1.      How will our data be returned?

  • We provide you with a report in PowerPoint which describes the sample workflow, MS data search parameters, and highlights of the data analysis.
  • We provide the final quantitative data as an excel sheet containing the raw summed signal to noise, percent relative abundance (normalized when appropriate), and total number of peptides matched to each respective protein. We can also provide data on identified proteins, before applying quant filters (e.g. S/N Threshold, isolation specificity).
  • For quantified PTM sites, we provide the data as an excel sheet containing summed signal to noise, percent relative abundance (normalized when appropriate), modified amino acid site with associated ModScore, peptide motif, and total number of quantified spectral matches to each modified peptide.
  • We suggest investigators to collect their raw files of MS analysis.

 2.      How are the data normalized?

 In most cases, we provide sum signal to noise of each detected protein along with scaled relative abundance (%). In some experiments, it may be more appropriate to normalize based on a single protein of interest (eg: bait protein from a quant-IP) or to forgo normalization all together.

  • Scaled relative abundance (%)Here the total signal for each TMT channel is calculated. Normalization factors (ideally 1-1.5) are calculated so that the total signal in each TMT channel is equal to the highest channel. The raw summed signal to noise for each protein is then multiplied by the normalization factor for each TMT channel. The TMT signal for all reagent channels for each protein is then summed, and the signal for each TMT channel is represented as a percentage of the total signal across all channels for that protein.
  • Scaled to a protein – Same as above. But the normalization factor is calculated based on a specific protein.

 3.      What is the difference between identified (protein assembly) and quantified proteins?

  • Protein assembly:  Here all unique and non-unique peptides are assigned to a single protein by parsimony and proteins are aggregated (ie: multiple isoforms collapsed into a single entry unless unique evidence exists for more than one isoform).  The resulting list of proteins represents all proteins identified before applying any quant filters
  • Protein Quant: To improve data quality, we apply two filters at the quantification level.
  • S/N Threshold is the minimum summed TMT signal to noise necessary for a peptide to be included in the quantified dataset. For 10/11-plex experiments, the threshold is set at 200; for 6-plex experiments, the threshold is 100.
  • Isolation specificity (IS) or purity indicates the amount of signal isolated for MS2 analysis that comes from the peptide of interest vs co-eluting species. IS is generally chosen to be greater than 0.5 in a scale of 0 – 1. Having too many interfering ions in a scan can cause data compression.

 4.      What is the difference between a Quantified Site and Quantified Composite Site for PTM analysis?

 If a peptide has more than one PTM site identified, it is called a composite site. Each PTM site will be identified with its unique ModScore, but quantified data will be provided for the peptide as a whole.

 5.      What do the special characters in my peptide sequence mean?

Some common Regular Expression characters used in peptide sequences:

“.”: Last AA in a peptide, typically found after lys or arg residues (trypsin digestion sites).

“]”: TMT modification.

“*”: Methionine Oxidation.

“.-“: terminal peptide in a protein.

“#”: STY Phosphorylation.

 6.      What is a One Hit wonder?

 If only one peptide is assigned to a protein, we call it a one hit wonder. Though only one peptide is required to identify a protein, confidence of detection and quantification increases with two or more peptides. 

7.      How can I visualize my data?

 Data analysis and visualization is up to you. When possible we provide a link to web viewer to help you jump-start your analysis. 

8.      Can TCMP help with method development or customized experiments?

 Please email us to arrange a time to talk about specialized needs or experiments. Extra fees may be charged for experiments requiring additional wet lab supplies, labor, or instrument time.

9.      How do I cite TCMP in publication?

The facility should be cited as the "Thermo Fisher Scientific Center for Multiplexed Proteomics at Harvard Medical School (https://tcmp.hms.harvard.edu)” in the acknowledgment section of any publications using data provided by TCMP. Acknowledgements of individual scientists for outstanding assistance are always appreciated.  Please send us a note to let us know once your paper is published

10.  Can I get a letter of recommendation and/or help with materials and methods for publications/grants?

Please contact us and we will be happy to assist you.

Methods References:

Sample Publications Using TCMP Core Facility:

  1. VRK1 as a synthetic lethal target in VRK2 promoter-methylated cancers of the nervous system.
    So J, Mabe NW, Englinger B, Chow KH, Moyer SM, Yerrum S, Trissal MC, Marques JG, Kwon JJ, Shim B, Pal S, Panditharatna E, Quinn T, Schaefer DA, Jeong D, Mayhew DL, Hwang J, Beroukhim R, Ligon KL, Stegmaier K, Filbin MG, Hahn WC. JCI Insight. 2022 Oct 10;7(19):e158755
  2. A trivalent nucleosome interaction by PHIP/BRWD2 is disrupted in neurodevelopmental disorders and cancer.
    Morgan MAJ, Popova IK, Vaidya A, Burg JM, Marunde MR, Rendleman EJ, Dumar ZJ, Watson R, Meiners MJ, Howard SA, Khalatyan N, Vaughan RM, Rothbart SB, Keogh MC, Shilatifard A. Genes Dev. 2021 Dec 1;35(23-24):1642-1656.
  3. Proteomic landscape of Japanese encephalitis virus-infected fibroblasts.
    Sharma KB, Chhabra S, Aggarwal S, Tripathi A, Banerjee A, Yadav AK, Vrati S, Kalia M. J Gen Virol. 2021 Sep;102(9)
  4. Actinin BioID reveals sarcomere crosstalk with oxidative metabolism through interactions with IGF2BP2.
    Ladha FA, Thakar K, Pettinato AM, Legere N, Ghahremani S, Cohn R, Romano R, Meredith E, Chen YS, Hinson JT. Cell Rep. 2021 Aug 10;36(6):109512.
  5. Diet posttranslationally modifies the mouse gut microbial proteome to modulate renal function.
    Lobel L, Cao YG, Fenn K, Glickman JN, Garrett WS. Science. 2020 Sep 18;369(6510):1518-1524.
  6. Quantitative Proteome Analysis of Atg5-Deficient Mouse Embryonic Fibroblasts Reveals the Range of the Autophagy-Modulated Basal Cellular Proteome.
    Sharma KB, Sharma M, Aggarwal S, Yadav AK, Bhatnagar S, Vrati S, Kalia M. mSystems. 2019 Nov 5;4(6):e00481-19
  7. An assay for de novo kinetochore assembly reveals a key role for the CENP-T pathway in budding yeast.
    Lang J, Barber A, Biggins S. Elife. 2018 Aug 17;7:e37819.
  8. Arsenic targets Pin1 and cooperates with retinoic acid to inhibit cancer-driving pathways and tumor-initiating cells.
    Kozono S, Lin YM, Seo HS, Pinch B, Lian X, Qiu C, Herbert MK, Chen CH, Tan L, Gao ZJ, Massefski W, Doctor ZM, Jackson BP, Chen Y, Dhe-Paganon S, Lu KP, Zhou XZ. Nat Commun. 2018 Aug 9;9(1):3069.
  9. Lessons in PROTAC Design from Selective Degradation with a Promiscuous Warhead.
    Bondeson DP, Smith BE, Burslem GM, Buhimschi AD, Hines J, Jaime-Figueroa S, Wang J, Hamman BD, Ishchenko A, Crews CM. Cell Chem Biol. 2018 Jan 18;25(1):78-87.e5.
  10. Transcription control by the ENL YEATS domain in acute leukaemia.
    Erb MA, Scott TG, Li BE, Xie H, Paulk J, Seo HS, Souza A, Roberts JM, Dastjerdi S, Buckley DL, Sanjana NE, Shalem O, Nabet B, Zeid R, Offei-Addo NK, Dhe-Paganon S, Zhang F, Orkin SH, Winter GE, Bradner JE. Nature. 2017 Mar 9;543(7644):270-274.
  11. Phthalimide conjugation as a strategy for in vivo target protein degradation.
    Winter GE, Buckley DL, Paulk J, Roberts JM, Souza A, Dhe-Paganon S, Bradner JE. Science. 2015 Jun 19;348(6241):1376-81.