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Spatiotemporal Proteomic Profiling of Human Cerebral Development*

  • Ugljesa Djuric
    Affiliations
    Laboratory Medicine and Pathology Program, University Health Network, Toronto, Ontario, M5G 2C4, Canada
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  • Deivid C. Rodrigues
    Affiliations
    Department of Developmental and Stem Cell Biology, The Hospital for Sick Children, Toronto, Ontario, M5G 1L7, Canada
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  • Ihor Batruch
    Affiliations
    Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, Ontario, M5G 1X5, Canada
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  • James Ellis
    Affiliations
    Department of Developmental and Stem Cell Biology, The Hospital for Sick Children, Toronto, Ontario, M5G 1L7, Canada

    Department of Molecular Genetics, University of Toronto, Toronto, Ontario, M5S 1A8, Canada
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  • Patrick Shannon
    Affiliations
    Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, Ontario, M5G 1X5, Canada

    Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, M5S 1A1, Canada; and
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  • Phedias Diamandis
    Correspondence
    To whom correspondence should be addressed: Laboratory Medicine and Pathobiology, University of Toronto; E-mail:.
    Affiliations
    Laboratory Medicine and Pathology Program, University Health Network, Toronto, Ontario, M5G 2C4, Canada

    Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, M5S 1A1, Canada; and

    Princess Margaret Cancer Centre, MacFeeters-Hamilton Centre for Neuro-Oncology Research, College Street 101, Toronto, ON, M5G 1L7, Canada
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  • Author Footnotes
    * The research was supported by the Department of Pathology at the University Health Network (PD) and Ontario Brain Institute (POND network to JE). DCR was supported by a RettSyndrome.org Mentored Fellowship. Authors declare no conflict of interest.
    This article contains supplemental material.
Open AccessPublished:July 07, 2017DOI:https://doi.org/10.1074/mcp.M116.066274
      Mass spectrometry (MS) analysis of human post-mortem central nervous system (CNS) tissue and induced pluripotent stem cell (iPSC)-based directed differentiations offer complementary avenues to define protein signatures of neurodevelopment. Methodological improvements of formalin-fixed, paraffin-embedded (FFPE) protein isolation now enable widespread proteomic analysis of well-annotated archival tissue samples in the context of development and disease. Here, we utilize a shotgun label-free quantification (LFQ) MS method to profile magnetically enriched human cortical neurons and neural progenitor cells (NPCs) derived from iPSCs. We use these signatures to help define spatiotemporal protein dynamics of developing human FFPE cerebral regions. We show that the use of high resolution Q Exactive mass spectrometers now allow simultaneous quantification of >2700 proteins in a single LFQ experiment and provide sufficient coverage to define novel biomarkers and signatures of NPC maintenance and differentiation. Importantly, we show that this abbreviated strategy allows efficient recovery of novel cytoplasmic, membrane-specific and synaptic proteins that are shared between both in vivo and in vitro neuronal differentiation. This study highlights the discovery potential of non-comprehensive high-throughput proteomic profiling of unfractionated clinically well-annotated FFPE human tissue from a diverse array of development and diseased states.
      Interrogation of translational outputs of biological tissues unifies some of the basic tenets of molecular biology. Proteomic signatures of cellular identity shed light on cell type-specific functions and can be used to elucidate molecular underpinnings of pathological processes. MS-based proteomic workflows have made great strides in achieving near genome-wide coverage and outlining draft atlases of the human proteome (
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      1The abbreviations used are: FFPE, formalin-fixed, paraffin-embedded; LFQ, label-free quantification; iPSC, induced pluripotent stem cell; NPC, neural progenitor cell; GW, gestational week; VZ, ventricular zone; IZ, intermediate zone; SP, subplate; Cx, Cortex.
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      ). As proteins represent a more functional readout of biologic activity, overlaying equivalent proteomic information through CNS development represents a gap in the field. Because obtaining well-annotated anatomical structures of fresh human brains for cellular isolation from different stages of development is much more difficult, neuronal differentiations in human-derived cell culture offer a complementary strategy for defining relevant cell type-specific proteome changes. Thus, to profile cerebral neurodevelopment-related protein dynamics we compared protein abundance changes between NPC- and neuron-rich cellular layers of the FFPE cerebrum to those found when neural precursors are induced to differentiate under defined culture conditions. Our parallel screens identify compartment- and time-specific protein abundance changes. These changes include both well-established, such as NES and SATB2, and novel, previously uncharacterized markers (Filamin C, FLNC, and Cellular retinoic acid biniding protein 1, CRABP1) of neurodevelopment.

      DISCUSSION

      With increasing technological advancements, the complex human proteome can now be resolved using mass spectrometry. Although large-scale efforts are profiling human tissues at large to define tissue-specific proteomic signatures, developmental- and region-specific proteomic discoveries are sparse. Combination of this emerging proteomic profiling technology with widely available archival human FFPE tissue promises to uncover large amounts of disease-relevant molecular information. This has recently been achieved in human B-cell lymphoma (
      • Deeb S.J.
      • Tyanova S.
      • Hummel M.
      • Schmidt-Supprian M.
      • Cox J.
      • Mann M.
      Machine learning based classification of diffuse large B-cell lymphoma patients by their protein expression profiles.
      ) and breast cancer (
      • Tyanova S.
      • Albrechtsen R.
      • Kronqvist P.
      • Cox J.
      • Mann M.
      • Geiger T.
      Proteomic maps of breast cancer subtypes.
      ). Here we use and validate a simplified and convenient adaptation of traditional proteomic profiling approaches to generate the first LFQ data set of iPS cell-mediated neuronal differentiations and a spatiotemporal snapshot of human neurodevelopment protein dynamics. Although we focused our current study to a well-defined cortical region, the practical modifications can be adapted to additional brain regions, developmental ages, and human diseases states. Higher protein yields can be achieved using carefully selected fresh or frozen tissue or by using higher protein quantities coupled with fractionation. The ability to rapidly perform profiling on microscopically defined regions however, makes this approach highly amenable for high-throughput screening of relevant complex and highly dynamic biological processes. Our modification of traditional protocols involving extensive fractionations and detergent cleanup steps, reduces the mass spectrometry time to under 2 h per sample enabling the quantification of 100 samples within 10 days of machine analysis time. This procedure is however not without its likely limitations and caveats. Primarily, a multitude of CNS proteins were not identified using our approach which could partly be a result of the limited area (anterior frontal lobe) we sampled or because of specific developmental windows we profiled. Additional regions and stages of maturation will likely increase total protein counts. With that said, we demonstrate the power and utility of spatiotemporal proteomic profiling in uncovering novel biological markers and pathological processes not possible by using highly fractionated static and bulk-tissue profiling approaches.
      Tissue heterogeneity is also a factor, as with many of the large-scale transcriptomics and proteomic screening efforts (
      • Kim M.S.
      • Pinto S.M.
      • Getnet D.
      • Nirujogi R.S.
      • Manda S.S.
      • Chaerkady R.
      • Madugundu A.K.
      • Kelkar D.S.
      • Isserlin R.
      • Jain S.
      • Thomas J.K.
      • Muthusamy B.
      • Leal-Rojas P.
      • Kumar P.
      • Sahasrabuddhe N.A.
      • Balakrishnan L.
      • Advani J.
      • George B.
      • Renuse S.
      • Selvan L.D.
      • Patil A.H.
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      • Sahu A.
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      • Sharma J.
      • Murthy K.R.
      • Syed N.
      • Goel R.
      • Khan A.A.
      • Ahmad S.
      • Dey G.
      • Mudgal K.
      • Chatterjee A.
      • Huang T.C.
      • Zhong J.
      • Wu X.
      • Shaw P.G.
      • Freed D.
      • Zahari M.S.
      • Mukherjee K.K.
      • Shankar S.
      • Mahadevan A.
      • Lam H.
      • Mitchell C.J.
      • Shankar S.K.
      • Satishchandra P.
      • Schroeder J.T.
      • Sirdeshmukh R.
      • Maitra A.
      • Leach S.D.
      • Drake C.G.
      • Halushka M.K.
      • Prasad T.S.K.
      • Hruban R.H.
      • Kerr C.L.
      • Bader G.D.
      • Iacobuzio-Donahue C.A.
      • Gowda H.
      • Pandey A.
      A draft map of the human proteome.
      ,
      • Wilhelm M.
      • Schlegl J.
      • Hahne H.
      • Gholami A.M.
      • Lieberenz M.
      • Savitski M.M.
      • Ziegler E.
      • Butzmann L.
      • Gessulat S.
      • Marx H.
      • Mathieson T.
      • Lemeer S.
      • Schnatbaum K.
      • Reimer U.
      • Wenschuh H.
      • Mollenhauer M.
      • Slotta-Huspenina J.
      • Boese J.H.
      • Bantscheff M.
      • Gerstmair A.
      • Faerber F.
      • Kuster B.
      Mass-spectrometry-based draft of the human proteome.
      ,
      • Kang H.J.
      • Kawasawa Y.I.
      • Cheng F.
      • Zhu Y.
      • Xu X.
      • Li M.
      • Sousa A.M.
      • Pletikos M.
      • Meyer K.A.
      • Sedmak G.
      • Guennel T.
      • Shin Y.
      • Johnson M.B.
      • Krsnik Z.
      • Mayer S.
      • Fertuzinhos S.
      • Umlauf S.
      • Lisgo S.N.
      • Vortmeyer A.
      • Weinberger D.R.
      • Mane S.
      • Hyde T.M.
      • Huttner A.
      • Reimers M.
      • Kleinman J.E.
      • Šestan N.
      Spatio-temporal transcriptome of the human brain.
      ). Even within our anatomically defined regions, proteins from intervening vasculature, connective tissue and blood cells can not be separated. At the protein level however, this can be easily and precisely addressed using companion cell-to-cell immunohistochemical analysis or comparison to purified collections of specific cell types. Indeed, our MACS enrichment protocol allowed us to obtain the first label-free proteome data set for human cortical neurons derived from iPSCs. Although we only focused on neuronal populations in our in vitro analysis, similar approaches with MACS-enriched astrocytes and oligodendrocytes can be used to identify additional cell type-specific developmental protein changes, as has recently been completed in the mouse brain tissue. Conversely, laser capture technologies of immunostained sections can be used to isolate specific cell populations or smaller regions prior to proteomic profiling from FFPE tissue. The added benefit of monitoring changes through time and space partly overcomes this issue as it allows selection and refinement of protein lists by linking dynamic protein changes to well understood developmental milestones. This was evident by robust detection of proteins like Filamin-C and CRABP1 which were only found in a subset of cells within the heterogeneous ventricular zone. As we show, having rich reference maps to compare with disease states also allows subtraction of specific inflammatory components of disease and analysis of specific changes in biological processes of interest (e.g. ectopic, premature or prolonged developmental milestones) in disease states like CMV encephalitis.
      In summary, we demonstrate that the dynamic and evolving function of specific anatomical brain subcompartments can be molecularly defined through this scalable proteomic technique. Assembly of such molecular atlases can serve as a reference resource in the understanding molecular programs that dictate human brain development and remodeling later in life and disease. The adaptability of this cost- and time-effective technique to rich supply of well-annotated archival FFPE human material will undoubtedly prove useful in delineating disease mechanisms in primary human tissue, not only in the CNS but throughout the human body. Such proteomic profiling will not only translate into diagnostic signatures but may also be instrumental for rational individualized drug development efforts.

      DATA AVAILABILITY

      The mass spectrometry proteomics data have been deposited to the ProteomeXchange consortium via the PRIDE (25) partner repository (http://www.ebi.ac.uk/pride/archive/) with the data set identifier PXD004075 (NPC/neuron data set), PXD004076 (FFPE data set) and PXD005065 (CMV data set). Annotated spectra files have also been deposited to MS-Viewer (http://msviewer.ucsf.edu/prospector/cgi-bin/msform.cgi?form=msviewer) (26) under the following search keys: vh4mxp5qze (NPC/neuron data set), owbnosn6ic (FFPE data set) and klehl8gfbx (CMV data set).

      Acknowledgments

      We thank Lilian Lee, Michelle Kushida and Heather Whetstone for technical expertise; Wei Wei and Alina Piekna for neuronal differentiation assistance. Authors thank all members of the Diamandis lab, Dr. Peter Dirks and Dr. Samer Hussain for helpful discussions.

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