Modeling Alzheimer's Disease Progression Using Disease Onset Time and Disease Trajectory Concepts Applied to CDR-SOB Scores From ADNI.
Ontology highlight
ABSTRACT:
SUBMITTER: Delor I
PROVIDER: S-EPMC3817374 | biostudies-other | 2013 Oct
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Using laser capture microdissection to isolate over 100 specific cell populations, we report the profiling of prostate cancer progression from benign epithelium to metastatic disease. By analyzing these expression signatures in the context of over 15,000 “molecular concepts”, or sets of biologically connected genes, we generated an integrative model of prostate cancer progression. Keywords: disease state analysis
2006-12-17 | GSE6099 | GEO
Project description:Using laser capture microdissection to isolate over 100 specific cell populations, we report the profiling of prostate cancer progression from benign epithelium to metastatic disease. By analyzing these expression signatures in the context of over 15,000 â??molecular conceptsâ??, or sets of biologically connected genes, we generated an integrative model of prostate cancer progression. Keywords: disease state analysis Experiment Design: â?¢ Type of experiment: This study used microarray experiments to profile prostate cancer progression through the isolation of pure cell populations using laser capture microdissection (LCM) and OmniPlex Whole Transcriptome (WTA) Amplification. â?¢ Experimental factors: This study profiled various pure cell populations isolated by LCM from prostate tissue. â?¢ The number of hybridizations performed in the experiment: A total of 104 hybridizations were performed. â?¢ The type of reference used for the hybridizations: A single commercially available pool of benign prostate tissue (CPP, Clontech) was used as the reference in all hybridizations. For the reference sample, 10 ng of CPP total RNA was converted into a WTA cDNA library in a volume of 25 µl and five 5 µl aliquots were amplified by WTA PCR and purified. Five ng aliquots of the pooled products were subjected to a second WTA PCR amplification in the presence of amino-allyl dUTP and all products were pooled before labeling to ensure equal representation across all of the hybridizations. â?¢ Hybridization design: For all hybridizations, the experimental prostate sample isolated by LCM was labeled with Cy5 and the common reference of benign pooled prostate as described above was labeled with Cy3. â?¢ Quality control steps taken: In order to assess biological and technical reproducibility, we captured identical regions from two specimens (PCA_11 and MET_HR_3) and subjected one sample, PCA_30, to two hybridizations. For PCA_11, adjacent regions of the same section were captured and treated as separate samples. For MET_HR_3, two separate foci of the liver metastasis (from separate frozen blocks) were captured and treated as separate samples. For PCA_30, two aliquots of the primary WTA PCR products were separately subjected to a second round of WTA PCR amplification. Replicates from PCA_30 and MET_HR_3 were also hybridized to arrays from each of the print runs utilized in this study. Samples used, extract preparation and labeling: â?¢ The origin of the biological sample (for instance, name of the organism, the provider of the sample) and its characteristics: All samples used were of human origin. The table in the MIAME Checklist (Supplmentary Methods) summarizes the samples used. Hybridizations are named according to the class of sample profiled and the background color is the same as shown in Figure S3. Two print runs were used during the course of the study and the indicated run for each hybridization is given. For prostate cancer samples, the Gleason patterns present on the sample section used are indicated, as well as the Gleason patterns of the actual cells isolated by LCM. A unique case number for all profiled samples is given. Descriptions of samples are given where applicable, including the location of metastatic foci. â?¢ Manipulation of biological samples and protocols used: Laser Capture Microdissection (LCM) was performed from frozen tissue sections with the SL Microtest device using µCUT software (MMI). Approximately 10,000 cells were captured for each sample. Serial sections were used if cells could not be obtained from a single section. Total RNA was isolated from captured cells with the RNAqueous Micro kit (Ambion) and treated with DNAse I according to the manufacturer's instructions. RNA quantification was perfomed using Ribogreen (Molecular Probes). â?¢ Protocol for preparing the hybridization extract: For each sample, total RNA (~10ng) in a volume of 25 µl was converted into an OmniPlex WTA cDNA library and amplified by WTA PCR using reagents and protocols according to the beta-commercial TransPlex WTA kit (Rubicon Genomics, Ann Arbor, MI). For each sample, a single 10 µl aliquot of the WTA cDNA library was amplified by WTA PCR and products were purified. Four or five 5 ng aliquots of product were subjected to a second WTA PCR amplification in the presence of amino-allyl dUTP for post amplification labeling and products were pooled before proceeding with the hybridization. Yields after all WTA PCR amplifications were between 2 to 5 μg per reaction, and reaction progress was monitored in real time using SYBR green I (Molecular Probes) on the iCycler IQ (BioRad) or ABI 7300 Real Time PCR system (Applied Biosystems). Real-time PCR amplifications were terminated at or before plateau phase as measured by fluorescence incorporation to preserve maximum representation. â?¢ Labeling protocol(s): For all WTA amplified cDNA samples with amino-allyl dUTP incorporated (10-20 ï?g), nuclease free water was added to 500 ï?l, the sample was transferred to a Microcon YM-30 filter, and centrifuged at 13,000 rcf at RT for 12 minutes. The spin column was inverted in a new collection tube and centrifuged at 13,000 rcf for 2 min at RT. The amount of sample was measured and the volume was adjusted to 5 ï?l with nuclease free water. For labeling, 5 ï?l of 1 M sodium bicarbonate (pH 9.0) was added and allowed to incubate at RT for 15 min. Nine ï?l of anhydrous DMSO were added to Mono Reactive Cy3 and Cy5 dye packs (Amersham, Buckinghamshire, England), mixed thoroughly, and 1 ï?l of the proper dye was added to each sample. The labeling mixture was incubated in the dark at RT for 1 hour. The reaction was stopped by the addition of 9 ï?l of 4 M Hydroxylamine for 15 min. For each labeling mixture, RNase free water was added to 100 ï?l, 500 ï?l of PB buffer was added and Cy3 and Cy5 mixtures were added to separate Qiaquick (Qiagen, Valencia, CA) spin columns and centrifuged at 13,000 rcf. Columns were washed twice with 700 ï?l of buffer PE, and centrifuged dry at 13,000 rcf for 2 min. To elute, 60 ï?l of EB buffer was placed on the column, incubated for 5 min and centrifuged at 13,000 rcf for 1 min. The Cy3 labeled and Cy5 labeled cDNA for each hybridization were mixed and 1.5 ï?l were used for analysis on a ND-1000 spectrophotometer. â?¢ External controls (spikes): Not utilized Hybridization procedures and parameters: â?¢ The protocol and conditions used during hybridization, blocking and washing: For hybridization, 40 ï?g of human Cot-1 DNA (Invitrogen, Carlsbad, CA) was added to 120 ï?l of labeled cDNA, the probe was transferred to a Microcon YM-30 filter and centrifuged at 13,000 rcf for 6 min at RT. The spin column was inverted in a new collection tube and centrifuged at 13,000 rcf for 2 min at RT. The eluted probe volume was adjusted to 18.6 ï?l with nuclease free water and the following were added: 4 ï?l of yeast tRNA (10 ï?g/ï?l, Invitrogen, Carlsbad, CA), 4.9 ï?l of 20X SSC and 0.84 ï?l of 10% SDS. The probe was denatured at 100 Co for 3 min and centrifuged at 13,000 rcf for 45 sec. The probe was added directly to the microarray and a cover slip was added. Slides were hybridized overnight in DIE-TECH 1 or 5 slide hybridization chambers in a 65 Co water bath. After hybridization, cover slips were removed by incubation in 2x SSC / 0.05% SDS. Slides were then washed in 2x SSC / 0.05% SDS for 5 min and 0.2x SSC / 0.05% SDS for five minutes. Slides were then washed in 0.2x SSC for 10 sec and centrifuged dry at 500 rpm for 5 min. Measurement data and specifications: â?¢ Type of scanning hardware and software used: Microarrays were scanned using a GenePix 4000B scanner (Axon Instruments, Union City, CA). â?¢ Type of image analysis software used: Images were gridded and spots were quantified using GenePix Pro 4.0 software (Axon Instruments, Union City, CA). â?¢ A description of the measurements produced by the image-analysis software and a description of which measurements were used in the analysis: The measurements made by GenePix Pro 4.0 are described in the sample files. For all hybridizations, the log2 normalized Median of Ratios (as described below) was used. â?¢ Data selection and transformation procedures: Arrays were auto-gridded by GenePix 4.0. Features flagged by GenePix as not found during grid
2010-06-05 | E-GEOD-6099 | biostudies-arrayexpress
Project description:Late-onset Alzheimer’s disease (LOAD) is the most common form of AD. However, modeling sporadic LOAD, without clear genetic predispositions, to capture hallmark neuronal pathologies such as extracellular amyloid-β (Aβ) plaque deposition, intracellular tau tangles, and neuronal loss, remains an unmet need. Here, we demonstrate that neurons generated by microRNA-based direct reprogramming of fibroblasts from patients affected by autosomal dominant AD (ADAD) and LOAD in a three-dimensional (3D) environment, effectively recapitulate key neuropathological features of AD without additional cellular or genetic insults. These LOAD neurons exhibit Aβ-dependent neurodegeneration, as treatment with β- or γ-secretase inhibitors before (but not subsequent to) Aβ deposit formation mitigated neuronal death. Moreover, inhibiting age-associated retrotransposable elements (RTEs) in LOAD neurons reduced both Ab deposition and neurodegeneration. Our study underscores the efficacy of modeling late-onset neuropathology of LOAD through high-efficiency microRNA-based neuronal reprogramming.
2024-08-05 | GSE252932 | GEO
Project description:Late-onset Alzheimer’s disease (LOAD) is the most common form of AD. However, modeling sporadic LOAD, without clear genetic predispositions, to capture hallmark neuronal pathologies such as extracellular amyloid-β (Aβ) plaque deposition, intracellular tau tangles, and neuronal loss, remains an unmet need. Here, we demonstrate that neurons generated by microRNA-based direct reprogramming of fibroblasts from patients affected by autosomal dominant AD (ADAD) and LOAD in a three-dimensional (3D) environment, effectively recapitulate key neuropathological features of AD without additional cellular or genetic insults. These LOAD neurons exhibit Aβ-dependent neurodegeneration, as treatment with β- or γ-secretase inhibitors before (but not subsequent to) Aβ deposit formation mitigated neuronal death. Moreover, inhibiting age-associated retrotransposable elements (RTEs) in LOAD neurons reduced both Ab deposition and neurodegeneration. Our study underscores the efficacy of modeling late-onset neuropathology of LOAD through high-efficiency microRNA-based neuronal reprogramming.
2024-08-05 | GSE267613 | GEO
Project description:Late-onset Alzheimer’s disease (LOAD) is the most common form of AD. However, modeling sporadic LOAD, without clear genetic predispositions, to capture hallmark neuronal pathologies such as extracellular amyloid-β (Aβ) plaque deposition, intracellular tau tangles, and neuronal loss, remains an unmet need. Here, we demonstrate that neurons generated by microRNA-based direct reprogramming of fibroblasts from patients affected by autosomal dominant AD (ADAD) and LOAD in a three-dimensional (3D) environment, effectively recapitulate key neuropathological features of AD without additional cellular or genetic insults. These LOAD neurons exhibit Aβ-dependent neurodegeneration, as treatment with β- or γ-secretase inhibitors before (but not subsequent to) Aβ deposit formation mitigated neuronal death. Moreover, inhibiting age-associated retrotransposable elements (RTEs) in LOAD neurons reduced both Ab deposition and neurodegeneration. Our study underscores the efficacy of modeling late-onset neuropathology of LOAD through high-efficiency microRNA-based neuronal reprogramming.
2024-08-05 | GSE253174 | GEO
Project description:Young-onset and late-onset Alzheimer's disease has different clinical presentations with late-onset presenting most often with memory deficits while young-onset often presents with a non-amnestic syndrome. However, it is unknown whether there are differences in presentation and progression of neuropsychiatric symptoms in young- versus late-onset Alzheimer's disease. We aimed to investigate differences in the prevalence and severity of neuropsychiatric symptoms in patients with young- and late-onset Alzheimer's disease longitudinally with and without accounting for the effect of medication usage. Sex differences were also considered in these patient groups. We included 126 young-onset and 505 late-onset Alzheimer's disease patients from National Alzheimer's Coordinating Center-Uniform Data Set (NACC-UDS) and Alzheimer's Disease Neuroimaging Initiative (ADNI). We investigated the prevalence and severity of neuropsychiatric symptoms using the Neuropsychiatric Inventory-Questionnaire over 4 visits with 1-year intervals, using a linear mixed-effects model. The prevalence of depression was significantly higher in young-onset than late-onset Alzheimer's disease over a 4-year interval when antidepressant usage was included in our analyses. Our findings suggest that neuropsychiatric symptom profiles of young- and late-onset Alzheimer's disease differ cross-sectionally but also display significant differences in progression.
| S-EPMC8050126 | biostudies-literature
Project description:To develop a disease progression model of Alzheimer's disease (AD) that shows cognitive decline from subjective cognitive impairments (SCI) to the end stage of AD dementia (ADD) and to investigate the effect of education level on the whole disease spectrum, we enrolled 565 patients who were followed up more than three times and had a clinical dementia rating sum of boxes (CDR-SB). Three cohorts, SCI (n = 85), amnestic mild cognitive impairment (AMCI, n = 240), and ADD (n = 240), were overlapped in two consecutive cohorts (SCI and AMCI, AMCI and ADD) to construct a model of disease course, and a model with multiple single-cohorts was estimated using a mixed-effect model. To examine the effect of education level on disease progression, the disease progression model was developed with data from lower (≤ 12) and higher (> 12) education groups. Disease progression takes 274.3 months (22.9 years) to advance from 0 to 18 points using the CDR-SB. Based on our predictive equation, it takes 116.5 months to progress from SCI to AMCI and 56.2 months to progress from AMCI to ADD. The rate of CDR-SB progression was different according to education level. The lower-education group showed faster CDR-SB progression from SCI to AMCI compared to the higher-education group, and this trend disappeared from AMCI to ADD. In the present study, we developed a disease progression model of AD spectrum from SCI to the end stage of ADD. Our disease modeling provides us with more understanding of the effect of education on cognitive trajectories.
| S-EPMC7544693 | biostudies-literature
Project description:A?, tau, and P-tau have been widely accepted as reliable markers for Alzheimer's disease (AD). The crosstalk between these markers forms a complex network. AD may induce the integral variation and disruption of the network. The aim of this study was to develop a novel mathematic model based on a simplified crosstalk network to evaluate the disease progression of AD. The integral variation of the network is measured by three integral disruption parameters. The robustness of network is evaluated by network disruption probability. Presented results show that network disruption probability has a good linear relationship with Mini Mental State Examination (MMSE). The proposed model combined with Support vector machine (SVM) achieves a relative high 10-fold cross-validated performance in classification of AD vs. normal and mild cognitive impairment (MCI) vs. normal (95% accuracy, 95% sensitivity, 95% specificity for AD vs. normal; 90% accuracy, 94% sensitivity, 83% specificity for MCI vs. normal). This research evaluates the progression of AD and facilitates AD early diagnosis.
| S-EPMC4718081 | biostudies-literature
Project description:Late-onset Alzheimer's disease (LOAD) is an age-related neurodegenerative disorder characterized by gradual loss of synapses and neurons, but its pathogenesis remains to be clarified. Neurons live in an environment constituted by neurons themselves and glial cells. In this review, we propose that the neuronal degeneration in the AD brain is partially caused by diverse environmental factors. We first discuss various environmental stresses and the corresponding responses at different levels. Then we propose some mechanisms underlying the specific pathological changes, in particular, hypothalamic-pituitary adrenal axis dysfunction at the systemic level; cerebrovascular dysfunction, metal toxicity, glial activation, and Aβ toxicity at the intercellular level; and kinase-phosphatase imbalance and epigenetic modification at the intracellular level. Finally, we discuss the possibility of developing new strategies for the prevention and treatment of LOAD from the perspective of environmental stress. We conclude that environmental factors play a significant role in the development of LOAD through multiple pathological mechanisms.
| S-EPMC5562669 | biostudies-other
Project description:The aim of this tutorial is to introduce the fundamental concepts of physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling with a special focus on their practical implementation in a typical PBPK model building workflow. To illustrate basic steps in PBPK model building, a PBPK model for ciprofloxacin will be constructed and coupled to a pharmacodynamic model to simulate the antibacterial activity of ciprofloxacin treatment.
| S-EPMC5080648 | biostudies-literature