f MRI guided Microarray analysis ImagingGuided Microarray Isolating

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f. MRI guided Microarray analysis Imaging-Guided Microarray: Isolating Molecular Profiles That Dissociate Alzheimer’s Disease

f. MRI guided Microarray analysis Imaging-Guided Microarray: Isolating Molecular Profiles That Dissociate Alzheimer’s Disease from Normal Aging A. C. Pereira, W. Wu & S. A. Small Ann NY Acad. Sci. 1097, Feb 2007 Combining Brain Imaging with Microarray: Isolating Molecules Underlying the Physiologic Disorders of the Brain A. Pierce & S. A. Small Neurochemical Research, Vol. 29, No. 6, June 2004

Crash course: The CELL and microarrays in 3 slides Cells internal processes and inter-cell

Crash course: The CELL and microarrays in 3 slides Cells internal processes and inter-cell communication based on proteins Goal: Figure out which proteins exist in a cell under some condition Condition – e. g. disease Many times – detect proteins differentially expressed – e. g. disease vs. control Basic: staining a specific protein and follow it under a microscope Next: The CELL

From DNA to Protein (Final) product – Protein Intermediate product m. RNA Idea: measure

From DNA to Protein (Final) product – Protein Intermediate product m. RNA Idea: measure m. RNA to get protein measurements Simultaneous measurements by hybridization

DNA Microarrays m. RNA – concatenation of nucleotides 4 types ATGC – pegs/holes Process

DNA Microarrays m. RNA – concatenation of nucleotides 4 types ATGC – pegs/holes Process Crush cell Wash all but m. RNA Glue lamps Spill on chip Shake well!

Sorry, 4 slides. . . Chip design – probes for genes Light on -->

Sorry, 4 slides. . . Chip design – probes for genes Light on --> Protein exists Light off --> No protein at the moment

Problem setting Given two sets of DNA microarrays: Disease Control Extract a set of

Problem setting Given two sets of DNA microarrays: Disease Control Extract a set of differentially expressed genes Feature selection for classification Biological significant features for downstream research

Problem setting revisited Given two sets of DNA microarrays: Disease Control + f. MRI

Problem setting revisited Given two sets of DNA microarrays: Disease Control + f. MRI measurements of the two populations Extract a small set of differentially expressed pathogenic-behaving genes Feature selection for classification Biological significant features for downstream research

Nervous System Diseases Multiple categorizations: Physiologic = molecular pathway Organic vs. Functional Anatomic vs.

Nervous System Diseases Multiple categorizations: Physiologic = molecular pathway Organic vs. Functional Anatomic vs. Physiologic Structural vs. Metabolic Invisible to (non functional) imaging Not evident under microscope, no histological markers Anatomic = loss/gain of tissue

A Needle in a Haystack Target: Find the one(? ) molecule that malfunctions: Multiple

A Needle in a Haystack Target: Find the one(? ) molecule that malfunctions: Multiple molecular pathways within a neuron Neuronal interconnection Cascade/ripple throughout the system Molecule -> Neuron (population) Neuron -> Other neuron -> Other molecules Molecules might be in the same neuron population (feedback) infeasible for standard statistical analysis

Aging and AD Cognitive decrease (AD and aging) Differential – vulnerable vs. resistant Memory

Aging and AD Cognitive decrease (AD and aging) Differential – vulnerable vs. resistant Memory Encoding Hippocampus Common process: Entorhinal Cortex Dentate Gyrus CA subfields Subiculum Synaptic Failure leads to: Cell loss / tangles / plaques Function, not structure!

Hippocampus AD Aging Known from postmortem, in -vitro, and f. MRI Interconn. Asses all

Hippocampus AD Aging Known from postmortem, in -vitro, and f. MRI Interconn. Asses all regions together

Microarray analysis Differential expression analysis “Blind” analysis Thousands of parameters simultaneously High false positives

Microarray analysis Differential expression analysis “Blind” analysis Thousands of parameters simultaneously High false positives rate (multiple comparisons, recall FDR) Poor signal-to-noise ratio Usually produce a “list” of differentially expressed genes “list” can be very long (up to hundreds)

Statistical Modeling Temporal model 2 nd stage for f. MRI Double subtraction With sickness

Statistical Modeling Temporal model 2 nd stage for f. MRI Double subtraction With sickness basal metabolic rate changes as well

Multiple Studies Why f. MRI and not postmortem? p. m. biased against earliest (and

Multiple Studies Why f. MRI and not postmortem? p. m. biased against earliest (and most discriminatory) stages Only f. MRI can image the cell-sickness stage EC found to be the primary source of dysfunction in AD What about normal aging? Age-related changes in the EC matched pathological decline Age-related changes in the dentate gyrus (DG), and subiculum (SUB), matched normal aging

Spatio-Temporal Model How a pathogenic molecule should behave? Differentially expressed in the EC (vs.

Spatio-Temporal Model How a pathogenic molecule should behave? Differentially expressed in the EC (vs. no differentially expression in the DG) Differences between AD and controls should be age independent once EC dysfunction begins it does not worsen across age groups or over time

Results 5 Molecules matched the pattern Much less than 100 s! Best molecule: VPS

Results 5 Molecules matched the pattern Much less than 100 s! Best molecule: VPS 35 Part of a complex that connects-to and transports substances within a cell A-beta – a known “smoking gun” for AD Experiments validated: Low VPS 35 --> High A-beta Required neuronal molecules in end-to-end transportation are not transported --> brain dysfunction

Conclusion Microarrays – noisy, unfocused results f. MRI – imaging in-vivo, not post-mortem Create

Conclusion Microarrays – noisy, unfocused results f. MRI – imaging in-vivo, not post-mortem Create statistical model (criteria) using f. MRI, for microarray differentiation Lack of specific methods Not a parametric model, like a thumb rule Nice example for research advance My personal research is on PD Lots of imaging data Any suggestions? Thanks!