Editing Proteomics (section)

==Emerging trends==
A number of emerging concepts have the potential to improve the current features of proteomics. Obtaining absolute quantification of proteins and monitoring post-translational modifications are the two tasks that impact the understanding of protein function in healthy and diseased cells. Further, the throughput and sensitivity of proteomic assays, often measured as samples analyzed per day and depth of proteome coverage, respectively, have driven development of cutting-edge instrumentation and methodologies.<ref>{{Cite journal |last1=Peters-Clarke |first1=Trenton M. |last2=Coon |first2=Joshua J. |last3=Riley |first3=Nicholas M. |date=2024-05-21 |title=Instrumentation at the Leading Edge of Proteomics |url=https://pubs.acs.org/doi/10.1021/acs.analchem.3c04497 |journal=Analytical Chemistry |language=en |volume=96 |issue=20 |pages=7976–8010 |doi=10.1021/acs.analchem.3c04497 |pmid=38738990 |issn=0003-2700}}</ref> For many cellular events, the protein concentrations do not change; rather, their function is modulated by post-translational modifications (PTM). Methods of monitoring PTM are an underdeveloped area in proteomics. Selecting a particular subset of protein for analysis substantially reduces protein complexity, making it advantageous for diagnostic purposes where blood is the starting material. Another important aspect of proteomics, yet not addressed, is that proteomics methods should focus on studying proteins in the context of the environment. The increasing use of chemical cross-linkers, introduced into living cells to fix protein-protein, protein-DNA and other interactions, may ameliorate this problem partially. The challenge is to identify suitable methods of preserving relevant interactions. Another goal for studying proteins is development of more sophisticated methods to image proteins and other molecules in living cells and real-time.<ref name="Weston & Hood 2004"/>

=== 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]].

===Human plasma proteome===
Characterizing the human plasma proteome has become a major goal in the proteomics arena, but it is also the most challenging proteomes of all human tissues.<ref>{{cite journal | vauthors = Anderson NL | title = The clinical plasma proteome: a survey of clinical assays for proteins in plasma and serum | journal = Clinical Chemistry | volume = 56 | issue = 2 | pages = 177–185 | date = February 2010 | pmid = 19884488 | doi = 10.1373/clinchem.2009.126706 | doi-access = free }}</ref> It contains immunoglobulin, cytokines, protein hormones, and secreted proteins indicative of infection on top of resident, hemostatic proteins. It also contains tissue leakage proteins due to the blood circulation through different tissues in the body. The blood thus contains information on the physiological state of all tissues and, combined with its accessibility, makes the blood proteome invaluable for medical purposes. It is thought that characterizing the proteome of blood plasma is a daunting challenge.

The depth of the plasma proteome encompasses a dynamic range of more than 10<sup>10</sup> between the highest abundant protein (albumin) and the lowest (some cytokines) and is thought to be one of the main challenges for proteomics.<ref name="Anderson_2014">{{cite journal | vauthors = Anderson L | title = Six decades searching for meaning in the proteome | journal = Journal of Proteomics | volume = 107 | issue = | pages = 24–30 | date = July 2014 | pmid = 24642211 | doi = 10.1016/j.jprot.2014.03.005 }}</ref> Temporal and spatial dynamics further complicate the study of human plasma proteome. The turnover of some proteins is quite faster than others and the protein content of an artery may substantially vary from that of a vein. All these differences make even the simplest proteomic task of cataloging the proteome seem out of reach. To tackle this problem, priorities need to be established. Capturing the most meaningful subset of proteins among the entire proteome to generate a diagnostic tool is one such priority. Secondly, since cancer is associated with enhanced glycosylation of proteins, methods that focus on this part of proteins will also be useful. Again: multiparameter analysis best reveals a pathological state. As these technologies improve, the disease profiles should be continually related to respective gene expression changes.<ref name="Weston & Hood 2004"/> Due to the above-mentioned problems plasma proteomics remained challenging. However, technological advancements and continuous developments seem to result in a revival of plasma proteomics as it was shown recently by a technology called plasma proteome profiling.<ref>{{cite journal | vauthors = Geyer PE, Kulak NA, Pichler G, Holdt LM, Teupser D, Mann M | title = Plasma Proteome Profiling to Assess Human Health and Disease | journal = Cell Systems | volume = 2 | issue = 3 | pages = 185–195 | date = March 2016 | pmid = 27135364 | doi = 10.1016/j.cels.2016.02.015 | doi-access = free | hdl = 11858/00-001M-0000-002B-A17E-4 | hdl-access = free }}</ref> Due to such technologies researchers were able to investigate inflammation processes in mice, the heritability of plasma proteomes as well as to show the effect of such a common life style change like weight loss on the plasma proteome.<ref>{{cite journal | vauthors = Malmström E, Kilsgård O, Hauri S, Smeds E, Herwald H, Malmström L, Malmström J | title = Large-scale inference of protein tissue origin in gram-positive sepsis plasma using quantitative targeted proteomics | journal = Nature Communications | volume = 7 | pages = 10261 | date = January 2016 | pmid = 26732734 | pmc = 4729823 | doi = 10.1038/ncomms10261 | bibcode = 2016NatCo...710261M }}</ref><ref>{{cite journal | vauthors = Geyer PE, Wewer Albrechtsen NJ, Tyanova S, Grassl N, Iepsen EW, Lundgren J, Madsbad S, Holst JJ, Torekov SS, Mann M | display-authors = 6 | title = Proteomics reveals the effects of sustained weight loss on the human plasma proteome | journal = Molecular Systems Biology | volume = 12 | issue = 12 | pages = 901 | date = December 2016 | pmid = 28007936 | pmc = 5199119 | doi = 10.15252/msb.20167357 }}</ref><ref>{{cite journal | vauthors = Liu Y, Buil A, Collins BC, Gillet LC, Blum LC, Cheng LY, Vitek O, Mouritsen J, Lachance G, Spector TD, Dermitzakis ET, Aebersold R | display-authors = 6 | title = Quantitative variability of 342 plasma proteins in a human twin population | journal = Molecular Systems Biology | volume = 11 | issue = 1 | pages = 786 | date = February 2015 | pmid = 25652787 | pmc = 4358658 | doi = 10.15252/msb.20145728 }}</ref>