Vasser Woolley Professor of Chemistry
B.A., Haverford College, 1991; Ph.D., University of Chicago, 1996
Dr. Dickson's group is developing novel spectroscopic, statistical, and imaging technologies for the study of dynamics in biology and medicine.
Nanomaterials for improved imaging: Noble metal quantum dots. We pioneered the use of few-atom Ag and Au nanoclusters for drastically improved sensitivity and dynamics in single molecule, cellular, and in vivo imaging. Ranging in size from 5~30 Ag and Au atoms, these ultrabright, biodegradable emitters are size-tunable to fluoresce from the blue to the near infrared. By tuning the encapsulating ligands, we have created dark states that can be optically depopulated to directly modulate and selectively detect nanocluster emission, independent of background emitters. We are pushing this first adaptation of molecular modulation schemes to simultaneously improve sensitivity and dynamics in fluorescence imaging. These nanomaterials, cluster physics, spectroscopy, and biophysical imaging approaches have been adopted by a large number of researchers worldwide, and their study and utilization remain an active program in our group.
Optically modulatable fluorescent proteins and spectroscopic biological imaging. We have developed new spectroscopic imaging techniques to selectively recover background-free signals of modulatable fluorophores and decode their dynamics for studying protein-protein interactions. Adapting the concept of optical modulation learned from Ag nanocluster photophysics, we built on Dr. Dickson’s original 1997 study that was identified as a key publication for W.E. Moerner’s 2014 Nobel Prize in Chemistry (Click here for Pictures of the Nobel Prize Ceremony!) to engineer the first optically modulatable fluorescent proteins and use them to distinguish bound vs. diffusing species in live cells. Tailoring and studying the fluorescent protein photophysics has enabled us to discriminate multiple spectrally overlapping emitters based on their modulation characteristics, expanding the dimensionality of fluorescence imaging. We are imaging viral entry into cells as well as quantitatively characterizing (normally obscured) transient protein-protein interactions leading to disease states. Providing up to 100-fold sensitivity gains over standard imaging, these optically modulatable dyes and spectroscopic imaging technologies are also being widely adopted for improved imaging on both commercial and research-based imaging systems.
Bioinformatics - Rapid determinations of antibiotic resistance. The inherent heterogeneity in biological and medical samples often obscures and delays appropriate treatment options. Our goal is to shorten the time needed to identify the best antibiotic treatment for bacterial infections in hospital settings by an order of magnitude. Our approach relies on measuring subtle, but significant shifts in the bacterial population signatures upon exposure to antibiotics. Using flow cytometry, we measure the scattered light and fluorescence of all bacteria exposed to various antibiotic concentrations, and compare to a paired no-antibiotic control. This results in multidimensional histograms of >100,000 bacterial detection events with subtle shifts often obscured by biological heterogeneity. By adaptively binning these histograms and calculating true linear distances between such multidimensional histograms as functions of antibiotic exposure, we have shown exquisite, statistically significant ability to rapidly determine appropriate treatments for lab and clinically-isolated strains. These multidimensional statistical metrics have enabled antibiotic susceptibility determinations to be shortened from ~48 hrs to ~4 hrs, post positive blood culture. We are currently working on shortening the preceding ~24-hour blood culture step by applying our statistics to very low numbers of detected bacteria, with very promising results, such that appropriate treatment can be given within 10 hrs of initial blood draw.
Bioinformatics – Comparative genomics. With the explosion of genetic data, rapid, quantitative methods of whole genome analysis are needed for metagenomics and diagnostics. Whether building phylogenetic trees, or looking for point mutations, comparing whole genomes is an extremely large, high dimension problem in statistics that is beautifully addressed using our adaptive multidimensional statistical methods. We treat each genomic dataset as a probability distribution and quantitatively compare intergenome relationships. Unknown species are rapidly identified as all similar genomes cluster based on our intergenome distances. We are also applying this to quantitatively map short strands to the appropriate sequence within a library, even in the presence of high sequencing error or rates of mutation. Many new applications of our probabilistic approaches are being explored.