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Digital pathology (DP) has transformative potential, especially for Alzheimer disease and related disorders. However, infrastructure barriers may limit adoption. To provide benchmarks and insights into implementation barriers, a survey was conducted in 2019 within National Institutes of Health's Alzheimer's Disease Centers (ADCs). Questions covered infrastructure, funding sources, and data management related to digital pathology. Of the 35 ADCs to which the survey was sent, 33 responded. Most respondents (81%) stated that their ADC had digital slide scanner access, with the most frequent brand being Aperio/Leica (62.9%). Approximately a third of respondents stated there were fees to utilize the scanner. For DP and machine learning (ML) resources, 41% of respondents stated none was supported by their ADC. For scanner purchasing and operations, 50% of respondents stated they received institutional support. Some were unsure of the file size of scanned digital images (37%) and total amount of storage space files occupied (50%). Most (76%) were aware of other departments at their institution working with ML; a similar (76%) percentage were unaware of multiuniversity or industry partnerships. These results demonstrate many ADCs have access to a digital slide scanner; additional investigations are needed to further understand hurdles to implement DP and ML workflows.

Additional file 1: Figure S1. CCL21 is still elevated in the CTE CSF in the male subset of cases. To determine if gender was driving the CSF related CCL21 elevation, only males from the CTE and AD groups were compared. Although the AD male sample size was reduced to 5 cases, CCL21 concentrations still trended towards significant increases in CTE as measured with a Mann–Whitney test. Each dot represents 1 case. Error bars represent mean ± SEM.

Additional file 7: Supplementary Table 7. Up-regulated and down-regulated pathway related to vesicle and membrane trafficking in isolated CD14+ monocytes and whole blood. The table shows the list of pathways that were found to be deregulated in both isolated CD14+ monocytes and whole blood in the PD/GBA vs CTRL/GBA groups (top), the deregulated genes in each pathway (first column), and the directionality of dysregulation (second and third column). Red “x” indicates genes dysregulated in whole blood, black “x” represents genes dysregulated in isolated CD14+ monocytes.

Additional file 6: Supplementary Table 6. Overlapping genes between monocytes and PPMI cohort (whole blood) in manifesting vs non-manifesting GBA-carriers. List of overlapping genes differentially expressed in monocytes and whole blood in manifesting vs non-manifesting carriers. Gene name, description of gene function and logFC and FDR in monocytes and whole blood are reported. In “grey” genes that represent PD-GWAS hits are highlighted.

Additional file 8: Supplementary Fig. 1. Characterization of genetic background of donor population. a) Representation of PCA analysis of ancestry of MDS values from the cohort of 158 subjects (PD/GBA, PD, CTRL/GBA, CTRL) compared to 1000 Genome Project samples (Phase 3). The different ancestry are represented in distinct colors (Orange: African; Gold: Ad Mixed American; Green: East Asian; Blue: European; Purple: South Asian; Black: study cohort). B) PCA considering only overlap of MDS values of donor cohort (black) with European ancestry (blue) and AJ ancestry (light blue). Supplementary Figure 2. Normalization and quality control of RNA-seq data from isolated CD14+ monocytes. a) Violin plot representing the contribution of each of the surrogate variables (as explained in the text) to the variability of expression data of the study cohort and residual (158 subjects). b) Violin plot representing the contribution of technical, demographical and clinical variables to the variability of expression data of the study cohort and residual (158 subjects). c) Heatmap representing the results of linear regression between the surrogate variables utilized for data normalization and technical variables (from RNA-seq analysis) and metadata. Coefficient of linear regression is reported in the heatmap for each correlation pair. d) Distribution of MDS values of study cohort identified a clear clustering based on batches used for RNA-seq analysis (batches 1 to 4). e) After regression of SVs, variability of MDS values is significantly reduced, with no significant outliers and no clustering based on experimental batches. Supplementary Fig. 3. Differential expression of GBA in CD14+ isolated monocytes. Box plot representing differential expression levels (normalized expression count) of GBA in isolated CD14+ monocytes (a and b). In b) data from isolated CD14+ monocytes of GBA-carriers and non-carriers within PD and CTRL subjects were combined and compared. Each dot represents a subject. Dots are colored based on GBA mutations (as reported in the legend: GBA mild mutations (N370S, E326K, R496H), GBA severe mutations (L444P/A456P/RecNciI, V394L, 84GG, 84GG/T369M, N370S/RecNciI)). p-value of different expression levels is reported on top (statistics: Mann-Whitney U test). GBA variants different from N370S are labeled in the boxplot. Supplementary Fig. 4. Differential expression of target genes in manifesting and non-manifesting carriers in monocytes. a) Volcano plot showing differentially expressed genes with FDR < 0.05 (green dots) in isolated monocytes from manifesting vs non-manifesting carriers. Genes related to targeted pathways (LRRK2, ATP13A2, NOTCH1 and TAU) are highlighted. b) Box plots of differential levels of expression of the targeted genes (ATP13A2, LRRK2, NOTCH1), between manifesting and non-manifesting carriers in isolated monocytes. Each dot represents a subject. Dots are colored based on GBA mutations (as reported in the legend: GBA mild mutations (N370S, E326K, R496H), GBA severe mutations (L444P/A456P/RecNciI, V394L, 84GG, 84GG/T369M, N370S/RecNciI)). Asterisks indicate significant p-value (* = p-value < 0.05, ** = p-value < 0.01, *** = p-value < 0.001, statistics: Mann-Whitney U test). Supplementary Fig. 5. Differential expression analysis in monocytes in PD patients with and without GBA-variants. a) Volcano-plot representing logFC (x-axes) and p-value (y-axes, -log10 p-value) of differential expressed genes between PD/GBA and PD as per nested interaction model. Highlighted in yellow are genes with FDR < 0.15 (44 total genes). ID labels of functionally relevant genes and of genes differentially expressed in whole blood (PPMI overlap of genes at nominal p-value 30% of total expression). Discrete relevant variables (Diagnosis, batches of RNAseq analysis (rna_batch), GBA mutations and GBA-related genetic status (carriers or non-carriers of GBA mutations), gender (Sex: male (M) and female (F)) are labeled at the top of the heatmap per each subject. b) Bar-plot reporting number of outlier genes per each subject (out of 158 subjects). Highlighted in orange samples with outlier gene count above 0.1%. c) Summary tables: on the left: number of outliers genes per cohort (PD/GBA, CTRL/GBA, PD, CTRL) (493 pairs); on the right: number of subjects per each cohort with at least one outlier gene (125 unique subjects total with at least one outlier gene). Supplementary Fig. 8. Scatter plot of selected genes identified through the OUTRIDER method. Scatter plot representing -log10 (p-value) of outliers genes identified in previous analysis. Supplementary Fig. 9. Cell proportion across patient and control cohorts. a) Monocyte counts (x106) in the GBA/PD, PD, GBA/CTRL, and CTRL groups in the cohort of isolated monocytes (NYMD cohort). Each dot represents the cell count of one subject. Cell count was available for 57 subjects in our cohort. Statistical analysis according to the Wilcoxon test is reported for the comparison between the four groups. b) Cell proportion (neutrophils, monocytes, lymphocytes, eosinophils, basophils) within whole blood samples in the four groups (GBA/PD, PD, GBA/CTRL, and CTRL) in the PPMI cohort. Comparison of each cell type percent across the four groups is reported on the box plots on the right side of the figure (statistical analysis: Wilcoxon test). Supplementary Fig. 10. Correlation between genes differentially expressed in isolated CD14+ monocytes and whole blood. We compared the directionality of differentially expression genes between a) GBA/PD vs GBA/CTRL and PD vs CTRL in CD14+ monocytes (n = 197); b) GBA/PD vs GBA/CTRL and PD vs CTRL in whole blood (n = 207); c) GBA/PD vs GBA/CTRL in CD14+ monocytes and whole blood (n = 16); d) PD vs CTRL in CD14+ monocytes and whole blood (n = 103). Supplementary Fig. 11. Correlation between gene expression levels in isolated CD14+ monocytes and whole blood. Genes with expression with more than 1 CPM in 30% of the samples were considered from both cohorts (discovery cohort: isolated CD14+ monocytes (total number of genes: 13711), validation cohort: whole blood - PPMI cohort (total number of genes: 18111)). Spearman correlation between levels of normalized mean gene expression across subjects within each cohort per sub-group of subjects was calculated (R = 0.78 p < 0.001). Genes were normalized with TMM and voom, as detailed in the main text. Supplementary Fig. 12. Validation in whole blood of differentially expressed genes in monocytes. a) Differential levels of expression of the targeted genes (ATP13A2, LRRK2, NOTCH1, between manifesting and non-manifesting carriers in whole blood from manifesting and non-manifesting GBA-mutation carriers. b) Differential normalized expression count of SNCA, LMNA, and GBA between PD/GBA and PD, compared to CTRL/GBA and CTRL subjects in whole blood. In a) and b) each dot represents a subject. Dots are colored based on GBA mutations (as reported in the legend: GBA mild mutations (N370S, E326K, R496H), GBA severe mutations (L444P/A456P/RecNciI, V394L, 84GG, 84GG/T369M, N370S/RecNciI)). p-value of different expression levels is reported on top (statistics: Mann-Whitney U test). c) Pathway enrichment analysis of differentially expressed genes in whole blood between PD/GBA vs PD subjects with p-value < 0.01 for GO terms are reported. Dark blue: pathways related to cell transport; Green: pathways related to immune response; Light blue: other pathways.

Additional file 5. Supplementary fig 5

Additional file 2. Supplementary fig 2

Additional file 10. Supplementary table 3

Additional file 8: Supplementary Fig. 1. Characterization of genetic background of donor population. a) Representation of PCA analysis of ancestry of MDS values from the cohort of 158 subjects (PD/GBA, PD, CTRL/GBA, CTRL) compared to 1000 Genome Project samples (Phase 3). The different ancestry are represented in distinct colors (Orange: African; Gold: Ad Mixed American; Green: East Asian; Blue: European; Purple: South Asian; Black: study cohort). B) PCA considering only overlap of MDS values of donor cohort (black) with European ancestry (blue) and AJ ancestry (light blue). Supplementary Figure 2. Normalization and quality control of RNA-seq data from isolated CD14+ monocytes. a) Violin plot representing the contribution of each of the surrogate variables (as explained in the text) to the variability of expression data of the study cohort and residual (158 subjects). b) Violin plot representing the contribution of technical, demographical and clinical variables to the variability of expression data of the study cohort and residual (158 subjects). c) Heatmap representing the results of linear regression between the surrogate variables utilized for data normalization and technical variables (from RNA-seq analysis) and metadata. Coefficient of linear regression is reported in the heatmap for each correlation pair. d) Distribution of MDS values of study cohort identified a clear clustering based on batches used for RNA-seq analysis (batches 1 to 4). e) After regression of SVs, variability of MDS values is significantly reduced, with no significant outliers and no clustering based on experimental batches. Supplementary Fig. 3. Differential expression of GBA in CD14+ isolated monocytes. Box plot representing differential expression levels (normalized expression count) of GBA in isolated CD14+ monocytes (a and b). In b) data from isolated CD14+ monocytes of GBA-carriers and non-carriers within PD and CTRL subjects were combined and compared. Each dot represents a subject. Dots are colored based on GBA mutations (as reported in the legend: GBA mild mutations (N370S, E326K, R496H), GBA severe mutations (L444P/A456P/RecNciI, V394L, 84GG, 84GG/T369M, N370S/RecNciI)). p-value of different expression levels is reported on top (statistics: Mann-Whitney U test). GBA variants different from N370S are labeled in the boxplot. Supplementary Fig. 4. Differential expression of target genes in manifesting and non-manifesting carriers in monocytes. a) Volcano plot showing differentially expressed genes with FDR < 0.05 (green dots) in isolated monocytes from manifesting vs non-manifesting carriers. Genes related to targeted pathways (LRRK2, ATP13A2, NOTCH1 and TAU) are highlighted. b) Box plots of differential levels of expression of the targeted genes (ATP13A2, LRRK2, NOTCH1), between manifesting and non-manifesting carriers in isolated monocytes. Each dot represents a subject. Dots are colored based on GBA mutations (as reported in the legend: GBA mild mutations (N370S, E326K, R496H), GBA severe mutations (L444P/A456P/RecNciI, V394L, 84GG, 84GG/T369M, N370S/RecNciI)). Asterisks indicate significant p-value (* = p-value < 0.05, ** = p-value < 0.01, *** = p-value < 0.001, statistics: Mann-Whitney U test). Supplementary Fig. 5. Differential expression analysis in monocytes in PD patients with and without GBA-variants. a) Volcano-plot representing logFC (x-axes) and p-value (y-axes, -log10 p-value) of differential expressed genes between PD/GBA and PD as per nested interaction model. Highlighted in yellow are genes with FDR < 0.15 (44 total genes). ID labels of functionally relevant genes and of genes differentially expressed in whole blood (PPMI overlap of genes at nominal p-value 30% of total expression). Discrete relevant variables (Diagnosis, batches of RNAseq analysis (rna_batch), GBA mutations and GBA-related genetic status (carriers or non-carriers of GBA mutations), gender (Sex: male (M) and female (F)) are labeled at the top of the heatmap per each subject. b) Bar-plot reporting number of outlier genes per each subject (out of 158 subjects). Highlighted in orange samples with outlier gene count above 0.1%. c) Summary tables: on the left: number of outliers genes per cohort (PD/GBA, CTRL/GBA, PD, CTRL) (493 pairs); on the right: number of subjects per each cohort with at least one outlier gene (125 unique subjects total with at least one outlier gene). Supplementary Fig. 8. Scatter plot of selected genes identified through the OUTRIDER method. Scatter plot representing -log10 (p-value) of outliers genes identified in previous analysis. Supplementary Fig. 9. Cell proportion across patient and control cohorts. a) Monocyte counts (x106) in the GBA/PD, PD, GBA/CTRL, and CTRL groups in the cohort of isolated monocytes (NYMD cohort). Each dot represents the cell count of one subject. Cell count was available for 57 subjects in our cohort. Statistical analysis according to the Wilcoxon test is reported for the comparison between the four groups. b) Cell proportion (neutrophils, monocytes, lymphocytes, eosinophils, basophils) within whole blood samples in the four groups (GBA/PD, PD, GBA/CTRL, and CTRL) in the PPMI cohort. Comparison of each cell type percent across the four groups is reported on the box plots on the right side of the figure (statistical analysis: Wilcoxon test). Supplementary Fig. 10. Correlation between genes differentially expressed in isolated CD14+ monocytes and whole blood. We compared the directionality of differentially expression genes between a) GBA/PD vs GBA/CTRL and PD vs CTRL in CD14+ monocytes (n = 197); b) GBA/PD vs GBA/CTRL and PD vs CTRL in whole blood (n = 207); c) GBA/PD vs GBA/CTRL in CD14+ monocytes and whole blood (n = 16); d) PD vs CTRL in CD14+ monocytes and whole blood (n = 103). Supplementary Fig. 11. Correlation between gene expression levels in isolated CD14+ monocytes and whole blood. Genes with expression with more than 1 CPM in 30% of the samples were considered from both cohorts (discovery cohort: isolated CD14+ monocytes (total number of genes: 13711), validation cohort: whole blood - PPMI cohort (total number of genes: 18111)). Spearman correlation between levels of normalized mean gene expression across subjects within each cohort per sub-group of subjects was calculated (R = 0.78 p < 0.001). Genes were normalized with TMM and voom, as detailed in the main text. Supplementary Fig. 12. Validation in whole blood of differentially expressed genes in monocytes. a) Differential levels of expression of the targeted genes (ATP13A2, LRRK2, NOTCH1, between manifesting and non-manifesting carriers in whole blood from manifesting and non-manifesting GBA-mutation carriers. b) Differential normalized expression count of SNCA, LMNA, and GBA between PD/GBA and PD, compared to CTRL/GBA and CTRL subjects in whole blood. In a) and b) each dot represents a subject. Dots are colored based on GBA mutations (as reported in the legend: GBA mild mutations (N370S, E326K, R496H), GBA severe mutations (L444P/A456P/RecNciI, V394L, 84GG, 84GG/T369M, N370S/RecNciI)). p-value of different expression levels is reported on top (statistics: Mann-Whitney U test). c) Pathway enrichment analysis of differentially expressed genes in whole blood between PD/GBA vs PD subjects with p-value < 0.01 for GO terms are reported. Dark blue: pathways related to cell transport; Green: pathways related to immune response; Light blue: other pathways.

Additional file 4: Supplementary Table 4. Number of subjects (PD and CTRL) with different GBA variants, reported in the text.

Additional file 1: Supplementary Table 1. Clinical characterization of study cohort. Summary of demographic, clinical and genetic features of the cohort of subjects (PD and CTRL) whose purified CD14+ monocytes were used for integrated genomic analysis.

Additional file 3. Supplementary fig 3

Additional file 3: Supplementary Table 3. GO terms pathways enrichment analysis. The table reports the GO terms and ID for the pathways that were tested for enrichment analysis of differentially expressed genes between the CTRL/GBA and PD/GBA cohorts.

Additional file 7. Supplementary fig 7

Additional file 1. Supplementary fig 1

Additional file 6. Supplementary fig 6

Additional file 14. Supplementary table 7

Additional file 11. Supplementary table 4

Additional file 15. Supplementary table 8

Additional file 3: Table S2. Full Association List of Males Only.

Additional file 6: Supplementary Table 6. Overlapping genes between monocytes and PPMI cohort (whole blood) in manifesting vs non-manifesting GBA-carriers. List of overlapping genes differentially expressed in monocytes and whole blood in manifesting vs non-manifesting carriers. Gene name, description of gene function and logFC and FDR in monocytes and whole blood are reported. In “grey” genes that represent PD-GWAS hits are highlighted.

Additional file 1: Supplemental figure 1 and supplemental tables 1, 2 and 3.

Additional file 4. Supplementary fig 4

Additional file 9. Supplementary table 2

Additional file 13. Supplementary table 6

Additional file 2: Table S1. Full Association List of All Cases.

Additional file 5: Supplementary Table 5. Summary of demographic, clinical and genetic features of the PPMI cohort (whole blood) of subjects (PD and CTRL) in this study.

Additional file 7: Supplementary Table 7. Up-regulated and down-regulated pathway related to vesicle and membrane trafficking in isolated CD14+ monocytes and whole blood. The table shows the list of pathways that were found to be deregulated in both isolated CD14+ monocytes and whole blood in the PD/GBA vs CTRL/GBA groups (top), the deregulated genes in each pathway (first column), and the directionality of dysregulation (second and third column). Red “x” indicates genes dysregulated in whole blood, black “x” represents genes dysregulated in isolated CD14+ monocytes.

Additional file 2: Supplementary Table 2. Targeted pathway enrichment in PD/GBA vs CTRL/GBA. List of endolysosomal pathways (from GO terms and curated pathways, i.e. ubiquitin pathway) reported in Fig. 5. p-value of enrichment as per Fisher exact test of each pathway within the set of up-regulated (UP) and down-regulated (DOWN) genes. Significant enriched pathways (at p-value < 0.15) are highlighted in red.

Additional file 1: Figure S1 Annotation procedure of blue hue ranges in Luxol fast blue, hematoxylin, and eosin-stained tiles. Representative tiles show the annotation method used for positive pixel counting in the Luxol fast blue, hematoxylin, and eosin (LH&E) stained histology tiles. For the annotation heatmap, the darker blue pixel range is highlighted as red while the lighter blue pixel range is highlighted as light blue. Figure S2 Correlation of slide-level probability estimates of cognitive impairment in matched brain donors between the two brain regions. Scatter plots showing the probability estimates of cognitive impairment by the top-performing models in the same brain donors between WSIs in the hippocampus and frontal cortex data sets. The blue line shows predictions from a linear model and grey error envelopes show 95% confidence intervals for the linear model. Figure S3 Scatterplot matrix of deep histopathology features with clinicopathologic features in the frontal cortex. Correlation analysis of deep histopathology results and clinicopathologic features: age, Braak score, ARTAG positivity in the hippocampus (coded as 0 = not present and 1 = present), cognitive label (coded as 0 = not cognitively impaired and 1 = cognitively impaired), probability of cognitive impairment as predicted by the top-performing model trained on the frontal cortex data, and median LFB staining intensity (pixel counts) in the top attention tiles in the frontal cortex data set. Upper right: rank correlation values and associated p-values (* = p < 0.05, ** = p < 0.01, *** = p < 0.001). Diagonal: histograms of variables. Lower left: Scatterplots with linear model trend lines for the variable pairs (red lines) and 95% confidence intervals (blue envelopes). This plot was made using the R package GGally (v. 2.1.2). CI = Cognitive impairment; ARTAG = Aging-related tau astrogliopathy; LFB = Luxol Fast Blue.

Additional file 5: Supplementary Table 5. Summary of demographic, clinical and genetic features of the PPMI cohort (whole blood) of subjects (PD and CTRL) in this study.

Parkinson's disease (PD) is genetically associated with the H1 haplotype of the MAPT 17q.21.31 locus, although the causal gene and variants underlying this association have not been identified.To better understand the genetic contribution of this region to PD and to identify novel mechanisms conferring risk for the disease, we fine-mapped the 17q21.31 locus by constructing discrete haplotype blocks from genetic data. We used digital PCR to assess copy number variation associated with PD-associated blocks, and used human brain postmortem RNA-seq data to identify candidate genes that were then further investigated using in vitro models and human brain tissue.We identified three novel H1 sub-haplotype blocks across the 17q21.31 locus associated with PD risk. Protective sub-haplotypes were associated with increased LRRC37A/2 copy number and expression in human brain tissue. We found that LRRC37A/2 is a membrane-associated protein that plays a role in cellular migration, chemotaxis and astroglial inflammation. In human substantia nigra, LRRC37A/2 was primarily expressed in astrocytes, interacted directly with soluble α-synuclein, and co-localized with Lewy bodies in PD brain tissue.These data indicate that a novel candidate gene, LRRC37A/2, contributes to the association between the 17q21.31 locus and PD via its interaction with α-synuclein and its effects on astrocytic function and inflammatory response. These data are the first to associate the genetic association at the 17q21.31 locus with PD pathology, and highlight the importance of variation at the 17q21.31 locus in the regulation of multiple genes other than MAPT and KANSL1, as well as its relevance to non-neuronal cell types.

Chronic traumatic encephalopathy (CTE) is a neurodegenerative disorder associated with exposure to head trauma. In 2015, a panel of neuropathologists funded by the NINDS/NIBIB defined preliminary consensus neuropathological criteria for CTE, including the pathognomonic lesion of CTE as "an accumulation of abnormal hyperphosphorylated tau (p-tau) in neurons and astroglia distributed around small blood vessels at the depths of cortical sulci and in an irregular pattern," based on review of 25 tauopathy cases. In 2016, the consensus panel met again to review and refine the preliminary criteria, with consideration around the minimum threshold for diagnosis and the reproducibility of a proposed pathological staging scheme. Eight neuropathologists evaluated 27 cases of tauopathies (17 CTE cases), blinded to clinical and demographic information. Generalized estimating equation analyses showed a statistically significant association between the raters and CTE diagnosis for both the blinded (OR = 72.11, 95% CI = 19.5-267.0) and unblinded rounds (OR = 256.91, 95% CI = 63.6-1558.6). Based on the challenges in assigning CTE stage, the panel proposed a working protocol including a minimum threshold for CTE diagnosis and an algorithm for the assessment of CTE severity as "Low CTE" or "High CTE" for use in future clinical, pathological, and molecular studies.

Additional file 2: Supplementary methods. Supplementary figures: Fig S1. Western Blot Analysis Investigating the presence of COVID-19 in Human Brain. Fig S2. Brain specimen dissections. Fig S3. SARS-COV2 RNA not detected in the postmortem midbrain of COVID-19 patients. Fig S4. Quality control of single nucleus data. Fig S5. Variance partition plot. Fig S6. Cell type composition across 15 major cell clusters. Fig S7. UMAP visualization of the distribution of canonical gene markers on annotated cell populations. Fig S8. Gene set enrichment analysis for marker genes of annotated populations. Fig S9. Comparison of marker genes of annotated populations from the current study and other published studies. Fig S10. Cell type composition among cases and controls. Fig S11. Immunohistochemistry of CD68 positive cells in the Choroid Plexus. Fig S12. Sub-clustering of microglial and monocyte/macrophage populations. Fig S13. KEGG pathway enrichment network. Fig S14. Pseudo-temporal trajectory score (PTS) analysis in microglia identifies gene expression signatures across different donors. Fig S15. UMAP projection based on the activity scores of 131 TF regulons. Fig S16. Regulon specificity scores (RSS) of each annotated cell population. Fig S17. Transcription factor activity scores across different cell types and brain regions.

Primary tauopathies are characterized neuropathologically by inclusions containing abnormal forms of the microtubule-associated protein tau (MAPT) and clinically by diverse neuropsychiatric, cognitive, and motor impairments. Autosomal dominant mutations in the MAPT gene cause heterogeneous forms of frontotemporal lobar degeneration with tauopathy (FTLD-Tau). Common and rare variants in the MAPT gene increase the risk for sporadic FTLD-Tau, including progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). We generated a collection of fibroblasts from 140 MAPT mutation/risk variant carriers, PSP, CBD, and cognitively normal controls; 31 induced pluripotent stem cell (iPSC) lines from MAPT mutation carriers, non-carrier family members, and autopsy-confirmed PSP patients; 33 genome engineered iPSCs that were corrected or mutagenized; and forebrain neural progenitor cells (NPCs). Here, we present a resource of fibroblasts, iPSCs, and NPCs with comprehensive clinical histories that can be accessed by the scientific community for disease modeling and development of novel therapeutics for tauopathies.

Chronic traumatic encephalopathy (CTE) is a neurodegeneration characterized by the abnormal accumulation of hyperphosphorylated tau protein within the brain. Like many other neurodegenerative conditions, at present, CTE can only be definitively diagnosed by post-mortem examination of brain tissue. As the first part of a series of consensus panels funded by the NINDS/NIBIB to define the neuropathological criteria for CTE, preliminary neuropathological criteria were used by 7 neuropathologists to blindly evaluate 25 cases of various tauopathies, including CTE, Alzheimer's disease, progressive supranuclear palsy, argyrophilic grain disease, corticobasal degeneration, primary age-related tauopathy, and parkinsonism dementia complex of Guam. The results demonstrated that there was good agreement among the neuropathologists who reviewed the cases (Cohen's kappa, 0.67) and even better agreement between reviewers and the diagnosis of CTE (Cohen's kappa, 0.78). Based on these results, the panel defined the pathognomonic lesion of CTE as an accumulation of abnormal hyperphosphorylated tau (p-tau) in neurons and astroglia distributed around small blood vessels at the depths of cortical sulci and in an irregular pattern. The group also defined supportive but non-specific p-tau-immunoreactive features of CTE as: pretangles and NFTs affecting superficial layers (layers II-III) of cerebral cortex; pretangles, NFTs or extracellular tangles in CA2 and pretangles and proximal dendritic swellings in CA4 of the hippocampus; neuronal and astrocytic aggregates in subcortical nuclei; thorn-shaped astrocytes at the glial limitans of the subpial and periventricular regions; and large grain-like and dot-like structures. Supportive non-p-tau pathologies include TDP-43 immunoreactive neuronal cytoplasmic inclusions and dot-like structures in the hippocampus, anteromedial temporal cortex and amygdala. The panel also recommended a minimum blocking and staining scheme for pathological evaluation and made recommendations for future study. This study provides the first step towards the development of validated neuropathological criteria for CTE and will pave the way towards future clinical and mechanistic studies.

Background: Induced pluripotent stem cells (iPSCs) derived from patients with neurodegenerative disease generally lack neuropathological confirmation, the gold standard for disease classification and grading of severity. The use of tissue with a definitive neuropathological diagnosis would be an ideal source for iPSCs. The challenge to this approach is that the majority of biobanked brain tissue was not meant for growing live cells, and thus was not frozen in the presence of cryoprotectants such as DMSO. Results: We report the generation of iPSCs from frozen non-cryoprotected dural tissue stored at −80°C for up to 11 years. This autopsy cohort included subjects with Alzheimer’s disease and four other neurodegenerative diseases. Conclusions: Disease-specific iPSCs can be generated from readily available, archival biobanked tissue. This allows for rapid expansion of generating iPSCs with confirmed pathology as well as allowing access to rare patient variants that have been banked.

Background Resistance to apoptosis is a paramount issue in the treatment of Glioblastoma (GBM). We show that targeting PARP by the small molecule inhibitors, Olaparib (AZD-2281) or PJ34, reduces proliferation and lowers the apoptotic threshold of GBM cells in vitro and in vivo. Methods The sensitizing effects of PARP inhibition on TRAIL-mediated apoptosis and potential toxicity were analyzed using viability assays and flow cytometry in established GBM cell lines, low-passage neurospheres and astrocytes in vitro. Molecular analyses included western blots and gene silencing. In vivo, effects on tumor growth were examined in a murine subcutaneous xenograft model. Results The combination treatment of PARP inhibitors and TRAIL led to an increased cell death with activation of caspases and inhibition of formation of neurospheres when compared to single-agent treatment. Mechanistically, pharmacological PARP inhibition elicited a nuclear stress response with up-regulation of down-stream DNA-stress response proteins, e.g., CCAAT enhancer binding protein (C/EBP) homology protein (CHOP). Furthermore, Olaparib and PJ34 increased protein levels of DR5 in a concentration and time-dependent manner. In turn, siRNA-mediated suppression of DR5 mitigated the effects of TRAIL/PARP inhibitor-mediated apoptosis. In addition, suppression of PARP-1 levels enhanced TRAIL-mediated apoptosis in malignant glioma cells. Treatment of human astrocytes with the combination of TRAIL/PARP inhibitors did not cause toxicity. Finally, the combination treatment of TRAIL and PJ34 significantly reduced tumor growth in vivo when compared to treatment with each agent alone. Conclusions PARP inhibition represents a promising avenue to overcome apoptotic resistance in GBM.