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Workflow Kits

iRT Kit

Retention time normalization kit for time-resolved mass spectrometry.

A single pack for 50 samples. 

1x iRT Kit

Product number: Ki-3002

USD

198.00

In stock

iRT – indexed Retention Time – is a dimensionless value that defines chromatographic retention of a peptide for a defined resin type (e.g. C18) relative to the iRT Standard. In contrast to empirical retention time, the iRT value of a peptide is stable and enables accurate prediction of peptide retention for any chromatographic setup.

The iRT Standard contains eleven non-naturally occurring synthetic peptides in a pooled mix. Peptides have been carefully optimized for stability, sensitivity and even retention time spacing over the gradient. The iRT Kit allows straightforward determination of peptide iRT values and calibration of chromatographic systems.

By calibrating your LC system with the iRT Kit you will dramatically increase the throughput of your targeted proteomics (MRM and PRM) experiments - even up to 20 times - by bundling more transitions into one run. The benefits of iRT standards are also important in the next-generation proteomic workflows such as DIA or SWATH. iRT peptides allow high-quality spectral library generation and precise normalization of your DIA or SWATH runs.

The iRT peptides work with both Biognosys software solutions: Spectronaut for DIA or SWATH data analysis and SpectroDive for targeted proteomics experiments.

Please contact us for support.

The 1x iRT Kit contains

  • Dissolution Buffer (1x 2.0 ml tube)
  • iRT Standard (1x 2.0 ml tube)

The kit components are sufficient for approximately 500 injections (or 50 samples) depending on sample volume and injection amount. 

The iRT Kit is used in time resolved mass spectrometry as a two-step process:

  1. Determine iRT values of target peptides in relation to the iRT Standard
  2. Calibrate LC system with iRT Standard and accurately predict retention time of target peptides

See the Quick Reference Card for detailed information on how to use the iRT Kit.

 

 

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Quick Reference Card 

iRT Kit Safety Data Sheet (MSDS)

iRT Kit reference sheet for transitions and sequence information (excel file)

FASTA of iRT peptides for shotgun search

iRT Peptides Volume Calculator (excel file)

iRT Real Sequences lists real iRT sequences and compares them to previously used nominal sequences (excel file)

 

 

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Visit Science Hub to access all scientific and technical content (publications, videos, application notes, posters and more) related to Biognosys technology, products and services.

Peer-reviewed papers

  1. Bruderer R, Sondermann J, Tsou CC, Barrantes-Freer A, Stadelmann C, et al. New targeted approaches for the quantification of data-independent acquisition mass spectrometry. Proteomics. 2017 Mar 20.
  2. Krokhin OV, Spicer V. Generation of accurate peptide retention data for targeted and data independent quantitative LC-MS analysis: Chromatographic lessons in proteomics. Proteomics. 2016; 16(23):2931-2936.
  3. Vowinckel J, Zelezniak A, Kibler A, Bruderer R, Muelleder M, Reiter L, Ralser M. Precise label-free quantitative proteomes in high-throughput by microLC and data-independent SWATH acquisition. bioRxiv Sep 5.
  4. Suwannatrai K, Suwannatrai A, Tabsripair P, Welbat JU, Tangkawattana S, et al. Differential Protein Expression in the Hemolymph of Bithynia siamensis goniomphalos Infected with Opisthorchis viverrini. PLoS Negl Trop Dis. 2016; 10(11):e0005104.
  5. Silva MC, Cheng C, Mair W, Almeida S, Fong H, Biswas MH, Zhang Z, et al. Human iPSC-Derived Neuronal Model of Tau-A152T Frontotemporal Dementia Reveals Tau-Mediated Mechanisms of Neuronal Vulnerability. Stem Cell Reports. 2016; 7(3):325-40. 
  6. Angeleri M, Muth-Pawlak D, Aro EM, Battchikova N. Study of O-Phosphorylation Sites in Proteins Involved in Photosynthesis-Related Processes in Synechocystis sp. Strain PCC 6803: Application of the SRM Approach. J Proteome Res. 2016; 15(12):4638-4652.
  7. Vialas V, Colomé-Calls N, Abian J, Aloria K, Alvarez-Llamas G, Antúnez O, et al. A multicentric study to evaluate the use of relative retention times in targeted proteomics. J Proteomics. 2016; 152:138-149.
  8. Voisinne G, García-Blesa A, Chaoui K, Fiore F, Bergot E, Girard L, et al. Co-recruitment analysis of the CBL and CBLB signalosomes in primary T cells identifies CD5 as a key regulator of TCR-induced ubiquitylation. Mol Syst Biol. 2016; 12(7):876.
  9. Müller DB, Schubert OT, Röst H, Aebersold R, Vorholt JA. Systems-level Proteomics of Two Ubiquitous Leaf Commensals Reveals Complementary Adaptive Traits for Phyllosphere Colonization. Mol Cell Proteomics. 2016; 15(10):3256-3269.
  10. Faridi P, Aebersold R, Caron E. A first dataset toward a standardized community-driven global mapping of the human immunopeptidome. Data Brief. 2016; 7:201-5.
  11. Kusebauch U, Campbell DS, Deutsch EW, Chu CS, Spicer DA, Brusniak MY, et al. Human SRMAtlas: A Resource of Targeted Assays to Quantify the Complete Human Proteome. Cell. 2016; 166(3):766-78.
  12. Bruderer R, Bernhardt OM, Gandhi T, Reiter L. High-precision iRT prediction in the targeted analysis of data-independent acquisition and its impact on identification and quantitation. Proteomics. 2016; 16(15-16): 2246-56.
  13. Trotta A, Suorsa M, Rantala M, Lundin B, Aro EM. Serine and threonine residues of plant STN7 kinase are differentially phosphorylated upon changing light conditions and specifically influence the activity and stability of the kinase. Plant J. 2016.
  14. Anjo SI, Lourenço AS, Melo MN, Santa C, Manadas B. Unraveling Mesenchymal Stem Cells' Dynamic Secretome Through Nontargeted Proteomics Profiling. Methods Mol Biol. 2016;1416:521-49.
  15. Pires AO, Mendes-Pinheiro B, Teixeira FG, Anjo SI, Ribeiro-Samy S, Gomes ED, et al. Unveiling the Differences of Secretome of Human Bone Marrow Mesenchymal Stem Cells, Adipose Tissue-Derived Stem Cells, and Human Umbilical Cord Perivascular Cells: A Proteomic Analysis. Stem Cells Dev. 2016 15; 25(14):1073-83.
  16. Kockmann T, Trachsel C, Panse C, Wahlander A, Selevsek N, Grossmann J, Wolski WE, Schlapbach R. Targeted proteomics coming of age - SRM, PRM and DIA performance evaluated from a core facility perspective. Proteomics. 2016.
  17. Stenemo M, Teleman J, Sjöström M, Grubb G, Malmström E, Malmström J, Niméus E. Cancer associated proteins in blood plasma: Determining normal variation. Proteomics. 2016; 16(13):1928-37.
  18. Singh KD, Roschitzki B, Snoek LB, Grossmann J, Zheng X, Elvin M, et al. Natural Genetic Variation Influences Protein Abundances in C. elegans Developmental Signalling Pathways. PLoS One. 2016; 11(3):e0149418.
  19. Vyas FS, Hargreaves AJ, Bonner PL, Boocock DJ, Coveney C, Dickenson JM. A1 adenosine receptor-induced phosphorylation and modulation of transglutaminase 2 activity in H9c2 cells: A role in cell survival. Biochem Pharmacol. 2016; 107: 41-58.
  20. Ortea I, Rodríguez-Ariza A, Chicano-Gálvez E, Arenas Vacas MS, Jurado Gámez B. Discovery of potential protein biomarkers of lung adenocarcinoma in bronchoalveolar lavage fluid by SWATH MS data-independent acquisition and targeted data extraction. J Proteomics. 2016; 138:106-114.
  21. Bollinger JG, Stergachis AB, Johnson RS, Egertson JD, MacCoss MJ. Selecting Optimal Peptides for Targeted Proteomic Experiments in Human Plasma Using In Vitro Synthesized Proteins as Analytical Standards. Methods Mol Biol. 2016; 1410: 207-221.
  22. Villeneuve LM, Purnell PR, Stauch KL, Callen SE, Buch SJ, Fox HS. HIV-1 transgenic rats display mitochondrial abnormalities consistent with abnormal energy generation and distribution. J Neurovirol. 2016 Feb 3.
  23. Faridi P, Aebersold R, Caron E. A first dataset toward a standardized community-driven global mapping of the human immunopeptidome. Data Brief. 2016; 7: 201-205.
  24. Mair W, Muntel J, Tepper K, Tang S, Biernat J, Seeley WW, et al. FLEXITau: Quantifying Post-translational Modifications of Tau Protein in Vitro and in Human Disease. Anal Chem. 2016; 88(7): 3704-3714.
  25. Holewinski RJ, Parker SJ, Matlock AD, Venkatraman V, Van Eyk JE. Methods for SWATH™: Data Independent Acquisition on TripleTOF Mass Spectrometers. Methods Mol Biol. 2016; 1410: 265-279.
  26. Russell MR, Walker MJ, Williamson AJ, Gentry-Maharaj A, Ryan A, Kalsi J, et al. Protein Z: A putative novel biomarker for early detection of ovarian cancer. Int J Cancer. 2016; 138(12): 2984-2992.
  27. Holman SW, McLean L, Eyers CE. RePLiCal: A QconCAT Protein for Retention Time Standardization in Proteomics Studies. J Proteome Res. 2016; 15(3):1090-1102.
  28. Kirk JA, Chakir K, Lee KH, Karst E, Holewinski RJ, Pironti G, Tunin RS, Pozios I, Abraham TP, de Tombe P, Rockman HA, Van Eyk JE, Craig R, Farazi TG, Kass DA. Pacemaker-induced transient asynchrony suppresses heart failure progression. Sci Transl Med. 2015; 7(319):319ra207.
  29. Feng Y, Picotti P. Selected Reaction Monitoring to Measure Proteins of Interest in Complex Samples: A Practical Guide. Methods Mol Biol. 2016;1394:43-56.
  30. Vuorijoki L, Isojärvi J, Kallio P, Kouvonen P, Aro EM, Corthals GL, Jones PR, Muth-Pawlak D. Development of a Quantitative SRM-Based Proteomics Method to Study Iron Metabolism of Synechocystis sp. PCC 6803. J Proteome Res. 2016; 15(1):266-279.
  31. Tyleckova J, Valekova I, Zizkova M, Rakocyova M, Marsala S, Marsala M, Gadher SJ, Kovarova H. Surface N-glycoproteome patterns reveal key proteins of neuronal differentiation. J Proteomics. 2016; 132:13-20.
  32. Caron E, Espona L, Kowalewski DJ, Schuster H, Ternette N, Alpízar A, et al. An open-source computational and data resource to analyze digital maps of immunopeptidomes. Elife. 2015 Jul 8;4. doi: 10.7554/eLife.07661.
  33. Thomas SN, Harlan R, Chen J, Aiyetan P, Liu Y, Sokoll LJ, Aebersold R, Chan DW, Zhang H. Multiplexed Targeted Mass Spectrometry-Based Assays for the Quantification of N-Linked Glycosite-Containing Peptides in Serum. Anal Chem. 2015 Oct 21.
  34. Martins-Marques T, Anjo SI, Pereira P, Manadas B, Girao H. Interacting Network of the Gap Junction (GJ) Protein Connexin43 (Cx43) is Modulated by Ischemia and Reperfusion in the Heart. Mol Cell Proteomics. 2015; 14(11): 3040-3055.
  35. Chen L, Li J, Guo T, Ghosh S, Koh SK, Tian D, et al. Global Metabonomic and Proteomic Analysis of Human Conjunctival Epithelial Cells (IOBA-NHC) in Response to Hyperosmotic Stress. J Proteome Res. 2015; 14(9): 3982-3995.
  36. Teixeira FG, Panchalingam KM, Anjo SI, Manadas B, Pereira R, Sousa N, Salgado AJ, Behie LA. Do hypoxia/normoxia culturing conditions change the neuroregulatory profile of Wharton Jelly mesenchymal stem cell secretome? Stem Cell Res Ther. 2015; 6:133.
  37. Schaefer KN, Geil WM, Sweredoski MJ, Moradian A, Hess S, Barton JK. Oxidation of p53 through DNA charge transport involves a network of disulfides within the DNA-binding domain. Biochemistry. 2015 Jan 27;54(3):932-41.
  38. Mueller S, Wahlander A, Selevsek N, Otto C, Ngwa EM, Poljak K, Frey AD, Aebi M, Gauss R. Protein degradation corrects for imbalanced subunit stoichiometry in OST complex assembly. Mol Biol Cell. 2015; 26(14):2596-608.
  39. Sjöström M, Ossola R, Breslin T, Rinner O, Malmström L, Schmidt A, Aebersold R, Malmström J, Niméus E. A Combined Shotgun and Targeted Mass Spectrometry Strategy for Breast Cancer Biomarker Discovery. J Proteome Res. 2015; 14(7):2807-18.
  40. Schlage P, Kockmann T, Kizhakkedathu JN, Auf dem Keller U. Monitoring matrix metalloproteinase activity at the epidermal-dermal interface by SILAC-iTRAQ-TAILS. Proteomics. 2015; 15(14):2491-502.
  41. Schubert OT, Gillet LC, Collins BC, Navarro P, Rosenberger G, Wolski WE, et al. Building high-quality assay libraries for targeted analysis of SWATH MS data. Nat Protoc. 2015; 10(3):426-41.
  42. DeBlasio SL, Johnson R, Sweeney MM, Karasev A, Gray SM, MacCoss MJ, Cilia M. The Potato leafroll virus structural proteins manipulate overlapping, yet distinct protein interaction networks during infection. Proteomics. 2015 Mar 19. doi: 10.1002/pmic.201400594.
  43. Frankl-Vilches C, Kuhl H, Werber M, Klages S, Kerick M, Bakker A, et al. Using the canary genome to decipher the evolution of hormone-sensitive gene regulation in seasonal singing birds. Genome Biol. 2015; 16(1):19.
  44. Liu Y, Buil A, Collins BC, Gillet LC, Blum LC, Cheng LY, et al. Quantitative variability of 342 plasma proteins in a human twin population. Mol Syst Biol. 2015; 11(2):786.
  45. Schaefer KN, Geil WM, Sweredoski MJ, Moradian A, Hess S, Barton JK. Oxidation of p53 through DNA Charge Transport Involves a Network of Disulfides within the DNA-Binding Domain. Biochemistry. 2015; 54(3):932-41.
  46. Tharakan R, Tao D, Ubaida-Mohien C, Dinglasan RR, Graham DR. Integrated Microfluidic Chip and Online SCX Separation Allows Untargeted Nanoscale Metabolomic and Peptidomic Profiling. J Proteome Res. 2015.
  47. Soste M, Hrabakova R, Wanka S, Melnik A, Boersema P, Maiolica A, et. al. A sentinel protein assay for simultaneously quantifying cellular processes. Nat Methods. 2014; 11(10):1045-8.
  48. Lebert D, Louwagie M, Goetze S, Picard G, Ossola R, Duquesne C, Basler K, Ferro M, Rinner O, Aebersold R, Garin J, Mouz N, Brunner E, Brun V. DIGESTIF: A Universal Quality Standard for the Control of Bottom-Up Proteomics Experiments. J Proteome Res. 2014 Dec 30.
  49. Sabino F, Hermes O, Egli FE, Kockmann T, Schlage P, Croizat P, et al. In Vivo Assessment of Protease Dynamics in Cutaneous Wound Healing by Degradomics Analysis of Porcine Wound Exudates. Mol Cell Proteomics. 2014 Dec 16. pii: mcp.M114.043414
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  53. Rosenberger G, Koh CC, Guo T, Röst HL, Kouvonen P, Collins BC, et al. A repository of assays to quantify 10,000 human proteins by SWATH-MS. Scientific Data. 2014; 1:140031.
  54. Wu Y, Williams EG, Dubuis S, Mottis A, Jovaisaite V, Houten SM, Argmann CA, Faridi P, Wolski W, Kutalik Z, Zamboni N, Auwerx J, Aebersold R. Multilayered genetic and omics dissection of mitochondrial activity in a mouse reference population. Cell. 2014; 158(6):1415-30.
  55. Schütz S, Fischer U, Altvater M, Nerurkar P, Peña C, Gerber M, Chang Y, Caesar S, Schubert OT, Schlenstedt G, Panse VG. A RanGTP-independent mechanism allows ribosomal protein nuclear import for ribosome assembly. Elife. 2014; 3:e03473.
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  59. Sjöholm K, Karlsson C, Linder A, Malmström J. A comprehensive analysis of the Streptococcus pyogenes and human plasma protein interaction network. Mol Biosyst. 2014; 10(7):1698-708. 
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If we forgot to include any paper using our technology in the list above, please don’t hesitate to let us know at info@biognosys.com.

Please contact us for support.

What does iRT stand for?

iRT is stands for indexed retention time. It is a dimensionless value that allows the transferring of retention time information of a peptide to different chromatographic set ups. 

What are iRT peptides?

iRT peptides are 11 non-naturally occurring chemically synthesized peptides that are used in the next generation proteomics workflows to accurately calculate the indexed retention time of peptides on a chromatographic set up. 

Why is it beneficial to add the iRT peptides to all my samples?

Apart from predicting the retention time the iRT peptides are used for mass calibration and QC monitoring of the LC-MS set up. In an HRM-MS experiment the iRT or HRM peptides are further used as anchor points for the data analysis. 

What is the difference between the iRT peptides and the HRM calibration peptides?

On top of the 11 peptides in the iRT kit the HRM calibration kit contains additional peptides. These are used in the older versions of Spectronaut. With the new algorithm of SN 9 the HRM peptides are no longer a requirement and can be replaced with the iRT peptides if desired. This has the advantage that only one set of peptides are required for customers using all three next-generation proteomics workflows offered by Biognosys. 

Do I really need to add the iRT peptides to all my runs because they make the per sample costs higher?

As of Spectronaut 9 the iRT peptides are not required in all cases for the samples used for spectral library generation. Yet, we strongly advise using them as in seldom cases spectral library generation can fail without them. For all DIA runs the peptides are required by the software. Adding the iRT peptides to the sample increase the per sample costs by less than 1% (~30cents/sample). Further, the dilution can be optimized on the newest generation of mass spectrometers to further reduce the per run cost additions.

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