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 31 - 40 of 62
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.
Karen H. Ashe
Specific CSF Abeta oligomers in unimpaired DIAN subjects
Compare CSF Abeta*56 and Abeta trimer levels in unimpaired carriers and non-carriers
Correlate CSF Abeta oligomers with Abeta1-42, tau and p-tau181
Relate CSF Abeta*56 and Abeta trimer levels to predicted age of onset
Ajay Verma MD Ph.D., VP, Development Sciences Biogen Idec
Proposal to evaluate the prognostic and diagnostic potential of selected AD biomarkers using plasma and CSF samples from DIAN registry
Evaluate total pyroglutamate-Aβ(pE3-Aβ) in plasma and CSF and other selected amyloid Aβ isoforms in CSF as prognostic/diagnostic markers of AD in symptomatic and pre-symptomatic populations of mutation carriers and non-carriers in the DIAN registry
Evaluate the CSF sTREM2 and VLP-1 markers as prognostic/diagnostic markers of AD in symptomatic and pre-symptomatic populations of mutation carriers and non-carriers in the DIAN registry
Evaluate the selected modified by oxidative stress tyrosine moieties in CSF as markers linked to amyloidogenesis, inflammation, and neuronal injury
Publish the findings from the above evaluations and share the results with Alzheimer’s disease research community
DIAN TU Biomarker Core Validation Testing
To develop and validate a method of measuring biomarker kit lot variability and new lot acceptance criteria
To establish and verify our internal quality control (QC) process for assay plate and sample acceptance criteria
To test DIAN TU drug interference on the ability of the AlzBio3 assay to detect CSF tau and ptau181
To validate the DIAN TU internal CSF pools as suitable internal controls to be used for Incurred Sample Reanalysis (ISR) during the trial
Antibody Signature of Alzheimer's Disease
To confirm whether AD has a specific and reproducible antibody signature that can be used as diagnostic test
Confirm that patients with PSEN-1 mutations have an Alzheimer-type signature
Determine whether PSEN-2 and APP mutation carrieres have an Alzheimer signature
Determine whether Alzheimer signature is present before the onset of dementia in mutation carriers
plasma proteomic markers associated with CSF biomarkers in AD
correlate CSF biomarkers with 700 plasma proteins using sliding threshold analysis
validate discovery of CSF Abeta threshold obtained with samples from sporadic AD
assess effects of APOE
find thresholds for tau, ptau and correlate plasma proteins with cognitive function
Soluble TREM2 in autosomal dominant Alzheimer’s disease
Determine whether and how many years before the estimated time point of symptom onset (EYO), mutation carriers (MC) of autosomal dominant Alzheimerâs disease (ADAD) show altered cerebrospinal fluid (CSF) and plasma levels of soluble TREM2 (sTREM2) compared to non-mutation carriers (NC).
Test whether differences in CSF and plasma levels of sTREM2 are associated with longitudinal changes in PIB-PET, FDG-PET, and structural MRI, rsfMRI, with other CSF biomarkers (AÃ42, T-tau, p-tau), clinical symptoms (MMSE, CDR-SOB) and cognitive performance (memory and executive functions).
Insulin and glucose levels in autosomal dominant Alzheimer's Disease
To measure fasting blood insulin and glucose levels in non-carriers, asymptomatic carriers, and symptomatic carriers
Relate alterations in insulin and glucose to biomarkers of AD pathology drawn from CSF, PET, and structural imaging
Identification of mutation-specific networks
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