[Show abstract][Hide abstract] ABSTRACT: Intratumoral heterogeneity contributes to cancer drug resistance, but the underlying mechanisms are not understood. Single cell analyses of patient-derived models and clinical samples from glioblastoma patients treated with EGFR tyrosine kinase inhibitors (TKIs) demonstrate that tumor cells reversibly up-regulate or suppress mutant EGFR expression, conferring distinct cellular phenotypes to reach an optimal equilibrium for growth. Resistance to EGFR TKIs is shown to occur by elimination of mutant EGFR from extrachromosomal DNA. After drug withdrawal, re-emergence of clonal EGFR mutations on extrachromosomal DNA follows. These results indicate a highly specific, dynamic and adaptive route by which cancers can evade therapies that target oncogenes maintained on extrachromosomal DNA.
[Show abstract][Hide abstract] ABSTRACT: Introduction: Technologies capable of multiple, quantitative, inexpensive, single-cell biomarker measurements (eg, microfluidics) are transforming biology by enabling systems analysis of microscopic samples at the single-cell level. Because the large data sets generated by these single-cell technologies are highly complex, meaningful interpretation of these multiparameter data sets requires novel bioinformatic tools. Here, we adapt self-organizing maps (SOMs), an unsupervised learning method which has found wide application in analysis of complex biological data sets (Tamayo et al, 1999), to analyze multiparameter, single-cell data sets including 1) proteomic measurements of signal transduction in clinical brain tumor specimens and 2) cytological features of human pluripotent stem cells.
Materials and Methods: To generate multiparameter, single-cell data sets, we utilized Microfluidic Image Cytometry (MIC), which combines the advantages of microfluidics and microscope-based cytometry to quantify multiple biomarkers at the single-cell level using immunocytochemistry (Sun et al, 2010). For clinical brain tumor specimens, we simultaneously quantified four signaling proteins (EGFR, PTEN, phospho-Akt and phospho-S6). For human embryonic (ES) and induced pluripotent stem cells (iPS), we quantified either protein biomarkers of pluripotency (OCT4, SSEA1) or 39 cytological features including nuclear morphology (DAPI) and cell cycle progression (incorporation of 5-ethynyl-2´-deoxyuridine, a marker of S-phase). All data sets included ~1,000 cells per sample.
SOMs were created using the Kohonen R package (Wehrens & Buydens, 2007). A SOM grid consists of a number of units each characterized by a multiparameter vector (eg, EGFR, PTEN, pAkt and pS6 levels). Vectors characterizing the SOM grid are trained so as to represent the global measurement space. Single-cell data are then mapped to the SOM grid based on similarity to the SOM units, and each sample is plotted as the frequency of cells in that sample that map to each SOM unit. SOM grid frequencies of each sample SOM grid are finally subjected to unsupervised hierarchical clustering.
Results and Discussion: To demonstrate the clinical application of SOMs in oncology, we used MIC to analyze the oncogenic PI3K/Akt/mTOR signaling pathway in a panel of 19 human brain tumor biopsies. SOM analysis of the MIC data revealed a diversity of signaling patterns that would have been masked by population-average measurements, including evidence of inter- and intra-tumoral heterogeneity. SOMs also stratified patients into molecularly-defined subsets that were predictive of tumor progression and patient survival.
To test the application of SOMs in stem cell biology, we measured either protein biomarkers of pluripotency (eg, OCT4 expression) or 39 cytological features (eg, nuclear morphology, cell cycle progression) in human ES and iPS cells. SOMs identified phenotypic changes dynamically occuring during differentiation. Additionally, in a panel of ES and iPS cells, SOMs revealed biomarker patterns that were predictive of the differentiated and pluripotent phenotypes.
Conclusions: The successful utilization of single-cell, quantitative diagnostic technologies including microfluidics will require bioinformatic tools to facilitate meaningful analysis of multiparameter, single-cell data. Here, we have demonstrated that SOMs are a robust, enabling tool for systems analysis of multiparameter, single-cell data.
References: Sun et al, Cancer Res (2010); 70:6128-38.
Tamayo et al, Proc Natl Acad Sci U S A (1999); 96: 2907-12.
Wehrens and Buydens, J Stat Soft (2007); 21: 1-19.
[Show abstract][Hide abstract] ABSTRACT: Nanoparticles are regarded as promising transfection reagents for effective and safe delivery of nucleic acids into a specific type of cells or tissues providing an alternative manipulation/therapy strategy to viral gene delivery. However, the current process of searching novel delivery materials is limited due to conventional low-throughput and time-consuming multistep synthetic approaches. Additionally, conventional approaches are frequently accompanied with unpredictability and continual optimization refinements, impeding flexible generation of material diversity creating a major obstacle to achieving high transfection performance. Here we have demonstrated a rapid developmental pathway toward highly efficient gene delivery systems by leveraging the powers of a supramolecular synthetic approach and a custom-designed digital microreactor. Using the digital microreactor, broad structural/functional diversity can be programmed into a library of DNA-encapsulated supramolecular nanoparticles (DNA⊂SNPs) by systematically altering the mixing ratios of molecular building blocks and a DNA plasmid. In vitro transfection studies with DNA⊂SNPs library identified the DNA⊂SNPs with the highest gene transfection efficiency, which can be attributed to cooperative effects of structures and surface chemistry of DNA⊂SNPs. We envision such a rapid developmental pathway can be adopted for generating nanoparticle-based vectors for delivery of a variety of loads.
[Show abstract][Hide abstract] ABSTRACT: The clinical practice of oncology is being transformed by molecular diagnostics that will enable predictive and personalized medicine. Current technologies for quantitation of the cancer proteome are either qualitative (e.g., immunohistochemistry) or require large sample sizes (e.g., flow cytometry). Here, we report a microfluidic platform-microfluidic image cytometry (MIC)-capable of quantitative, single-cell proteomic analysis of multiple signaling molecules using only 1,000 to 2,800 cells. Using cultured cell lines, we show simultaneous measurement of four critical signaling proteins (EGFR, PTEN, phospho-Akt, and phospho-S6) within the oncogenic phosphoinositide 3-kinase (PI3K)/Akt/mammalian target of rapamycin (mTOR) signaling pathway. To show the clinical application of the MIC platform to solid tumors, we analyzed a panel of 19 human brain tumor biopsies, including glioblastomas. Our MIC measurements were validated by clinical immunohistochemistry and confirmed the striking intertumoral and intratumoral heterogeneity characteristic of glioblastoma. To interpret the multiparameter, single-cell MIC measurements, we adapted bioinformatic methods including self-organizing maps that stratify patients into clusters that predict tumor progression and patient survival. Together with bioinformatic analysis, the MIC platform represents a robust, enabling in vitro molecular diagnostic technology for systems pathology analysis and personalized medicine.
Cancer Research 08/2010; 70(15):6128-38. · 9.28 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Microfluidic image cytometry (MIC) has been developed to study phenotypes of various hPSC lines by screening several chemically defined serum/feeder-free conditions. A chemically defined hPSC culture was established using 20 ng mL(-1) of bFGF on 20 microg mL(-1) of Matrigel to grow hPSCs over a week in an undifferentiated state. Following hPSC culture, we conducted quantitative MIC to perform a single cell profiling of simultaneously detected protein expression (OCT4 and SSEA1). Using clustering analysis, we were able to systematically compare the characteristics of various hPSC lines in different conditions.
Lab on a Chip 05/2010; 10(9):1113-9. · 5.70 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We demonstrated a convenient, flexible and modular synthetic approach for preparation of a small library of DNA-encapsulated supramolecular nanoparticles SNPs superset DNA and RGD-SNPs superset DNA with different sizes and RGD target ligand coverage for targeted gene delivery.
Chemical Communications 03/2010; 46(11):1851-3. · 6.38 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Many biological and biomedical laboratory assays require the use of antibodies and antibody fragments that strongly bind to their cell surface targets. Conventional binding assays, such as the enzyme-linked immunosorbent assay (ELISA) and flow cytometry, have many challenges, including capital equipment requirements, labor intensiveness, and large reagent and sample consumption. Although these techniques are successful in mainstream biology, there is an unmet need for a tool to quickly ascertain the relative binding capabilities of antibodies/antibody fragments to cell surface targets on the benchtop at low cost. We describe a novel cell capture assay that enables several candidate antibodies to be evaluated quickly as to their relative binding efficacies to their cell surface targets. We used chimeric rituximab and murine anti-CD20 monoclonal antibodies as cell capture agents on a functionalized microscope slide surface to assess their relative binding affinities based on how well they capture CD20-expressing mammalian cells. We found that these antibodies' concentration-dependent cell capture profiles correlate with their relative binding affinities. A key observation of this assay involved understanding how differences in capture surfaces affect the assay results. This approach can find utility when an antibody or antibody fragment against a known cell line needs to be selected for targeting studies.