1. MAE 6291 Bionanotechnology and Biosensors Goals: 1. learn about nanotechnology-based biosensors molecules (analytes) detected molecules used to provide specificity transducing modalities (light, mass, electricity) assay formats (label-free, sandwich, labels) processes affecting time to get signal and sensitivity (analyte diffusion, binding kinetics) multiplex methods (e.g. hybridization arrays) massively parallel DNA sequencing methods biological significance of assays

2. 2. At end of course, be able to design assay for 1 or more analytes using different modalities, predict sensitivity, specificity, describe expected technical challenges 3. Have framework for considering clinical utility: how well do results correlate with medical state? is concentration or presence/absence critical? will assay fill need or create problems? 4. Gain experience reading papers in field critically Hopefully, good intro/entrée into world of molec. biol. for engineers, should be growth sector (!)

3. Format - lecture discussion % grade will aim to have students present segments of papers in each class.25 homework  1 every 1 - 2 classes to learn how to use what we cover.25 will try to include demonstrations – e.g. ELISA, fluorescence microscopy, gene chip, pcr take-home midterm exam.25 student presentation/or take-home final exam.25

4. Papers, lecture notes, homework, previous week’s homework answers, announcements will be on Blackboard Contact info – Prof. Jon Silver Phillips Hall 738 jesilver@gwu.edujesilver@gwu.edu, cell 240 893 7020

5. What makes something a “bio” sensor? target molecule is biological molecule used to provide recognition specificity is biol. (enzyme, antibody, aptamer) analog of biol. process contributes to sensor design e.g. evolution/selection for improved functionality design mimics biological organ – e.g. compound eye

6. Molecules (things) to be detected ions – e.g. Na + small molecules (MW < 600g/mole=10 -21 g, or  50 atoms – e.g. glucose) peptides – short string of amino acids oligonucleotides – short string of nucleic acids = bases A, G, C, T (U) – joined via sugar-PO 4 proteins – string(s) of up to ~1000 amino acids viruses -  1000 + proteins + NA genome (>10 4 bases) larger organisms – bacteria, protists, cells nucleic acid sequence

7. Protein = linear polymer of amino acids (aa) chains from a few to  1000 aa long aa order encoded in order of bases in DNA order of aa’s determines protein’s structure, interacting surfaces, properties, function

8. All NH 2 -CHX-COOH side groups X differ hydrophobic chains hydrophobic rings polar, not charged + charge at neut pH - charge at neut pH give proteins highly variable chemcial surfaces for specific identification and inter-action with other molecules

9. Model of transmembrane protein showing charged surface regions (red -, blue +), and some drug molecules in binding pockets. Note complexity of surface allowing complex interaction with other molecules http://www.pnas.org/content/104/1/42/F6.ex pansion.html

10. Base pairing – at edges – holds strands together; each bp = weak bond (  1 k B T) but runs of complementary sequence -> tight binding; can be used for specific recogni- tion of NA’s with compl. sequence Nucleic acids – polymers of “bases” Cheap to make  mol of DNA chains with arbitrary seq. up to  100 bases long for specific sensing elements (  1$/base)

11. Molecules used to provide specificity Enzymes – e.g. glucose oxidase Antibodies Nucleic acids – hybridization Aptamers – ss NAs that bind small molecules natural and engineered Antibody variants and substitutes

12. Glucose oxidase  600 aa protein enzyme that binds and oxidizes glucose. Ribbon model of its aa backbone, por- tions of which form helices. Note size, complexity relative to glucose, a simple sugar typical of small molecule targets ~ 3 nm

13. Antibody – class of proteins with common structure: region that is invariant and region that varies a lot (in different ab’s), the latter having high affinity for some other molecule (antigen) Nature’s “professional biosensor” molecule

14. Ball and stick model of crystal structure of portion of antibody (left) binding protein from HIV (green, right). Variable region of antibody (purple) Antibodies are most common molecules used to make bio-assays specific Antibodies to particular antigens can be generated in animals, then made in large quantities in vitro

15. Single-stranded (ss) nucleic acids (NA’s) often used to detect complementary ssNA’s because of incredible specificity 1 base mismatch can be detected in a 20 base long dna How many different 20 base sequences are there? 4 20 = 10 12

16. Aptamer = single stranded nucleic acid that happens to have high affinity for another molecule Aptamers can be engineered and selected for ability to bind particular targets ss NA’s can also fold into shapes that bind other molecules besides complementary NA’s

17. Assay formats bulk solution (e.g. signal generated by molecules coming together on DNA) surface sensors (the majority) captured analyte -> signal directly e.g. due to mass,  index of refraction sandwich – capture analyte, then add labeled molecule/particle that binds analyte label provides enhanced signal – e.g. radio-isotope fluorescence inc. mass (e.g. gold beads) enzyme on second antibody can generate multiple signal mol. dyes or chemi-luminescence = signal amp.

18. More assay formats “homogeneous” assays (no washing needed) “coincidence” – require 2 or more specific binding events (e.g. sandwich, increases specificity) massively parallel hybridization arrays: different DNA species in each position DNA synthesized in situ DNA attached to micron-sized via photo-lithography beads in wells etched in silicon

19. Specialized processes/formats target amplification (rather than signal amplification) NA targets can be copied enzymatically (pcr, polymerase chain reaction) to yield  10 9 replicates before detection massively parallel DNA sequencing in arrays of wells, each containing many copies of a different dna fragment made by pcr in DNA “thickets”, each containing many copies of a different dna fragment grown on glass by pcr

20. Signal transduction methods light – colorimetry (dyes), luminescence, fluorescence, fl. res. energy transfer (FRET-sensitive to nm separation) evanescent wave effects to reduce bkgd surface plasmon resonance (SPR) electrochemical – oxidation/reduction rxns on surface transfer electrons to/from ions in solution -> current alters V-I relations, often transiently e.g. glucose oxidase sensors electrical – field effect transistors (FETs) nearby charge affects V-I relation ion sensitive-FETS used in new dna sequencing meth. carbon nanotube FETs

21. Transduction methods - mechanical micro/nano cantilevers, analyte binding changes mass ->  in resonance freq. electrical or optical read-out DNA tethering micron-sized beads beads visualized microscopically, binding molecules alter tether properties -> new kind of single-molecule sensors

22. Goalsincreased sensitivity increased parallelization Lots of room for innovation miniaturization cost reduction use of new nanoscale phenomena

23. Clinical Utility – what is it useful to detect? Infectious disease agents – e.g. viruses whose presence always indicates clinically significant infection or contamination – HIV, HBV, HCV, polio, malaria But other infectious agents are normally present in environment, so detection may or may not be clinically significant – e.g. streptococci

24. Proteins absolutely diagnostic of cancer – e.g. fusion protein (bcl) that only occurs in chronic myelogenous leukemia (a result of a chromosomal translocation) But this is exception: most proteins are normally present; their concentrations may change in disease but often they change in many conditions, so changes are not diagnostic, though possibly suggestive Our ability to detect things is outstripping our ability to know what to do with the results

25. Example – prostate specific antigen (psa) serum level elevated (>4ng/ml) in blood of men with prostate cancer, but also in men with prostate inflammation not elevated in all men with prostate cancer (false negatives); elevated in some men without any disease (false positives) another problem – overdiagnosis many men with prostate cancer detected by PSA and biopsy (bx) have such slow growing disease they would never have symptoms and dye of something else; elevated PSA -> medical tests and procedures (bx, surgery) that often have severe side-effects, sometimes providing no benefit

26. After >10 years of PSA testing, clinical trials with > 100,000 men showed PSA screening -> increased diagnosis (expected) but no improved survival Other quandaries: Genetic tests can identify people with increased risk of senile dementia for which no preventive measures are known Genetic tests can identify people with increased risk of some cancers for which we have no effective screening tests (ovarian cancer)

27. Some new tests identify patterns of altered protein levels or genetic changes in patients with breast cancer that are reported to correlate with worse prognosis -> altered chemotherapy The correlations between panels of “biomarkers” and clinical state result from data-mining studies which are subject to statistical pitfalls – e.g. large # of possible patterns increase chance that some pattern will correlate with outcome in any finite study - but won’t be reproducible Implication – need to be cautious about over-estimating clinical value of diagnostic tests made possible by new technology, esp. given escalating costs

28. Processes affecting time to detect analyte and sensitivity (subject of next 2 classes) Binding kinetics – of analyte to sensor mass action drives binding concentrations of analyte and capture probe very important often limit sensitivity How does analyte get to capture molecule? diffusion (usually on small scales): t  x 2 (not x) result of random (Brownian) collisions fast over short distances (nm), slow over long (mm); scale determined by D (diff. const.)

29. Flow (advection) – often used to introduce sample, label into sensor, wash out non-binding proteins Competition between advection and diffusion: narrow sensor channel reduces time for analyte to diffuse to sensor surface but also reduces amount of sample that can be introduced and increases viscous drag flow replenishes analyte depleted from region near to sensor (so speeds up binding) but if too fast, analyte molecules leave chamber before they can bind balance between flow rate and diffusion rate optimizes performance but sets limits to how fast device can function