HYPERPOLARIZED MICRO-NMR SYSTEM AND METHODS

Abstract

Described herein are micro-coil hyperpolarized NMR systems and methods for measuring metabolic flux in living and non-living samples. Such systems can perform high throughput measurements (with multiple coils) of metabolic flux without destroying the material, making it useful to analyze tumor biopsies, cancer stem cells, and the like. In certain embodiments, a hyperpolarized micromagnetic resonance spectrometer (HMRS), described herein, is used to achieve real-time, significantly more sensitive (e.g., 10.sup.3-fold more sensitive) metabolic analyses of live cells or non-living samples. In this platform, a suspension mixed with hyperpolarized metabolites is loaded into a miniaturized detection coil (e.g., about 2 L), where the flux analysis can be completed within a minute without significant changes in viability. The sensitive and rapid analytical capability of the provided systems enables rapid assessment of metabolic changes by a given drug, which may direct therapeutic choices in patients.

Claims

1. A method of sample analysis, the method comprising: providing a sample comprising a hyperpolarized substance within a micro coil and exposing said sample and micro coil to a magnetic field; and detecting an NMR signal for the sample.

2. The method of claim 1, wherein the method comprises metabolic imaging.

3. The method of claim 1, wherein the sample comprises a member selected from the group consisting of a living sample and a non-living sample.

4. The method of claim 1, wherein the living sample comprises a biological sample.

5. The method of claim 4, wherein the biological samples comprises a member selected from the group consisting of a suspension of cells, a solid tissue sample, a porous structure encapsulating cells, a tumor organoid, biological cells, proteins and/or metabolites, bacteria, yeast, an enzymatic system, and blue/green algae.

6. The method of claim 1, wherein the non-living sample comprises a member selected from the group consisting of a synthetic cell system, a synthetic enzymatic system, and chemical molecules.

7. The method of claim 1, further comprising hyperpolarizing the sample.

8. The method of claim 7, wherein hyperpolarization is performed via Para-Hydrogen Induced Polarization (PHIP), Dynamic Nuclear Polarization (DNP), Spin-Exchange Optical Pumping, Parahydrogen Induced Polarization or other Gas-Induced Polarization.

9. The method of claim 7, wherein hyperpolarizing the sample comprises introducing a radical source.

10. The method of claim 9, wherein the radical source comprises a member selected from the group consisting of organic persistent free radicals, photo-induced non-persistent radicals, nanodiamonds or metabolites which can form temporary radicals, and silicon particles.

11. The method of claim 1, wherein the hyperpolarized substance comprises an active nucleus (e.g., any NMR active nucleus).

12. The method of claim 11, wherein the active nucleus comprises a member selected from the group consisting of .sup.1H, .sup.13C, .sup.31P, .sup.15N, and .sup.19F.

13. The method of claim 1, wherein the hyperpolarized substance comprises a member selected from the group consisting of hyperpolarized .sup.13C-pyruvate, .sup.13C-DHA, and .sup.13C-VitC (Vitamin C), .sup.13C-fructose, .sup.13C-glucose, .sup.13C-glutamine, .sup.13C-senne, .sup.13C-glycine, and .sup.13C-acetate.

14. The method of claim 1, wherein the biological cells comprise a quantity selected from the group consisting of no greater than 200 k cells, no greater than 150 k cells, no greater than 100 k cells, no greater than 50 k cells, no greater than 25 k cells, and no greater than 10 k cells.

15. The method of claim 1, wherein the sample comprises a volume selected from the group consisting of no greater than 100 no greater than 50 no greater than 25 no greater than 10 no greater than 5 and from about 1 to 3 L.

16. The method of claim 1, wherein the micro coil comprises a miniaturized radiofrequency (RF) coil.

17. The method of claim 1, wherein the sample is placed within a chamber containing the micro coil, said micro coil surrounding the sample.

18. The method of claim 1, wherein the magnetic field is from about 0.5T to 7T.

19. The method of claim 1, wherein the magnetic field is about 1T.

20. The method of claim 1, further comprising determining (e.g., by a processor of a computing device) metabolic data for the sample from the NMR signal.

21. The method of claim 20, comprising determining metabolic flux.

22. The method of claim 20 or 21, comprising monitoring an enzymatic reaction rate.

23. The method of claim 22, wherein the enzymatic reaction rate is monitored at a rate less than 500 pmol/sec, less than 100 pmol/sec, less than 50 pmol/sec, or about 30 pmol/sec.

24. The method of claim 1, comprising, for each of a plurality of samples, placing said sample in a separate micro coil chamber or compartment, thereby enabling high throughput signal and/or metabolic data detection.

25. The method of claim 24, comprising placing each of the plurality of samples in its separate micro coil chamber or compartment using configurations of multiple (e.g., 2, 4, 8, 16, 48, 96, 192, 384, 1536, e.g., a high throughput platform) micro coils or micro coil portions.

26. The method of claim 25, wherein each micro coil or micro coil portion is surrounding a separate sample.

27. The method of claim 1, wherein the micro coil has a diameter no greater than 5 mm, e.g., no greater than 3 mm, e.g., no greater than 2 mm, e.g., no greater than 1.5 mm, e.g., no greater than 1.0 mm, e.g., no greater than 0.5 mm.

28. The method of claim 1, wherein the micro coil has a diameter in a range from about 0.5 mm to about 2 mm.

29. The method of claim 1, wherein the micro coil has a diameter in a range from about 0.5 mm to about 1.5 mm.

30. The method of claim 1, wherein the sample comprises cells from a tumor biopsy and/or cancer stem cells.

31. The method of claim 1, further comprising determining metabolic data from the NMR signal, wherein the sample comprises cells acquired from a patient during surgery, and the determining of the metabolic data takes place within 10 minutes of acquisition of the cells.

32. The method of claim 31, wherein the surgery is brain surgery.

33. The method of claim 31, wherein the determining of metabolic data takes place within 5 minutes, e.g., within 3 minutes, e.g., within 1 minute of acquisition of the cells.

34. The method of claim 31, wherein the method comprises intraoperative analysis of tissue sample obtained during surgery.

35. The method of claim 1, wherein the sample comprises cells acquired from a subject following administration of a drug.

36. The method of claim 35, further comprising monitoring treatment efficacy.

37. A hyperpolarized micro-NMR system comprising: a magnet for producing a magnetic field; a micro-coil chamber sized and shaped to contain a sample in suspension and a hyperpolarized substance; a first tuning circuit; a detector for detecting an NMR signal for the sample; and a processor of a computing device for executing instructions to determine metabolic data for the sample from the NMR signal.

38. The micro-NMR system of claim 37, wherein the first tuning circuit is a tuning circuit for .sup.1H.

39. The micro-NMR system of claim 37, further comprising a second tuning circuit.

40. The micro-NMR system of claim 39, wherein the second tuning circuit is a tuning circuit for .sup.13C.

41. The micro-NMR system of claim 37, further comprising a mechanical switch.

42. The micro-NMR system of claim 41, wherein the mechanical switch selects the first tuning circuit.

43. The micro-NMR system of claim 41, wherein the mechanical switch selects the second tuning circuit.

44. The micro-NMR system of claim 37, wherein the magnetic field is from about 0.5T to 7T.

45. The micro-NMR system of claim 37, wherein the magnetic field is about 1T.

46. The micro-NMR system of claim 37, wherein the processor of the computer device determines metabolic flux.

47. The micro-NMR system of claim 37, wherein the system monitors an enzymatic reaction rate.

48. The micro-NMR system of claim 47, wherein the enzymatic reaction rate is less than 500 pmol/sec, less than 100 pmol/sec, less than 50 pmol/sec, or about 30 pmol/sec.

49. The micro-NMR system of claim 37, configured to perform a method of sample analysis, the method comprising: providing a sample comprising a hyperpolarized substance within a micro coil and exposing said sample and micro coil to a magnetic field; and detecting an NMR signal for the sample.

50. The micro-NMR system of claim 37, wherein the micro coil has a diameter no greater than 5 mm, e.g., no greater than 3 mm, e.g., no greater than 2 mm, e.g., no greater than 1.5 mm, e.g., no greater than 1.0 mm, e.g., no greater than 0.5 mm.

51. The micro-NMR system of claim 37, wherein the micro coil has a diameter in a range from about 0.5 mm to about 2 mm.

52. The micro-NMR system of claim 37, wherein the micro coil has a diameter in a range from about 0.5 mm to about 1.5 mm.

53. The micro-NMR system of claim 37, wherein the system comprises a polarization module.

54. The micro-NMR system of claim 53, wherein the polarization module comprises a member selected from the group consisting of a Para-Hydrogen Induced Polarization (PHIP) module, a Dynamic Nuclear Polarization (DNP) module, a Spin-Exchange Optical Pumping module, a Parahydrogen Induced Polarization module, and other Gas-Induced Polarization module.

Description

BRIEF DESCRIPTION OF DRAWINGS

[0055] The foregoing and other objects, aspects, features, and advantages of the present disclosure will become more apparent and better understood by referring to the following description taken in conduction with the accompanying drawings, in which:

[0056] FIGS. 1A-1C show a hyperpolarized micromagnetic resonance spectrometer (HMRS) platform, according to an illustrative embodiment of the invention.

[0057] FIG. 1A shows a schematic of glycolytic metabolism in cancer and normal cells, according to an illustrative embodiment of the invention.

[0058] FIG. 1B shows a schematic of an HMRS assay, according to an illustrative embodiment of the invention. .sup.13C-enriched pyruvate is hyperpolarized and mixed with cell suspension. Once the suspension is loaded into the micro-coil, .sup.13C NMR acquisition starts in a 1.05 Tesla magnetic field.

[0059] FIG. 1C shows an optical picture of the micro-coil probe in the HMRS, according to an illustrative embodiment of the invention. The mechanical switch was used for changing the resonance mode of the micro-coil. Inset shows the micro-coil embedded in a PDMS block. Net volume inside the coil is 2 L.

[0060] FIGS. 2A-2C show analysis of metabolic flux using HMRS.

[0061] FIG. 2A shows NMR spectra of hyperpolarized metabolites from 50,000 UOK262 cells. Each spectrum was acquired every four seconds with a 30 radio-frequency pulse. Lac, lactate; Pyr-H, pyruvate hydrate; Pyr, pyruvate.

[0062] FIG. 2B shows a plot of the NMR signals of pyruvate and lactate peaks shown in FIG. 2A. Each signal was quantified from the integral of each area.

[0063] FIG. 2C shows a plot of the ratio of lactate signal to total signal (sum of pyruvate, pyruvate hydrate, and lactate signal) with time. All measurements were performed in duplicate. Error bars show the standard deviation.

[0064] FIGS. 3A-3D show sensitive and non-destructive analysis of glycolytic flux with HMRS.

[0065] FIG. 3A shows titration data of the flux metric of UOK262 cells.

[0066] FIG. 3B shows profiling of the flux metric in five different cell lines. UOK262 (kidney cancer), U87 (glioblastoma), Jurkat (acute T-cell leukemia), K562 (chronic myeloid leukemia), and HK-2 (kidney).

[0067] FIG. 3C shows NMR spectra of hyperpolarized metabolites acquired from malignant cancer cells (UOK262) and nonmalignant ones (HK-2). 10.sup.5 cells were used for each HMRS assay.

[0068] FIG. 3D shows a comparison of viability before and after HMRS assays. Two cancer cell lines (K562 and UOK262) were tested. The Before measurement was conducted 15 minutes before the HMRS assay, and the After measurement was conducted 15 minutes after the HMRS assay. All measurements were performed in duplicate. Error bars show the standard deviation.

[0069] FIGS. 4A-4C show quantification of metabolic flux in leukemia stem cells.

[0070] FIG. 4A shows a representative plot of flow cytometry of leukemia cells from a mouse with MLL-AF9 AML. The leukemia cells, collected from a mouse bone marrow, were sorted using the gates indicated in the plot.

[0071] FIG. 4B shows median fluorescence intensity of c-Kit in the leukemia stem cells (c-Kit.sup.Hi) and leukemia non-stem cells)(c-Kit.sup.Lo) after 20 hours in media.

[0072] FIG. 4C shows profiling of the flux metric in the leukemia cells.

[0073] FIGS. 5A-5G show rapid assessment of drug treatment response with HMRS.

[0074] FIG. 5A shows measurement of K562 cell counts after imatinib (GLEEVEC) treatment.

[0075] FIG. 5B shows viability of K562 cells after 24 hours of treatment.

[0076] FIG. 5C show NMR spectra of hyperpolarized metabolites acquired by the HMRS after 24 hours of treatment.

[0077] FIG. 5D shows a plot of normalized metric /.sub.0 after treatment. The metric .sub.0 was calculated from non-treated K562 cells (control).

[0078] FIG. 5E shows a schematic of metabolic analysis with different treatment durations. The dose of imatinib was set to 1 M.

[0079] FIG. 5F shows a plot of normalized metric with different durations. The metric was calculated from non-treated K562 cells (duration was 0 hour).

[0080] FIG. 5G shows a plot of normalized lactate concentrations from cell culture media with different durations. Each lactate signal was acquired by a conventional high-field NMR spectrometer. All measurements were performed in triplicate. Error bars show the standard deviation.

[0081] FIGS. 6A-6E show HMRS integrated with a microfluidic system for multiple analyses, according to an illustrative embodiment of the invention.

[0082] FIG. 6A shows a schematic of the integrated HMRS platform, according to an illustrative embodiment of the invention. FIG. 6A shows a tuning/matching circuit for .sup.1H, a mechanical switch, and a tuning/matching circuit for .sup.13C. The mechanical switch is designed for selecting the tuning/matching circuit between .sup.1H and .sup.13C.

[0083] FIG. 6B shows an assay schematic, according to an illustrative embodiment of the invention.

[0084] FIG. 6C shows sequential spectra of hyperpolarized pyruvate collected from multiple analyses.

[0085] FIG. 6D shows quantification and comparison of NMR signals from 12 analyses. NMR signal in each analysis (I) was normalized by the first signal of the same analysis (I.sub.0). All data sets were fitted to a single model of decay of hyperpolarized spin states. The 12 analyses yielded R.sub.2 of 99.50.4% and root-mean-square-error (RMSE) of 3.071.05%.

[0086] FIG. 6E shows multiple analyses of glycolytic flux from a single dissolution of hyperpolarized pyruvate. Each analysis consists of five NMR acquisitions (15 seconds in total), followed by a 15-second interval to stabilize the sample after movement. The overall glycolytic flux metric was 34.69 (pmol/sec) per 10.sup.5 cells, which was well matched to the of K562 (33.242.51) presented in FIG. 3B. The measurements were performed in triplicate. Error bars show the standard deviation.

[0087] FIG. 7 shows a schematic of [1-.sup.13C] pyruvate metabolism in cells. The circle indicates .sup.13C in pyruvate and lactate. When [1-.sup.13C] pyruvate is converted to lactate, the 13C stays in the same carboxyl group (COOH). Pyruvate and lactate is imported/exported through MCTs. LDH, lactate dehydrogenase; PDH, pyruvate dehydrogenase; MCT, monocarboxylate transporter.

[0088] FIGS. 8A-8C show metabolic flux measurements using the HMRS.

[0089] FIG. 8A shows a plot of NMR spectra of hyperpolarized metabolites in 50,000 UOK262 cells. This is an extended plot of FIG. 2A.

[0090] FIG. 8B shows a comparison of the pyruvate signal plotted in FIG. 8A with the simulated data. The simulation was conducted with an assumption that there was no metabolic reaction during the measurement. The simulated data models a spin-lattice relaxation decay with a relaxation constant T.sub.1 of pyruvate (70 sec).

[0091] FIG. 8C shows a comparison of the lactate signal plotted in FIG. 8A with the simulated data. The simulation was conducted with an assumption that there was no metabolic reaction during the measurement. The simulated data models a spin-lattice relaxation decay with a relaxation constant T.sub.1 of lactate (40 sec).

[0092] FIG. 9 shows a mathematical analysis of the increase rate of lactate relative to total carbon (lactate+pyruvate+pyruvate hydrate) with time. The variation of hyperpolarized signal levels (dPyr/dt or dLac/dt) can be described by a simplified mathematical model. In this model, the variation is dependent on the conversion rate constant (k.sub.PL or k.sub.Lp) and the spin-lattice relaxation time (T.sub.pyr or T.sub.Lac). Because the ratio of the pyruvate hydrate level to the pyruvate level is constant under the same pH condition, it can be assumed that (Lac+Pyr+Pyr-H)(Lac+Pyr). Because the pyruvate signal level is significantly larger than the lactate level, it can be assumed that (Lac/Pyr)<<1. In this model, the increase rate of lactate relative to total carbon can be derived as a constant, k.sub.PL. Lac, lactate; Pyr, pyruvate; Pyr-H, pyruvate hydrate.

[0093] FIG. 10 shows a comparison of NMR signals from three different coils. All the micro-coils had the same length, 1.3 mm. In each micro-coil, 28 mol of [1-.sup.13C] pyruvic acid was loaded for the experiments. NMR experiments were conducted with the following parameters: the number of 90 pulses, 20; the repetition time between pulses, 4 sec. All measurements were performed in duplicate.

[0094] FIG. 11 shows a comparison of viability before and after HMRS assays. This is an extended plot of FIG. 3D right panel. Two cancer cell lines (K562 and UOK262) were tested. The Before measurement was conducted 15 minutes before the HMRS assay, and the After measurement was conducted 15 minutes after the HMRS assay. All measurements were performed in duplicate. Error bars show the standard deviation.

[0095] FIG. 12 shows viability after imatinib treatment on K562 cells. The viability of K562 cells was compared after 24 hours and 96 hours of treatment. All measurements were performed in triplicate. Error bars show the standard deviation.

[0096] FIG. 13 shows changes in protein expression and phosphorylation levels after treatment. After 24 hours of imatinib treatment to K562 cells, the oncogenic tyrosine kinase, BCR-ABL, and the metabolic enzyme, LDHA, were analyzed by western blotting.

[0097] FIG. 14 shows a high-throughput analysis of hyperpolarized pyruvate. NMR signal of hyperpolarized pyruvate was acquired by the HMRS system integrated with the microreservoir. Twelve analyses were performed from a single dissolution of hyperpolarized pyruvate; each analysis comprises five NMR signals. After one analysis was finished (e.g., five NMR signals were acquired), intact hyperpolarized pyruvate was loaded into the micro-coil for further analysis.

[0098] FIG. 15 shows an immunoblot analysis for c-Myc in leukemia stem cells (c-Kit.sup.Hi) and leukemia non-stem cells (c-Kit.sup.Lo).

[0099] FIG. 16A shows real-time analysis of cancer metabolism with hyperpolarized .sup.13C-pyruvate. The left image shows a T2-weighted MRI image of a GIST (gastrointestinal stromal tumor) mouse xenograft. Dotted lines indicate the tumor region. The center image shows a hyperpolarized NMR spectra from kidney (top), tumor (middle), and muscle (bottom). The spectrum from the tumor is significantly different than the spectra from kidney and muscle. The right image shows a false-color image demonstrating the higher levels of lactate in the tumor region.

[0100] FIG. 16B shows real-time analysis of brain metabolism with hyperpolarized .sup.13C-DHA and .sup.13C-VitC (Vitamin C), according to an illustrative embodiment of the invention.

[0101] FIG. 17 shows real-time analysis of pulmonary perfusion with hyperpolarized 13-C-hydroxyethylpropionate, according to an illustrative embodiment of the invention.

[0102] FIG. 18 shows a schematic of nuclei with magnetic moments that can be analyzed without any labeling. In certain embodiments, a NMR signal comes from the polarization (e.g., difference in state occupancy). Accordingly, the higher the magnetic field, the higher the polarization, and the higher the NMR signal. Note that a magnetic signal is not absorbed by biomaterial (e.g., tissue). A limitation of NMR is that nuclear polarization in a magnetic field is poor. For example, even in a high magnetic field (e.g., 10 Tesla), the polarization is only 0.0044%. Due to poor polarization, NMR is limited to the analysis of highly concentrated samples.

[0103] FIG. 19 shows a schematic that depicts how hyperpolarized NMR can be used to overcome limitations of NMR. For example, by using radicals with radiofrequency (RF) irradiation, the polarization of nuclei can be enhanced significantly (e.g., greater than 10,000-fold). Accordingly, hyperpolarized NMR can be applied to biological reactions in live cells.

[0104] FIG. 20 shows data where hyperpolarized NMR is used to overcome limitations faced by NMR. The left panel of FIG. 20 shows that 70 mM of .sup.13C pyruvate requires more than 1 hour of acquisition time in order to see an NMR peak. However, after hyperpolarization, the same only requires about 1 second of acquisition time.

[0105] FIG. 21 shows that a smaller coil used in hyperpolarized micro-NMR systems provides higher sensitivity (e.g., higher signal-to-noise ratio). The present disclosure also describes alternative embodiments where sensitivity is increased. FIG. 21 also depicts mathematical formulations describing how the size of a coil affects signal-to-noise ratio.

[0106] FIG. 22 shows monitoring and sensitive analysis of enzymatic reactions (30 pmol/sec).

[0107] FIGS. 23A-23B show a 3D-printed multi-well micro-reservoir that supplies different samples for NMR analyses, according to an illustrative embodiment of the invention.

[0108] FIG. 24 is a block diagram of an example network environment for use in the methods and systems for analysis of spectrometry data, according to an illustrative embodiment.

[0109] FIG. 25 is a block diagram of an example computing device and an example mobile computing device, for use in illustrative embodiments of the invention.

[0110] FIGS. 26-28 show a method of sample analysis, according to an illustrative embodiment of the invention.

DETAILED DESCRIPTION

[0111] Throughout the description, where compositions are described as having, including, or comprising specific components, or where methods are described as having, including, or comprising specific steps, it is contemplated that, additionally, there are compositions of the present invention that consist essentially of, or consist of, the recited components, and that there are methods according to the present invention that consist essentially of, or consist of, the recited processing steps.

[0112] It should be understood that the order of steps or order for performing certain action is immaterial so long as the invention remains operable. Moreover, two or more steps or actions may be conducted simultaneously.

[0113] The mention herein of any publication, for example, in the Background section, is not an admission that the publication serves as prior art with respect to any of the claims presented herein. The Background section is presented for purposes of clarity and is not meant as a description of prior art with respect to any claim.

[0114] Described herein is a rapid and sensitive NMR platform designed for metabolic flux analysis on a small number of cells. Termed hyperpolarized micromagnetic resonance spectrometer (HMRS), in certain embodiments, the system is (i) equipped with a dual-tuned micro-coil circuit for sensitive detection of .sup.13C molecules, (ii) optimized to process a small mass of biological sample, and (iii) integrated with a microfluidic system for high-throughput analysis. The HMRS system can quantify glycolytic flux with 1000-fold higher sensitivity than conventional DNP-NMR approaches. This system was used to quantify metabolic flux in leukemia stem cells in vitro and to assess drug treatment response before any changes in cell viability or proliferation has occurred. Furthermore, the platform analyzed more than 10 samples of hyperpolarized molecules in one experiment demonstrating improved features compared to other systems that are used to study metabolic flux.

[0115] Aerobic glycolysis or Warburg effect is a unique metabolic feature of cancer cells, closely related to multiple oncogenic signaling pathways. As the glycolytic flux represents the activity of the major metabolic pathway at any given moment, it has emerged as a potent biomarker for cancer diagnosis and treatment response monitoring. There has been a great need for methods that could perform rapid flux analyses in mass-limited samples, including cancer stem cells, and the HMRS system presented herein presents a solution for methods that could perform rapid flux analyses in mass-limited samples.

[0116] The developed HMRS system has several advantages over conventional techniques for metabolic flux analysis. First, the detection modality of the provided system is based on magnetic resonance, which makes a flux analysis completely non-destructive, thereby allowing repeated measurements as well as downstream molecular analyses using other techniques. Second, a miniaturized detection coil of the provided system increases the filling-factor of the target molecules and enhances the sensitivity. Third, by exploiting hyperpolarization techniques such as DNP hyperpolarization techniques, the provided system monitors metabolic reactions in real-time without any signal averaging. Fourth, the provided system can perform multiple analyses with an integrated microfluidic system, enabling the high-throughput system for analyzing hyperpolarized molecules as well as the potential to integrate this platform into other high-throughput approaches.

[0117] Based on at least these technical advantages, the HMRS platform quantitatively profiled metabolic fluxes in leukemia stem cells and assessed therapeutic responses in cancer cells much earlier than any changes in viability, hardly achievable using conventional methods. The present disclosure provides surprising opportunities for stem cell research where rapid and sensitive analytical capabilities are critical. Moreover, the provided disclosure can be used to investigate how stem cells metabolically respond to external stimuli, such as drug treatment.

[0118] Further studies with drugs targeting metabolic differences in leukemia stem cells and hematopoietic stem cells can lead to more effective therapeutic strategies to cure cancer. The HMRS platform can also be applicable for rapid metabolic flux analysis in clinically relevant samples, such as biopsy specimens or patient-derived organoids, in a continuous manner to assess treatment response.

[0119] Other features can advance the HMRS system. For the analysis of different metabolic pathways, hyperpolarization of metabolites other than pyruvate can be readily applied to the HMRS. For the analysis of smaller number of cells, there are multiple strategies.

[0120] First, a smaller coil for NMR signal acquisition can be implemented. A coil with a diameter of less than 1.4 mm can provide a higher sensitivity; however, precise loading of sample into a sub-microliter space within a limited time frame is not trivial. One strategy to overcome this challenge is to integrate a micro pneumatic actuator into the HMRS, which moves liquid sample rapidly and precisely into a micro-coil;

[0121] Second, high-order shimming coils can be designed to make a magnetic field more homogeneous. In certain embodiments, provided system are equipped with first-order shimming coils, which provided a limited homogeneity of magnetic field (0.14-ppm FWHM) in the micro-coil circuit region. Since the NMR signal is proportional to the number of target nuclei experiencing the same magnetic field, more homogenous magnetic field leads to narrower NMR peaks and a higher SNR. Adding second- and third-order shimming coils into the permanent magnet can improve the homogeneity of magnetic field, thereby leading to the enhanced sensitivity of the HMRS.

[0122] Third, a selective RF pulse can be applied for signal acquisition. In certain embodiments, the provided system acquired the NMR spectra using a single RF pulse, which excited all the metabolic substrates and products at the same time. A selective pulse designed to only excite the products (e.g., lactate) can leave the hyperpolarized spin states of substrates (e.g., pyruvate) relatively intact. The benefit of slowly-decaying hyperpolarized substrates allows flux analysis for extended periods of time, providing an opportunity to monitor multiple metabolic pathways.

[0123] The HMRS platform provides opportunities for rapid and sensitive exploration of metabolic dynamics in biologically relevant systems.

Applications of Hyperpolarized NMR

[0124] In certain embodiments, hyperpolarized NMR is used for intra-operative tissue analysis. For example, during brain surgery, a tumor biopsy sample is analyzed by frozen sectioning, which take from about 20 to about 30 minutes. Hyperpolarized NMR allows for metabolic information of biopsy samples to be analyzed within a minute before frozen sectioning. Metabolic dynamics in about 50,000 cells in suspension are analyzed. In certain embodiments, the provided systems and methods are modified such that that solid tissue samples can be analyzed.

[0125] In certain embodiments, hyperpolarized NMR is used for treatment efficacy monitoring after drug treatment. For example, characterization of leukemia cells might take a couple of hours or days. Hyperpolarized NMR allows for metabolic information after treatment to be analyzed within a minute before FACS, Western blot, or PCT, for example. As described herein, analysis of tyrosine kinase inhibitor (TKI) was studied to determine its effect on metabolism. In certain embodiments, the provided systems and methods can be used for other TKI treatments (e.g., ibrutinib for CLL).

[0126] Conventional hyperpolarized NMR systems require more than tens of millions of cells. By contrast, the present disclosure provides for hyperpolarized micro-NMR systems that can analyze a small number of cells.

Identification of Targets and Therapeutic Treatment

[0127] The present disclosure describes hyperpolarized micro-NMR systems and methods for identifying a variety of drug targets in metabolic pathways (e.g., for early-stage discovery). In certain embodiments, the disclosed systems and methods facilitate high-throughput screening of various compounds involved in metabolic pathways.

[0128] Moreover, the disclosed systems and methods can direct a therapeutic regimen based on efficacy monitoring and can support therapeutic development by identifying points of intervention (e.g., targets) that correspond to metabolic differences. For example, hyperpolarized micro-NMR can profile metabolic characteristics in a small number of cells (e.g., no greater than 200 k cells, no greater than 150 k cells, no greater than 100 k cells, no greater than 50 k cells, no greater than 25 k cells, or no greater than 10 k cells). Accordingly, the disclosed systems and methods provide the ability to investigate heterogeneity of a tumor from a patient, thereby revealing effective therapeutic efficacy. This cannot be achieved by systems that profile metabolic characteristics over only a large number of cells, e.g., systems with resolution requiring signal averaging over large numbers of cells.

[0129] Drugs can target a variety of metabolic pathways, for example, metabolic pathways in cancer and metabolic pathways in non-cancerous diseases and other conditions, such as metabolic pathways in diabetes and in fatty liver disease.

[0130] Drugs can also change in the endogenous metabolism of the cell. In certain embodiments, the drug can be hyperpolarized, and its metabolic product in the cell is detected and quantified.

[0131] For example, drugs, can include receptor tyrosine kinase inhibitors and metabolic enzyme inhibitors (e.g., IDHi, e.g., LDHi, e.g., DCA for PDHK), imatinib (e.g., for chronic myelogenous leukemia (CIVIL), e.g., for acute lymphoblastic leukemia (ALL), e.g., for gastrointestinal stromal tumor (GIST)), rapamycin (e.g., for kidney cancer), enasidenib, an epidermal growth factor receptor (EGFR)-targeting tyrosine kinase inhibitor (e.g., Erlotinib (e.g., for lung cancer)), a BCR-ABL tyrosine kinase inhibitor (e.g., Erlotinib (e.g., for lung cancer)), a PI3K inhibitor (e.g., Idelalisib (e.g., for CLL)), a FLT3-ITD tyrosine kinase inhibitor (e.g., Quizartinib (e.g., for AML)), an inhibitor of PDK (e.g., Dichloroacetate (DCA)), an indirect inhibitor of mTORC1 (e.g., Metformin (e.g., for pancreatic cancer)), and P450.

Examples

Hyperpolarized Micromagnetic Resonance Spectrometer

[0132] FIG. 1A illustrates the fundamental difference in glycolytic metabolism between cancer and normal cells. Pyruvate, a 3-carbon intermediate product of glycolysis, is predominantly converted to lactate by lactate dehydrogenase (LDH, EC 1.1.2.4) in the cytoplasm or to acetyl-CoA by pyruvate dehydrogenase (PDH, EC 1.2.4.1) in the mitochondria. LDH is typically overexpressed in cancer cells, which drives the conversion from pyruvate to lactate. In normal cells, pyruvate is oxidized to a greater degree and used for ATP generation in the mitochondria. When [1-.sup.13C] pyruvate enters into cells directly through the monocarboxylate transporters (MCTs), it enters in the intracellular pool of pyruvate and is metabolized, producing higher [1-.sup.13C] lactate in cancer cells as compared to normal cells (FIGS. 1A and 7). Hyperpolarization of [1-.sup.13C] pyruvate then provides a means of probing this last step of glycolysis non-invasively with a dramatic MR signal enhancement.

[0133] FIG. 1B shows the HMRS assay scheme. Samples (cell suspension) are mixed with hyperpolarized [1-.sup.13C] pyruvate and then loaded into the micro-coil (1.4-mm diameter, 2-volume) in the HMRS system (FIG. 1C). The HMRS system acquires NMR signals of pyruvate and lactate in a 1.05 Tesla (B.sub.0) permanent magnet (nanoScan, Mediso). Because pyruvate and lactate have relatively long spin-lattice relaxation times, T1, at 1.05 Telsa (71 and 43 seconds, respectively), the hyperpolarization states of these molecules do not decay significantly during the metabolic reactions and signal acquisition. It is important to note that the whole process of analyzing metabolic flux is completed within two minutes (FIG. 1B). The micro-coil probe circuit was implemented to resonate at the Larmor frequencies of .sup.1H and .sup.13C in our permanent magnet (44.69 and 11.24 MHz, respectively). A permanent magnet, in general, provides a cost-effective solution for MR research, but it suffers from temporal and spatial inhomogeneity of a magnetic field. These problems were addressed with the .sup.1H resonance mode in the micro-coil; the drift and inhomogeneity were diagnosed and calibrated with first-order shimming in the .sup.1H resonance mode. The resonance mode was then switched from .sup.1H mode to .sup.13C mode immediately before .sup.13C NMR experiments (FIG. 1C).

[0134] The HMRS system acquired NMR spectra every four seconds with a 30 RF pulse and quantified them based on the peak area. Utilizing 50,000 UOK262 cells (a renal cell carcinoma), it was observed that the lactate peak was detected from the first NMR spectrum and its signal level increased relative to pyruvate with time (FIGS. 2A and 2B). Considering that lactate has a shorter T.sub.1 than pyruvate, the relative increase of lactate indicated that the cells inside the micro-coil were consuming pyruvate and producing lactate during the measurement (FIGS. 8A-8C).

[0135] Because the .sup.13C NMR signal of pyruvate is significantly larger than those of lactate and pyruvate hydrate, the increase rate of .sup.13C lactate signal relative to .sup.13C total signal(pyruvate+pyruvate hydrate+lactate) can be equivalent to the conversion rate constant of pyruvate to lactate, k.sub.PL (FIG. 9). The overall conversion rate from pyruvate to lactate in the HMRS assay, therefore, can be assumed to be the product of kPL and the initial amount of pyruvate, [Pyr]t=0, loaded into the system (=28109 mol). The overall conversion rate (kPL[Pyr]t=0) was defined as a metabolic flux metric .

Analysis of Metabolic Flux in Live Cells with 1000-Fold Higher Sensitivity

[0136] In order to assess the sensitivity of the described approach, titration experiments were performed with UOK262 cells, demonstrating detection sensitivity down to 10.sup.4 cancer cells with a linear response to cell numbers (R.sub.2>99%, FIG. 3A). Conventional hyperpolarized NMR techniques typically require much larger number of cells (>10.sup.7) for metabolic flux analysis, whereas the HMRS platform required on the order of 10.sup.4 cells of interest. This 1000-fold higher sensitivity is one of the key advantages of the HMRS system, which was achieved, for example, with the miniaturization of the coil design. The signal-to-noise ratio (SNR) of NMR signals depends on the magnetic field induced by unit current flowing through the coil, thus a smaller coil generates a higher SNR than a larger one with the same amount of target molecules (FIG. 10). Another contributing factor for a higher SNR is the filling factor of the target molecule. The described micro-coil, embedded into a polymer block, allowed the sample to fill the entire coil, maximizing the filling-factor 1) for NMR detection.

[0137] Five different cell lines, UOK262, U87, Jurkat, K562, and HK-2 (FIG. 3B), were profiled. UOK262 showed approximately 2-fold higher flux when compared to benign renal tubular cells, HK-2 (40.6 and 21.0 pmol/sec, respectively) (FIG. 3C). Cell viability was further assessed before and after the HMRS assay with two cancer cell lines, UOK262, as a representative of solid cancer, and K562, a chronic myeloid leukemia (CIVIL) cell line, as a representative of liquid cancer. The cell loss was negligible during the experiments, and cell viability was not significantly different (p-value=NS, FIG. 3D and FIG. 11), thereby demonstrating another advantage of the HMRS platform (e.g., non-destructive analysis of metabolic flux).

Quantification of Metabolic Flux in Leukemia Stem Cells

[0138] Leukemia stem cells (LSCs, or leukemia-initiating cells), defined by their ability to initiate and re-establish malignancy upon transplantation, are more resistant to conventional therapeutic regimens as compared to bulk leukemia populations. Considering that distinct metabolic features confer the survival benefits to cancer cells, understanding LSC's reliance on specific metabolic pathways has the potential to illuminate more effective therapeutic strategies to target cancer stem cells. With this in mind, increased sensitivity of the HMRS platform was taken advantage of to quantify metabolic flux in primary LSCs, previously not possible using conventional approaches. LSCs in acute myeloid leukemia (AML) driven by MLL-AF9 oncogene are of particular interest as MLL-AF9 is related to deregulated expression of Myc33,34, which potentially mediates metabolic reprogramming in cancer. LSCs, collected from the bone marrow in MLL-AF9 AML mice, were sorted based on the surface protein, c-Kit (CD117), and assayed rapidly within 24 hours non-invasively (FIGS. 4A and 4B). Interestingly, LSCs (c-Kit.sup.Hi) demonstrated nearly 2-fold higher flux than leukemia non-stem cells (c-Kit.sup.Lo) (FIG. 4C). Given that the provided approach is non-destructive, it was possible to further assay the status of c-Myc in these primary cells by immunoblot (FIG. 16), which confirmed that c-Kit.sup.Hi cells also exhibited high expression levels of Myc. While further experiments can be performed to characterize the control of metabolism by c-Myc in primary c-Kit.sup.Hi LSCs, the experiments using hyperpolarized substrates provides quantitative evidence of differential metabolism in the LSC compartment.

Rapid Quantitative Assessment of Drug Treatment Response

[0139] Since metabolic changes can be induced by anticancer drug treatments before major clinicopathological changes occur, any metabolic changes related to the pathway can be an early indicator of the treatment efficacy. Accordingly, the HMRS platform was applied for rapid assessment of drug-treatment efficacy, based on the advantages of high sensitivity and non-destructiveness for metabolic flux analysis.

[0140] Imatinib (GLEEVEC), a BCR-ABL tyrosine kinase inhibitor developed for chronic myeloid leukemia (CML) treatment, has been shown to affect glycolytic activity in cancer cells. It was reasoned that glycolytic flux analysis on K562 cells with imatinib treatment would be a model to assess time dependent changes in drug action. After 24 hours of drug treatment, the rate of cell proliferation was slightly decreased, and the cell viability was not significantly changed at concentrations as high as 1 M imatinib (FIGS. 5A, 5B, and FIG. 12). However, using the HMRS, it was found that the lactate peak decreased by approximately 75% after 24 hours of 1 M imatinib treatment (FIGS. 5C and 5D). This reduction in the glycolytic flux could be explained by the signaling pathways targeted by the treatment: the phosphorylation level of the target protein, BCR-ABL, decreased even at 0.25 M imatinib treatment, which led to down-regulation of the PI3K/AKT/mTOR pathway and decreased phosphorylation of a key metabolic enzyme, LDH-A (FIG. 13). By assessing different time points, it was found that the drug effect could be detected as early as 3 hours post-treatment (FIGS. 5E and 5F). Contrary to the flux analysis, the analysis of metabolite concentrations using a conventional high-field NMR spectrometer showed much slower response to treatment (FIG. 5G). Without wishing to be bound to any theory, this significant difference between the analyses of flux and pool sizes can be explained as following: the flux indicates the metabolic activity at a given moment, but the pool size indicates the integration of the metabolic activity over a certain period. Imatinib starts to affect metabolic activities in K562 cells as early as 3 hours post-treatment, and the decreased glycolytic activity begins to change the lactate pool size, which is large (on the order of mM). In the provided assay, it required about 12 hours to make a significant change in the pool size. These results demonstrate another advantage of the HMRS flux analysis: rapid assessment of treatment efficacy.

Multiple Analyses in a Single Dissolution

[0141] While current approaches provide a means of rapidly measuring metabolic fluxes, there remains a need for developing high-throughput approaches. In order to address this need, the HMRS platform was advanced to enable high-throughput analysis of hyperpolarized molecules. The DNP method provides greater than 20% polarization for .sup.13C nuclei, but it has some limitations in throughput. For example, (i) the DNP method takes more than an hour for hyperpolarization with relatively expensive chemical compounds, and ii) the hyperpolarized state decays to thermal equilibrium rapidly with RF excitation pulses, which allows for only one NMR experiment. There is a great need for approaches that enable a high-throughput analysis with the DNP-NMR method. It was hypothesized that multiple NMR analyses could be performed if intact hyperpolarized molecules could be supplied constantly into the micro-coil in a time frame proportional to the T.sub.1 relaxation time of the hyperpolarized molecule.

[0142] A 3D-printed micro-reservoir was designed to hold samples for multiple experiments (e.g., up to 100 L) and was connected to the inlet of the micro-coil channel (FIG. 6A). The channel outlet was connected to a syringe pump (PH2000, Harvard Apparatus) with a pulling rate of 5 L/sec. FIG. 6B describes the assay scheme of high-throughput analysis from a single dissolution; i) the syringe pump pulled the sample (Z1) to the micro-coil region, followed by .sup.13C NMR signal acquisition, ii) after the acquisition, the syringe pump pulled the intact sample (Z2) into the micro-coil region, and iii) another 13C NMR signal acquisition started. Integrated with the micro-reservoir, the HMRS platform successfully conducted 12 analyses of the hyperpolarized spin states from a single dissolution (FIGS. 6C, 6D, and FIG. 14). All 12 analyses were fit to a single decay model with T.sub.1 of 67 seconds and RF pulse of 60 flip angle, which yielded 99.50.4% of R.sub.2 and 3.071.05% of root-mean-square error between analyses (FIG. 6D). Moreover, this approach was extended to the multiple analyses of glycolytic flux in cancer cells from a single dissolution (FIG. 6E). The provided integrated HMRS platform, separating the sample reservoir from the micro-coil (where RF pulses were applied), achieved multiple analyses from a single dissolution of hyperpolarized molecules. This analytical capability shows the potential of the HMRS for high-throughput flux analysis in cells.

Methods and Materials

Microcoil Fabrication

[0143] Thin magnet wire (Belden 8042, 32 AWG) was wound five times around a metal rod (diameter of 1.4 mm), and it was embedded into polydimethylsiloxane (PDMS) (Down Corning). After PDMS curing for overnight at 80 C., the wire was pulled out, which formed a miniaturized solenoid coil (micro-coil) with a microfluidic channel.

3D-Printed Micro-Reservoir

[0144] A CAD software (3Ds MAX, Autodesk) and a 3D-printer (Micro Plus Hi-Res, EnvisionTec) was used to design and print a micro-reservoir. The outlet (1.4-mm diameter) of the micro-reservoir was designed to fit into the microfluidic channel and the inlet of the micro-reservoir (10-mm diameter) was designed for easy-loading of sample. The maximum capacity of the micro-reservoir was about 100 L.

Drug (Imatinib) Treatment on K562 Cells

[0145] K562 cells were plated with a concentration of 100,000 cells/mL in 10 mL complete RPMI media. 10 L of complete RPMI media, dimethyl sulfoxide (DMSO), 0.25 mM imatinib in DMSO, and 1 mM imatinib in DMSO were loaded for control sample, 0.1% DMSO-treated sample, 0.25 M and 1.0 M imatinib-treated samples, respectively. Imatinib was purchased from Cayman Chemical. Two sets of four flasks (two flasks per each sample) were prepared and stored in an incubator for 24 hours. Cell number and viability were checked after 24 hours, and cells were washed with complete RPMI media once and prepared for hyperpolarization experiments.

Hyperpolarization Using Dynamic Nuclear Polarization (DNP)

[0146] A SPINLab polarizer (GE) was used to polarize [1-.sup.13C] pyruvate. The preparation steps for polarization are the following: i) .sup.13C-enriched pyruvate sample was prepared with radical: 15 mM of AH-111501 (GE) was mixed in [1-.sup.13C] pyruvic acid (Sigma Aldrich: 677175) thoroughly. ii) Buffer solution for dissolution was prepared: 0.4 mM of ethylenediaminetetraacetic acid (EDTA) was added in 40 mM of Trizma hydrochloride solution (Sigma Aldrich: T2663). iii) 100 L of the pyruvate sample from step i) and 20 mL of the buffer solution from step ii) were loaded into the SPINLab polarizer (3 Tesla, 0.98K). After 90 minutes of polarization, the pyruvate sample was quickly dissolved into an ice-cold flask with 120 L of 10N sodium hydroxide solution (Fisher Scientific: SS255) to make the dissolved sample neutral (pH7.4) and reach 37 C. faster. The dissolved pyruvate sample was added into cell suspension with a ratio of 1:10.

Cell Culture

[0147] K562 and Jurkat cells were grown in Gibco RPMI-160 medium (Thermo Fisher Scientific: 11875-093). UOK262 and HK-2 cells were grown in Advanced DMEM/F-12 (Thermo Fisher Scientific: 12634-010). U87 cells were grown in EMEM (ATCC: 30-2003). All media were supplemented with 10% fetal bovine serum (FBS) and penicillin-streptomycin.

Cell Number and Viability

[0148] The cell number and the viability were measured using CELLOMETER Mini cell counter (Nexcelom Bioscience); the cell suspension was mixed at 1:1 ratio with the trypan blue solution before the measurement. For each measurement, the cell suspension was diluted to make its concentration within a range of 0.8-210.sup.6/mL.

FACS Sorting of Leukemia Cells

[0149] Bone marrow from leukemia mice transplanted with MLL-AF9 leukemia cells were processed and stained with Mac1-PacBlue and c-Kit-PeCy7 (34). The top 15% and lowest 20% of c-Kit cells were sorted as c-Kit.sup.Hi and c-Kit.sup.Lo cells, respectively, using BD FACS Aria III instrument. Cells were cultured in RPMI media containing SCF (long/ml), IL-3 (long/ml) and IL-6 (long/ml) for 20 hours before hyperpolarization experiments.

Western Blot Analysis

[0150] K562 cells, washed with cold PBS w/o Ca+Mg and concentrated by centrifugation (3000 rpm, 10 min), were lysed in RIPA buffer (Thermo Scientific: 89901) containing protease and phosphatase inhibitors (Thermo Scientific: 78480, 78420). The concentration of protein lysates was quantified using a BCA assay. Protein lysates were resolved using NuPAGE pre-cast gels and transferred to NOVEX polyvinylidene difluoride (PVDF) membrane (Thermo Fisher Scientific: LC2002), as described in the manufacturer's protocol. Target proteins in the membrane were immunoblotted with primary antibodies; BCR-ABL (Cell Signaling: 3902), phospho BCR-ABL (Cell Signaling: 3901), LDHA (Cell Signaling: 2012), phospho LDHA (Cell Signaling: 8176), and -actin (Cell Signaling: 8457). The horseradish peroxidase (HRP) conjugated secondary antibodies (Santa Cruz Biotechnology: SC-2004) were then incubated with the membrane. Protein bands were detected on X-ray film (Thermo Fisher Scientific: 34090) using the chemiluminescent (ECL) HRP substrate (Thermo Fisher Scientific: 34080).

Smaller Coil is More Sensitive for Mass-Limited Samples

[0151] FIG. 10 shows that smaller coils are more sensitive for mass-limited samples. It was found that NMR signal depends on the diameter of a micro-coil. FIG. 10 depicts a plot that tests micro-coils having diameters of 1.4 mm to measure a concentration of 17.5 mole in a sample, 2.8 mm to measure a concentration of 17.5 mole in a sample, and 2.8 mm a concentration of 70 mole in a sample that yielded a NMR signal (a.u.) of about 40, 20, and 80 respectively. Accordingly. FIG. 10 shows that the same amount of target material can be detected at higher sensitivity when using a smaller coil (e.g., having a diameter of 1.4 mm) compared to a larger coil (e.g., having a diameter of 2.8 mm). However, using a coil that has too small of a diameter can present issues with sample loading. Accordingly, the systems provided herein are well-fitted to measure metabolic flux in limited about of cells in a sample (e.g., cancer stem cells, e.g., stem cells, e.g., a patient biopsy sample).

High-Throughput Micro-NMR Devices

[0152] To develop a micro-NMR system that would conduct multiple hyperpolarized NMR experiments, the provided systems and methods were designed to continuously supply hyperpolarized molecules into the NMR coil.

[0153] A 3D-printed micro-reservoir was designed to supply samples for high-throughput experiments (FIGS. 6A-6E). An exemplary assay scheme of high-throughput analysis from a single dissolution of hyperpolarized molecules is depicted in FIGS. 6A-6E.

[0154] FIG. 6B shows that microfluidic system pulls the sample Z1 into the micro-coil region, followed by .sup.13C NMR signal acquisition. After the acquisition, the microfluidic system pulls another intact sample (Z2) into the micro-coil region, and another .sup.13C NMR signal acquisition is started. This process is continuously repeated.

[0155] FIGS. 6D and 6E show that more than 12 analyses of hyperpolarized molecules are achievable with 99.5% of R2 between analyses (FIG. 6D). Moreover, multiple analyses of metabolic activities in live cells can be done with a single dissolution (FIG. 6E).

[0156] FIG. 6F shows a 3D-printed multi-well micro-reservoir that supplies different samples for NMR analyses, according to an illustrative embodiment of the invention. Different samples in each well are loaded into the micro-coil region, where .sup.13C NMR signal is acquired. Each acquisition is finished within 1 second, and the sample switching time is less than 5 seconds. Accordingly, an analysis of 8 samples can be finished within 1 minute.

Illustrative Network Environment

[0157] FIG. 24 shows an illustrative network environment 2400 for use in the methods and systems for analysis of spectrometry data corresponding to particles of a sample, as described herein. In brief overview, referring now to FIG. 24, a block diagram of an exemplary cloud computing environment 2400 is shown and described. The cloud computing environment 2400 may include one or more resource providers 2402a, 2402b, 2402c (collectively, 2402). Each resource provider 2402 may include computing resources. In some implementations, computing resources may include any hardware and/or software used to process data. For example, computing resources may include hardware and/or software capable of executing algorithms, computer programs, and/or computer applications. In some implementations, exemplary computing resources may include application servers and/or databases with storage and retrieval capabilities. Each resource provider 2402 may be connected to any other resource provider 2402 in the cloud computing environment 2400. In some implementations, the resource providers 2402 may be connected over a computer network 2408. Each resource provider 2402 may be connected to one or more computing device 2404a, 2404b, 2404c (collectively, 2404), over the computer network 2408.

[0158] The cloud computing environment 2400 may include a resource manager 2406. The resource manager 2406 may be connected to the resource providers 2402 and the computing devices 2404 over the computer network 2408. In some implementations, the resource manager 2406 may facilitate the provision of computing resources by one or more resource providers 2402 to one or more computing devices 2404. The resource manager 2406 may receive a request for a computing resource from a particular computing device 2404. The resource manager 2406 may identify one or more resource providers 2402 capable of providing the computing resource requested by the computing device 2404. The resource manager 2406 may select a resource provider 2402 to provide the computing resource. The resource manager 2406 may facilitate a connection between the resource provider 2402 and a particular computing device 2404. In some implementations, the resource manager 2406 may establish a connection between a particular resource provider 2402 and a particular computing device 2404. In some implementations, the resource manager 2406 may redirect a particular computing device 2404 to a particular resource provider 2402 with the requested computing resource.

[0159] FIG. 25 shows an example of a computing device 2500 and a mobile computing device 2550 that can be used in the methods and systems described in this disclosure. The computing device 2500 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The mobile computing device 2550 is intended to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart-phones, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to be limiting.

[0160] The computing device 2500 includes a processor 2502, a memory 2504, a storage device 2506, a high-speed interface 2508 connecting to the memory 2504 and multiple high-speed expansion ports 2510, and a low-speed interface 2512 connecting to a low-speed expansion port 2514 and the storage device 2506. Each of the processor 2502, the memory 2504, the storage device 2506, the high-speed interface 2508, the high-speed expansion ports 2510, and the low-speed interface 2512, are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate. The processor 2502 can process instructions for execution within the computing device 2500, including instructions stored in the memory 2504 or on the storage device 2506 to display graphical information for a GUI on an external input/output device, such as a display 2516 coupled to the high-speed interface 2508. In other implementations, multiple processors and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system).

[0161] The memory 2504 stores information within the computing device 2500. In some implementations, the memory 2504 is a volatile memory unit or units. In some implementations, the memory 2504 is a non-volatile memory unit or units. The memory 2504 may also be another form of computer-readable medium, such as a magnetic or optical disk.

[0162] The storage device 2506 is capable of providing mass storage for the computing device 2500. In some implementations, the storage device 2506 may be or contain a computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. Instructions can be stored in an information carrier. The instructions, when executed by one or more processing devices (for example, processor 2502), perform one or more methods, such as those described above. The instructions can also be stored by one or more storage devices such as computer- or machine-readable mediums (for example, the memory 2504, the storage device 2506, or memory on the processor 2502).

[0163] The high-speed interface 2508 manages bandwidth-intensive operations for the computing device 2500, while the low-speed interface 2512 manages lower bandwidth-intensive operations. Such allocation of functions is an example only. In some implementations, the high-speed interface 2508 is coupled to the memory 2504, the display 2516 (e.g., through a graphics processor or accelerator), and to the high-speed expansion ports 2510, which may accept various expansion cards (not shown). In the implementation, the low-speed interface 2512 is coupled to the storage device 2506 and the low-speed expansion port 2514. The low-speed expansion port 2514, which may include various communication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet) may be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter.

[0164] The computing device 2500 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a standard server 2520, or multiple times in a group of such servers. In addition, it may be implemented in a personal computer such as a laptop computer 2522. It may also be implemented as part of a rack server system 2524. Alternatively, components from the computing device 2500 may be combined with other components in a mobile device (not shown), such as a mobile computing device 2550. Each of such devices may contain one or more of the computing device 2500 and the mobile computing device 2550, and an entire system may be made up of multiple computing devices communicating with each other.

[0165] The mobile computing device 2550 includes a processor 2552, a memory 2564, an input/output device such as a display 2554, a communication interface 2566, and a transceiver 2568, among other components. The mobile computing device 2550 may also be provided with a storage device, such as a micro-drive or other device, to provide additional storage. Each of the processor 2552, the memory 2564, the display 2554, the communication interface 2566, and the transceiver 2568, are interconnected using various buses, and several of the components may be mounted on a common motherboard or in other manners as appropriate.

[0166] The processor 2552 can execute instructions within the mobile computing device 2550, including instructions stored in the memory 2564. The processor 2552 may be implemented as a chipset of chips that include separate and multiple analog and digital processors. The processor 2552 may provide, for example, for coordination of the other components of the mobile computing device 2550, such as control of user interfaces, applications run by the mobile computing device 2550, and wireless communication by the mobile computing device 2550.

[0167] The processor 2552 may communicate with a user through a control interface 2558 and a display interface 2556 coupled to the display 2554. The display 2554 may be, for example, a TFT (Thin-Film-Transistor Liquid Crystal Display) display or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology. The display interface 2556 may comprise appropriate circuitry for driving the display 2554 to present graphical and other information to a user. The control interface 2558 may receive commands from a user and convert them for submission to the processor 2552. In addition, an external interface 2562 may provide communication with the processor 2552, so as to enable near area communication of the mobile computing device 2550 with other devices. The external interface 2562 may provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces may also be used.

[0168] The memory 2564 stores information within the mobile computing device 2550. The memory 2564 can be implemented as one or more of a computer-readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units. An expansion memory 2574 may also be provided and connected to the mobile computing device 2550 through an expansion interface 2572, which may include, for example, a SIMM (Single In Line Memory Module) card interface. The expansion memory 2574 may provide extra storage space for the mobile computing device 2550, or may also store applications or other information for the mobile computing device 2550. Specifically, the expansion memory 2574 may include instructions to carry out or supplement the processes described above, and may include secure information also. Thus, for example, the expansion memory 2574 may be provided as a security module for the mobile computing device 2550, and may be programmed with instructions that permit secure use of the mobile computing device 2550. In addition, secure applications may be provided via the SIMM cards, along with additional information, such as placing identifying information on the SIMM card in a non-hackable manner.

[0169] The memory may include, for example, flash memory and/or NVRAM memory (non-volatile random access memory), as discussed below. In some implementations, instructions are stored in an information carrier and, when executed by one or more processing devices (for example, processor 2552), perform one or more methods, such as those described above. The instructions can also be stored by one or more storage devices, such as one or more computer- or machine-readable mediums (for example, the memory 2564, the expansion memory 2574, or memory on the processor 2552). In some implementations, the instructions can be received in a propagated signal, for example, over the transceiver 2568 or the external interface 2562.

[0170] The mobile computing device 2550 may communicate wirelessly through the communication interface 2566, which may include digital signal processing circuitry where necessary. The communication interface 2566 may provide for communications under various modes or protocols, such as GSM voice calls (Global System for Mobile communications), SMS (Short Message Service), EMS (Enhanced Messaging Service), or MMS messaging (Multimedia Messaging Service), CDMA (code division multiple access), TDMA (time division multiple access), PDC (Personal Digital Cellular), WCDMA (Wideband Code Division Multiple Access), CDMA2000, or GPRS (General Packet Radio Service), among others. Such communication may occur, for example, through the transceiver 2568 using a radio-frequency. In addition, short-range communication may occur, such as using a Bluetooth, Wi-Fi, or other such transceiver (not shown). In addition, a GPS (Global Positioning System) receiver module 2570 may provide additional navigation- and location-related wireless data to the mobile computing device 2550, which may be used as appropriate by applications running on the mobile computing device 2550.

[0171] The mobile computing device 2550 may also communicate audibly using an audio codec 2560, which may receive spoken information from a user and convert it to usable digital information. The audio codec 2560 may likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of the mobile computing device 2550. Such sound may include sound from voice telephone calls, may include recorded sound (e.g., voice messages, music files, etc.) and may also include sound generated by applications operating on the mobile computing device 2550.

[0172] The mobile computing device 2550 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a cellular telephone 2580. It may also be implemented as part of a smart-phone 2582, personal digital assistant, or other similar mobile device.

[0173] Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0174] These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms machine-readable medium and computer-readable medium refer to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term machine-readable signal refers to any signal used to provide machine instructions and/or data to a programmable processor.

[0175] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0176] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0177] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.