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Generating Cell Type-Specific Protein Signatures from Non-symptomatic and Diseased Tissues

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Abstract

Here we demonstrate a technique to generate proteomic signatures of specific cell types within heterogeneous populations. While our method is broadly applicable across biological systems, we have limited the current work to study neural cell types isolated from human, post-mortem Alzheimer’s disease (AD) and aged-matched non-symptomatic (NS) brains. Motivating the need for this tool, we conducted an initial meta-analysis of current, human AD proteomics studies. While the results broadly corroborated major neurodegenerative disease hypotheses, cell type-specific predictions were limited. By adapting our Formaldehyde-fixed Intracellular Target-Sorted Antigen Retrieval (FITSAR) method for proteomics and applying this technique to characterize AD and NS brains, we generated enriched neuron and astrocyte proteomic profiles for a sample set of donors (available at www.fitsarpro.appspot.com). Results showed the feasibility for using FITSAR to evaluate cell-type specific hypotheses. Our overall methodological approach provides an accessible platform to determine protein presence in specific cell types and emphasizes the need for protein-compatible techniques to resolve systems complicated by cellular heterogeneity.

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Abbreviations

Aβ:

Amyloid-beta

AD:

Alzheimer’s disease

D:

Donor

DAPI:

4′,6-diamidino-2-phenylindole

ECM:

Extracellular matrix

ER:

Endoplasmic reticulum

F:

Female

FACS:

Fluorescence-activated cell sorting

FSC:

Forward scatter

FITSAR:

Formaldehyde-fixed Intracellular Target-Sorted Antigen Retrieval

GFAP:

Glial fibrillary acidic protein

H:

Hallmark gene sets

K:

KEGG gene sets

M:

Male

NS:

Non-symptomatic

O1:

Oligodendrocyte marker 1

PBS:

Phosphate saline buffer

PFA:

Paraformaldehyde

PM:

Plasma membrane

PPI:

Protease and phosphatase inhibitor

R:

Reactome gene sets

R#:

Region #

SDS:

Sodium dodecyl sulfate

SSC:

Side scatter

Tryp.:

Trypsin

TUBB3:

β-III tubulin

y.o.:

Years old

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Acknowledgments

The authors would like to thank Rebecca Hamelin and Brown’s Animal Care Facility for providing sentinel rats for method optimization, Dr. Edward Stopa, Terra D. Velilla, and the Brain Tissue Resource Center for providing primary human brain samples, Mark Dooner and the COBRE Flow Cytometry Core at Rhode Island Hospital for his assistance with FACS runs, Dr. TuKiet Lam, Jean Kanyo, Wei Wei Wang, and Keck Mass Spectrometry & Proteomics Resource Core at Yale University for the mass spectrometry sample preparation and data analysis, Dr. Thomas Neubert for his feedback on the proteomics analyses, Dr. Thomas Wisniewski, Dr. Arline Faustin, and the New York University Alzheimer’s Disease Center (funded in part by PHS Grant P30 AG08051) for providing primary human brain tissue sections, and Dr. Ryosuke Kita for his help on using data spectra.

Funding

This work was supported by the National Institutes of Health (R01 AR063642 to EMD, T32 to JSS via T32 AG052909 (Wisniewski, Scharfman)), the National Science Foundation (CAREER CBET 1253189 and EAGER CBET 1547819 to EMD, GRFP 2014183678 to JSS), the Cure Alzheimer’s Fund (SAL), and the Alzheimer’s Disease Resource Center at NYU Langone (SAL and JSS).

Author Contributions

JSS, LAC, HCC, CF, SAL, and EMD designed the study and wrote the manuscript. JSS conducted all experimental work, including cell isolation, FACS, WBs, and proteomics experiments. JSS and LAC conducted all bioinformatics analyses. JSS and HCC conducted all confocal imaging.

Conflict of interest

No benefits in any form have been or will be received from a commercial party related directly or indirectly to the subject of this manuscript.

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Authors and Affiliations

  1. Department of Molecular Pharmacology, Physiology, and Biotechnology, Brown University, Providence, RI, 02912, USA

    Jessica S. Sadick & Eric M. Darling

  2. Neuroscience Institute, NYU Langone Medical Center, New York, NY, 10016, USA

    Jessica S. Sadick & Shane A. Liddelow

  3. Department of Biostatistics, Brown University, Providence, RI, 02912, USA

    Lorin A. Crawford

  4. Center for Statistical Sciences, Brown University, Providence, RI, 02912, USA

    Lorin A. Crawford

  5. Center for Computational Molecular Biology, Brown University, Providence, RI, 02912, USA

    Lorin A. Crawford

  6. Center for Biomedical Engineering, Brown University, Providence, RI, 02912, USA

    Harry C. Cramer III, Christian Franck & Eric M. Darling

  7. School of Engineering, Brown University, Providence, RI, 02912, USA

    Harry C. Cramer III, Christian Franck & Eric M. Darling

  8. Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI, 53706, USA

    Harry C. Cramer III & Christian Franck

  9. Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, 53706, USA

    Christian Franck

  10. Department of Neuroscience and Physiology, NYU Langone Medical Center, New York, NY, 10016, USA

    Shane A. Liddelow

  11. Department of Pharmacology and Therapeutics, The University of Melbourne, Melbourne, VIC, 3010, Australia

    Shane A. Liddelow

  12. Department of Orthopaedics, Brown University, Providence, RI, 02912, USA

    Eric M. Darling

Authors
  1. Jessica S. Sadick
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  2. Lorin A. Crawford
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  4. Christian Franck
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Correspondence to Eric M. Darling.

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Sadick, J.S., Crawford, L.A., Cramer, H.C. et al. Generating Cell Type-Specific Protein Signatures from Non-symptomatic and Diseased Tissues. Ann Biomed Eng 48, 2218–2232 (2020). https://doi.org/10.1007/s10439-020-02507-y

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  • DOI: https://doi.org/10.1007/s10439-020-02507-y

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