Quantitative Proteomics for the Development and Manufacturing of Human-Induced Pluripotent Stem Cell-Derived Neural Stem Cells Using Data-Independent Acquisition Mass Spectrometry

DeepMS: super-fast peptide identification using end-to-end deep learning method

Mass spectrometry (MS) has emerged as a powerful omics analysis technique, particularly in proteomics, where the initial step involves identifying MS spectra as peptide sequences. However, this process often requires substantial computational resources and expertise, taking hours or even days to complete, thereby limiting the widespread adoption of MS-based omics technologies. To overcome this challenge, we have developed DeepMS, a deep learning-based spectra identification algorithm that overcomes the speed limitations of traditional spectra identification methods. We conducted comprehensive benchmark tests, comparing six deep learning algorithms. Based on the results, we selected the VGG16 algorithm as the core model for DeepMS. This algorithm enables super-fast, end-to-end identification of peptide sequences from MS spectra with high accuracy. DeepMS is adaptable to post-translational modifications, enhancing its versatility. In fact, its identification speed surpasses the generation rate of MS spectra, enabling super-fast identification. Furthermore, we demonstrate the practical application of DeepMS in microorganism detection, highlighting its utility in clinical testing. Through the implementation of DeepMS, our aim is to revolutionize the field of MS-based proteomics and facilitate the broader application of omics technologies, opening new avenues for rapid and efficient analysis in various research and clinical domains.

Exploring the Therapeutic Potential of the DOT1L Inhibitor EPZ004777 Using Bioinformatics and Molecular Docking Approaches in Acute Myeloid Leukemia

BACKGROUND

Acute myeloid leukemia (AML) is a malignancy characterized by the clonal expansion of hematopoietic stem and progenitor cells, often associated with mutations such as NPM1. DOT1L inhibitors have shown potential as new therapeutic opportunities for NPM1-mutant AML. The aim of this study was to investigate potential alternative targets of the small-molecule inhibitor EPZ004777, in addition to its primary target, DOT1L, using RNA sequencing data from the NCBI-GEO database (GSE85107).

METHODS

Differentially expressed genes (DEGs) were identified through bioinformatic analysis, followed by pathway enrichment analysis to uncover the relevant biological pathways. Additionally, molecular docking analysis was conducted to assess the binding affinity of EPZ004777 with the proteins CT45A3, HOXA4, SNX19, TPBG, and ZNF185, which were identified as significantly DEGs. The protein structures were obtained from AlphaFold and the Protein Data Bank.

RESULTS

EPZ004777 significantly altered gene expression. Oncofetal genes (CT45A3, TPBG) and genes associated with oncogenic pathways (HOXA4, ZNF185, SNX19) were downregulated, while the pro-apoptotic gene BEX3 was upregulated. Pathway enrichment analysis revealed the suppression of the Rap1 signaling pathway and cell adhesion molecules, which may reduce the invasiveness of AML cells. Additionally, upregulation of immune-related pathways suggests enhanced anti-tumor immune responses. Molecular docking analysis demonstrated that EPZ004777 has strong binding potential with SNX19, TPBG, and ZNF185 proteins.

CONCLUSIONS

EPZ004777 has been identified as a potent modulator of SNX19, TPBG, and ZNF185 associated with apoptosis and tumor progression in AML.

Proteomic Approach Using DIA-MS Identifies Morphogenesis-Associated Proteins during Cardiac Differentiation of Human iPS Cells

Human-induced pluripotent stem cell (hiPSC)-derived cardiomyocytes have potential applications in regenerative medicine. The quality by design (QbD) approach enables the efficiency and quality assurance in the manufacturing of hiPSC-derived products. It requires a molecular understanding of hiPSC differentiation throughout the differentiation process; however, information on cardiac differentiation remains limited. Proteins associated with the early stages of cardiac differentiation would be useful in the cardiomyocyte quality assessment. Here, we performed quantitative proteomics of hiPSC intermediate cells in the early phase of cardiac differentiation to better understand their molecular characteristics. Proteomic profiles suggested that day 5-7 cells were in the morphogenetic stage of cardiac differentiation. Trophoblast glycoprotein (TPBG) was the most up-regulated protein in the morphogenetic stage; it was previously shown to be up-regulated during differentiation into neural stem cells. Proteomics of TPBG-knockdown cells revealed that TPBG is involved in cell proliferation and is related to the cardiomyocyte yield, suggesting that it could be used as a marker in QbD development. Our approach helps us understand the molecular basis of hiPSC differentiation and could be a powerful tool in QbD-based manufacturing.

Proteomics applications in next generation induced pluripotent stem cell models

INTRODUCTION

Induced pluripotent stem (iPS) cell technology has transformed biomedical research. New opportunities now exist to create new organoids, microtissues, and body-on-a-chip systems for basic biology investigations and clinical translations.

AREAS COVERED

We discuss the utility of proteomics for attaining an unbiased view into protein expression changes during iPS cell differentiation, cell maturation, and tissue generation. The ability to discover cell-type specific protein markers during the differentiation and maturation of iPS-derived cells has led to new strategies to improve cell production yield and fidelity. In parallel, proteomic characterization of iPS-derived organoids is helping to realize the goal of bridging in vitro and in vivo systems.

EXPERT OPINIONS

We discuss some current challenges of proteomics in iPS cell research and future directions, including the integration of proteomic and transcriptomic data for systems-level analysis.