In the last part, some preliminary results on the clinical translation of single-cell proteomic chips will be presented. 104 Figure 3.7 The influence of the mTOR inhibitor PP242 on the analyzed protein levels for GBM cell lines and xenograft neurosphere tumor models, as a function of pO2.
INTRODUCTION
N ANOTECHNOLOGY INNOVATIVE TOOLS FOR CANCER RESEARCH
C ELLULAR HETEROGENEITY AND SINGLE - CELL TECHNOLOGY
O NCOGENIC SIGNALING NETWORK IN CANCER CELLS
If our system were macroscopically large, we would call i “the chemical potential of protein i.” The smaller the fluctuations (i.e. the narrower the histogram), the more resilient the distribution is to change24.
M ICROFLUIDIC - BASED MICROCHIP PLATFORM FOR SINGLE - CELL PROTEOMICS
P HYSICAL APPROACHES TOWARD UNDERSTANDING CANCER
Therefore, populations of cells are transformed into distributions of points in feature space31, which makes it possible to identify the steady state of the cell population (or more strictly, the steady state of the signal coordination) by establishing a single cell ensemble and the probability distribution function which is of maximum entropy82. These approaches, which are unique to single-cell measurements and are one of the central topics of this thesis, stand in contrast to traditional biology thinking that rejects the heterogeneity of the system in favor of a more streamlined (but ultimately non-predictive) description.
T HESIS OVERVIEW
We empirically inferred the signaling network from quantitative functional proteomic analysis of single cell counts isolated from a glioblastoma-derived murine model of resistance to mTOR kinase inhibitors. Chapter 5 will present some preliminary results on the clinical translation of single-cell proteomic arrays.
R EFERENCES
Microchip platforms for multiplex single-cell functional proteomics with applications to immunology and cancer research. A microfluidic platform for systems pathology: multiparameter single cell signal measurements of clinical brain tumor specimens.
DEVELOPMENT OF THE MICROCHIP: SINGLE-CELL FUNCTIONAL
I NTRODUCTION
E XPERIMENTAL METHODS
- Enabling technologies: DNA Encoded Antibody Library (DEAL) and DNA barcode
- Design and fabrication of single-cell proteomic chip
- Protocol of single-cell proteomic assays
- Results extraction, calibration and conversion
- Cell culture, stimulation and drug treatment
R ESULTS AND DISCUSSION
- Unique information disclosed from single-cell analysis
- Stripping experimental uncertainty out of the biological variation
- Validation of SCBC technology with conventional methods
- Constructing protein-protein correlation networks to reveal the signaling coordination . 52
R EFERENCES
A PPENDIX A: S UPPLEMENTARY M ETHODS
- Synthesis of DNA-antibody conjugates
- Microfluidic flow patterning of DNA barcode microarray
- DNA spot microarray for conjugate validation, cross-reactivity check and other bulk
- Step by step protocol of single-cell proteomic assay
- Protocol for immunoblot assay
A PPENDIX B: S UPPLEMENTARY T ABLES
APPLICATIONS IN FUNDAMENTAL CANCER BIOLOGY: HYPOXIA
I NTRODUCTION
In most solid organ cancers, increased interstitial pressure, vascular constriction, abnormal blood vessel leakage, and edema result in a hypoxic microenvironment, especially in the center of the tumor1-5. The second is the protein fluctuations, which are histograms of the observation frequency versus the measured protein levels. This view appears to be correct using the ATP-competitive mTOR inhibitor PP24223 on both GBM cell lines, as well as a neurosphere culture model grown from a human-derived GBM xenograft tumor that also expresses the EGFRvIII mutation.
Finally, we discuss a quantitative version of Le Chatelier's principle that relies on the single cell proteomics assays as input and, unlike the mean field model, allows for the explicit treatment of protein-protein correlations. The theory is validated by using it to predict the effect of changes in pO2 on the mean numbers of the pO2.
E XPERIMENTAL METHODS
- Microchip design and fabrication
- Experiment steps and procedures
- Cell lines and reagents
- Protein assays on bulk cell culture
- Protein calibration for bulk and SCBC assays
The validation of the DNA-antibody conjugates included separate calibrations for each of the different immunoassays, as well as quantifying the cross-reactivity between these immunoassays (Fig. 3.3). The description of the microwell-based multiplex immunoassays based on statistical cell counts followed the protocols described in Chapter 2. The calibration at right used the microwell format of the spotted arrays and provided calibration data for the bulk cell assays.
For both SCBC and bulk assays, these ranges are generally within the linear response regime of the calibration curves. Since the volume of the microchambers is known, these calibration curves allow a transformation from the fluorescence intensity to the number of molecules for each analyzed protein, with the caveat that the standard proteins may not be exactly the same as their counterparts from the GBM cells.
P HYSICAL APPROACHES
- A mean-field model for understanding protein fluctuations
- Single-cell ensemble, a basis for making predictions
- Fluctuations describe the response to small perturbations
- A quantitative version of the principle of Le Chatelier
First is the condition that the numerical values of the chemical potentials are determined by the given average numbers, the Ni's, of the proteins. Proof: Say we make a small change in the value of the chemical potential i from its current equilibrium value to some new value i i. The change in the mean is proportional to the variance of the distribution of fluctuations.
Taylor's theorem states that at leading order the change in a function is the sum of the changes. It shows that the new distribution is the maximum entropy distribution of the functional form Eq.
R ESULTS AND DISCUSSION
- Single-cell data collected from SCBCs
- Protein fluctuations reveal a deregulation in mTORC1 signaling near 1.5% oxygen
- Steady-state kinetic model identifies a switch in mTORC1 signaling near 1.5% pO 2
- Quantitative Le Chatelier's principle identifies a phase transition in mTORC1 signaling
We now look towards gaining a better mechanistic understanding of the behavior of mTOR signaling near 1.5% pO2 via a steady state kinetic model. This Figure 3.8 The network hypothesis and accompanying steady state kinetic model describing relationships between HIF-1α, p-mTOR, PP242 and pO2 in U87 EGFRvIII cells reveals a shift in mTOR regulation below 1.5% pO2. The coordination of mTOR-associated signaling states, as a function of pO2, is reflected in an analysis of the relevant eigenvalues (state strength) and their composition of the protein-protein covariance matrix (state composition).
Coordination of mTOR with its effectors, p-ERK and p-P70S6K, dominates the composition of the three lowest-amplitude eigenvectors, which exhibit unique behavior between 2–1.5% pO2. The proof comes from the near-zero eigenvalues of the covariance matrix; associated eigenvectors are those localized to phosphoproteins associated with mTORC1 signaling.
C ONCLUSION
R EFERENCES
Mammalian target of rapamycin inhibition promotes response to epidermal growth factor receptor kinase inhibitors in PTEN-deficient and PTEN-intact glioblastoma cells. Use of an orthotopic xenograft model to assess the effect of epidermal growth factor receptor amplification on the radiation response of glioblastoma. Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns.
Bnip3 mediates hypoxia-induced inhibition of mammalian target of rapamycin by interacting with Rheb.
A PPENDIX A: S UPPLEMENTARY T ABLES
Mean intensity and standard deviations (SD) are shown with or without addition of PP242, and at different O2.
APPLICATIONS IN PRECLINICAL CANCER RESEARCH
I NTRODUCTION
To test this idea, we took advantage of a clinically relevant model of acquired resistance to cancer drugs to understand the general nature of resistance and to identify combinations of targeted therapies for effective treatment. GBM39 expresses high levels of the epidermal growth factor receptor (EGFR) variant(v)III oncogene, which sensitizes tumor cells to the mechanistic target of rapamycin (mTOR) inhibitor CC214-214. Here, we determined the structure of hyperactivated phosphoprotein networks, including those associated with mTOR signaling, using multiplex phosphoprotein assays from a statistical number of individual cancer cells 15 , 16 that were untreated, responded to CC214-2, and were resistant. on CC214-2.
The evolution of that structure between untreated and responsive states provides guidance for selecting targeted therapy combinations that can successfully halt tumor growth. It also provides guidance on identifying those therapies and combinations of therapies that will not be effective.
E XPERIMENTAL METHODS
- Establishment of in vivo mouse xenograft model recapitulating the clinical scenario of
- MicroPET/CT characterizations
- Immunohistochemistry (IHC) and immunoblotting
- Preparing single-cell suspension from solid tumors
- Magnetic-activated cell sorting (MACS) and plating
- Microchip design, fabrication and experimental procedures
- DEAL based cell capturing and viability test
- Single Nucleotide Polymorphism (SNPs) analysis
- In vivo drug treatment
David James (UCSF, San Francisco, USA) and verified by luciferase reporter expression before starting the in vivo experiments. Third, cells must be healthy after sorting to provide representative information of the tumor. The production of SCBC, the protein panel validation and calibration and the experimental procedures of the single cell proteomic assay follow the same protocol described in Chapter 2.
Representative images of cells were obtained using a Nikon Eclipse TS100 scope equipped with a Canon S51S camera (Fig. 4.8). Access code is GSE53042. a) Biochemical analysis of the drug aims to decrease regulation with single or combined treatments.
P HYSICAL APPROACHES
- Collective behaviors in signaling coordination: signaling modes hypothesis
- Principal component analysis (PCA)
- Quantifying the functional heterogeneity
- Partial least square (PLS) modeling of immunohistochemical data
The calculated dissimilarity coefficients are used as indices for quantifying the functional heterogeneity of the tumor. V0 can be understood as the part of the solid tumor that was not involved in tumor growth, and V0' was the part that was involved in tumor growth24. The use of effective drug combinations can significantly shut down tumor progression and thus lead to large values of the cell cycle target τ as expected (Fig. 4.10).
The Q2cum index measures the global contribution of the h first principal components to the model's predictive capacity. The orange part of the table represents the calibration phase of the model and the blue part represents the prediction phase.
R ESULTS AND DISCUSSION
- Single-cell proteomic analysis of three drug treatment stages
- Signaling modes extraction by PCA predicts effective therapy strategies
- In vivo validation of predicted therapy strategies
- In vitro perturbation identifies the fast network adaptation mechanism
- PLS modeling on tissue analysis independently confirms the signaling modes
The SCBC dataset enabled statistical analyzes of the correlation of functional proteins in tumor cells for all three conditions. This is manifested in an almost 10-fold drop in the functional heterogeneity of the cell population (Figure 4.12 b). PCA analysis of the protein-protein covariance matrix reflects coordination of protein signaling, but not absolute protein.
The calibration phase of the model was built using part of the observations (orange part). It also provides independent confirmation of the two signaling modes shown in the single-cell assay.
C ONCLUSION
R EFERENCES
Identification of molecular features correlating with glioblastoma sensitivity to EGFR kinase inhibition using an intracranial xenograft assay panel. Patient tumor EGFR and PDGFRA gene amplifications maintained in an invasive intracranial xenograft model of glioblastoma multiforme. The mTOR kinase inhibitors, CC214-1 and CC214-2, preferentially block the growth of EGFRvIII-activated glioblastomas.
Use of nuclear modification in the discovery of CC214-2, an orally available, selective inhibitor of mTOR kinase. Cross talk between the PI3K/mTOR and MEK/ERK pathways involved in maintaining self-renewal and tumorigenicity of glioblastoma stem-like cells.
A PPENDIX A: S UPPLEMENTARY FIGURES
Statistical analysis of the quantitative IHC plots for additional mTOR biomarkers (quantitative values listed in Table S2; C: CC214-2 responsive samples; R: CC214-2 resistant samples; D: dasatinib; U: U0126; stop: xenografts collected after interruption of treatments); *P<0.05; **P<0.005; ***P≤0.0005, N.S., not significant; (Student's t-test, values represent the mean of three independent section fields).
A PPENDIX B: S UPPLEMENTARY TABLES
TRANSLATING SINGLE-CELL FUNCTIONAL PROTEOMICS INTO
- I NTRODUCTION
- E XPERIMENTAL METHODS
- Surface chemistry optimization for clinical applications
- High throughput solutions
- The protocol and workflow of analyzing patient biopsy samples
- R ESULTS AND DISCUSSION
- Fast signaling adaptation of a pediatric GBM tumor to lapatinib: a patient sample
- Prediction on effective therapy combination
- C ONCLUSION
- R EFERENCES
Then, a library of amino-modified ssDNA diluted in a mixture of DMSO and deionized water (v/v=3:2) with a final concentration of 300 μM and mixed with a 2 mM solution of BS3 (a linker molecule containing an amine-reactive N-hydroxysulfosuccinimide (NHS) ester at each Figure 5.1 Reaction scheme for covalent DNA patterning Schematic illustration of chemical patterning to produce high-density crossbanded ssDNA arrays A second flow sampling step directed perpendicular to the first is used to generate unique addresses at crossband intersections.
The color-coded DNA oligomers illustrate the patterning/hybridization sequence for making the final array. a, bottom) Validation of cross-stripe barcode microarray. The plot on the right gives the fluorescence intensity profile for the vertical line through the two unit squares.
