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.
Displaying 21 - 30 of 60
Characterization of γ-Secretase in fibroblasts derived from FAD patients harboring PSEN1or PSEN2 mutations
Determine in vitro γ-secretase activity for Aβ40 and Aβ42 production using fibroblast cell membranes
Characterize the γ-secretase complexes in fibroblast cell membranes
Examine the sensitivity of various modulators and inhibitors to γ-secretase in fibroblast cell membranes
Comprehensive CSF tau profiling in DIAN using a novel mass spectrometry
Determine stage of the disease where additional p-tau isoforms (particularly p-tau Thr 217) begin to differ between MC and NC based on EYO and Aβ status, and compare this to the currently used immunoassay measures of tau and p-tau181.
Determine longitudinal change (quantitative change / mean and SD) of the various p-tau isoforms identified as it relates to disease stage and how this compares to the currently used immunoassay measures of tau and p-tau181.
Assess the association between baseline and longitudinal CSF MS p-tau isoforms and other measures of disease (neuroimaging- FDG �PET, MRI volumetrics and tau �PET; clinical- CDR, cognition).
Compare the comprehensive CSF tau profile identified in DIAN with that completed and underway in sAD at Washington University by Drs. Barth�lemy and Bateman.
Neuropathological findings of brains of persons with familial AD (FAD) due to the A431E PSEN1 mutation
To thoroughly characterize and describe the neuropathological findings of 11 brains of persons with familial AD (FAD) due to the A431E PSEN1 mutation.
To correlate pathological findings with clinical findings in these patients.
To explain specific clinical and imaging features of the A431E mutation by quantifying aspects of the neuropathology.
Assessment of CSF YKL-40, VILIP-1, and calbindin as diagnostic and prognostic markers
Examine the utility of CSF VILIP-1, YKL-40, and calbindin D28K (alone or in combination with CSF tau, p-tau181, and A?42) in the identification of mutation carrier status in family members of individuals with familial AD compared to non-mutation carrier status.
Investigate whether CSF levels of VILIP-1, YKL-40, and calbindin D28K (alone or in combination with CSF tau, p-tau181, and A?42) can predict symptomatic onset over the study follow-up period.
Determine whether CSF levels of VILIP-1, YKL-40, and calbindin D28K correlate with other CSF and imaging markers of preclinical AD pathology such CSF tau, p-tau181, A?42, and amyloid load on PET-PIB in familial AD.
Prof. Ralph Martins
Investigating blood and CSF lipid biomarkers in autosomal dominantly inherited Alzheimer's disease.
Identify early modifications in plasma, CSF and platelet lipid profiles of DIAN participants which may reveal critical information about the pathobiological cascade that culminates in symptomatic disease.
Demonstrate any correlation that may exist across the two major macromolecule subclasses in the blood and CSF, i.e. lipids and selected proteins (Aβ, ApoE, phospho and total tau) and also contribute to a panel of biomarkers to detect AD through a blood test.
Correlate the DIAN lipid profile with brain imaging data obtained from MRI, FDG-PET and PiB-PET scans and determine whether there exists a link between biochemical alterations occurring in the periphery and brain.
Development and characterization of iPS derived cellular model for Alzheimer's Disease
We are a group working on the molecular and functional characterization of neuropsychiatric illness using patient specific cellular models. LCl lines from the DIAN repository are fully characterized and would serve as informative control in our attempts to model disease phenotypes.
Marc I. Diamond
Seeding of Tau Aggregation: A Novel Plasma and CSF Biomarker for Alzheimer’s Disease
1) To determine the sensitivity and specificity of our seeding assay for identifying subjects with AD.
2) To determine when in the course of the disease process the seeding assay becomes positive.
3) To examine how seeding activity correlates with validated biomarkers in a well-studied population.
Defining the mechanisms of AD pathogenesis in human IPSC-derived neurons
To measure the influence of pathogenic FAD mutations on APP and tau metabolism in human neurons
John M. Ringman, M.D., M.S.
Metabolic profiling of biofluids in Alzheimer's Disease
Identify the compounds in the combined gas chromatography-mass spectrometry profiles that differentiate the samples collected from patients carrying and not carrying causative FAD mutations. Further analyses will compare presymptomatic and symptomatic persons carrying FAD mutations.
Identify peaks in the combined liquid chromatography-mass spectrometry profiles that differentiate samples collected from causative FAD mutation carriers and non-carriers, and presymptomatic and symptomatic FAD mutation carriers and identify the responsible compounds.
Establish and apply quantitative assays to the compounds identified in Aims 1 and 2.
Integrate all the compounds that are identified in Aims 1 and 2 into a biochemical overview, and rationalize these data with the extant knowledge about Alzheimer's Disease pathology.
Richard J. Perrin
Quantitative label-free proteomics for CSF Biomarkers in Dominantly Inherited AD
To evaluate and map the temporal patterns of novel CSF biomarkers, measured in DIAN samples using quantitative label-free proteomics. Data will be aligned according to expected symptom onset; values from carriers (N=120) and non-carriers (N=80) will be compared (as in Bateman RJ, et al. NEJM 2012).
To integrate proteomics data into the context of known AD-associated changes for this cohort (Bateman RJ, NEJM 2012), to compare novel and existing markers, and to compare this autosomal dominant dataset to a sporadic AD dataset already derived from proteomic analysis of a Knight ADRC cohort.