2 - 39798943
VL - 14
JO - GigaScience
JF - GigaScience
SN - 2047-217X
M1 - giae110
ER -
ID: 129632075