Single-cell spatial proteomic imaging for human neuropathology

A framework for quantitative multiplexed imaging and feature selection in human brain

Archival, formalin fixed paraffin embedded (FFPE) brain regions are predominantly used for human neurodegenerative disease research. Fluorescence imaging on adult brain FFPE is confounded by endogenous tissue autofluorescence that can overwhelm signals originating from antibody bound fluorophores (Additional file 1: Fig. S1A) [8, 9, 19]. With this in mind, we created a framework for high resolution, quantitative, multiplexed imaging of archival brain regions (Fig. 1a) using MIBI-TOF. Because antibodies are detected using elemental mass tags, MIBI-TOF images possess no equivalent endogenous signal from the biological matrix (i.e., no equivalent ‘autofluorescence’). Brain tissue sections were stained with a cocktail of primary antibodies (36-plex panel), where each antibody is labeled with a unique elemental mass tag (Fig. 1a, Additional file 1: Fig. S1B, Table S1). Stained brain sections were imaged using an ion beam, which liberates these mass reporters as secondary ions (Fig. 1a). The spatial distribution of elemental reporters is converted into an N-dimensional image where each channel of this image corresponds to one of the primary antibodies (Fig. 1a). Quantitative pixel-level information was extracted to produce global expression summaries and fed into downstream pipelines for data-driven analysis of cell phenotypes and protein aggregate classification (see Methods: Global expression pattern) [10]. Ultimately, these data were used to construct tabular summaries of molecular features describing overall tissue architecture, as well as the spatial distribution of cells and proteopathies (Fig. 1a).

Fig. 1figure 1

Workflow and Features Extracted from MIBI-TOF Spectral Images. a MIBI-TOF experimental workflow begins with the staining of FFPE brain regions (e.g., hippocampus, substantia nigra, striatum, locus coeruleus, medulla oblongata, cerebellum) on conductive gold sides, and imaging with a panel of 36 antibodies, each labeled with a unique elemental mass tag. Stained tissue sections were rasterized and resulted in a 36-dimensional image revealing protein expression at unique spatial coordinates in a selected field of view (FOV). MIBI-TOF generates data to quantify and visualize tissue architecture, which allows analysis of global protein expression and distribution, discretization into biological units for exploring marker relationships within the biological units, and investigation of distance relationships and neighborhoods among these biological units. be Montage of 196 tiled FOVs MIBI-TOF image from a post-mortem FFPE archival human hippocampus from an individual with AD dementia (ADD). Each tile is a 500μm2 spectral image acquired at low-resolution scan resolving ~ 1 µm2/pixel. Representative 11 out or 36 spectral images were pseudo-colored and overlaid to show the spatial distribution and expression of structural and molecular markers. Abbreviations: DG, Dentate Gyrus; CA, Conus Ammonis; Lmol, molecular layer

To capture salient neuropathologic changes, the antibody staining panel targeted proteins for delineating major neuronal cell lineages, proteopathies, and vascular structures (Additional file 1: Fig. S1B, Table S1). Antibody specificities were validated against multiple brain regions by standard single-plex IHC (Additional file 1: Fig. S1C), benchmarking optimal titers and emphasizing the concordance of chromogenic IHC with MIBI-TOF imaging, like seen on our previous validation study with multiple other tissue organs [16]. MIBI spectral images may not completely recapitulate the visual features of IHC, due to the differences in chromogenic light capturing vs quantitative ion count-based construction of MIBI images. Particularly, in this study we only scanned a single depth of 100–300 nm tissue [16, 20]. Thus, cell type markers which meander in CNS tissue, such as Iba1, may have been partially captured from the 5 µm thick FFPE tissue. However, given we are capturing a large area of tissue in the x and y domains these markers still give valuable information about how different cell types interact locally. We used the resulting 36-plex panel to reveal the cytoarchitecture of an entire coronal section of human hippocampus (Fig. 1b–e). Hippocampus consists of anatomically distinct regions with structural boundaries that are evident from pseudo-colored overlays. Figure 1b–e illustrates a 31 mm2 hippocampal region from a person with AD dementia (ADD, Additional file 1: Table S2B). The granule cell layer of dentate gyrus (DG) is highlighted by MAP2 and HH3 (Fig. 1b, DG white arrow), while the boundary between white and gray matter can be delineated based on expression of MBP and CD56 (Fig. 1b). In line with previous work, glutamatergic terminals within the hilus, Cornu Ammonis (CA), and molecular layers (Lmol) are marked by the presynaptic vesicular proteins VGLUT1 and VGLUT2 (Fig. 1b) [21,22,23]. Calbindin (CB), calretinin (CR), and parvalbumin (PV) appear as contiguous parcels in the hippocampus (Fig. 1d). As seen in rat hippocampus [23], our human data showed that CB borders the DG and hilus, CR structures the Lmol, and PV is within CA4-CA1 [24]. As expected, NFTs and NTs are highlighted by PHF1-TAU immunoreactivity in the CA1 subfield, while Aβ plaques depicted by Aβ42-immunoreactivity predominantly in hilus and Lmol of the ADD hippocampus (Fig. 1e). Spectral images of all simultaneously acquired markers are illustrated in Additional file 1: Fig. S4I. Taken together, this comprehensive survey of brain phenotypes highlights the capability of our approach to visualize multiplexed signatures that reveal salient structural hallmarks and proteopathies in human hippocampus.

Multiplex imaging protein signatures organize anatomical structures

Given the visual differences in expression across hippocampus subfields, we next sought to validate the strength of the 36-plex panel in a quantifiable manner across a broader set of brain regions (Fig. 2). We used the 36-plex panel to analyze a tissue microarray (TMA) containing six different brain regions from cognitively normal individuals (Fig. 2a; Additional file 1: Table S2A). A total of 24 FOVs (4 per brain region) was acquired at low-resolution scan resolving ~ 1 µm2/pixel. A subset of markers (25-plex), namely phenotypic and structural targets, was selected for unsupervised clustering of the FOVs and markers. Firstly, except for medulla oblongata (MO), unsupervised clustering organized the different FOVs into similar anatomical brain regions (Fig. 2b). Secondly, mean pixel intensity z-score heatmap shows that many markers exhibited noticeable gradients that are consistent with canonical brain region compositions. For instance, calcium-binding proteins (PV, CB, and CR), that play important roles in memory processes, show laminar distributions and densities within and across the brain regions (Fig. 2b, c). These patterns look like published protein and gene expression profiles in mouse, rat and human, [25,26,27]. Notably within the cerebellum cluster, we observed an enrichment of CB with inhibitory VGAT, and CR with excitatory VGLUT2 markers possible highlighting presence of Purkinje cells in concordance previous reports on human and non-human primate cerebellum (Fig. 2b, c) [28, 29]. Likewise, TH and GAD65/67 are enriched in FOVs for SN and LC marking presence of dopaminergic neurons (Fig. 2b, d). The lower z-scores across most markers of the CA1 brain region can be explained by rarefaction of neuropil in the TMA core. Furthermore, FOVs from tissue sample of a cognitive impaired individual (CA1-ADD, CA1- AD dementia) were grouped in an unsupervised manner from normative (CA1), with these 25-plex phenotypic and structural targets (Additional file 1: Figs. S2B, S2C). Suggesting, cytoarchitectural alterations (Additional file 1: Fig. S2B), apart from gain of pan AD disease markers like PHF1Tau and Aβ, in the CA1-ADD tissue sampled (Additional file 1: Fig. S2C). Altogether, much like multiplexed analysis of the immune system and other tissues [10, 13, 15, 30,31,32,33], combinatorial protein expression patterns provided a snapshot of functional organization or proteopathy within brain regions. This low-level analysis of multiplexed proteomic data could serve as a guide for ‘fingerprinting' human brain and be used to model progression in neurodegeneration.

Fig. 2figure 2

Global phenotypic expression organizes CNS sub-regions in a data driven manner. a Schematic of brain regions used in construction of TMA; circle highlights 3 mm cores isolated from FFPE tissues. The photomicrograph of the brain TMA cores stained with Luxol Fast Blue/Hematoxylin and Eosin (LFB/H&E) and the rastered areas, highlighted in black boxes, are shown in Additional file 1: Fig. S2A. b Heatmap of mean z-score distribution of pixel expression of proteins per rastered FOV (row normalized). Columns and rows are hierarchically clustered (Euclidean distance). Variance between and among FOVs are stratified in an unsupervised manner into similar anatomical regions. Black boxes highlight distribution of different calcium binding proteins, PV predominantly expression in cerebellum, midbrain, medullar similar to previous reports in mouse and rat1, while CB expression is enriched in stratum moleculare cerebellum layer highlighting presence of Purkinje cells, and CR in the LC c, d Tiled spectral images that were pseudo-colored to show distribution of calcium-binding protein PV, CB and CR (c) and TH and GAD65/67 (d). Enlargement of the boxed areas in c and d show that CB+ Purkinje neuron in CBL co-localized with PV, CR and VGAT, and TH+ dopaminergic neurons in SN and LC co-localized with GAD65/67. Each brain region is composed of 4 tiled FOVs. Each FOV is a 500 µm2 spectral image acquired at low-resolution scan resolving ~ 1 µm2/pixel. Abbreviations: CA1, Cornu Ammonis 1; STR, Striatum; SN, Substantia Nigra; LC, Locus Coeruleus; CBL, Cerebellum; MO, Medulla Oblongata; CB, CALBINDIN; CR, CALRETININ; PV, PARVALBUMIN; TH, Tyrosine Hydroxylase; VGAT, Vesicular GABA Transporter; GAD65/67, Glutamate Decarboxylase 65/67

Discretized cellular and pathological features identify lineage and disease pathology-specific subclusters

We next discretized cells and proteopathies as biological units: neurons, astrocytes, microglia, vasculature, tau neurofibrillary tangles-neuropil threads (NFT-NTs), and Aβ plaques. Segmentation of planar brain tissue sections has inherent problems due to shape, texture, the disjointed features from neuronal and glial processes in the tissue section, but distant from their cell bodies, and from nonconforming shapes defining protein aggregates. As shown in Fig. 3a (dashed lines), we observed these various facets of planar imaging by both standard IHC and MIBI-TOF. To capture these attributes, two segmentation methods were used to partition neuronal perikaryons, microglia, astrocytes, and their processes, endothelial and their vascular-boundaries, Aβ plaques, and NFT-NTs (Fig. 3b). An adapted version of DeepCell was used for nuclear segmentation (Fig. 3b) [34,35,36]. For features not associated with nuclei, a pixel intensity thresholding-based method was used to partition cell body microglial and astrocyte projections, larger vascular structures, Aβ plaques, and NFT-NTs (Fig. 3b, see Methods: Object segmentation).

Fig. 3figure 3

Spatial Organization and Molecular Identities of Cells and Proteopathy in ADD hippocampus is Revealed with Single-Cell and Object Segmentation. a Cartoons show examples of cells (astrocytes, microglia, vessels, neurons, and immune cells) and possible planar views in CNS tissue (dashed lines numbered 1 through 4). The representative photomicrograph (IHC-DAB) and MIBI-TOF (pseudo-colored spectral images) visually exemplify the corresponding focal planes (numbered 1 through 4) for the different cells, which are identified by their pan-markers (GFAP: astrocytes, Iba1-CD45: microglia, CD31-CD105-MCT1: vasculature, MAP2: neurons, CD45: immune cells). b Conceptual overview of nuclear and object segmentation approaches. With cellular segmentation, nuclear associated features (nuclei interiors, nuclei borders) are obtained using a pixel-based convolution neural network (DeepCell neural network) and custom pixel expansion around the nuclei, to generate cell segmentation masks for cell lineage assignments. Object segmentation was used to capture microglia and astrocytic processes, vascular boundaries, Aβ plaques, and PHF1-TAU NFTs and NTs. Masks for both discretized biological units are integrated to reveal their relationships in silico. c Photomicrograph (on the conductive gold slide, left panel) and corresponding MIBI-TOF spectral image (HH3 for nuclei, right panel) of human ADD hippocampus. White box highlights the re-rastered region that was used for analysis. d UMAP visualization of all segmented objects across all hippocampal FOVs, colored by lineage and proteopathy identities. Inset shows only Aβ plaques, and NFT-NTs overlaid on the UMAP. e Cell Phenotype Maps (CPM) of nuclear and object segmented masks labeled by their phenotype. f Composite CPM of all segmented masks with six inserts for a zoomed in view of cell identity overlaid onto segmentation masks for the hippocampal region. Regions 1–6 represent zoomed in images representative areas of CPM’s in e

The same 36-plex stain of ADD hippocampus from Fig. 1b was chosen for the discretization analysis to capture a greater breadth of morphological and pathological landscapes. With nuclear segmentation, we identified 15,270 nuclei-associated cells across 85 connected imaging FOVs which were classified using manual gating (Additional file 1: Table S4, see Methods: Manual gating ADD hippocampus). With the pixel-based approach, we identified 19,332 features not associated with nuclei that included microglia, astrocytes, endothelial cells, Aβ plaques, and NFT-NTs (Additional file 1: Table S4). Neuron masks identified by nuclear segmentation were integrated with masks of the object segmented data (microglia, astrocytes, vasculature, Aβ plaques, and NFTs-NTs) to obtain a comprehensive repertoire of neuronal and non-neuronal cell types as well as disease features. We then generated a UMAP [37] plot organized by lineage and proteopathy markers to analyze the relationship between these features (see Methods: 1024 × 1024 ADD Images). Extracted cellular and proteopathy features mapped to four cell-type clusters (neurons, microglia, astrocytes, and vasculature) and two pathologic component clusters (Aβ plaques and NFTs-NTs) (Fig. 3d, inset).

NFT-NT masks (cyan) mostly interrelated with the neuron (dark red) cloud (Fig. 3d, Additional file 1: Fig. S3K). This is consistent with known accrual of NFT-NTs in intraneuronal compartments [6, 38]. Aβ plaques shared the UMAP space mainly with astrocyte (dark green, Fig. 3d, Additional file 1: Fig. S3L) and neuronal populations (Fig. 3d, Additional file 1: S3M) as well as scattered with microglia (dark yellow) and vascular groups (Fig. 3d). In accordance with previous reports, both astrocytes and neurons highly express Aβ, and GFAP(+) reactive astrocytes accumulate Aβ in the process of clearance in AD [39,40,41].

By mapping the masks of these gated objects back onto the coordinates of the original segmented images (Fig. 3e), we created a cell phenotype map (CPM) that illustrated a more even distribution of hippocampal microglia relative to astrocytes (Fig. 3e, f1, Additional file 1: Fig. S3A). Astrocytes were enriched in hippocampal white matter and vascular regions (Fig. 3e, f2, Additional file 1: Fig. S3B) as they are integral parts of CNS white matter and blood–brain-barrier (BBB) architecture [42, 43]. Vascular CPM charts large and micro vessel boundaries (Fig. 3e, f3, Additional file 1: Fig. S3C). In the neuron CPM, masks are enriched in the granule cell layer of DG band, and are scattered throughout the image (Fig. 3e, f4, Additional file 1: Fig. S3D). These data highlight the utility of the combined segmentation approaches, enabling analyses analogous to those achieved in other tissue where spatial organization, expression, and molecular identities of single cells and proteopathy can now be determined and organized for human CNS.

Fig. 4figure 4

Cellular and proteopathy composition of hippocampal neuroanatomy. a Representation of the three imaged hippocampi to identify differences in AD pathological changes. Included are samples from people who were diagnosed as cognitively normal (CN), cognitively impaired, no dementia (CIND), or Alzheimer’s Disease dementia (ADD). LFB and H&E histochemical staining of a serial section of each hippocampal slice imaged (Top). Pseudo-color overlay of CALRETININ (CR), CALBINDIN (CB), MAP2, and Histone H3 (HH3) acquired in each sample representing high-level neuronal morphology (Bottom). b Mask overlays of the cells and proteopathies collected by the segmentation methods described in Fig. 2, broken down by hippocampal neuroanatomy (Top) and cell or proteopathy subtype (Bottom). c Relative composition of each cell and proteopathy subtype within each anatomical subregion of each individual. d Ratio of proteopathy-associated cells to proteopathy-free cells in each anatomical region of each individual. Ratio of 1 indicates an equal number of proteopathy-associated cells to proteopathy-free cells, where PHF1-TAU NFT-NTs cell associations are shown in the top-panel and Aβ plaque cell associations in the bottom-panel. Callout indicates ADD, CA1 microglia with a high number of NFT-NTs associated microglia (green circle). e Normalized mean expression of microglia phenotyping channels in CA1 region of each individual, broken down by microglia with NFT-NTs association or those without. f Normalized mean expression of microglia phenotyping channels in CA1 region of each individual, broken down by microglia with Aβ plaque association or those without. g UMAP projections of CA1 microglia phenotyping channels in ADD. Gray represents CA1 microglia from CN, CIND samples (S4K-L for CN, CIND expression maps). Projection was calculated using Iba1, CD45, CD33, APOE, PHF1-TAU, Aβ42, Aβ40, and Pan Aβ markers. CIND and ADD are subsampled so that all conditions are represented by 447 microglia, the total number of CA1 microglia in the CN condition. h Image representation of tau-tangle and amyloid associated phenotypes described in E and F found in ADD CA1 microglia. Masks of microglia (yellow), tau NFT-NTs (cyan), and Aβ plaques (pink) (Left). Inset of microglia expressing phenotypic and proteopathy markers (Right). Abbreviations: CN, cognitively normal; CIND, cognitive impairment no dementia; ADD, AD dementia; DG, Dentate Gyrus CA1-4, Cornu Ammonis 1–4

Top-down spatial organization of cellular and proteopathy composition distinguishes hippocampal subregions and disease status

We combined our discretization strategies with prior knowledge of human hippocampal anatomy (i.e., top-down) to identify neuropathologic changes across stages of cognitive decline (defined by Thal phase, Braak stage, CERAD and ADNC scores, Additional file 1: Table S2B). Coronal hippocampal sections from three individuals were imaged, focusing on the DG to CA4 through CA1 subregions, and expanding outward (Fig. 4). Samples from cognitively normal (CN), cognitively impaired with no dementia (CIND), and ADD subjects were used to capture a wide spectrum of pathologic changes (Fig. 4a). Both MCI and CIND are attempts to define a state in-between normal cognitive function and dementia. Mild Cognitive Impairment (MCI) is a clinical diagnosis [44]. Cognitive Impairment with no Dementia (CIND) is determined by neuropsychological test results [45, 46]. Single-plex spectral images of CN (Additional file 1: Fig. S4A–C), CIND (Additional file 1: Fig. S4D–F) and ADD (Additional file 1: Fig. S4G–I) hippocampus demonstrated larger, qualitative structures that can be captured by tiling numerous imaged fields together. For instance, VGLUT1 and VGLUT2 positivity demarking the CA and DG borders, respectively.

Given the known differences and progression of disease within AD hippocampal subregions [4], we first segregated all cells and protein aggregates into DG, CA4, CA3, CA2, and CA1 associations (Fig. 4b). We determined these boundaries for the subregions based on prior knowledge of morphological characteristics [47, 48] and expression of neuronal and pan-CNS markers including CALBINDIN, CALRETININ, MAP2, CD56, SYNAPTOPHYSIN (SYP), VGLUT1, and VGLUT2 (Fig. 4a, Additional file 1: Fig. S4) that have previously been shown to highlight boundaries [21, 24, 49]. Spectral images of CALBINDIN, CALRETININ, and MAP2, specifically showed anatomical delineation of CA4–CA2 (CALBINDIN), DG and Alveus (CALRETININ), joined by neuronal cytoarchitecture from CA4 to the subiculum (MAP2) (Fig. 4a, bottom panel). We then assigned cells and protein aggregates to their respective structures (Fig. 4b, Additional file 1: Table S5A). To assign cell identities, we applied a combination of FlowSOM [50] and manual meta-clustering strategies to parse out the objects into six distinct categories: neurons, microglia, endothelial cells, and non-immune glia which included both astrocytes and oligodendrocytes (Fig. 4b, lower panel). Astrocytes and oligodendrocytes were grouped together based on MBP, MAG, and GFAP expression, as their expression patterns overlapped enough at this lower resolution to make it difficult to assign individual labels for these subtypes.

Comparing the proportion of each cell type within the CA and DG subregions, we observed a higher proportion of protein aggregates in the CA1 subfield relative to other regions within the same sample, regardless of cognitive status (Fig. 4c, Additional file 1: Table S5B). In addition, the total number of protein aggregates increased in the tissue of cognitively impaired individuals (Additional file 1: Fig. S4J). We then considered whether proteopathies tended to lay in proximity to cell somas bound by our cellular segmentation. For each cell subtype, we counted the number of cells in direct proximity (i.e., overlapping pixels) to each proteopathy subtype and divided this value by the number of cells with no proteopathy overlap (Fig. 4d). Most cells did not exhibit direct proximity to Aβ plaque objects, particularly in the CN and CIND conditions. In the ADD individual, more cells lay in proximity to Aβ plaques, particularly for neurons in the CA1 and CA2 subfields, similar to previous reports [51]. For PHF1-TAU labeled NFT-NTs, a similar trend was found. In particular, microglia surrounding NFT-NTs doubled the number of microglia independent of NFT-NTs in the ADD CA1 subfield (Fig. 4d, green circle).

Building upon this observation, we contrasted those microglia that were NFT-NT associated (positive) or not (negative) (Fig. 4e). Microglia markers associated with reactivity [51], APOE, Iba1, CD33, and CD45, were consistently higher in NFT-NTs(+) relative to NFT-NTs(−) CA1 microglia, across all samples, with the highest expression in ADD (Fig. 4e). Microglia interacting with Aβ plaques did not show as strong of a difference in these reactive markers compared to microglia independent of Aβ plaques (Fig. 4f). Projecting an equal subset of microglia from all three samples onto an 2D-UMAP embedding, shows subsets of these reactive microglia associated with PHF1-TAU, particularly high Iba1(+) and APOE(+) in CA1 ADD cells (Fig. 4g, green circle); and that while these cells exist across all individuals, they are most prominent in ADD (Fig. 4g, h, Additional file 1: Fig. S4K–L). This differential state of expression further reflects the possibility that immune cell reactivity may play a central role in AD severity, particularly an association with PHF1-TAU formation in CA1 subregion [51].

Bottom-up, data driven neighborhood analysis identifies common regions of neuropathology across individuals and severity

Given the level of neuropathological organization identified using previously defined hippocampal subregions, we investigated what other levels of spatial order could be revealed with a more data-driven approach to our multiplexed images. In addition to including the DG and CA regions, we also included cells from additional areas of the hippocampus captured including the alveus and subiculum. To this end, we employed a bottom-up workflow to isolate common signatures of hippocampal spatial identities, independent of previously known neuroanatomy or cognitive status. Using CytoM

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