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Phenotyping Multiple Subsets of Immune Cells In Situ in Formalin-Fixed, Paraffin-Embedded Tissue Sections

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Proteomic Methods in Neuropsychiatric Research

Part of the book series: Advances in Experimental Medicine and Biology ((PMISB,volume 974))

Abstract

Some somatic illnesses such as peripheral tumours can present with psychiatric symptoms. Many of these are characterized by changes in biomarkers related to the inflammation or immune response. Here, we describe a multispectral imaging protocol that can be used to phenotype immune and other cell types through simultaneous imaging of multiple proteins in sections of peripheral solid tumours and other tissues. This approach can also be used to assess the spatial organization of these cells within the tissue.

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References

  1. Patel V, Chisholm D, Parikh R, Charlson FJ, Degenhardt L, Dua T et al (2016) Addressing the burden of mental, neurological, and substance use disorders: key messages from Disease Control Priorities, 3rd edition. Lancet 387:1672–1685

    Article  PubMed  Google Scholar 

  2. American Psychiatric Association (2013) Diagnostic and statistical manual of mental disorders: (DSM-5). 5th edn. American Psychiatric Publishing, Arlington. (31 May 2013). ISBN-13: 978–0890425558

    Google Scholar 

  3. http://www.who.int/classifications/icd/en/bluebook.pdf

  4. Guest FL, Guest PC, Martins-de-Souza D (2016) The emergence of point-of-care blood-based biomarker testing for psychiatric disorders: enabling personalized medicine. Biomark Med 10:431–443

    Article  CAS  PubMed  Google Scholar 

  5. Piccillo GA, Musco A, Manfrini S, Mondati E, Guastella T (2005) Two clinical cases of insulinoma misdiagnosed as psychiatric conditions. Acta Biomed 76:118–122

    PubMed  Google Scholar 

  6. Ding Y, Wang S, Liu J, Yang Y, Liu Z, Li J, Zhang B et al (2010) Neuropsychiatric profiles of patients with insulinomas. Eur Neurol 63:48–51

    Article  PubMed  Google Scholar 

  7. Renca S, Santos G, Cerejeira J (2013) An insulinoma presenting with hypochondriac delusions and food refusal. Int Psychogeriatr 25:1909–1911

    Article  PubMed  Google Scholar 

  8. Winston KY, Dawrant J (2014) A rare case of hypoglycaemia due to insulinoma in an adolescent with acutely altered mental status. J Pediatr Endocrinol Metab 27:773–776

    Article  PubMed  Google Scholar 

  9. Yedinak CG, Fleseriu M (2014) Self-perception of cognitive function among patients with active acromegaly, controlled acromegaly, and non-functional pituitary adenoma: a pilot study. Endocrine 46:585–593

    Article  CAS  PubMed  Google Scholar 

  10. Iglesias P, Bernal C, Díez JJ (2014) Curious cases: acromegaly and schizophrenia: an incidental association? Schizophr Bull 40:740–743

    Article  PubMed  PubMed Central  Google Scholar 

  11. Dimopoulou C, Geraedts V, Stalla GK, Sievers C (2015) Neuropsychiatric and cardiometabolic comorbidities in patients with previously diagnosed Cushing’s disease: a longitudinal observational study. BMJ Open 5:e006134. doi:10.1136/bmjopen-2014-006134

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Izmailov R, Guest PC, Bahn S, Schwarz E (2011) Algorithm development for diagnostic biomarker assays. Int Rev Neurobiol 101:279–298

    Article  PubMed  Google Scholar 

  13. Schwarz E, Guest PC, Steiner J, Bogerts B, Bahn S (2012) Identification of blood-based molecular signatures for prediction of response and relapse in schizophrenia patients. Transl Psychiatry 2:e82

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Chan MK, Krebs MO, Cox D, Guest PC, Yolken RH, Rahmoune H et al (2015) Development of a blood-based molecular biomarker test for identification of schizophrenia before disease onset. Transl Psychiatry 5:e601. doi:10.1038/tp.2015.91

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Freudenreich O, Brockman MA, Henderson DC, Evins AE, Fan X, Walsh JP et al (2010) Analysis of peripheral immune activation in schizophrenia using quantitative reverse-transcription polymerase chain reaction (RT-PCR). Psychiatry Res 176:99–102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Herberth M, Koethe D, Cheng TM, Krzyszton ND, Schoeffmann S, Guest PC et al (2011) Impaired glycolytic response in peripheral blood mononuclear cells of first-onset antipsychotic-naive schizophrenia patients. Mol Psychiatry 16:848–859

    Article  CAS  PubMed  Google Scholar 

  17. Xu Y, Yao Shugart Y, Wang G, Cheng Z, Jin C, Zhang K et al (2016) Altered expression of mRNA profiles in blood of early-onset schizophrenia. Sci Rep 6:16767. doi:10.1038/srep16767

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Wang L, Lockstone HE, Guest PC, Levin Y, Palotás A, Pietsch S et al (2010) Expression profiling of fibroblasts identifies cell cycle abnormalities in schizophrenia. J Proteome Res 9:521–527

    Article  CAS  PubMed  Google Scholar 

  19. Johansson AS, Owe-Larsson B, Hetta J, Lundkvist GB (2016) Altered circadian clock gene expression in patients with schizophrenia. Schizophr Res 174:17–23

    Article  PubMed  Google Scholar 

  20. Krishnamurthy D, Harris LW, Levin Y, Koutroukides TA, Rahmoune H, Pietsch S et al (2013) Metabolic, hormonal and stress-related molecular changes in post-mortem pituitary glands from schizophrenia subjects. World J Biol Psychiatry 14:478–489

    Article  PubMed  Google Scholar 

  21. Stelzhammer V, Alsaif M, Chan MK, Rahmoune H, Steeb H, Guest PC et al (2015) Distinct proteomic profiles in post-mortem pituitary glands from bipolar disorder and major depressive disorder patients. J Psychiatr Res 60:40–48

    Article  PubMed  Google Scholar 

  22. Muller N, Schwarz M (2006) Schizophrenia as an inflammation-mediated dysbalance of glutamatergic neurotransmission. Neurotox Res 10:131–148

    Article  CAS  PubMed  Google Scholar 

  23. Lizano PL, Keshavan MS, Tandon N, Mathew IT, Mothi SS, Montrose DM et al (2016) Angiogenic and immune signatures in plasma of young relatives at familial high-risk for psychosis and first-episode patients: a preliminary study. Schizophr Res 170:115–122

    Article  PubMed  Google Scholar 

  24. Lai CY, Scarr E, Udawela M, Everall I, Chen WJ, Dean B (2016) Biomarkers in schizophrenia: a focus on blood based diagnostics and theranostics. World J Psychiatry 6:102–117

    Article  PubMed  PubMed Central  Google Scholar 

  25. Medzhitov R, Shevach EM, Trinchieri G, Mellor AL, Munn DH, Gordon S et al (2011) Highlights of 10 years of immunology in Nature Reviews Immunology. Nat Rev Immunol 11:693–702

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Mandy FF, Bergeron M, Minkus T (1997) Evolution of Leukocyte Immunophenotyping as Influenced by the HIV/AIDS Pandemic: a short history of the development of gating strategies for CD4^+ T-Cell enumeration. Cytometry 30:157–165

    Article  CAS  PubMed  Google Scholar 

  27. Loken MR (1990) Immunofluorescence techniques. In: Flow cytometry and sorting, 2nd edn. Wiley-Blackwell, New York, p 341–353. (21 Jun. 1990), ISBN-13: 978-0471562351

    Google Scholar 

  28. Zola H, Swart B, Banham A, Barry S, Beare A, Bensussan A et al (2007) CD molecules 2006--human cell differentiation molecules. J Immunol Methods 318:1–5

    Article  Google Scholar 

  29. Lagouros E, Salomao D, Thorland E, Hodge DO, Vile R, Pulido JS (2009) Infiltrative T regulatory cells in enucleated uveal melanomas. Trans Am Ophthalmol Soc 107:223–228

    PubMed  PubMed Central  Google Scholar 

  30. Walling MA, Novak JA, Shepard JR (2009) Quantum dots for live cell and in vivo imaging. Int J Mol Sci 10:441–491

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Akinfieva O, Nabiev I, Sukhanova A (2013) New directions in quantum dot-based cytometry detection of cancer serum markers and tumor cells. Crit Rev Oncol Hematol 86:1–14

    Article  PubMed  Google Scholar 

  32. Chen T, Srinivas C (2015) Group sparsity model for stain unmixing in brightfield multiplex immunohistochemistry images. Comput Med Imaging Graph 46(Pt 1):30–39

    Article  PubMed  Google Scholar 

  33. Buchwalow IB, Minin EA, Boecker W (2005) A multicolor fluorescence immunostaining technique for simultaneous antigen targeting. Acta Histochem 107:143–148

    Article  CAS  PubMed  Google Scholar 

  34. Tóth ZE, Mezey E (2007) Simultaneous visualization of multiple antigens with tyramide signal amplification using antibodies from the same species. J Histochem Cytochem 55:545–554

    Article  PubMed  Google Scholar 

  35. Stack EC, Wang C, Roman KA, Hoyt CC (2014) Multiplexed immunohistochemistry, imaging, and quantitation: a review, with an assessment of Tyramide signal amplification, multispectral imaging and multiplex analysis. Methods 70:46–58

    Article  CAS  PubMed  Google Scholar 

  36. https://www.researchgate.net/publication/283979457_Automated_5-plex_fluorescent_immunohistochemistry_with_tyramide_signal_amplification_using_antibodies_from_the_same_species

  37. Mansfield JR (2014) Multispectral imaging: a review of its technical aspects and applications in anatomic pathology. Vet Pathol 51:185–210

    Article  CAS  PubMed  Google Scholar 

  38. Mansfield JR (2014) Imaging in cancer immunology:phenotyping immune cell subsets in situ in FFPE tissue sections. MLO Med Lab Obs 46:12–13

    PubMed  Google Scholar 

  39. Zhou Y, Guang H, Pu H, Zhang J, Luo J (2016) Unmixing multiple adjacent fluorescent targets with multispectral excited fluorescence molecular tomography. Appl Optics 55:4843–4849

    Article  Google Scholar 

  40. http://www.perkinelmer.co.uk/Content/LST_Software_Downloads/inFormUserManual.pdf

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Author information

Authors and Affiliations

  1. Andor Technology, 5 Millennium Way, Belfast, BT12 7AL, UK

    James R. Mansfield

  2. Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP, Cidade Universitária Zeferino Vaz), Rua Monteiro Lobato 255 F/01, 13083-862, Campinas, Brazil

    Paul C. Guest

  3. Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Jared Burks

Authors
  1. James R. Mansfield
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  2. Paul C. Guest
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  3. Jared Burks
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Correspondence to James R. Mansfield .

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Editors and Affiliations

  1. Department of Biochemistry and Tissue Biology, University of Campinas (UNICAMP), Campinas, Brazil

    Paul C. Guest

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Mansfield, J.R., Guest, P.C., Burks, J. (2017). Phenotyping Multiple Subsets of Immune Cells In Situ in Formalin-Fixed, Paraffin-Embedded Tissue Sections. In: Guest, P. (eds) Proteomic Methods in Neuropsychiatric Research. Advances in Experimental Medicine and Biology(), vol 974. Springer, Cham. https://doi.org/10.1007/978-3-319-52479-5_32

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