Search DIAN Tissue Requests
In order to avoid the situation where two investigators study the same research question, please search our database to determine if your topic has already been studied. If you find that your topic or a related topic has already been submitted, you may wish to contact the investigator to inquire about his/her findings to determine how you might proceed. You may wish to collaborate or modify your request to avoid overlap. The results below reflect requests made since online requests have been accepted. As such, not all fields will have data as certain information, such as aims, were not collected until recently. If an entry has been assigned an ID # (e.g. DIAN-T1004), the full request has been submitted and is either approved, disapproved or in process.
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Mitochondrial deficits in AD patient iPSC-derived neurons
Investigate abnormalities in mitochondrial dynamics and quality control in patient iPSC-derived human neurons
Investigate molecular mechanism underlying mitochondrial dynamic and quality control abnormalities in patient iPSC-derived human neurons
Mechanistic and functional assessment of sporadic early-onset AD
Investigate amyloid-related mechanisms of familial and sporadic EOAD patient-derived fibroblasts, patient iPSC-derived neurons and directly converted neurons
Assess the function, structure and potential pathological pathways of sporadic EOAD, familial EOAD, healthy and LOAD patient-derived iPSC-derived neurons and directly converted neurons
Dr. Tammie Benzinger
Imaging Tauopathy in the Dominantly Inherited Alzheimer Network (DIAN)
Aim 1: To study the temporal dynamics of tau deposition (using AV-1451). This proposal studies tauopathy in relation to existing biomarkers within DIAN participants including: CSF (CSF tau, p-tau, Aβ42), neuroimaging [Aβ PET and structural and functional magnetic resonance imaging (MRI)], and cognitive performance [clinical dementia rating sum of boxes (CDR-SB) and neuropsychometric testing]. We will relate AV1451 uptake to EYO to determine timing of tau changes relative to conversion to symptomatic AD. Hypothesis: PET tau changes occur after Aβ (CSF Aβ42 and PET Aβ) and after soluble CSF tau but before MRI (structural or functional) or neuropsychometric changes in DIAN.
Aim 2: To study the spatial (both local and distributed) changes of tau deposition (using AV-1451). We will examine the spread of tauopathy along structural and functional connections. We will identify the spatial pattern of tauopathy during the crucial transition from preclinical to symptomatic AD. Additionally, using novel mathematical models, we will correlate the topography of tau PET with both cross-sectional and longitudinal spatial patterns seen with other imaging biomarkers (Aβ PET and MRI [structural and functional]). Hypothesis: Phenoconversion from cognitively normal to symptomatic dementia is associated with neocortical tau deposition. The spread of tau deposition is predicted by functional/structural connections.
Aim 3 (exploratory): Study the relationship between in vivo tau deposition and neuropathology. We will perform quantitative measures of cortical tauopathy NFT, NP, and neuropil thread (NT) burden in twenty-five brain areas using tau-immunostained sections and automated stereological methods. We will perform quantitative autoradiography with [3H]AV-1451 in the same tissue samples to validate receptor binding specificity and quantify tauopathy. Neuropathological results will be aligned with in vivo imaging using an ex vivo MRI prior to sectioning. Hypothesis: There will be a strong correlation between tau burden as assessed by AV-1451 in vivo imaging, [3H]AV-1451 autoradiography, and anti-tau antibodies.
Development of blood-based proteomic biomarkers of Alzheimer's disease
Our study aims to identify a blood-based biomarker panel using SWATH-MS that can detect AD at an early asymptomatic stage
Longitudinal changes of the microglia activity markers sTREM2 and Progranulin in CSF of Dominantly Inherited Alzheimer’s disease
To determine longitudinal changes in CSF sTREM2 and CSF PGRN levels across the range of EYO.
To determine the association between baseline levels of CSF sTREM2 and CSF PGRN and their rate of change with longitudinal cognition, brain structure and metabolism at different stages of ADAD
Assess the cross-sectional association between CSF sTREM2 or CSF PGRN and AV1451 PET across different EYO.
Role of amylin in AD
Test the hypothesis that the ratio of serum amylin to CSF amylin levels increases in fAD compared to age-matched cognitively normal individuals.
Test the hypothesis that amylin interacts with Abeta to form mixed amylin-Abeta oligomers within CSF and serum in patients with fAD. We propose to measure the levels of mixed Abeta-amylin oligomers within CSF and serum in patients with fAD versus age-matched cognitively normal individuals.
Identification of mutation-specific networks. Amended
To generate RNA-seq data from brain tissue from DIAN participants (mutation carriers and non-carriers)
To identify genes differentially expressed or spliced in mutation carriers vs LOAD, and vs controls
To identify gene and mutation-specific networks and pathways
Lipidomics study of preclinical plasma biomarkers for Alzheimer´s disease
Establish a preclinical biomarker for AD
Determine lipid profiles in preclinical AD
Compare lipid profiles in DIAN and Framingham
Determine longitudinal changes of lipid profiles during disease
AD pathology in advanced cellular models
Generation of iPSCs from AD patient fibroblasts
Differentiation of AD iPSCs into neurons
Analysis of cellular networks dysfunction
Leptin and metabolic signaling in autosomal dominant Alzheimer’s disease
To determine if alterations in levels of plasma leptin and related metabolic markers are evident in cognitively normal subjects with autosomal dominant Alzheimer’s disease
To determine if alterations in plasma leptin levels and related metabolic markers correlate with changes in Alzheimer’s disease biomarkers in subjects with autosomal dominant Alzheimer’s disease
To determine if levels of plasma leptin and related metabolic markers correlate with worsening Alzheimer’s disease biomarkers and progression of cognitive decline over time (age) in subjects with autosomal dominant Alzheimer’s disease