Label-free vibrational imaging of different Aβ plaque types in Alzheimer's disease reveals sequential events in plaque development - PubMed (original) (raw)

Label-free vibrational imaging of different Aβ plaque types in Alzheimer's disease reveals sequential events in plaque development

Dominik Röhr et al. Acta Neuropathol Commun. 2020.

Abstract

The neuropathology of Alzheimer's disease (AD) is characterized by hyperphosphorylated tau neurofibrillary tangles (NFTs) and amyloid-beta (Aβ) plaques. Aβ plaques are hypothesized to follow a development sequence starting with diffuse plaques, which evolve into more compact plaques and finally mature into the classic cored plaque type. A better molecular understanding of Aβ pathology is crucial, as the role of Aβ plaques in AD pathogenesis is under debate. Here, we studied the deposition and fibrillation of Aβ in different plaque types with label-free infrared and Raman imaging. Fourier-transform infrared (FTIR) and Raman imaging was performed on native snap-frozen brain tissue sections from AD cases and non-demented control cases. Subsequently, the scanned tissue was stained against Aβ and annotated for the different plaque types by an AD neuropathology expert. In total, 160 plaques (68 diffuse, 32 compact, and 60 classic cored plaques) were imaged with FTIR and the results of selected plaques were verified with Raman imaging. In diffuse plaques, we detect evidence of short antiparallel β-sheets, suggesting the presence of Aβ oligomers. Aβ fibrillation significantly increases alongside the proposed plaque development sequence. In classic cored plaques, we spatially resolve cores containing predominantly large parallel β-sheets, indicating Aβ fibrils. Combining label-free vibrational imaging and immunohistochemistry on brain tissue samples of AD and non-demented cases provides novel insight into the spatial distribution of the Aβ conformations in different plaque types. This way, we reconstruct the development process of Aβ plaques in human brain tissue, provide insight into Aβ fibrillation in the brain, and support the plaque development hypothesis.

Keywords: Alzheimer’s disease; Amyloid plaque; Amyloid-beta; FTIR; Fibril; Human; Imaging; Infrared, Raman; Microspectroscopy; Oligomer.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing financial interests.

Figures

Fig. 1

Fig. 1

Workflow. Fourier-transform infrared (FTIR) and Raman imaging were applied to selected sample areas. Subsequently, the sample was immunostained against amyloid beta (Aβ) and imaged with light microscopy. The resulting (spectral) images were spatially aligned to generate a precisely overlaid, unified dataset. An experienced neuropathologist (B.D.C.B) annotated Aβ-IHC images to the different plaque types. Based on this data, spectral analysis was conducted and statistically evaluated

Fig. 2

Fig. 2

Immunohistochemical and FTIR imaging of control tissue (1) diffuse (2), compact (3) and classic cored plaques (4). A Immunohistochemical staining against amyloid beta (Aβ). B Ratio between the Amide II and CH stretching bands. Red indicates high protein concentrations. C Ration between the main β-sheet band and non-β-sheet band of the Amide I. Red indicates high β-sheet levels

Fig. 3

Fig. 3

FTIR analysis of an exemplary classic cored plaque and its compartments. A Anti-Aβ immunostaining B Areas of spectral similarity, identified by hierarchical cluster analysis (HCA), that correspond to surrounding tissue (green), corona (yellow) and core (red). C Area-normalized FTIR spectra in the range 1780–1480 cm−1. The red core spectrum shows a prominent shoulder around 1628 cm−1, and reduced absorbance around 1655 cm−1. A difference spectrum (black) in the in the range 1700–1600 cm−1 reveals minor bands around 1683 cm−1 and 1694 cm−1. The lipid-associated ester band around 1738 cm−1 is decreased in both the corona and the core

Fig. 4

Fig. 4

Amide I band analysis. A Mean Amide I bands of all plaque spectra from each plaque type, core spectra generated by hierarchial cluster analysis (HCA), and gray matter control spectra. The shoulder around 1628 cm−1 indicates β-sheet protein. B Cutout of mean difference spectra between plaque spectra and their respective surrounding spectra. Note the shift to lower wavenumbers and the increased absorbance around 1620 cm−1. C Visualization of sub-bands of the Amide I in the region 1700–1600 cm−1. The marked local minima indicate bands that are relevant for protein secondary structure. Note the substantial increase of the band around 1628 cm−1 alongside the plaque development sequence. The band around 1693 cm−1 displays little change, whereas the band around 1682 cm−1 increases, and the bands around 1657 cm−1, and 1639 cm−1 decrease

Fig. 5

Fig. 5

Statistical analysis. The boxplots present spectroscopic ratios derived from control, plaque, and core spectra. The red bar indicates the median value, the blue boxes range between the first and third quartile. The black whiskers extend the extremes of the distribution, excluding outliers (black crosses). The significance bars announce the confidence levels. A Ratios between the Amide II band and the CH stretching bands, indicating protein accumulation. B Ratios between the Amide I band of β-sheets and non-β-sheet structures, indicating β-sheet levels. C The scatterplot illustrates the correlation between protein and β-sheet levels in plaques. A successive accumulation of β-sheet protein alongside the plaque development sequence is apparent. D The negative height difference of the bands around 1628 and 1693 cm−1 in 2nd derivative spectra, indicating increased proportions of parallel β-sheets

Fig. 6

Fig. 6

Proposal of Aβ conformations in the different plaque types. A depicts the exemplary plaques from Fig. 2. B Based on our observations, we propose the depicted composition of Aβ conformations in the different plaque types. The symbols are used to indicate the hypothetical location, density, and mixture of Aβ conformation in a simplified fashion

Similar articles

Cited by

References

    1. Araki K, Yagi N, Ikemoto Y, Yagi H, Choong C-J, Hayakawa H, et al. Synchrotron FTIR micro-spectroscopy for structural analysis of Lewy bodies in the brain of Parkinson’s disease patients. Sci Rep. 2015;5:17625. doi: 10.1038/srep17625. - DOI - PMC - PubMed
    1. Arends YM, Duyckaerts C, Rozemuller JM, Eikelenboom P, Hauw JJ. Microglia, amyloid and dementia in Alzheimer disease: A correlative study. Neurobiol Aging. 2000;21:39–47. doi: 10.1016/S0197-4580(00)00094-4. - DOI - PubMed
    1. Barth A. Infrared spectroscopy of proteins. Biochim Biophys Acta Bioenerg. 2007;1767:1073–1101. doi: 10.1016/j.bbabio.2007.06.004. - DOI - PubMed
    1. Benseny-Cases N, Álvarez-Marimon E, Castillo-Michel H, Cotte M, Falcon C, Cladera J. Synchrotron-based fourier transform infrared microspectroscopy (μFTIR) study on the effect of Alzheimer’s Aβ amorphous and fibrillar aggregates on PC12 cells. Anal Chem. 2018;90:2772–2779. doi: 10.1021/acs.analchem.7b04818. - DOI - PubMed
    1. Benseny-Cases N, Cócera M, Cladera J. Conversion of non-fibrillar β-sheet oligomers into amyloid fibrils in Alzheimer’s disease amyloid peptide aggregation. Biochem Biophys Res Commun. 2007;361:916–921. doi: 10.1016/j.bbrc.2007.07.082. - DOI - PubMed

Publication types

MeSH terms

Substances

LinkOut - more resources