Editing Proteomics (section)

=== Systems biology ===
Advances in quantitative proteomics would clearly enable more in-depth analysis of cellular systems.<ref name="Bensimon_2012" /><ref name="Sabidó_2012" /> Another research frontier is the analysis of single cells,<ref>{{cite journal | vauthors = Slavov N | title = Scaling Up Single-Cell Proteomics | language = English | journal = Molecular & Cellular Proteomics | volume = 21 | issue = 1 | pages = 100179 | date = January 2022 | pmid = 34808355 | pmc = 8683604 | doi = 10.1016/j.mcpro.2021.100179 }}</ref><ref>{{cite journal | vauthors = Derks J, Leduc A, Wallmann G, Huffman RG, Willetts M, Khan S, Specht H, Ralser M, Demichev V, Slavov N | display-authors = 6 | title = Increasing the throughput of sensitive proteomics by plexDIA | journal = Nature Biotechnology | pages = 50–59 | date = July 2022 | volume = 41 | issue = 1 | pmid = 35835881 | doi = 10.1038/s41587-022-01389-w | pmc = 9839897 }}</ref> and protein covariation across single cells<ref>{{cite journal | vauthors = Slavov N | title = Learning from natural variation across the proteomes of single cells | journal = PLOS Biology | volume = 20 | issue = 1 | pages = e3001512 | date = January 2022 | pmid = 34986167 | pmc = 8765665 | doi = 10.1371/journal.pbio.3001512 | doi-access = free }}</ref> which reflects biological processes such as protein complex formation, immune functions,<ref>{{Cite journal | vauthors = Huffman RG, Leduc A, Wichmann C, di Gioia M, Borriello F, Specht H, Derks J, Khan S, Emmott E, Petelski AA, Perlman DH | display-authors = 6 |date=2022-03-18 |title=Prioritized single-cell proteomics reveals molecular and functional polarization across primary macrophages | journal = bioRxiv |pages=2022.03.16.484655 |doi=10.1101/2022.03.16.484655| s2cid = 247599981 }}</ref> as well as cell cycle and priming of cancer cells for drug resistance<ref>{{cite journal | vauthors = Leduc A, Huffman RG, Cantlon J, Khan S, Slavov N | title = Exploring functional protein covariation across single cells using nPOP | journal = Genome Biology | volume = 23 | issue = 1 | pages = 261 | date = December 2022 | pmid = 36527135 | pmc = 9756690 | doi = 10.1186/s13059-022-02817-5 | doi-access = free }}</ref> Biological systems are subject to a variety of perturbations ([[cell cycle]], [[cellular differentiation]], [[carcinogenesis]], [[environment (biophysical)]], etc.). [[Transcription (genetics)|Transcriptional]] and [[Translation (biology)|translational]] responses to these perturbations results in functional changes to the proteome implicated in response to the stimulus. Therefore, describing and quantifying proteome-wide changes in protein abundance is crucial towards understanding biological phenomenon more [[Holism|holistically]], on the level of the entire system. In this way, proteomics can be seen as complementary to [[genomics]], [[transcriptomics]], [[epigenomics]], [[metabolomics]], and other [[Omics|-omics]] approaches in integrative analyses attempting to define biological [[phenotype]]s more comprehensively. As an example, ''The Cancer Proteome Atlas'' provides quantitative protein expression data for ~200 proteins in over 4,000 tumor samples with matched transcriptomic and genomic data from [[The Cancer Genome Atlas]].<ref>{{cite journal | vauthors = Li J, Lu Y, Akbani R, Ju Z, Roebuck PL, Liu W, Yang JY, Broom BM, Verhaak RG, Kane DW, Wakefield C, Weinstein JN, Mills GB, Liang H | display-authors = 6 | title = TCPA: a resource for cancer functional proteomics data | journal = Nature Methods | volume = 10 | issue = 11 | pages = 1046–1047 | date = November 2013 | pmid = 24037243 | pmc = 4076789 | doi = 10.1038/nmeth.2650 }}</ref> Similar datasets in other cell types, tissue types, and species, particularly using deep shotgun mass spectrometry, will be an immensely important resource for research in fields like [[Cancer|cancer biology]], [[Developmental biology|developmental]] and [[stem cell]] biology, [[medicine]], and [[Evolution|evolutionary biology]].