fMRI 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 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 mRNA

Idea: measure mRNA to get protein measurements

Simultaneous measurements by hybridization

DNA Microarrays

mRNA – concatenation of nucleotides

4 types ATGC – pegs/holes

Process Crush cell Wash all but mRNA Glue lamps

Spill on chip Shake well!

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 differentially expressed genes Feature selection for classification Biological significant features for downstream

research

Problem setting revisited

Given two sets of DNA microarrays: Disease Control

+ fMRI 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: Organic vs. Functional Anatomic vs. Physiologic Structural vs. Metabolic

Physiologic = molecular pathway 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 molecular pathways within a neuron Neuronal interconnection Cascade/ripple throughout the system

Molecule -> Neuron (population) Neuron -> Other 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 Encoding Hippocampus

Entorhinal Cortex Dentate Gyrus CA subfields Subiculum

Common process: Synaptic Failure leads to: Cell loss / tangles / plaques

Function, not structure!

AD Aging Known from

postmortem,in-vitro, and fMRI

Interconn. Asses all

regions together

Hippocampus

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 2nd stage for fMRI

Double subtraction

With sickness - basal metabolic rate changes as well

Control DiseaseVulnerable

Resistant

Multiple Studies

Why fMRI and not postmortem? p.m. biased against earliest (and most

discriminatory) stages Only fMRI 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. 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 100s! Best molecule: VPS35 Part of a complex that connects-to and

transports substances within a cell A-beta – a known “smoking gun” for AD Experiments validated:

Low VPS35 --> High A-beta Required neuronal molecules in end-to-end

transportation are not transported --> brain dysfunction

Conclusion

Microarrays – noisy, unfocused results fMRI – imaging in-vivo, not post-mortem Create statistical model (criteria) using fMRI,

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!