Mass Spectrometry

Overview

Mass Spectrometer

The mass spectrometry (MS) laboratory is a Core Service at Barts Cancer Institute and is currently managed by the Centre for Haemato-Oncology. MS has a variety of applications in cell and molecular biology and cancer research, including the analysis of:

  • Pharmacokinetics and pharmacodynamics of drugs
  • Samples' protein composition
  • Protein structures
  • Post-translational modifications of proteins
  • Enzymatic activity

MS is also the preferred analytical method in metabolomics and proteomics.

The MS Laboratory supports BCI researchers through collaborations in a variety of high quality mass spectrometry-based analyses.

The support covers both planning and carrying out proteomics, metabolomics and drug analyses.

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Information for Users

There is a charge to cover MS and reagent costs that the unit incurs to prepare and analyse samples.

There are also services available to external collaborators; if you are interested in collaborating or accessing services offered by the mass spectrometry facility, please contact us to discuss how we may be able to help you:

Internal users

Please visit the BCI Intranet

Equipment

massspec01 ltq-orbitrap-xltm thermofisher scientific

The lab is equipped with three LC-MS/MS systems based on different types of mass spectrometers serving different analytical purposes.

  • LTQ-Orbitrap XL connected to a nanoACQUITY LC - untargeted proteomics analyses

  • Q-TOF connected to a microACQUITY LC - untargeted metabolomics analyses

  • TSQ Vantage connected to an Accela or nanoACQUITY LC - targeted analyses of drugs, metabolites and polypeptides

Services

The MS facility can provide access to advanced MS, proteomics and metabolomics technologies.

Proteomics

A series of analytical techniques that enable:
  1. Protein identification
  2. Determination of protein post-translational modifications such as: phosphorylation, acetylation, and more
  3. Relative quantification of proteins and their modifications
  4. Absolute protein quantification in targeted mode
  5. Molecular weight determination of intact proteins (top-down proteomics)

Metabolomics

Metabolite identification/characterisation; untargeted analysis of metabolites; metabolic profiling and targeted analysis of metabolites by LC-MS/MS.

Sample Preparation

mass_spec_4

The success of mass spectrometry experiments is largely dependent on sample preparation. Therefore, it is strongly recommended that our experienced MS laboratory staff perform the critical steps of the relevant preparation procedures.

Researchers submitting samples need to consult with MS staff regarding the initial steps of sample preparation.

Please contact us prior to starting your MS analysis sample preparation.

Not all “off-the-shelf” methods are applicable to MS analyses, and thus sample preparation protocols and data analysis pipelines must be tailored to the particular application and research question.

Data Analysis

Proteomics data are normally analysed by means of Mascot-driven database searches. Quantitative proteomics data are analysed using appropriate software depending on whether experiments are label-based or label-free. The results are provided in electronic format, including the protein hits, sequences of the corresponding peptides and, if applicable, the location of the modification sites and relative quantities.

Metabolomics experiments should be tailored to the type of analytes of interest (polar, unpolar or both) and data is analysed using the Waters software family.

Targeted analysis of small molecules or proteins often requires optimisation of parent and fragment masses and chromatographic conditions.

The facility has led and provided analytical support to a range of studies of varying natures:

  • Metabolite quantification (Refs 1 and 2).
  • Pharmacokinetic analysis of drugs (Refs 3-5).
  • Proteomic and phosphoproteomics analysis of kinase signalling in a variety of cancer types (Refs 6-14).

In a recent illustrative example (Ref 14), we investigated how the tumour microenvironment modulates the activity of cell signalling pathways in vivo. To achieve this, human colorectal cancer cells were grown either in vitro (cell culture) or in vivo (as tumour xenografts in mice).

We then measured and compared the proteomes and phosphoproteomes of these cells by MS. Due to the specificity of MS, it was possible to distinguish proteins and phosphorylation sites in stromal cells (mouse sequences) from those in cancer cells (human sequences) within tumours.


This would have not been possible with the use of Western blots or other immunochemical techniques as antibodies cross-react with proteins of closely related species. We identified and quantified several thousands of phosphorylation sites modulated by the tumour microenvironment in cancer cells and found that such an environment extensively modulated signalling pathway activity, despite producing only modest changes in absolute protein expression.

This resulted in differences in how two different PI3K inhibitors, currently in clinical trials, induced apoptosis in vivo relative to when the same cells where grown in vitro. This study illustrated how the specificity of MS provides biological insights not achievable through the use of immunochemical techniques.

Publications

Recent published studies supported by the MS facility

  1. Prognostic and therapeutic impact of argininosuccinate synthase-1 control in bladder cancer as monitored longitudinally by PET imaging. Cancer Res. January 2014. [Epub ahead of print]. DOI: 10.1158/0008-5472.CAN-13-1702
    Allen M, Luong P, Hudson C, Leyton J, Delage B, Ghazaly E, Cutts R, Yuan M, Syed N, Lo Nigro C, Lattanzio L, Chmielewska-Kassassir M, Tomlinson I, Roylance R, Whitaker HC, Warren AY, Neal D, Freeza C, Beltran L, Chelala C, Jones LJ, Wu BW, Bomalaski JS, Jackson RC, Lu YJ, Crook T, Lemoine NR, Mather S, Foster J, Sosabowski J, Avril N, Li CF, Szlosarek PW (2013).
  2. Arginine Deprivation With Pegylated Arginine Deiminase Induces Death Of Acute Myeloid Leukaemia Cells In Vivo. Blood: blood-2014-10-608133. [Epub ahead of print]
    Miraki-Moud, F., Ghazaly, E. A., Ariza-McNaughton, L., Hodby, K. A., Clear, A., Anjos-Afonso, F., Liapis, K., Grantham, M., Sohrabi, F., Cavenagh, J., Bomalaski, J. S., Gribben, J. G., Szlosarek, P. W., Bonnet, D., Taussig, D. C. (2015)
  3. Safety and Pharmacokinetics Of Clofarabine In Combination With High-Dose Cytarabine and Liposomal Daunorubicin In Pediatric AML: Results Of a Phase 1 Combination Study By The ITCC Consortium. In: Blood (ASH Annual Meeting Abstracts) 2013; 122 (21): Abstract 2693.
    Zwaan, C. M., Dworzak, M., Klingebiel, T., Rossig, C., Leverger, G., Stary, J., De Bont, E. S., Ramnarian, S., Bertrand, Y., Ghazaly, E. A. And Reinhardt, D. (2013)
  4. T-Cell Dysfunction In CLL Is Mediated Not Only By PD-1/PD-L1 But Also By PD-1/PD-L2 Interactions - Partial Functionality Is Maintained In PD-1 Defined CD8 Subsets and This Can Be Further Promoted By Ibrutinib Treatment. In: Blood (ASH Annual Meeting Abstracts) 2013; 122 (21): Abstract 4120.
    McClanahan, F., Miller, S., Riches, J. C., Ghazaly, E. A., Day, W. P., Capasso, M. and Gribben J. G. (2013)
  5. ProGem1: Phase I first-in-human study of the novel nucleotide, NUC-1031, in adult patients with advanced solid tumors. J. Clin. Oncol. 31(15), 2013: 2576.
    Ghazaly, E. A., Luong, P., Chmielewska-Kassasir, M., Hudson, C., Bomalaski, J. S., Wozniak, L., Avril, N. E., Joel, S. P. and Szlosarek, P. W. (2013)
  6. A comprehensive untargeted UPLC-MS based metabolomic analysis of ASS1-deficient solid tumor cell lines treated with arginine deiminase. In: AACR; Cancer Res 2013; 73(8 Suppl): Abstract nr 1885. DOI: 10.1158/1538-7445.AM2013-1885
    Ghazaly, E. A., Joel, S., Gribben, J. G., Mohammad, T., Emiloju, O., Stavraka, C., Hopkins, T., Gabra, H., Wasan, H., Habib, N. A., Leonard, R. C. F., McGuigan, C., Slusarcyzk, M. and Blagden, S. P. (2013)
  7. Calpain interacts with class IA phosphoinositide 3-kinases regulating their stability and signaling activity. Proc. Natl. Acad. Sci. U.S.A. 108, 16217-16222.
    Casado, P., and Cutillas, P. R. (2011)
  8. A self-validating quantitative mass spectrometry method for assessing the accuracy of high-content phosphoproteomic experiments. Mol. Cell. Proteomics: MCP 10, M110 003079.
    Beltran, L., Chaussade, C., Vanhaesebroeck, B., and Cutillas, P. R. (2011)
  9. Phosphoproteomic analysis of leukemia cells under basal and drug-treated conditions identifies markers of kinase pathway activation and mechanisms of resistance. Mol. Cell. Proteomics: MCP 11, 453-466.
    Alcolea, M. P., Casado, P., Rodriguez-Prados, J. C., Vanhaesebroeck, B., and Cutillas, P. R. (2012)
  10. Phosphoproteomics data classify hematological cancer cell lines according to tumor type and sensitivity to kinase inhibitors. Genome Biol. 14, R37. Casado, P., Alcolea, M. P., Iorio, F., Rodriguez-Prados, J. C., Vanhaesebroeck, B., Saez-Rodriguez, J., Joel, S., and Cutillas, P. R. (2013)
  11. Kinase-substrate enrichment analysis provides insights into the heterogeneity of signaling pathway activation in leukemia cells. Sci. Signal 6, rs6. Casado, P., Rodriguez-Prados, J. C., Cosulich, S. C., Guichard, S., Vanhaesebroeck, B., Joel, S., and Cutillas, P. R. (2013)
  12. Polyamine production is downstream and upstream of oncogenic PI3K signalling and contributes to tumour cell growth. Biochem. J. 450, 619-628. 
    Rajeeve, V., Pearce, W., Cascante, M., Vanhaesebroeck, B., and Cutillas, P. R. (2013)
  13. Environmental stress affects the activity of metabolic and growth factor signaling networks and induces autophagy markers in MCF7 breast cancer cells. Mol. Cell. Proteomics: MCP 13, 836-848.
    Casado, P., Bilanges, B., Rajeeve, V., Vanhaesebroeck, B., and Cutillas, P. R. (2014)
  14. Cross-species Proteomics Reveals Specific Modulation of Signaling in Cancer and Stromal Cells by Phosphoinositide 3-kinase (PI3K) Inhibitors. Mol. Cell. Proteomics: MCP 13, 1457-1470.
    Rajeeve, V., Vendrell, I., Wilkes, E. H., Torbett, N., and Cutillas, P. R. (2014)
  15. Empirical inference of circuitry and plasticity in a kinase signaling network. Proc. Natl. Acad. Sci. U.S.A.
    Wilkes, E. H., Terfve, C., Saez-Rodriguez, J. and Cutillas, P. R. (2015) 

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