Ligand-clustered “patchy” nanoparticles for modulated cellular uptake and in vivo tumor targeting

Author Manuscript: 2012 August 05.A matter of presentation: The manner in which polyvalent ligands are presented to a cell—homogeneously or in spatially defined groupings on a nanoparticle surface—may play an important role in cellular uptake. This aspect is investigated for the first time using a linear dendritic polymer construct to pattern the surfaces of nanoparticles with variable-sized ligand clusters in different spatial arrangements.National Institutes of Health (U.S.) (NIH NIBIB Grant 5R01EB008082-02)MIT-Harvard Center of Cancer Nanotechnology ExcellenceNational Science Foundation (U.S.

Genomic Pathogen Typing Using Solid-State Nanopores.

In clinical settings, rapid and accurate characterization of pathogens is essential for effective treatment of patients; however, subtle genetic changes in pathogens which elude traditional phenotypic typing may confer dangerous pathogenic properties such as toxicity, antibiotic resistance, or virulence. Existing options for molecular typing techniques characterize the critical genomic changes that distinguish harmful and benign strains, yet the well-established approaches, in particular those that rely on electrophoretic separation of nucleic acid fragments on a gel, have room for only incremental future improvements in speed, cost, and complexity. Solid-state nanopores are an emerging class of single-molecule sensors that can electrophoretically characterize charged biopolymers, and which offer significant advantages in terms of sample and reagent requirements, readout speed, parallelization, and automation. We present here the first application of nanopores for single-molecule molecular typing using length based "fingerprints" of critical sites in bacterial genomes. This technique is highly adaptable for detection of different types of genetic variation; as we illustrate using prototypical examples including Mycobacterium tuberculosis and methicillin-resistant Streptococcus aureus, the solid-state nanopore diagnostic platform may be used to detect large insertions or deletions, small insertions or deletions, and even single-nucleotide variations in bacterial DNA. We further show that Bayesian classification of test samples can provide highly confident pathogen typing results based on only a few tens of independent single-molecule events, making this method extremely sensitive and statistically robust

Two Principal Modes for Nanopore Discrimination of Pathogen Genomic Variation.

<p>Mode I: Direct length detection according to analyte translocation dwell time and depth enables discrimination of longer vs. shorter fragments; i.e: whether or not an insertion or deletion is present (left). Mode II: Prior to translocation, samples are exposed to a restriction enzyme that cuts at the site of a SNV or short indel or mutation. Detection of cleaved vs. uncleaved DNA fragments in the nanopore reveals whether or not the critical genomic variation is present.</p

Gaussian Mixture Models of DNA Fragments for Actual Mode II Pathogen Typing at the SNV Level.

<p>(a) Diagram of the main steps in sample preparation, detection, and classification: PCR fragments from isolated pathogens are subjected to a restriction digest, which recognizes and cuts only one genomic variant. Nanopore translocations are used to classify the pathogen according to the combination of fragment lengths detected. (b) The <i>mazG</i> gene of the avirulent <i>M</i>. <i>tuberculosis</i> strain H37Ra is not cut by NaeI (942 bp), while the same gene in the closely related virulent strain H37Rv, which differs by only a single A-to-C mutation, is cut by NaeI (621bp + 321 bp). (c) Gaussian mixture model (one component) fit to translocations of <i>mazG</i> fragments from H37Ra. (d) Gaussian mixture model (two components) fit to translocations of <i>mazG</i> fragments from H37Rv. (e) Posterior probabilities for correctly identifying the H37Ra and H37Rv strains as a function of number of translocation events collected from an unknown sample, simulated using bootstrap sampling from nanopore translocation data. (f) The <i>parC</i> gene of the multi-drug-resistant MRSA strain FPR3757 is not cut by BseRI (886 bp) due to a single C-to-A mutation, while the closely related and less resistant strain HOU-MR is cut by BseRI (640bp + 245 bp). (g) Gaussian mixture model (one component) fit to translocations of <i>parC</i> fragments from FPR3757. (h) Gaussian mixture model (two components) fit to translocations of <i>parC</i> fragments from HOU-MR. (i) Posterior probabilities for correctly identifying the FPR3757 and HOU-MR strains as a function of number of translocation events collected from an unknown sample, simulated using bootstrap sampling from nanopore translocation data.</p

Translocation Event Diagrams Uniquely Identify DNA Fragment Lengths in a Single Nanopore.

<p>(a) 100 bp at 1 nM. (b) 200 bp at 1 nM. (c) 900 bp at 1 nM. (d) 1000 bp at 1 nM. (e) 1:1 combination of 100 bp and 900 bp, total concentration 2 nM. (f) Semilog(x) distributions of translocation dwell times for all samples (a)-(e). Translocations for all samples were collected in a single nanopore (4.8 nm diameter, effective thickness ~7 nm) with a +300 mV bias relative to <i>trans</i> (open pore current: 13 nA). To facilitate visualization of population density, a random white noise offset below the acquisition rate of this data (-2 μs < Δ<i>t</i> < +2 μs, acquisition rate 250 kHz) has been added to each <i>t</i>_D.</p