Mass spectrometry-based techniques for single-cell analysis

Xu, Xin; Jiang, Xuening; Shi, Meiyun; Yin, Lei

doi:10.1039/d3an00370a

Cited by 4 publications

(2 citation statements)

References 122 publications

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“…At present, due to the continuous updating of mass spectrometry detection technology, the determination of many tiny components of the human body can be easily accomplished by mass spectrometry, which is of great significance for expanding the detection spectrum and discovering new disease markers. Moreover, the technology has the advantages of gradual maturity and increasing convenience, which has already been popularized in various clinical laboratories, and at the same time, it is being used in many fields such as drug metabolism, biomedicine, and genomics research ( 23 , 24 ). Histones form the core of the eukaryotic ribosome and their N-terminal amino acid residues can be covalently modified to induce transcription ( 25 ).…”

Section: Discussionmentioning

confidence: 99%

Combining proteomic markers to construct a logistic regression model for polycystic ovary syndrome

Tong,

Wu,

Zhuang

et al. 2023

Front. Endocrinol.

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IntroductionProteomics technology has been used in various fields in recent years for the Q6 exploration of novel markers and the study of disease pathogenesis, and has become one of the most important tools for researchers to explore unknown areas. However, there are fewer studies related to the construction of clinical models using proteomics markers.MethodsIn our previous study we used DIA proteomics to screen for proteins that were significant in 31 PCOS patients compared to women of normal reproductive age. In this study, we used logistic regression among these protein markers to screen out variables with diagnostic value and constructed logistic regression models.ResultsWe constructed a logistic model using these protein markers, where HIST1H4A (OR=1.037) was an independent risk factor for polycystic ovary syndrome and TREML1 (OR=0.976) were protective factors for the disease. The logistic regression model equation is: Logit (PCOS) =0.036*[HIST1H4A]-0.024*[TREML1]-16.368. The ROC curve analyzing the diagnostic value of the model has an AUC value of 0.977 and a Youden index of0.903, which gives a cutoff value of 0.518 at this point. The model has a sensitivity of 93.5% and a specificity of 96.8%. Calibration curves show fair consistency of the model.DiscussionOur study is the first to use proteomic results with clinical biochemical data to construct a logistic regression model, and the model is consistent. However, our study still needs a more complete sample to confirm our findings.

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Section: Discussionmentioning

confidence: 99%

Combining proteomic markers to construct a logistic regression model for polycystic ovary syndrome

Tong,

Wu,

Zhuang

et al. 2023

Front. Endocrinol.

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“…This is combined with the MALDI-mediated detection of peptides [190]. The optimized usage of reversed-phase liquid chromatography is crucial prior to the direct injection of peptides using ESI [191] or related techniques for MS analysis [192]. The detailed characterization of proteoforms can be carried out using Fourier-transform ion cyclotron resonance MS (FTMS) [193].…”

Section: Mass Spectrometric Protein Identificationmentioning

confidence: 99%

Mass Spectrometry-Based Proteomic Technology and Its Application to Study Skeletal Muscle Cell Biology

Dowling,

Swandulla,

Ohlendieck

2023

Cells

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Voluntary striated muscles are characterized by a highly complex and dynamic proteome that efficiently adapts to changed physiological demands or alters considerably during pathophysiological dysfunction. The skeletal muscle proteome has been extensively studied in relation to myogenesis, fiber type specification, muscle transitions, the effects of physical exercise, disuse atrophy, neuromuscular disorders, muscle co-morbidities and sarcopenia of old age. Since muscle tissue accounts for approximately 40% of body mass in humans, alterations in the skeletal muscle proteome have considerable influence on whole-body physiology. This review outlines the main bioanalytical avenues taken in the proteomic characterization of skeletal muscle tissues, including top-down proteomics focusing on the characterization of intact proteoforms and their post-translational modifications, bottom-up proteomics, which is a peptide-centric method concerned with the large-scale detection of proteins in complex mixtures, and subproteomics that examines the protein composition of distinct subcellular fractions. Mass spectrometric studies over the last two decades have decisively improved our general cell biological understanding of protein diversity and the heterogeneous composition of individual myofibers in skeletal muscles. This detailed proteomic knowledge can now be integrated with findings from other omics-type methodologies to establish a systems biological view of skeletal muscle function.

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Multimodal techniques and strategies for chemical and metabolic imaging at the single-cell level

Kesharwani,

Gujrati

2024

Current Opinion in Biotechnology

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Mass spectrometry-based techniques for single-cell analysis

Abstract: Cell is the most basic structural unit and plays a vital role in the function of organism. Studying the heterogeneity of cells, especially the qualitative and quantitative analysis of proteins...

Cited by 4 publications

References 122 publications

Combining proteomic markers to construct a logistic regression model for polycystic ovary syndrome

Combining proteomic markers to construct a logistic regression model for polycystic ovary syndrome

Mass Spectrometry-Based Proteomic Technology and Its Application to Study Skeletal Muscle Cell Biology

Multimodal techniques and strategies for chemical and metabolic imaging at the single-cell level

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