WO2009143504A2 - Methods of microrna detection and differentiation - Google Patents

Abstract

Embodiments of the present disclosure relate to surface-enhanced Raman spectroscopic (SERS) systems and methods of using the SERS systems to identify and differentiate an analyte.

Description

METHODS OF MICRORNA DETECTION AND DIFFERENTIATION

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to co-pending U.S. provisional application entitled "Methods of miRNA Detection and Differentiation," having Serial No. 61/055,477, filed May 23, 2008, which is entirely incorporated herein by reference.

SEQUENCE LISTING

The present disclosure includes a sequence listing incorporated herein by reference in its entirety.

BACKGROUND

MicroRNAs (miRNAs) are small (19-25 nucleotides), non-coding RNAs regulating gene expression through mRNA degradation or translation inhibition. Recent studies have revealed that miRNAs have key roles in regulatory pathways including development, apoptosis, cell proliferation and differentiation, organ development and cancer, and it has been suggested that up to 30% of the human genome may be regulated by miRNAs.

The recently recognized role of miRNAs in regulating aspects of hematopoietic cell proliferation and differentiation has led researchers to investigate the role of miRNAs in cancer predisposition. It appears that some miRNAs may serve as biomarkers for disease, particularly in the development of some cancers. Expression studies of various tumor types have revealed specific alterations in miRNA profiles. For example, miR-15 and miR-16 are frequently deleted and/or downregulated in B cell chronic lymphocytic luekemia, and levels of miR-143 and miR-145 are reduced in colorectal cancer. Recent studies suggest that clusters of miRNAs may also act as potent oncogenes. For example, miRNA clusters on human chromosomes (M I RN 17-92) have been found to be regulated by transcription factors, e.g., c-Myc that are over-expressed in many human cancers. The findings that miRNAs are associated with cancer suggest that miRNA expression profiles may be useful to classify and diagnose human cancers. In fact, cancer-specific miRNA expression patterns have been identified in every cancer analyzed to date.

Development of analytical methods for rapid and sensitive identification of miRNA is essential for both the discovery of potential biomarkers of disease pathogenesis and the high-throughput detection of miRNA expression profiles for diagnosis of human cancers. Current miRNA diagnostic methods are poorly developed and have limited sensitivity and breadth.

SUMMARY

Embodiments of the present disclosure relate to surface-enhanced Raman spectroscopic (SERS) systems and methods of using the SERS systems to identify and differentiate an analyte.

Briefly described, embodiments of the present disclosure include a method for identifying and differentiating individual miRNAs comprising: obtaining a unique surface enhanced Raman spectroscopy (SERS) spectrum of a first miRNA, obtaining a unique SERS spectrum of a second miRNA, and analyzing the difference in the spectra of the first miRNA and the second miRNA using a chemometric method of analysis.

In another embodiment, a method for identifying an individual miRNA in a sample includes applying the sample to a SERS platform, obtaining a unique SERS spectrum for the individual miRNA, and analyzing the unique SERS spectrum to identify the individual miRNA.

In another embodiment, a method for identifying and differentiating individual miRNA's includes obtaining a unique SERS spectrum for each of two or more miRNA and analyzing the difference in the spectra of each of the miRNA using a chemometric method of analysis.

BRIEF DESCRIPTION OF THE DRAWINGS

Many aspects of this disclosure can be better understood with reference to the following drawings. The components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Moreover, in the drawings, like reference numerals designate corresponding parts throughout the several views.

FIG. 1 illustrates sample location for substrates 1 , 2, 4, and 5. Samples are imiR- 16 (16), miR-21 (21), miR-24a (24a), miR-133a (133a), and let-7a (7a).

FIG. 2 illustrates representative SERS spectra for each unrelated miRNA sample.

FIG. 3 illustrates representative SERS spectra for each of the let-7 miRNA samples.

FIG. 4 illustrates a two-dimensional scores plot for PC3 vs PC2 using the normalized first derivative spectra for 118 spectra collected over four substrates for five miRNA samples.

FIGS. 5A-5E illustrate PLS-DA Y predicted plots. Each plot predicts a sample as belonging to or not belonging to the specified miRNA class. Each sample is symbol coded according to its known identity: let-7a (▼), miR-16(*), miR-21 (■), miR-24a(+), and miR-133a(0).

FIGS. 6A-6H illustrate PLS-DA Y predicted plots. Each plot predicts a sample as belonging to or not belonging to the specified miRNA class. Each sample is symbol coded according to its known identity: let-7a (T), let-7b(^*), let-7c (■), let-7d(+), let-7e(0), let-7f (A), let-7g(star symbol), and let-7i(«).

DETAILED DESCRIPTION

Before the present disclosure is described in greater detail, it is to be understood that this disclosure is not limited to particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present disclosure will be limited only by the appended claims.

Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit (unless the context clearly dictates otherwise), between the upper and lower limit of that range, and any other stated or intervening value in that stated range, is encompassed within the disclosure. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges and are also encompassed within the disclosure, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the disclosure.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present disclosure, the preferred methods and materials are now described.

All publications and patents cited in this specification are herein incorporated by reference as if each individual publication or patent were specifically and individually indicated to be incorporated by reference and are incorporated herein by reference to disclose and describe the methods and/or materials in connection with which the publications are cited. The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present disclosure is not entitled to antedate such publication by virtue of prior disclosure. Further, the dates of publication provided could be different from the actual publication dates that may need to be independently confirmed.

As will be apparent to those of skill in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the present disclosure. Any recited method can be carried out in the order of events recited or in any other order that is logically possible.

The following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how to perform the methods and use the compositions and compounds disclosed and claimed herein. Efforts have been made to ensure accuracy with respect to numbers (e.g., amounts, temperature, etc.), but some errors and deviations should be accounted for. Unless indicated otherwise, parts are parts by weight, temperature is in ⁰C, and pressure is at or near atmospheric. Standard temperature and pressure are defined as 20 ⁰C and 1 atmosphere.

Before the embodiments of the present disclosure are described in detail, it is to be understood that, unless otherwise indicated, the present disclosure is not limited to particular materials, reagents, reaction materials, manufacturing processes, or the like, as such can vary. It is also to be understood that the terminology used herein is for purposes of describing particular embodiments only, and is not intended to be limiting. It is also possible in the present disclosure that steps can be executed in different sequence where this is logically possible.

It must be noted that, as used in the specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a support" includes a plurality of supports. In this specification and in the claims that follow, reference will be made to a number of terms that shall be defined to have the following meanings unless a contrary intention is apparent. Definitions

Use of the phrase "peptides", "polypeptide", or "protein" is intended to encompass a protein, a glycoprotein, a polypeptide, a peptide, fragments thereof and the like, whether isolated from nature, of viral, bacterial, plant, or animal (e.g., mammalian, such as human) origin, or synthetic, and fragments thereof. Polypeptides are disclosed herein as amino acid residue sequences. Those sequences are written left to right in the direction from the amino to the carboxy terminus. In accordance with standard nomenclature, amino acid residue sequences are denominated by either a three letter or a single letter code as indicated as follows: Alanine (Ala, A), Arginine (Arg, R), Asparagine (Asn, N), Aspartic Acid (Asp, D), Cysteine (Cys, C), Glutamine (GIn, Q), Glutamic Acid (GIu, E), Glycine (GIy, G), Histidine (His, H), lsoleucine (lie, I), Leucine (Leu, L), Lysine (Lys, K), Methionine (Met, M), Phenylalanine (Phe, F), Proline (Pro, P), Serine (Ser, S), Threonine (Thr, T), Tryptophan (Trp, W), Tyrosine (Tyr, Y), and Valine (VaI, V).

Use of the term "nucleotide" is intended to encompass molecules which comprise the structural units of RNA and DNA. A nucleotide is composed of a nitrogenous base and a five-carbon sugar (either ribose or 2'-deoxyribose), and one to three phosphate groups. A nucleobase and sugar comprise a nucleoside. Cyclic nucleotides are a form comprised of a phosphate group bound to two of the sugar's hydroxyl groups. Ribonucleotides are nucleotides where the sugar is ribose, and deoxyribonucleotides contain the sugar deoxyribose. Nucleotides can contain either a purine or pyrimidine base.

Use of the term "polynucleotide" is intended to encompass DNA, RNA, and miRNA whether isolated from nature, of viral, bacterial, plant or animal (e.g., mammalian, such as human) origin, or synthetic; whether single-stranded or double- stranded; or whether including naturally or non-naturally occurring nucleotides, or chemically modified. As used herein, "polynucleotides" include single or multiple stranded configurations, where one or more of the strands may or may not be completely aligned with another. The terms "polynucleotide" and "oligonucleotide" shall be generic to polydeoxynucleotides (containing 2-deoxy-D-ribose), to polyribonucleotides (containing D-ribose), to any other type of polynucleotide which is an N-glycoside of a purine or pyrimidine base, and to other polymers in which the conventional backbone has been replaced with a non-naturally occurring or synthetic backbone or in which one or more of the conventional bases has been replaced with a non-naturally occurring or synthetic base. An "oligonucleotide" generally refers to a nucleotide multimer of about 2 to 100 nucleotides in length, while a "polynucleotide" includes a nucleotide multimer having any number of nucleotides greater than 1 , although they are often used interchangeably.

Use of the term "affinity" can include biological interactions and/or chemical interactions. The biological interactions can include, but are not limited to, bonding or hybridization among one or more biological functional groups located on the first biomolecule and the second biomolecule. In this regard, the first (or second) biomolecule can include one or more biological functional groups that selectively interact with one or more biological functional groups of the second (or first) biomolecule. The chemical interaction can include, but is not limited to, bonding among one or more functional groups (e.g., organic and/or inorganic functional groups) located on the biomolecules.

Discussion

In accordance with the purpose(s) of the present disclosure, as embodied and broadly described herein, embodiments of the present disclosure, in one aspect, relate to surface-enhanced Raman spectroscopic (SERS) systems and methods of using the SERS systems to identify and differentiate an analyte. The present disclosure provides, in general, methods and systems for the detection, analysis, and/or differentiation of one or more miRNA. One aspect, among others, provides methods and systems for the identification and differentiation of two or more miRNA using SERS systems including a SERS substrate including an array of nanostructures. In addition, methods of the present disclosure can use chemometric analysis to differentiate between/among two or more miRNA.

As used in the present disclosure, to identify and differentiate is to determine the presence of and distinguish between two or more analytes (e.g., miRNA). Two analytes are distinguishable when there is a measurable and statistically significant difference between their SERS spectra (e.g., a statistically significant difference is about 0.1%, 1%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, or 40% or more difference).

In particular, the SERS system of the present disclosure can be used to identify and differentiate, one or more types of miRNA. The miRNA can be from the same or different family (e.g., let 7 family) of miRNA. In an embodiment, two or more miRNA can be identified and differentiated from one another.

Embodiments of the present disclosure also relate to methods of using the SERS system to detect miRNA in a sample. The SERS system can enhance the detection of miRNA by orders of magnitude (about 10⁸) relative to normal Raman in a reproducible manner and distinguish between/among two or more miRNA using chemometric methods of analysis (e.g., principal component analysis, cluster analysis, partial least squares discriminant analysis (PLS-DA).

Embodiments of the present disclosure include samples selected from the group consisting of: blood, saliva, tears, phlegm, sweat, urine, plasma, lymph, spinal fluid, a cell, a microorganism, a combination thereof, and aqueous dilutions thereof. In an embodiment, the sample is analyzed directly for miRNA. In another embodiment, the RNA isolated from the sample using standard RNA isolation protocols to collect miRNA as aqueous samples (e.g., mirVana miRNA isolation kit commercially available from Ambion).

In a preferred aspect, the analyte is miRNA. miRNA's are small, non-coding RNA's, 19-25 nucleotides (nt) in length, that regulate gene expression through degradation or translation inhibition. miRNA's have key regulatory roles in development, apoptosis, virus replication, and are potentially important biomarkers of disease, particularly cancer.

The SERS system can include those described in Patent Applications having serial numbers 11/495,980; 11/376,661 ; and 11/256,395 to Zhao, et al., each of which is incorporated herein by reference.

In an embodiment, the nanorods can be formed using a modified oblique angle deposition (OAD) technique/system (additional details are described in U.S. Patent Application 2007/0166539, which is incorporated herein by reference). For example, the OAD system can include a two-axis substrate motion system in a physical vapor deposition (PVD) device (e.g., thermal evaporation, e-beam evaporation, sputtering growth, pulsed laser deposition, and the like) that operates at temperatures lower than the melting point of the material used to form the nanorods. In an embodiment, the substrate motion system provides two rotation movements: one is the polar rotation, which changes angle between the substrate surface normal and the vapor source direction, and one is the azimuthal rotation, where the sample rotates about its center axis of rotation (e.g., normal principle axis). Embodiments of the OAD system can include a physical vapor deposition (PVD) device, such as thermal evaporation, e-beam evaporation, molecular beam epitaxy (MBE), sputtering growth, pulsed laser deposition, combinations thereof, and the like, to form the nanorods.

In an embodiment, the nanorods are in an array, and the array of nanorods can be defined as having a distance of about 10 to 30 nm, about 10 to 60 nm, about 10 to 100 nm, about 10 to 150 nm, or about 10 to 200 nm, between each of the nanostructures. Alternatively, the array of nanorods can be defined as having an average density of about 11 to 2500/μm².

The length is the largest dimension of the nanorod and is the dimension extending from the substrate. The nanorod can have a length of about 10 nm to 5000 nm, about 10 nm to 4000 nm, about 10 nm to 3000 nm, about 10 nm to 2000 nm, about 10 nm to 1000 nm, about 10 nm to 500 nm, about 10 nm to 250 nm, about 10 nm to 100 nm, or about 10 nm to 50 nm. The length depends, at least in part, upon the deposition time, deposition rate, and the total amount of evaporating materials. The diameter is the dimension perpendicular to the length. The diameter of the nanorod is about 10 to 30 nm, about 10 to 60 nm, about 10 to 100 nm, or about 10 to 150 nm. One or more of the dimensions of the nanorod could be controlled by the deposition conditions and the materials.

An angle, β, is formed between the nanorod and the substrate. In an embodiment, the angle can be about 40° to 50 °, about 30 ° to 60 °, or about 30 ° to 75 °. The nanorods of each of the embodiments described herein can include an angle, β. The angle will help to promote the SERS "hot spot" sites on the surface, and increase the enhancement of SERS signals of the molecules located in-between the nanorods.

Embodiments of the present disclosure include a rapid, sensitive test for the identification and differentiation of miRNA. In an embodiment, a first miRNA and a second miRNA can be identified and differentiated in less than about one minute. In another embodiment, at least two miRNA can be identified and differentiated in less than about one minute. However, longer time frames can be used to obtain one or more SERS spectra. In another embodiment, a single nucleotide difference between individual miRNA's can be detected.

Embodiments of the present disclosure include a method for identifying and differentiating individual miRNA's comprising: obtaining a unique SERS spectrum for each of two or more miRNA and analyzing the difference in the spectra of each of the miRNA using a chemometric method of analysis. In an embodiment, the method further comprises determining the type of each of the two or more miRNA.

Embodiments of the present disclosure include a method for identifying and differentiating individual miRNAs comprising obtaining a unique SERS spectrum of a first miRNA, obtaining a unique SERS spectrum of a second miRNA, and analyzing the difference in the spectra of the first miRNA and the second miRNA using a chemometric method of analysis. In an embodiment, the first miRNA is in a first sample and the second miRNA is in a second sample. The unique SERS spectrum includes the SERS spectrum uniquely characteristic for the miRNA sequence. Thus, each unique SERS spectrum can be used to identify and differentiate each of the miRNA between/among different samples. In an embodiment, the unique SERS spectrum of a first miRNA is distinguishable from the unique SERS spectrum of a second miRNA. Without being bound by any particular theory, the compositional difference (e.g., percent composition) in the miRNA is at least one factor that accounts for the unique SERS spectra.

Embodiments of the present disclosure include a SERS platform comprising a Ag nanorod array substrate. In an embodiment, an angle, β, is formed between the nanorod and the substrate, wherein the angle can be about 30° to 75°. As stated above, the SERS platform may be fabricated by oblique angle deposition (OAD).

Embodiments of the present disclosure include chemometric methods of analysis selected from the group consisting of: principal component analysis (PCA), cluster analysis, partial least squares discriminant analysis (PLS-DA), and a combination thereof to distinguish between the miRNA.

PCA is a method of data analysis for building linear multivariate models of complex data sets that reduces the dimensionality of large data sets and allows spectral similarities and differences to be easily visualized. These models are developed using orthogonal basis vectors usually called principal components. The PC that contains the greatest variance is labeled PC 1 , while the vector containing the second most variance is termed PC 2, etc. Thus, PCs model the most statistically significant variations in the data set and are primarily used to reduce the dimensionality of the sample matrix prior to the use of clustering methods such as hierarchical cluster analysis (HCA). HCA utilizes the PC scores to produce a dendrogram that semi-quantitatively reveals the similarity of the SERS spectra and displays the possible classification of the samples into their prospective classes. PLS-DA is a supervised classification method that utilizes a priori knowledge of calibration spectra to calculate variables that emphasize spectral differences between classes. PLS-DA builds new axes (called latent variables - LVs) much like PCA. However, unlike PCA, LVs are created to maximize between-group variance and minimize within-group variance to optimize classification of samples into known classes. PLS-DA requires a training data set of spectra to which the sample identity is known to build a classification model that can then be used to predict the identity (i.e., class) of unknown spectra.

Embodiments of the present disclosure include a method for identifying an individual miRNA in a sample comprising applying the sample to a SERS platform, obtaining a unique SERS spectrum for the individual miRNA, and analyzing the unique SERS spectrum to identify the individual miRNA. In an embodiment, the analysis can include chemometric methods.

Embodiments of the present disclosure include a method of identifying and differentiating individual miRNAs comprising the use of SERS, wherein a unique SERS spectra of a first miRNA sample is distinguishable from a second unique SERS spectra of a second miRNA sample. The first and second miRNA samples can be distinguished using chemometric methods of analysis.

EXAMPLES Example 1 Introduction

MicroRNAs (miRNAs) are small (19-25 nucleotides) non-coding RNAs regulating gene expression through mRNA degradation or translation inhibition (Scherr, M.; Eder, M. Curr Opin MoI Ther 2004, 6, 129-135; Bartel, D. P. Ce// 2004, 116, 281-297; Zhang, B.; Wang, Q.; Pan, X. J Cell Physiol 2007 , 210, 279-289, which are herein incorporated by reference for the corresponding discussion). Recent studies have revealed that miRNAs have key roles in regulatory pathways including development, apoptosis, cell proliferation and differentiation, organ development and cancer (Bartel, D. P. Cell 2004, 116, 281-297; Bartel, B. Nat Struct MoI Biol 2005, 12, 569-571 ; Yeung, M. L.; Bennasser, Y.; Jeang, K. T. Curr Med Chem 2007, 14, 191-197; Pfeffer, S.; Voinnet, O. Oncogene 2006, 25, 6211-6219; Nair, V.; Zavolan, M. Trends Microbiol 2006, 14, 169- 175, which are herein incorporated by reference for the corresponding discussion), and it has been suggested that up to 30% of the human genome may be regulated by miRNAs (Lewis, B. P.; Burge, C. B.; Bartel, D. P. Ce// 2005, 120, 15-20, which is herein incorporated by reference for the corresponding discussion).

The recently recognized role of miRNAs in regulating aspects of hematopoietic cell proliferation and differentiation has led researchers to investigate the role of miRNAs in cancer predisposition. It appears that some miRNAs may serve as biomarkers for disease, particularly in the development of some cancers. Expression studies of various tumor types has revealed specific alterations in miRNA profiles, for example, miR-15 and miR-16 are frequently deleted and/or downregulated in B cell chronic lymphocytic luekemia (Calin, G. A.; Croce, C. M. Semin Oncol 2006, 33, 167- 173; Cimmino, A.; Calin, G. A.; Fabbri, M.; lorio, M. V.; Ferracin, M.; Shimizu, M.; Wojcik, S. E.; Aqeilan, R. I.; Zupo, S.; Dono, M.; Rassenti, L.; Alder, H.; Volinia, S.; Liu, C. G.; Kipps, T. J.; Negrini, M.; Croce, C. M. Proc Natl Acad Sci U S A 2005, 102, 13944-13949, which are herein incorporated by reference for the corresponding discussion), and levels of miR-143 and miR-145 are reduced in colorectal cancer (Michael, M. Z.; SM, O. C; van Hoist Pellekaan, N. G.; Young, G. P.; James, R. J. MoI Cancer Res 2003, 1, 882-891 , which is herein incorporated by reference for the corresponding discussion). Recent studies suggest that clusters of miRNAs may also act as potent oncogenes. For example, miRNA clusters on human chromosomes (MIRN17-92) have been found to be regulated by transcription factors, e.g., c-Myc that are over-expressed in many human cancers (Gaur, A.; Jewell, D. A.; Liang, Y.; Ridzon, D.; Moore, J. H.; Chen, C; Ambros, V. R.; Israel, M. A. Cancer Res 2007, 67, 2456- 2468; Hammond, S. M. Cancer Chemother Pharmacol 2006, 58 Suppl 1, s63-68; He, L.; Thomson, J. M.; Hemann, M. T.; Hernando-Monge, E.; Mu, D.; Goodson, S.; Powers, S.; Cordon-Cardo, C; Lowe, S. W.; Hannon, G. J.; Hammond, S. M. Nature 2005, 435, 828-833; Tagawa, H.; Seto, M. Leukemia 2005, 19, 2013-2016, which are herein incorporated by reference for the corresponding discussion). The findings that miRNAs are associated with cancer suggest that miRNA expression profiles may be useful to classify and diagnose human cancers. In fact, cancer-specific miRNA expression patterns have been identified in every cancer analyzed to date (Calin, G. A.; Croce, C. M. Cancer Res 2006, 66, 7390-7394, which is herein incorporated by reference for the corresponding discussion).

Development of analytical methods for rapid and sensitive identification of miRNA is essential for both the discovery of potential biomarkers of disease pathogenesis and the high-throughput detection of miRNA expression profiles for diagnosis of human cancers. Current miRNA diagnostic methods are poorly developed and have limited sensitivity and breadth. In this disclosure, we demonstrate that surface-enhanced Raman scattering (SERS) is capable of rapid identification of miRNAs.

SERS has been previously used in biological sciences and can be applied to the study of viruses, bacteria, proteins, and nucleic acids (Carey, P. R. Biochemical Applications of Raman and Resonance Raman Spectroscopies; Academic Press: New York, 1982, which is herein incorporated by reference for the corresponding discussion). Several examples exist for the interrogation of DNA structure with SERS; however, previous studies did not attempt to differentiate between sequences (Kneipp, K.; Flemming, J. J. MoI. Structure 1986, 145, 173-179; Nabiev, I. R.; Sokolov, K. V.; Voloshin, O. N. J. Raman Spectrosc. 1990, 21, 333-336; Otto, C; Tweel, T. J. J. v.; deMul, F. F. M.; Greve, J. J. Raman Spectrosc. 1986, 17; Suh, J. S.; Moskovits, M. J. Am. Chem. Soc. 1986, 708, 4711-4718; Thornton, J.; Force, R. K. Appl. Spectrosc. 1991 , 45, 1522-1526, which are herein incorporated by reference for the corresponding discussion). This has been due to the lack of reproducibility of SERS substrates. It has been shown that normal Raman signal inherent to viruses can be used for classification of different viruses and strains (Long, D. A. Raman Spectroscopy, McGraw-Hill: New York, 1977, which is herein incorporated by reference for the corresponding discussion). Given that these unique viral Raman signatures are heavily dependent on the nucleic acid sequences, it is likely that Raman is an ideal technique for the classification of different nucleic acids sequences, such as miRNAs. However, normal Raman signals are typically very weak due to small scattering cross-sections, limiting its use as a diagnostic tool where miRNA concentrations are relatively low. Nonetheless, the Raman signal can be significantly enhanced in a closely related technique, SERS, in which the scattering molecule is in close proximity to certain metal surfaces with nanometer-scale surface asperities (Moskovits, M. J. Raman Spectros. 2005, 36, 485- 496, which is herein incorporated by reference for the corresponding discussion). In the case of SERS, the incident light excites local surface plasmon modes in the rough enhancing substrate, which magnify the electromagnetic field experienced by the adsorbate, and therefore enhance the scattering intensity by orders of magnitude (Moskovits, M. J. Raman Spectros. 2005, 36, 485-496, which is herein incorporated by reference for the corresponding discussion).

The attributes (i.e., sensitivity and specificity) of SERS theoretically make it an ideal candidate for miRNA identification and classification; however, there are two challenges that have limited the realization and widespread application of SERS-based biosensors. First, the morphology of the substrate nanostructure is primarily responsible for the enhancement of the Raman scattering. Currently, few fabrication methods exist for creating reproducible, highly ordered arrays of nano-sized features, and the methods that are available are time-consuming, tedious, and require expensive equipment (Felidj, N.; Aubard, J.; Levi, G.; Krenn, J. R.; Salerno, M.; Schider, G.; Lamprecht, B.; Leitner, A.; Aussenegg, F. R. Phys. Rev. B 2002, 65, 075419; Haynes, C. L.; Van Duyne, R. P. J. Phys. Chem. B 2001 , 105, 5599-5611 ; Jensen, T. R.; Schatz, G. C; Van Duyne, R. P. J. Phys. Chem. B 1999, 103, 2394-2401 ; Kahl, M.; Voges, E. Physical Review B 2000, 61, 14078-14088; Liao, P. F.; Stern, M. B. Optical Letters 1982, 7, 483-485, which are herein incorporated by reference for the corresponding discussion). Second, the Raman spectra are complicated to interpret due to the extremely complex structure of miRNAs, which undergo numerous vibrational transitions. Thus, new fabrication and data analysis techniques are required to take advantage of SERS-based detection.

It has recently been shown that oblique angle vapor deposition (OAD) can be used to prepare aligned silver nanorod arrays with surface morphologies required for surface-enhanced Raman scattering (SERS) substrates (Chaney, S. B.; Shanmukh, S.; Zhao, Y.-P.; Dluhy, R. A. Appl. Phys. Lett. 2005, 87, 31908-31910; Shanmukh, S.; Jones, L.; Driskell, J.; Zhao, Y.; Dluhy, R.; Tripp, R. Nano Lett. 2006, 6, 2630-2636; Zhao, Y.-P.; Chaney, S. B.; Shanmukh, S.; Dluhy, R. A. J. Phys. Chem. B 2006, 110, 3153-3157, which are herein incorporated by reference for the corresponding discussion). The OAD method of substrate preparation facilitates the selection of nanorod size, shape, density, alignment, orientation, and composition, while the procedure is reproducible and relatively simple. Furthermore, the flexibility of this technique is advantageous for designing a nanostructured substrate with the greatest surface enhancement.

The similar chemical makeup of miRNAs translates to similar SERS spectra for different miRNA sequences. Chemometric techniques highlight the minute spectral differences and can objectively differentiate between similar spectra. Chemometrics is a well-established field that has been successfully applied to spectroscopy, but typically has been reserved for IR and normal Raman spectra. While there are a few examples of chemometric analysis of SERS data in the literature, they have focused on the identification of bacteria and have yet to be applied to miRNA classification (Jarvis, R. M.; Brooker, A.; Goodacre, R. Faraday Discuss. 2006, 132, 281-292; Jarvis, R. M.; Goodacre, R. Anal. Chem. 2004, 76, 40-47, which are herein incorporated by reference for the corresponding discussion). Application of multivariate analysis to SERS has been limited due to the irreproducibility limitations of SERS as discussed above. With the development of stable OAD fabricated SERS substrates, it is necessary to explore the effectiveness of chemometrics as applied to SERS spectra.

To demonstrate the powerful utility of SERS-based biosensing performed with our OAD prepared substrates, coupled with chemometric analysis of the spectral data, results on the classification of different sequences of miRNA are presented. Two series of experiments were performed. In the first, SERS spectra of five unrelated miRNAs were collected, and the samples were classified using principal component analysis (PCA) and partial least squares discriminate analysis (PLS-DA). In a second study, eight miRNAs from the let-7 family were analyzed to establish the discriminating power of SERS-based sensing. Experimental

Substrate Preparation

The OAD fabrication of aligned silver nanorod arrays, as SERS substrates, has been previously described in detail (Shanmukh, S.; Jones, L.; Driskell, J.; Zhao, Y.; Dluhy, R.; Tripp, R. Nano Lett. 2006, 6, 2630-2636, which are herein incorporated by reference for the corresponding discussion). Glass microscope slides were cut into 1 x 1 cm pieces, carefully cleaned with hot piranha solution (80% sulfuric acid, 20% hydrogen peroxide), and rinsed with Dl water. The substrates were then dried with a stream of N₂(g) before loading into a custom-designed electron beam/sputtering evaporation (E-beam) system (Torr International, New Windsor, NY). Thin films of Ti (20 nm) and Ag (500 nm) were evaporated onto the substrates at an angle normal to the surface at a rate of <1.0 A/s and 3.5-4.0 A/s, respectively. The Ti served as an adhesion layer. The substrates were then rotated with computer controlled motors (Labview) to 86° with respect to the surface normal. Ag nanorods were grown at this oblique angle in which Ag was deposited at a rate of 2.5-3.0 A/s for 2 h. As reported elsewhere, these deposition conditions result in optimal SERS substrates with overall nanorod lengths of ~900 nm, diameters of ~100 nm, and densities of ~13 nanorods/ μm miRNAs

Five unrelated human miRNAs were synthesized and provided as dehydrated samples by Dharmacon (Table 1): hsa-miR-21 , hsa-Iet-7a, hsa-miR-16, hsa-miR-24a, and hsa-miR-133a. Each miRNA was resuspended in diethyl pyrocarbonate (DEPC)- treated water at a concentration of 1 mg/mL The DEPC eliminates RNAse activity. Eight members of the let-7 family were also synthesized and provided at a concentration of 1 mg/mL in water by Dharmacon. The samples and their sequences are given in Table 2.

Table 1. Sequences for the unrelated miRNAs.

Table 2. Sequence for the let-7 family miRNAs. Sequence differences from let-7a are highlighted in bold.

SERS Measurements

SERS spectra were acquired using a Renishaw inVia Raman microscope system. A 785 nm near-IR diode laser was used as the excitation source. The laser was focused into ~12 x 30 μm spot using a 5x objective. The laser power was set to 10%, where the power at the sample surface was measured to be ~120 mW. Extended Scan spectra with a spectral range of 400-1800 cm^"1 were collected using a 10 s exposure.

The miRNA sequences were spotted onto the substrates in 1-μL aliquots and allowed to dry. Each sequence was spotted in triplicate on a single chip, with 3 spectra recorded for each spot, for a total of 9 spectra per sequence on a single chip. To ensure reproducibility of spectra from substrate to substrate, each miRNA was applied to three different substrates. The overall sampling design used to investigate the five unrelated miRNAs is shown in FIG. 1. DEPC-treated water was used as a control. The study of the let-7 family of miRNAs followed a similar sampling design. Data Analysis

SERS spectra of the miRNA were imported into The Unscrambler version 9.6 (CAMO Software AS, Woodbridge, NJ) software, where the first derivative of each spectrum was computed using a nine-point Savitzky-Golay algorithm. The first derivative eliminated the need for manual and subjective baseline-correction of each spectrum. Each derivative spectrum was then normalized with respect to its maximum value such that the values in the vertical axis ranged from -1 to 1. Normalization accounts for slight differences in the enhancement factors provided by each substrate. The normalized first derivative spectra were then imported into MATLAB version 7.2 (The Mathworks Inc., Natick, MA) and analyzed with PCA and PLS-DA using the PLS Toolbox version 4.0 (Eigen Vector Research Inc., Wenatchee, WA). Results

Surface-Enhanced Raman Spectroscopy of the Samples

Measurement of the SERS spectrum was examined for each miRNA. 1.0 μL of miRNA in DEPC-treated water was applied to a SERS substrate, allowed to dry, and the SERS signal was collected for 10 s. FIG. 2 shows a representative raw spectrum for each of the miRNA sequences. The spectrum revealed several features between 400 and 1800 cm^"1. Slight differences in the spectra are observed. For example, the let-7a sample does not have a band at 1400 cm^"1 or 930 cm^"1, and its band at 1048 cm^"1 is greater in intensity than the 1006 cm^"1 band. In contrast, each of the other miRNA samples has bands at 1400 and 930 cm^"1, and the 1048 cm^"1 band is approximately equal to the 1006 cm^"1 band. The SERS bands have not been assigned to specific molecular vibrations in this study. Spectral interpretation is possible; however, this can be extremely tedious. The chemical composition of miRNA is very similar (i.e., C, G, A, and/or U) and slight variations in base content, orientation, and adsorption to the sensor will result in measurable differences in the SERS spectra. These issues that cause spectral interpretation to be challenging can be exploited using chemometrics to classify miRNAs directly without having to decipher the spectra.

SERS spectra for the eight let-7 miRNA samples were collected, and are shown in FIG. 3. These samples are much more similar than those analyzed in FIG. 2, and this is exemplified in the similarity of their spectra. Each let-7 sample provides the same number of Raman bands located at the same position. Upon close inspection of the raw data, slight variations in the relative intensities of the bands at 796, 731 , and 656 cm^'1 can be observed. However, it is even more apparent in this study that the integration of chemometrics is important for the accurate classification of the samples based on their SERS spectra.

Chemometric Analysis and miRNA Classification

The excellent reproducibility of the OAD method of substrate fabrication is the key factor in facilitating viral strain identification. As discussed above, spectral differences between virus strains are visualized, but manual inspection of each spectrum for classification is not realistic. The reproducible spectra lend themselves to the application of multivariate chemometric methods for classification and sequence identification.

While the SERS bands are consistent for each spectrum, the baseline varies considerably; therefore, the first derivative spectra (9 point Savitzky-Golay) for the miRNA samples were calculated. In these first derivative spectra, the baselines are nearly identical (data not shown). Differences in the peak intensity are related to slight variations in the SEF between locations on the substrate; thus, each spectrum was normalized with respect to its maximum intensity to account for this heterogeneity. The normalized first derivative spectra were then processed using PCA. A PCA model of the data was generated for the 132 spectra of the unrelated miRNAs using the spectral range of 400-1800 cm^"1. The two-dimensional scores plot of PC3 versus PC2 is shown in FIG. 4. The scores plot reveals well-separated clusters for the let-7a and miR-133a samples; however, there is significant overlap in the miR-16, miR-21 , and miR-24a clusters. While PCA is great for reducing large dimensional data and for exploring clusters of like data, it emphasizes the total variance; thus its primary function is not class discrimination.

PLS-DA utilizes PCA to reduce the dimensionality of the data, but this method uses a priori knowledge of a training set to maximize between group variance and minimize within group variance; thus it serves primarily as a method of class discrimination. Two PLS-DA models were created, one for the sample set of unrelated miRNAs, and one for the let-7 miRNAs. The Y-predicted plots for each of the models are presented in FIGS. 5 and 6, respectively. These plots show that all of the spectra are correctly assigned membership to only their class. Summaries of the PLS-DA results for each of these studies are presented in Tables 3 and 4. Table 3. Summary of PLS-DA results (training data and cross validated data) for the unrelated miRNA samples.

Table 4. Summary of PLS-DA results (training data and cross validated data) for the let-7 miRNAs. All spectra (145) have been included.

Discussion

The unrelated miRNA samples provided unique SERS spectra that were distinguishable from the other miRNA samples. To determine a basis for these unique miRNA, the composition of each miRNA sample in terms of base percentage is examined (Table 5). An obvious compositional difference between let-7a and the other four miRNAs is the absence of cytosine in the let-7a sample. It is likely that the absence of cytosine results in a unique SERS spectrum for let-7a. The difference in miR-133a and the other strains is less clear. miR-133a has the greatest percentage of cytosine, however, it is only 13% greater than miR-24a. miR-133a also has the smallest percentage of guanine and adenosine compared to the other samples. PCA of the unrelated miRNAs was only able to cluster the let-7a and miR-133a. None of the three indistinguishable samples have extreme (high or low) percentages of any of the bases. PLS-DA provided a better model for classification of the unrelated sequences and resulted in perfect discrimination. Based on the analysis of this initial investigation, but without being bound by any particular theory, percent composition is a factor that influences the SERS spectrum.

Analysis of the let-7 family of miRNAs suggests that the composition of the sequence (i.e., number of each nucleotide) is a key factor leading to unique spectra. The sequences of the let-7 miRNAs are very similar, with single nucleotide differences for some samples (e.g., let-7a, let-7c, let-7f; see Table 2). Successful classification of each of these samples using PLS-DA confirms that base composition is at least in part responsible for the unique spectra; however, it is not possible to conclude that this is the only factor influencing the spectra.

SERS is an extremely surface-sensitive technique. The molecules closest to the substrate experience the greatest electric field and, as a result, provide the majority of the SERS signal. Additionally, the local environment may affect secondary structure that can affect the SERS bands. These inherent properties of SERS suggest that SERS spectra of miRNA samples with identical composition but different primary structure can also be identified using SERS. Systematic investigations are necessary to determine the impact of miRNA sequence on the SERS signature of miRNA. With this knowledge, the experimental design could be altered, increasing the potential for miRNA classification with SERS spectral fingerprinting. Table 5. Composition of miRNA samples as percentages of base pairs.

Conclusions

There is a crucial need for the development of a rapid, sensitive test for the identification of miRNAs. We have presented the development of a SERS-based biosensor and its application to the rapid detection and differentiation of different miRNAs. We have shown that the OAD fabrication method is capable of economically producing robust, reproducible biosensing SERS substrates which provide extremely high enhancement factors. miRNA samples were directly applied to the OAD prepared substrates without pretreatment, and a SERS viral fingerprint was collected in 10 s. Chemometric methods of data analysis facilitated the classification of the miRNA samples based on spectral differences. We have presented a case in which 5 of the 5 chemically distinct samples were correctly classified as a result of intrinsic SERS spectra, and nearly all of the similar samples were correctly classified. With a more detailed investigation into the mechanism for differentiation, the detection format could be optimized for increased specificity to differences in miRNA sequence. This example demonstrates the power of SERS to differentiate individual miRNAs in less than one minute when coupled to chemometric methods for data analysis.

It should be noted that ratios, concentrations, amounts, and other numerical data may be expressed herein in a range format. It is to be understood that such a range format is used for convenience and brevity, and thus, should be interpreted in a flexible manner to include not only the numerical values explicitly recited as the limits of the range, but also to include all the individual numerical values or sub-ranges encompassed within that range as if each numerical value and sub-range is explicitly recited. To illustrate, a concentration range of "about 0.1 % to about 5%" should be interpreted to include not only the explicitly recited concentration of about 0.1 wt% to about 5 wt%, but also include individual concentrations (e.g., 1%, 2%, 3%, and 4%) and the sub-ranges (e.g., 0.5%, 1.1%, 2.2%, 3.3%, and 4.4%) within the indicated range. The term "about" can include ±1%, ±2%, ±3%, ±4%, ±5%, ±6%, ±7%, ±8%, ±9%, or ±10%, or more of the numerical value(s) being modified. In embodiments where "about" modifies 0 (zero), the term "about" can include ±1%, ±2%, ±3%, ±4%, ±5%, ±6%, ±7%, ±8%, ±9%, ±10%, or more of 0.00001 to 1. In addition, the phrase "about 'x' to 'y'" includes "about 'x' to about 'y'".

It should be emphasized that the above-described embodiments of the present disclosure are merely possible examples of implementations, and are merely set forth for a clear understanding of the principles of the disclosure. Many variations and modifications may be made to the above-described embodiments. All such modifications and variations are intended to be included herein within the scope of this disclosure and protected by the following claims.

Claims

CLAIMS Therefore, at least the following is claimed:

1. A method for identifying and differentiating individual miRNAs comprising: obtaining a unique surface enhanced Raman spectroscopy (SERS) spectrum of a first miRNA; obtaining a unique SERS spectrum of a second miRNA; and analyzing the difference in the spectra of the first miRNA and the second miRNA using a chemometric method of analysis.

2. The method of any of claims 1 and 3-11 , wherein the first miRNA is in a first sample and the second miRNA is in a second sample.

3. The method of any of claims 1-2 and 4-11 , further comprising determining the type of the first miRNA and the second miRNA.

4. The method of any of claims 1-3 and 5-11 , wherein the first miRNA and the second miRNA are in the same family.

5. The method of any of claims 1-3 and 6-11 , wherein the first miRNA and the second miRNA are each in different families.

6. The method of any of claims 1-5 and 7-11 , wherein the unique SERS spectrum of the first miRNA is distinguishable from the unique SERS spectrum of the second miRNA.

7. The method of any of claims 1-6 and 8-11 , wherein a SERS platform comprises a Ag nanorod array substrate.

8. The method of any of claims 1-7 and 9-11 , wherein an angle, β, is formed between the nanorod and the substrate, wherein the angle can be about 30 ° to 75 °.

9. The method of any of claims 1-8 and 11 , wherein the chemometric method is selected from the group consisting of: principal component analysis (PCA), cluster analysis, partial least squares discriminant analysis (PLS-DA), and a combination thereof.

10. The method of claim 9, wherein the chemometric method is PLS-DA.

11. The method of any of claims 1 -10, wherein the first miRNA and the second miRNA are differentiated in less than about one minute.

12. A method for identifying an individual miRNA in a sample comprising: applying the sample to a SERS platform; obtaining a unique SERS spectrum for the individual miRNA; and analyzing the unique SERS spectrum to identify the individual miRNA.

13. The method of any of claims 12 and 14-17, wherein the SERS platform comprises a Ag nanorod array substrate.

14. The method of any of claims 13 and 15-17, wherein an angle, β, is formed between the nanorod and the substrate, wherein the angle can be about 30 ° to 75 °.

15. The method of any of claims 12-14 and 16-17, wherein the unique SERS spectrum of the miRNA is analyzed using a chemometric method of analysis.

16. The method of claim 15, wherein the chemometric method is selected from the group consisting of: principal component analysis (PCA), cluster analysis, partial least squares discriminant analysis (PLS-DA), and a combination thereof.

17. The method of claim 16, wherein the chemometric method is PLS-DA.

18. A method for identifying and differentiating individual miRNA's comprising: obtaining a unique SERS spectrum for each of two or more miRNA; and analyzing the difference in the spectra of each of the miRNA using a chemometric method of analysis.

19. The method of claim 18, further comprising: determining the type of each of the two or more miRNA.