Cyclic multiplex fluorescent immunohistochemistry and machine learning reveal distinct states of astrocytes and microglia in normal aging and Alzheimer’s disease

Journal of Neuroinflammation

Clara Muñoz-Castro¹  Jordan D. Marks²  Colin G. Magdamo³  Zhaozhi Li³  Sudeshna Das⁴  Bradley T. Hyman⁴  Alberto Serrano-Pozo⁴  Matthew P. Frosch⁵  Ayush Noori⁶ 

[1] Departamento de Bioquímica y Biología Molecular, Facultad de Farmacia, Universidad de Sevilla, 41012, Sevilla, Spain;Instituto de Biomedicina de Sevilla (IBiS)-Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, 41013, Sevilla, Spain;Department of Neurology, Massachusetts General Hospital, 02114, Boston, MA, USA;MassGeneral Institute for Neurodegenerative Disease, 114 16th Street, 02129, Charlestown, MA, USA;Massachusetts Alzheimer’s Disease Research Center, 02129, Charlestown, MA, USA;Harvard Medical School, 02115, Boston, MA, USA;Department of Neurology, Massachusetts General Hospital, 02114, Boston, MA, USA;MassGeneral Institute for Neurodegenerative Disease, 114 16th Street, 02129, Charlestown, MA, USA;Department of Neurology, Massachusetts General Hospital, 02114, Boston, MA, USA;MassGeneral Institute for Neurodegenerative Disease, 114 16th Street, 02129, Charlestown, MA, USA;Massachusetts Alzheimer’s Disease Research Center, 02129, Charlestown, MA, USA;Department of Neurology, Massachusetts General Hospital, 02114, Boston, MA, USA;MassGeneral Institute for Neurodegenerative Disease, 114 16th Street, 02129, Charlestown, MA, USA;Massachusetts Alzheimer’s Disease Research Center, 02129, Charlestown, MA, USA;Harvard Medical School, 02115, Boston, MA, USA;Department of Pathology, Massachusetts General Hospital, 02114, Boston, MA, USA;MassGeneral Institute for Neurodegenerative Disease, 114 16th Street, 02129, Charlestown, MA, USA;Massachusetts Alzheimer’s Disease Research Center, 02129, Charlestown, MA, USA;Harvard Medical School, 02115, Boston, MA, USA;Harvard College, 02138, Boston, MA, USA;Department of Neurology, Massachusetts General Hospital, 02114, Boston, MA, USA;MassGeneral Institute for Neurodegenerative Disease, 114 16th Street, 02129, Charlestown, MA, USA;Massachusetts Alzheimer’s Disease Research Center, 02129, Charlestown, MA, USA;

关键词: Alzheimer’s disease;    Amyloid plaques;    Astrocytes;    Immunohistochemistry;    Microglia;    Neurofibrillary tangles;    Neuropathology;    Tau;   

DOI  :  10.1186/s12974-022-02383-4

来源: Springer

【 摘 要 】

BackgroundAstrocytes and microglia react to Aβ plaques, neurofibrillary tangles, and neurodegeneration in the Alzheimer’s disease (AD) brain. Single-nuclei and single-cell RNA-seq have revealed multiple states or subpopulations of these glial cells but lack spatial information. We have developed a methodology of cyclic multiplex fluorescent immunohistochemistry on human postmortem brains and image analysis that enables a comprehensive morphological quantitative characterization of astrocytes and microglia in the context of their spatial relationships with plaques and tangles.MethodsSingle FFPE sections from the temporal association cortex of control and AD subjects were subjected to 8 cycles of multiplex fluorescent immunohistochemistry, including 7 astroglial, 6 microglial, 1 neuronal, Aβ, and phospho-tau markers. Our analysis pipeline consisted of: (1) image alignment across cycles; (2) background subtraction; (3) manual annotation of 5172 ALDH1L1+ astrocytic and 6226 IBA1+ microglial profiles; (4) local thresholding and segmentation of profiles; (5) machine learning on marker intensity data; and (6) deep learning on image features.ResultsSpectral clustering identified three phenotypes of astrocytes and microglia, which we termed “homeostatic,” “intermediate,” and “reactive.” Reactive and, to a lesser extent, intermediate astrocytes and microglia were closely associated with AD pathology (≤ 50 µm). Compared to homeostatic, reactive astrocytes contained substantially higher GFAP and YKL-40, modestly elevated vimentin and TSPO as well as EAAT1, and reduced GS. Intermediate astrocytes had markedly increased EAAT2, moderately increased GS, and intermediate GFAP and YKL-40 levels. Relative to homeostatic, reactive microglia showed increased expression of all markers (CD68, ferritin, MHC2, TMEM119, TSPO), whereas intermediate microglia exhibited increased ferritin and TMEM119 as well as intermediate CD68 levels. Machine learning models applied on either high-plex signal intensity data (gradient boosting machines) or directly on image features (convolutional neural networks) accurately discriminated control vs. AD diagnoses at the single-cell level.ConclusionsCyclic multiplex fluorescent immunohistochemistry combined with machine learning models holds promise to advance our understanding of the complexity and heterogeneity of glial responses as well as inform transcriptomics studies. Three distinct phenotypes emerged with our combination of markers, thus expanding the classic binary “homeostatic vs. reactive” classification to a third state, which could represent “transitional” or “resilient” glia.

【 授权许可】

CC BY