Considerations for mass spectrometry-based multi-omic analysis of clinical samples

Teclemariam, Esei T; Pergande, Melissa R; Cologna, Stephanie M.

doi:10.1080/14789450.2020.1724540

Cited by 4 publications

(3 citation statements)

References 115 publications

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“…Until this is done, we will be unable to advance our methods or thus deep and routine proteomic analyses to the extent that is both necessary and sufficient for unbiased identification of genuine highly selective biomarkers and drug targets. That said, there are indeed critical ongoing efforts to improve both sample and data analyses, and the best of these must be integrated into a continuously developing unified approach to proteoform and thus proteome analysis [29,81,88,[257][258][259][260][261][262][263][264][265][266][267][268][269][270][271][272][273][274]. We are hopeful and confident of a more collaborative and unbiased future for the discipline of proteomics.…”

Section: What Next?mentioning

confidence: 99%

Proteomes Are of Proteoforms: Embracing the Complexity

2021

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Proteomes are complex—much more so than genomes or transcriptomes. Thus, simplifying their analysis does not simplify the issue. Proteomes are of proteoforms, not canonical proteins. While having a catalogue of amino acid sequences provides invaluable information, this is the Proteome-lite. To dissect biological mechanisms and identify critical biomarkers/drug targets, we must assess the myriad of proteoforms that arise at any point before, after, and between translation and transcription (e.g., isoforms, splice variants, and post-translational modifications [PTM]), as well as newly defined species. There are numerous analytical methods currently used to address proteome depth and here we critically evaluate these in terms of the current ‘state-of-the-field’. We thus discuss both pros and cons of available approaches and where improvements or refinements are needed to quantitatively characterize proteomes. To enable a next-generation approach, we suggest that advances lie in transdisciplinarity via integration of current proteomic methods to yield a unified discipline that capitalizes on the strongest qualities of each. Such a necessary (if not revolutionary) shift cannot be accomplished by a continued primary focus on proteo-genomics/-transcriptomics. We must embrace the complexity. Yes, these are the hard questions, and this will not be easy…but where is the fun in easy?

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Section: What Next?mentioning

confidence: 99%

Proteomes Are of Proteoforms: Embracing the Complexity

2021

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“…Furthermore, the comprehensive integration of omics data, such as genomics and transcriptomics, can provide a summary of how an entire system is behaving. 7 The differential proteomes of human SM between subjects with Duchenne muscular dystrophy (DMD), Becker muscular dystrophy (BMD), and healthy individuals have been elucidated [8][9][10] ; differences between DMD and healthy tissue are also seen in cell culture, 11 mouse, [12][13][14][15][16] pig, 17 and canine models for DMD. 18,19 These studies revealed variations in the extracellular matrix proteomes between DMD and BMD, which may contribute to differences in symptom severity.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, significant advances have been made in biomarker and drug discovery, as well as personalized medicine. Furthermore, the comprehensive integration of omics data, such as genomics and transcriptomics, can provide a summary of how an entire system is behaving 7 …”

Section: Introductionmentioning

confidence: 99%

Relative quantification of progressive changes in healthy and dysferlin‐deficient mouse skeletal muscle proteomes

Golding,

Li,

Blank

et al. 2023

Muscle and Nerve

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Introduction/AimsIndividuals with dysferlinopathies, a group of genetic muscle diseases, experience delay in the onset of muscle weakness. The cause of this delay and subsequent muscle wasting are unknown, and there are currently no clinical interventions to limit or prevent muscle weakness. To better understand molecular drivers of dysferlinopathies, age‐dependent changes in the proteomic profile of skeletal muscle (SM) in wild‐type (WT) and dysferlin‐deficient mice were identified.MethodsQuadriceps were isolated from 6‐, 18‐, 42‐, and 77‐wk‐old C57BL/6 (WT, Dysf+/+) and BLAJ (Dysf−/−) mice (n = 3, 2 male/1 female or 1 male/2 female, 24 total). Whole‐muscle proteomes were characterized using liquid chromatography‐mass spectrometry with relative quantification using TMT10plex isobaric labeling. Principle component analysis was utilized to detect age‐dependent proteomic differences over the lifespan of, and between, WT and dysferlin‐deficient SM. The biological relevance of proteins with significant variation was established using Ingenuity Pathway Analysis.ResultsOver 3200 proteins were identified between 6‐, 18‐, 42‐, and 77‐wk‐old mice. In total, 46 proteins varied in aging WT SM (p < .01), while 365 varied in dysferlin‐deficient SM. However, 569 proteins varied between aged‐matched WT and dysferlin‐deficient SM. Proteins with significant variation in expression across all comparisons followed distinct temporal trends.DiscussionProteins involved in sarcolemma repair and regeneration underwent significant changes in SM over the lifespan of WT mice, while those associated with immune infiltration and inflammation were overly represented over the lifespan of dysferlin‐deficient mice. The proteins identified herein are likely to contribute to our overall understanding of SM aging and dysferlinopathy disease progression.

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