Method for determining viral titer in a sample using Raman spectroscopy

Abstract

A method for quantifying viral titer in a sample using Raman spectroscopy is disclosed. The method includes providing a sample, providing a model for determining viral titer in the sample, illuminating the sample with a light source, and acquiring a Raman spectrum of the sample. The method further involves quantifying the viral titer of the sample by applying the viral component of the Raman spectrum to the model for determining viral titer. Another aspect of the present disclosure relates to a method for generating a model suitable for quantifying viral titer in a sample.

Description

[Technical field]

[0001] This application claims the benefit of priority to U.S. Provisional Patent Application No. 63 / 281,441, filed November 19, 2021, which is incorporated by reference in its entirety herein.

[0002] This invention was made with Government support under R01 GM109988 awarded by the National Institutes of Health. The Government has certain rights in this invention.

[0003] The present disclosure relates to a method for determining virus titer in a sample using Raman spectroscopy. [Background technology]

[0004] The ability to rapidly identify and determine lentiviral titers is important for many biomedical challenges, from gene editing to drug and vaccine development. Lentiviruses are enveloped viruses that have been shown to efficiently deliver genetic information to reprogram cells, which is particularly useful in immunotherapy (Kalos et al., “T Cells with Chimeric Antigen Receptors Have Potent Antitumor Effects and Can Establish Memory in Patients with Advanced Leukemia,” Science Translational Medicine 3(95):95ra73(2011); Brentjens et al., “Eradication of Systemic B-Cell Tumors by Genetically Targeted Human T Lymphocytes Co-Stimulated by CD80 and Interleukin-15,” Nature Medicine 9(3):279-286(2003); Zuffere et al., “Multiply Attenuated Lentiviral Vector Achieves Efficient Gene Delivery In Vivo,” Nat. Biotechnol. 15(9):871-875(1997)). When producing these viruses for therapy, lentiviruses are modified to contain the necessary information to modify cells and also prevent viral replication to prevent unintended infection (Zuffere et al., "Multiply Attenuated Lentiviral Vector Achieves Efficient Gene Delivery In Vivo," Nat. Biotechnol. 15(9):871-875 (1997); Wang et al., "Clinical Manufacturing of CAR T Cells: Foundation of a Promising Therapy," Molecular Therapy-Oncolytics 3:16015 (2016)).This means that each transforming virus can only transform one cell, therefore knowing the effective titer of the transforming virus is important to know the dose and expectations for therapy.

[0005] Current methods to characterize viruses and determine virus titers include ELISA (Wu et al., “Digital Single Virus Electrochemical Enzyme-Linked Immunoassay for Ultrasensitive H7N9 Avian Influenza Virus Counting,” Analytical Chemistry 90(3):1683-1690(2018)), PCR (Carr et al., “Development of a Real-Time RT-PCR for the Detection of Swine-lineage Influenza A(H1N1)Virus Infections,” Journal of Clinical Virology 45(3):196-199(2009); Pivert et al., “A First Experience of Transduction for Differentiated HepaRG Cells using Lentiviral Technology,” Scientific Reports 9(1):12910(2019)), and cell culture (Gueret et al., “Rapid Titration of Adenoviral Examples of methods include "Infectivity by Flow Cytometry in Batch Culture of Infected HEK293 Cells," Cytotechnology 38(1-3):87-97(2002)). Although these methods are reliable for detecting and quantifying viruses, they have some drawbacks. These methods often involve infecting a known number of cells and then performing an analysis such as PCR to determine successful reprogramming. The assays and cell culture can be time consuming when considering incubation times and require significant sample preparation. This means that these methods can take days to weeks to provide results. The ability to rapidly characterize these viruses is important in drug and vaccine development.

[0006] SERS utilizes plasmonic metal nanostructures to enhance the Raman signal of an analyte, thus providing a molecular fingerprint based on the vibrational modes of the analyte (Kneipp et al., “Ultrasensitive Chemical Analysis by Raman Spectroscopy,” Chemical Reviews 99(10):2957-2976(1999); Moskovits, M., “Surface-Enhanced Spectroscopy,” Reviews of Modern Physics 57(3):783-826(1985); Stiles et al., “Surface-Enhanced Raman Spectroscopy,” Annual Review of Analytical Chemistry 1(1):601-626(2008)).SERS has demonstrated the ability to detect virus particles and provide molecular fingerprints based on the composition of the virus (Shanmukh et al., “Rapid and Sensitive Detection of Respiratory Virus Molecular Signatures Using a Silver Nanorod Array SERS Substrate,” Nano Letters 6(11):2630-2636(2006); Lim et al., “Identification of Newly Emerging Influenza Viruses by Surface-Enhanced Raman Spectroscopy,” Analytical Chemistry 87(23):11652-11659(2015); Dardir et al., “SERS Nanoprobe for Intracellular Monitoring of Viral Mutations,” Journal of Physical Chemistry C 124(5):3211-3217(2020); Paul et al., “Bioconjugated Gold Nanoparticle Based SERS Probe for Ultrasensitive Identification of Mosquito-Borne Viruses Using Raman Fingerprinting,”Journal of Physical Chemistry C 119(41):23669-23675(2015), Verduin et al., “RNA-Protein Interactions and Secondary Structures of Cowpea Chlorotic Mottle Virus for In Vitro Assembly,”Biochemistry 23(19):4301-4308(1984)).Different influenza strains have been distinguished by differences in SERS spectra arising from different surface proteins on the viral envelope (Lim et al., “Identification of Newly Emerging Influenza Viruses by Surface-Enhanced Raman Spectroscopy,” Analytical Chemistry 87(23):11652-11659(2015)). SERS can also distinguish between adenovirus, HIV, and rhinovirus particles based on signal changes arising from the different nucleic acids and amino acids that make up each virus (Shanmukh et al., “Rapid and Sensitive Detection of Respiratory Virus Molecular Signatures Using a Silver Nanorod Array SERS Substrate,” Nano Letters 6(11):2630-2636(2006)).Many SERS studies of viruses have used aptamers (Dardir et al., “SERS Nanoprobe for Intracellular Monitoring of Viral Mutations,” Journal of Physical Chemistry C 124(5):3211-3217(2020); Negri et al., “Identification of Virulence Determinants in Influenza Viruses,” Analytical Chemistry 86(14):6911-6917(2014)) or antibodies (Paul et al., “Bioconjugated Gold Nanoparticle Based SERS Probe for Ultrasensitive Identification of Mosquito-Borne Viruses Using Raman Fingerprinting,” Journal of Physical Chemistry C 119(41):23669-23675(2015); Driskell et al., “Low-Level Detection of Viral Pathogens by a This involves the development of a SERS-based assay by functionalizing nanostructures with a surface-enhanced Raman scattering based immunoassay, “Analytical Chemistry 77(19):6147-6154 (2005).”

[0007] The present disclosure is directed to overcoming current deficiencies in determining viral titers. Summary of the Invention

[0008] A first aspect of the present disclosure is directed to a method of quantifying viral titer in a sample using Raman spectroscopy. The method includes providing a sample, providing a model for determining viral titer in the sample, illuminating the sample with a light source, and acquiring a Raman spectrum of the sample. The method further involves quantifying the viral titer of the sample by applying the viral component of the Raman spectrum to the model for determining viral titer.

[0009] Another aspect of the present disclosure is directed to a method for generating a model suitable for quantifying virus titer in a sample. The method includes providing two or more samples, each containing a known virus type and a corresponding titer, subjecting each sample to Raman spectroscopy to generate a reference spectrum for each known virus type and titer, identifying virus type-specific components from the reference spectra, and determining a score for the components for each sample of known virus type and known titer. The method further includes generating a model for quantifying virus titer based on the score.

[0010] The use of SERS was investigated to provide a simpler and faster approach to determine viral titer. SERS was used to detect and differentiate viral particles in formulation media. Two viral particle types, one containing an inserted gene and one without, were analyzed at various concentrations. Multivariate curve resolution (MCR) was then used to differentiate the spectra and determine the viral titer of the particles with the inserted gene. This was performed in solution using both silver and gold substrates at different excitation wavelengths. Spectral differences resulting from the use of different substrates and excitation wavelengths were also investigated.

[0011] As shown in the accompanying examples, SERS provides a rapid approach to determine viral titers with less sample preparation than current methods. While SERS has already been shown to distinguish between viral types and strains, the methodology described herein allows for viral titers to be determined using direct SERS measurements. Results are presented using lentiviral particles, but this same methodology can be applied to other viral types. This technique can also be used to quantify modifications to the viral genome. [Brief description of the drawings]

[0012] [Figure 1] Schematic of the experimental setup. Two viral particle types were analyzed by SERS. One particle contained a vector encoding green fluorescent protein (GFP) and one did not. The SERS spectra were analyzed using multivariate curve resolution (MCR) to determine the component spectrum arising from the GFP vector. This component was then used to determine the viral titer of the GFP-encoded particles. [Figure 2A] Shown are SERS spectra acquired for LentiArray CRISPR negative control lentivirus with (grey) and without (black) GFP (FIG. 2A) and LentiArray CRISPR negative control lentivirus with different concentrations of GFP (FIG. 2B) at 50,000 TU / mL on a gold substrate. The shaded areas indicate the standard deviation across all spectra obtained at each concentration. [Figure 2B] Shown are SERS spectra acquired for LentiArray CRISPR negative control lentivirus with (grey) and without (black) GFP (FIG. 2A) and LentiArray CRISPR negative control lentivirus with different concentrations of GFP (FIG. 2B) at 50,000 TU / mL on a gold substrate. The shaded areas indicate the standard deviation across all spectra obtained at each concentration. [Diagram 3]1 shows SERS spectra of LentiArray CRISPR negative control lentivirus without GFP at various concentrations. The shaded area indicates the standard deviation across all spectra acquired on a gold substrate. [Figure 4] FIG. 13 is a graph showing average SERS spectra of LV-MAX medium acquired on a gold substrate with standard deviations outlined. [Figure 5A] Figure 5A shows the MCR model of the LentiArray CRISPR negative control lentivirus with GFP developed using spectra acquired at 500TU / ml, 5,000TU / mL, and 50,000TU / mL. The model was validated using a spectrum at 10,000TU / mL. A spectrum of water was subtracted from each concentration prior to MCR analysis. Figure 5A is a graph showing the spectra of the three component loadings of the model. [Figure 5B] Figure 5B is a graph showing the score of each spectrum in component 2 plotted against component 3. The score in component 2 shows a relationship with the viral titer. [Figure 5C] FIG. 5C is a graph showing the mean score for each concentration plotted against the virus titer. [Figure 5D] FIG. 5D is a bar graph of the average score for component 2 of lentiviral particles without GFP (50,000 TU / mL) and storage medium of the particles plotted compared to the average score for particles without GFP (50,000 TU / mL). [Figure 6] FIG. 13 shows the average SERS spectrum of the highest concentration of lentiviral particles with GFP (black) and the component spectra of the MCR model used to quantify the particles with GFP (grey). [Figure 7] Graph showing viral titer determination by cellular fluorescence. Inset shows fluorescence image overlaid on bright field image for each concentration analyzed. [Figure 8A]Analysis of LentiArray CRISPR negative control lentivirus with and without GFP using a silver SERS substrate and 532 nm laser excitation. Figure 8A is a graph showing the average SERS spectra of lentivirus with (grey) and without (black) GFP at 50,000 TU / mL. [Figure 8B] All spectra acquired at this concentration were used to develop a three-component MCR model, and Figure 8B is a graph showing the component loadings of this MCR model. [Figure 8C] Figure 8C is a three-dimensional plot of the scores for each of the three components. Only spectra of particles containing GFP scores high in component 3. [Figure 8D] FIG. 8D is a graph showing a comparison of component spectra resulting from a GFP vector obtained using silver (black) and gold (grey) substrates. [Figure 9] FIG. 1 shows SERS spectra collected from a lentiviral particle encoding GFP (top), an identical lentiviral particle but without the GFP gene (middle), and a lentiviral particle with no RNA within the capsid (empty capsid, bottom). Spectroscopic differences are observed that can be correlated with the gene content present in the virus. All spectra are collected using 785 nm excitation with a gold SERS substrate. [Figure 10A] 10A shows the MCR model for the evaluation of modified lentiviral particles designated JLV1, and FIG 10B is a graph showing the average SERS spectra for different concentrations of JLV1. [Figure 10B] FIG. 10B is a three-dimensional plot of the scores for each of the three components of the JLV1 sample. [Figure 10C] FIG. 10C is a graph showing the component loadings of this MCR model. [Figure 10D] FIG. 10D shows the average score for each sample for component 1 (10 3 -10 6 TU / mL). [Figure 10E]Figure 10E is a calibration curve for determining the viral titer of JLV1 ranging from 10 to 10 TU / mL. [Figure 10F] Figures 10F-H are graphs showing a comparison of spectral component scores for a JLV1 sample with two components plotted against each other: component 1 vs. component 2 (Figure 10F), component 1 vs. component 3 (Figure 10G), and component 2 vs. component 3 (Figure 10H). [Figure 10G] Figures 10F-H are graphs showing a comparison of spectral component scores for a JLV1 sample with two components plotted against each other: component 1 vs. component 2 (Figure 10F), component 1 vs. component 3 (Figure 10G), and component 2 vs. component 3 (Figure 10H). [Figure 10H] Figures 10F-H are graphs showing a comparison of spectral component scores for a JLV1 sample with two components plotted against each other: component 1 vs. component 2 (Figure 10F), component 1 vs. component 3 (Figure 10G), and component 2 vs. component 3 (Figure 10H). DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] definition Before describing the method, it should be understood that the present invention is not limited to the specific methodology described, as these may vary. It should also be understood that the terms used herein are for the purpose of describing only the specific versions or embodiments, and are not intended to limit the scope of the embodiments herein, which are limited only by the appended claims. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the embodiments of the present invention, the preferred methods, devices, and materials are described herein. All publications mentioned herein are incorporated by reference in their entirety. Nothing herein should be construed as an admission that the embodiments of the present invention are not entitled to antedate such disclosure by prior invention.

[0014] It should be noted that as used in this specification and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise.

[0015] Unless otherwise stated, any numerical value, such as a concentration or concentration range described herein, should be understood in all instances to be modified by the term "about." Thus, numerical values ​​typically include ±10% of the recited value, or more specifically, ±5% of the recited value, or ±3%, ±2%, or ±1% of the recited value. For example, a concentration of 1 mg / mL includes 0.9 mg / mL to 1.1 mg / mL. Similarly, a concentration range of 1% to 10% (w / v) includes 0.9% (w / v) to 11% (w / v). As used herein, the use of numerical ranges expressly includes all individual numerical values ​​within that range, including all possible subranges, integers within that range, and fractions of values, unless the context clearly indicates otherwise.

[0016] Unless otherwise indicated, the term "at least" preceding a series of elements should be understood to refer to every element in the series. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the invention described herein. Such equivalents are intended to be encompassed by the present invention.

[0017] As used herein, the terms "comprise," "includes," "included," "including," "having," "having," "containing," "containing," or any other variation thereof, are understood to imply the inclusion of a specified integer or group of integers but not the exclusion of any other integer or group of integers, and are intended to be non-exclusive or open-ended. For example, a composition, mixture, process, method, article, or device that includes a list of elements is not necessarily limited to only those elements and may include other elements not expressly listed or inherent to such composition, mixture, process, method, article, or device. Furthermore, unless expressly stated to the contrary, "or" refers to an inclusive or, not an exclusive or. For example, condition A or condition B is satisfied by any one of the following: A is true (or present) and B is false (or absent), A is false (or absent), B is true (or present), and both A and B are true (or present). As used herein, the conjunction term "and / or" between multiple listed elements is understood to encompass both individual and combined options. For example, when two elements are joined by "and / or", the first option refers to the applicability of the first element without the second element. The second option refers to the applicability of the second element without the first element. The third option refers to the applicability of the first and second elements together. Any one of these options is understood to be within the meaning and thus meets the requirements of the term "and / or" as used herein. The simultaneous applicability of multiple options is also understood to be within the meaning and thus meets the requirements of the term "and / or".

[0018] As used herein, the term "consists of," or variations such as "consist of" or "consisting of," as used throughout this specification and claims, indicates the inclusion of any recited integer or group of integers, but indicates that additional integers or groups of integers cannot be added to the specified method, structure, or composition.

[0019] As used herein, the term "consists essentially of," or variations such as "consist essentially of" or "consisting essentially of," as used throughout the specification and claims, refers to the inclusion of any recited integer or group of integers, and refers to the inclusion of any recited integer or group of integers that do not materially change the basic or novel characteristics of the specified method, structure, or composition.

[0020] It should also be understood that the terms "about," "approximately," "generally," "substantially," and the like, used herein when referring to dimensions or characteristics of preferred inventive components, indicate that the described dimensions / characteristics are not precise boundaries or parameters, as would be understood by one of ordinary skill in the art, but do not exclude minor variations that are functionally the same or similar. At a minimum, such references involving numerical parameters include variations that do not alter the least significant digit using mathematical and industrial principles accepted in the art (e.g., rounding, measurement, or other systematic errors, manufacturing tolerances, and the like).

[0021] How to quantitate viral titer in a sample A first aspect of the present disclosure is directed to a method of quantifying viral titer in a sample using Raman spectroscopy. The method includes providing a sample and providing a model for determining viral titer in the sample. The sample is illuminated with a light source and a Raman spectrum of the sample is obtained. The method further involves quantifying the viral titer of the sample by applying the viral component of the Raman spectrum to the model for determining viral titer.

[0022] Viruses are submicroscopic infectious agents (typically smaller than bacteria) that can only replicate inside the living cells of another organism. Viruses can have RNA or DNA-based genomes. According to the methods described herein, viruses encompass any naturally occurring virus, modified virus, or viral vector. Thus, while the viral titer of any wild-type virus can be assessed according to the present disclosure, it will be understood that the utility of the methods disclosed herein extends to assessing the viral titer of mutant or modified viruses (e.g., viruses that contain one or more nucleic acid substitutions, insertions, deletions, or translocations compared to wild-type or naturally occurring viruses, or that are missing a large portion of the genetic material that codes for viral proteins) or viral vectors.

[0023] The viral titer determined according to the method disclosed herein is described in the context of detecting viral particles. A virus particle or viral particle is a virus that is independent of its host (i.e., not found in a cell), but contains the viral genome and the viral capsid (or viral envelope). The method described herein is also suitable for determining the titer of virus-like particles. Virus-like particles are small particles that contain certain proteins from the viral envelope, but do not contain any or all of the genetic material from the virus and cannot cause infection. Virus-like particles (VLPs) can be naturally occurring or can be synthesized by individual expression of viral structural proteins that self-assemble into virus-like structures. Synthetic virus-like particles can also include liposomes or polymer particles that have been modified to display and / or contain viral proteins, such as viral structural proteins. For the purposes of this disclosure, when reference is made to determining the titer of viral particles, this should be understood to encompass determining the titer of all forms of viral and virus-like particles, i.e., naturally occurring VLPs, synthetic VLPs, liposome-based VLPs, polymeric particle VLPs, etc.

[0024] In some embodiments, the viral titer of the sample is determined in a sample that contains one or more types of viral particles.In some embodiments, the sample contains two or more types of viral particles, for example, two types of viral particles, three types of viral particles, four types of viral particles, five types of viral particles, or six or more types of viral particles.

[0025] In any embodiment, two or more virus particle types differ by one or more genetic elements.For example, two or more virus particle types in a sample can differ by one or more gene insertions, substitutions, translocations, or deletions in the virus particle genome.In some embodiments, two or more virus particles differ by gene insertion, for example, the insertion of an exogenous gene or part thereof into the virus particle genome.One or more genetic differences, which can also cause one or more protein differences between virus particle types, provide the basis for identifying virus type-specific components that are useful for quantifying virus titer, as described herein.

[0026] The methods disclosed herein are also suitable for determining the titer of viral vectors in a sample. Viral vectors are modified, non-infectious forms of viruses that are commonly used to introduce genetic material into target cells (e.g., genes for therapeutic applications). Thus, viral vectors have particular utility, such as in gene therapy, cell therapy, or other molecular applications, and their production is central to the gene therapy and cell therapy industries. In some embodiments, the viral titer of a sample is determined in a sample that contains one or more viral vectors. In some embodiments, a sample contains two or more viral vectors, such as two viral vectors, three viral vectors, four viral vectors, five viral vectors, or six or more viral vectors.

[0027] In any embodiment, the titer of the viral vector produced by the packaging cell line can be monitored or evaluated by the methods disclosed herein. In the field of gene therapy and cell therapy, it is particularly important to be able to measure the titer produced with high sensitivity so that the production process, such as production from a producer cell line, can be accurately monitored and controlled. Thus, the virus does not need to be fully functional or wild type to be monitored or evaluated by the methods disclosed herein.

[0028] The term "viral titer" refers to the amount of virus (i.e., viral particles, VLPs, and / or viral vectors) present in a given volume. Any type of viral titer can be assessed in the present invention, for example, physical viral titer, functional viral titer (also referred to as infectious viral titer), or transducing viral titer can be assessed.

[0029] In certain embodiments, physical viral titer may be assessed. Physical viral titer is a measure of the concentration of viral particles in a sample, and is usually based on the presence of viral proteins or viral nucleic acids, i.e., viral components. Physical titer may be expressed as viral particles per mL (VP / mL), viral genomes per mL (vg / mL), viral copies per mL, or RNA copies per mL, and may be determined using the methods described herein. Physical titering does not necessarily distinguish between empty or defective viral particles and particles that can infect cells. Thus, physical viral titer may be distinguished from functional titer or infectious titer, which determines how many produced particles are capable of infecting cells, and transducing viral titer, which determines how many functional viral particles contain the gene of interest (e.g., for viral vector production, transducing viral titer may be relevant). Thus, determining physical titer is not equivalent to determining functional titer, unless all particles in the sample are functional. In fact, functional titers are often 100-1000 times lower than physical titers.

[0030] Alternatively, functional titer or infectious titer may be measured or assessed in the present invention, which is a measure of the amount of viral particles present in a particular volume that can infect target cells. Functional titer may be expressed as plaque forming units per mL (pfu / mL), infectious units per mL (ifu / mL), or transfection units per mL (TU / mL).

[0031] Transduction titer is a measure of the amount of viral particles present in a particular volume that can infect target cells and contain the gene of interest. Transduction titer can be expressed as transduction units / mL and can be evaluated using the assays described herein. Those skilled in the art will understand that functional titer or transduction titer can be determined by scaling down any value obtained for physical titer. As mentioned above, the fold difference between physical titer and functional titer or transduction titer is well understood in the art. Thus, in one aspect of the invention, functional titer or transduction titer can be determined indirectly by the method of the invention (e.g., through scaling down the value obtained for physical titer). Thus, the method of the invention can include an additional step of scaling down the determination of physical titer to determine functional titer or transduction titer.

[0032] The method of the present invention can monitor or evaluate virus titer. Thus, the method of the present invention can determine virus titer, e.g., the level, amount, or concentration of virus particles present in a sample. Thus, the method can determine, in particular, whether the level, amount, or concentration of virus increases or plateaus over time (e.g., by assaying samples at different time points), or fluctuates (e.g., increases, decreases, or is similar) compared to different samples (e.g., assayed at the same or similar time points). In this way, the methods disclosed herein can be used, for example, to evaluate the efficiency of a virus production method (e.g., detection or determination of high levels of virus can indicate an efficient method, while low levels of virus can indicate a suboptimal production method), or to determine the importance of certain factors in a virus production method, for example, by comparing the virus titer measured by comparing with the virus titer measured in other modified production methods (for the same or different viruses).

[0033] The methods of the present invention can also be used to evaluate any process downstream of the virus production process, for example, to determine whether any such process has affected the virus titer. In any embodiment, the methods disclosed herein are suitable for evaluating purification methods, which can be used to determine whether such purification methods have any effect on titer, for example, whether the titer increased, decreased, or remained the same after such purification compared to the virus titer present in the sample before purification. The methods disclosed herein can further be used to evaluate large-scale production of viruses, for example, viral particles, VLPs, or viral vectors, which can be particularly important for the production of viral particles or vectors for gene therapy.

[0034] The methods disclosed herein can determine the increase or decrease in the viral titer of one sample relative to another sample. In any embodiment, the methods disclosed herein are suitable for determining an increase of about 1%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% of the viral titer to which the measurements are compared, and a decrease of about 1%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% of the viral titer to which the measurements are compared. Comparable viral titers can be within 5% of the viral titers to which the measurements are compared.

[0035] In this regard, it will be understood that for some purposes, it may be desirable to assess the viral titer before performing the method, and after and / or during the method, to determine whether any changes or fluctuations have occurred in the viral titer. The methods disclosed herein may further comprise a step of comparing the viral titer, for example, with the viral titer in different samples (at the same or different time points), or in the same sample at different time points. In another embodiment, the method may be used to determine the extent of viral infection in a subject, for example, to determine whether the infection has been successfully treated or reduced. In such a method, it may be desirable to compare the viral titer in samples, for example, samples of the same type from a subject at different time points, to determine whether the viral titer increases, decreases, or remains the same over time. Alternatively or additionally, it may be desirable to compare the viral titer in a sample from an individual with a viral titer measurement previously obtained for a condition, which may indicate, for example, the stage of infection and / or prognosis.

[0036] Alternatively, the methods disclosed herein may not determine the actual amount, level, or concentration of virus in a sample, but may determine whether the amount, level, or concentration is above or below an acceptable threshold, e.g., for a production method, the threshold may determine whether an acceptable level of virus particles is present in the sample. It will be understood that the methods disclosed herein may determine whether the level of virus titer is increased, decreased, or equivalent to a previously assayed sample, and thus, in certain applications, it may not be necessary to determine the actual virus titer (e.g., the amount or concentration of virus present).

[0037] According to the method of determining the viral titer in a sample described herein, suitable samples include any sample that contains or is expected to contain one or more viral particles, VLPs, or viral vectors. The viral titer in a sample can be measured in situ, in real time, by Raman spectroscopy, or can be performed ex situ on the sample.

[0038] By "in situ" it is meant that the measurement to obtain the intensity of Raman scattered light in a culture capable of producing viral particles is made from the primary culture environment in which the viral particles are produced, and not from a sample extracted from the primary culture environment. Thus, by making the measurement "in situ", there is no need for a liquid handling step. Thus, removal of the sample from its environment may not be required for certain applications of the invention, and in situ measurement of the sample may be preferred. In situ measurement of the sample may allow periodic evaluation of the viral titer in the sample without the need for an actual sampling step in which a portion of the sample is removed. The viral titer evaluation in this respect may be measured accurately and sensitively in real time, without the need for additional steps that may introduce costs and errors.

[0039] Alternatively, the methods disclosed herein may be performed ex situ on a sample. By "ex situ" is meant that the measurement to obtain the intensity of Raman scattered light in a culture capable of producing viral particles is performed from a sample extracted from the primary culture environment in which the viral particles are produced after one or more liquid handling steps. In certain embodiments, the methods disclosed herein may include a step of sampling.

[0040] The origin of the sample used in the methods described herein may be a cell culture in which the virus is produced. Thus, the sample may be one of the culture media (e.g., DMEM, MEM, SFII, LVMAX, Texmacs, or PBS, optionally containing serum, L-glutamine, and / or other components), which may additionally contain packaging cells, for example, if collected during the virus production process. Alternatively, the sample may be a partially purified sample or a cell-free sample obtained during virus harvesting. The sample may be, for example, a sample virus for medical use, which requires quality testing before marketing, sale, or use. The sample may also be a sample from a subject (e.g., a human or mammalian subject) suspected of being infected by a virus. Thus, the sample may be a biological sample, such as a blood, saliva, sputum, plasma, serum, cerebrospinal fluid, urine, or fecal sample. Other sample sources include those from open water or public water supplies.

[0041] The method of determining virus titer in a sample described herein involves illuminating the sample with a light source and acquiring a Raman spectrum of the sample. Raman spectroscopy measures the change in wave number of monochromatic light scattered by samples to provide information about their chemical composition, physical state, and environment. This is possible due to the way incident light quanta interact with vibrational modes present in the molecules that comprise the sample. These modes have specific vibrational frequencies and scattering intensities under a given set of physical conditions, which allows the amount of a given analyte of interest to be quantified. Unlike infrared absorption spectroscopy, in which the absorption of light of different energies from a broadband light source is measured, Raman spectroscopy measures the difference in energy of monochromatic incident light relative to scattered light, which is known as the Raman shift.

[0042] Raman spectra provide a "molecular fingerprint" that allows qualitative and quantitative analysis of a sample by providing information about the vibrational states of the molecule. Many molecules have atomic bonds that can exist in many vibrational states. Such molecules can absorb incident radiation that coincides with a transition between two of its allowed vibrational states and subsequently emit radiation. Most frequently, the absorbed radiation is re-emitted at the same wavelength, a process designated Rayleigh or elastic scattering. In some cases, the re-emitted radiation can contain slightly more or slightly less energy than the absorbed radiation (depending on the allowed vibrational states of the molecule and the initial and final vibrational states). The result of the energy difference between the incident and re-emitted radiation appears as a shift in wavelength between the incident and re-emitted radiation, and the degree of difference is designated the Raman shift (RS), measured in units of wavenumbers (reciprocal length). When the incident light is substantially monochromatic (single wavelength), as in the case of using a laser source, scattered light of differing frequencies can be more easily distinguished from Rayleigh scattered light.

[0043] In any embodiment, the Raman spectroscopy utilized in accordance with the methods disclosed herein is surface-enhanced Raman spectroscopy (SERS), a surface-sensitive technique that enhances Raman scattering by molecules adsorbed on rough metal surfaces (e.g., rough silver or gold surfaces) or by nanostructures such as plasmonic-magnetic silica nanotubes on a substrate.

[0044] In any embodiment, the application of SERS in the method of determining virus titer described herein is carried out using a planar (or flat) substrate, such as a silicon, quartz, or glass substrate. Planar substrates can also be made of materials including, but not limited to, semiconductors (e.g., Si, GaAs, GaAsP, and Ge), oxides (e.g., SiO2, Al2O3), and polymers (e.g., polystyrene, polyacetylene, polyethylene, etc.). In another embodiment, the substrate is a non-planar substrate, such as a cylindrical or conical substrate (e.g., an optical fiber or a pipette tip). The substrate can be a microfabricated or nanofabricated substrate, such as a substrate with a regular array, such as a micropattern, such as a dot array, a line array, or a well array, or a similar nanopattern. In one embodiment, SERS according to the method described herein is carried out using a metal substrate, and the metal is selected from gold, silver, copper, and platinum, and alloys thereof. In one embodiment, SERS for determining virus titer is carried out on a gold substrate. In another embodiment, SERS for determining virus titer is carried out using a silver substrate.

[0045] In any embodiment, the application of SERS in the methods disclosed herein is carried out using a substrate modified to contain nanostructures. Suitable nanostructures include, but are not limited to, nanorods, nanowires, nanotubes, nanospirals, nanospheres, nanotriangles, nanostars, combinations thereof, etc., and uniform arrays of each. Nanostructures (of the above types) may be made of one or more materials, such as, but not limited to, metals, metal oxides, metal nitrides, metal oxynitrides, metal carbides, doped materials, polymers, multicomponent compounds, compounds (e.g., compounds or precursor compounds, or organic or inorganic compounds), and combinations thereof. Metals may include, but are not limited to, silver, nickel, aluminum, silicon, gold, platinum, palladium, titanium, copper, cobalt, zinc, other transition metals, composites thereof, oxides thereof, nitrides thereof, silicides thereof, phosphides thereof, oxynitrides thereof, carbides thereof, and combinations thereof. In any embodiment, the material is silver or gold. In any embodiment, the composition of the nanostructures is the same as the composition of the substrate material. In any embodiment, the composition of the nanostructures is different from the substrate material.

[0046] Many commercially available SERS substrates can be used in carrying out the methods of the present disclosure, including, but not limited to, various SERS substrates available from Silmeco ApS (Copenhagen, Denmark), Horiba Scientific (Piscataway, NJ), Ocean Optics (Orlando, FL), Ato ID (Vilnius, Lithuania), Enhanced Spectrometry (San Jose, CA), and SERSitive (Warsaw, Poland). Alternatively, those skilled in the art will understand that customized SERS substrates can be used alternatively, and these can be customized with respect to nanostructured materials as well as any surface-bound reagents that facilitate viral particle binding / orientation.

[0047] The disclosed method involves providing a sample for which information about viral titer is desired, introducing the sample onto a substrate or nanostructures on a substrate, illuminating the sample with a light source, and obtaining a Raman spectrum of the sample, which is used to determine the viral titer in the sample. As described above and in the examples, the sample shall be evaluated in undiluted form, using one or more dilutions (including serial dilutions of 10x, 100x, etc.), or both.

[0048] In any embodiment, the light source utilized to illuminate the sample is a narrow bandwidth laser. Suitable wavelengths include, but are not limited to, 300-1200 nm, 350-1100 nm, 400-1100 nm, 400-1064 nm, 450-1064 nm, 500-1064 nm, 550-1064 nm, 600-1064 nm, 650-1064 nm, 700-1064 nm, 450-1100 nm, or 500-1100 nm. In one embodiment, the light source is a narrow bandwidth laser having a wavelength of 550-1064 nm. In one embodiment, the light source is a narrow bandwidth laser having a wavelength of 400-1064 nm.

[0049] In any embodiment, the light source is a narrow bandwidth laser having a wavelength of about 800 nm, about 785 nm, about 750 nm, about 725 nm, about 700 nm, about 675 nm, about 650 nm, about 625 nm, about 600 nm, about 575 nm, about 550 nm, about 532 nm, about 525 nm, or about 500 nm. Preferably, the light source is a narrow bandwidth laser having a wavelength of about 785 nm, about 640 (e.g., 638 nm), or about 532 nm.

[0050] In any embodiment, acquiring a Raman spectrum from a sample according to the methods described herein includes acquiring a spectrum with an exposure time of about 100 ms, 150 ms, 200 ms, 250 ms, 300 ms, 350 ms, 400 ms, 450 ms, 500 ms, 550 ms, 600 ms, 650 ms, 700 ms, 750 ms, 800 ms, 850 ms, 900 ms, 950 ms, 1000 ms, or greater than 1000 ms. In any embodiment, the Raman spectrum is acquired using an exposure time of about 250 ms.

[0051] The Raman signal intensity is directly proportional to the power of the Raman laser used to excite the sample. The higher the laser power used, the greater the Raman signal, and the specific laser power used can be optimized for a specific sample. However, in any embodiment, the spectrum of the sample containing the virus can be obtained using a Raman laser with a power of about 1.9 mW, about 1.8 mW, about 1.7 mW, about 1.6 mW, about 1.50 mW, about 1.4 mW, about 1.3 mW, about 1.2 mW, about 1.1 mW, about 1 mW, about 0.9 mW, about 0.8 mW, about 0.7 mW, 0.6 mW, or 0.5 mW. In any embodiment, the spectrum is obtained with a Raman laser with a power of about 1.50 mW. In any embodiment, the spectrum is obtained with a Raman laser with a power of 0.6 mW.

[0052] The Raman spectrum obtained from illuminating the sample with a light source is then analyzed, and scores for viral components from the Raman spectrum are extracted. The peaks obtained for a particular Raman spectrum at a particular wavelength may correspond to viral particle-specific amino acids (e.g., present in capsid proteins, etc.) or nucleic acids (e.g., RNA or DNA encapsulated by viral particles), or may correspond to molecules / compounds that are non-viral (e.g., metabolites in culture), but result from the presence of the virus therein. In this way, Raman spectroscopy can be used to directly or indirectly detect the presence of virus in a sample, which can then be scored and applied to the corresponding model used to determine the viral titer in the sample. For example, as described herein, a suitable model for determining viral titer according to the methods described herein includes a viral particle-specific calibration curve. Thus, the viral particle-specific components used to generate the model are the viral components analyzed and scored from the Raman spectrum obtained from the experimental sample (i.e., the sample containing an unknown viral titer). Once the scores for viral particle-specific components are extracted from the Raman spectrum, this is applied to the model to quantify the viral particle-specific titer.

[0053] Another aspect of the present disclosure is directed to a method for generating a model suitable for quantifying a viral titer in a sample. The method includes providing two or more samples, each sample containing a known viral type and a corresponding titer, and subjecting each sample to Raman spectroscopy to generate a reference spectrum for each known viral type and titer. The method further includes identifying a viral type-specific component from the reference spectrum and determining a score for that component for each sample of known viral type and known titer. The method further includes generating a model for quantifying the viral titer based on the determined score.

[0054] To generate a model suitable for quantifying the viral titer in a sample, several samples containing known viral types and known amounts of corresponding viral titers are analyzed using Raman spectroscopy, particularly SERS.For some models, two or more samples are analyzed using Raman spectroscopy.To generate other models, three or more samples, four or more samples, five or more samples, six or more samples, seven or more samples, eight or more samples, nine or more samples, ten or more samples, fifteen or more samples, or twenty or more samples are required.

[0055] One or more Raman spectra are collected for each sample containing a known virus type and a known amount of the corresponding virus titer.In some embodiments, one or more spectra are collected for each of these samples.For example, 2 or more, 3 or more, 4 or more, 5 or more, 10 or more, 20 or more, 30 or more, 40 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, 100 or more, 200 or more, 300 or more, 400 or more, or 500 or more spectra are collected for each of these samples.

[0056] According to this aspect of the disclosure, the sample is subjected to Raman spectroscopy as described above. In an optional embodiment, the Raman spectroscopy is surface-enhanced Raman spectroscopy as described above, and the spectrum is obtained from the sample on a metal substrate (e.g., gold or silver) and / or nanostructures as described above.

[0057] In any embodiment, the light source utilized to illuminate the sample to obtain the reference spectrum is a narrow bandwidth laser. Suitable wavelengths include, but are not limited to, 300-1200 nm, 350-1100 nm, 400-1100 nm, 400-1064 nm, 450-1064 nm, 500-1064 nm, 550-1064 nm, 600-1064 nm, 650-1064 nm, 700-1064 nm, 450-1100 nm, or 500-1100 nm. In one embodiment, the light source is a narrow bandwidth laser having a wavelength of 550-1064 nm. In one embodiment, the light source is a narrow bandwidth laser having a wavelength of 400-1064 nm.

[0058] In any embodiment, subjecting each sample to Raman spectroscopy to generate a reference spectrum for each known virus type and titer comprises acquiring a spectrum with an exposure time of about 250 ms, in some embodiments, the spectrum is acquired at about 1.9 mW, about 1.8 mW, about 1.7 mW, about 1.6 mW, about 1.5 mW, about 1.4 mW, about 1.3 mW, about 1.2 mW, about 1.1 mW, about 1 mW, about 0.9 mW, about 0.8 mW, about 0.7 mW, about 0.6 mW, about 0.5 mW, about 0.4 mW, about 0.3 mW, about 0.2 mW, or about 0.1 mW.

[0059] In any embodiment, subjecting each sample to Raman spectroscopy to generate a reference spectrum for each known virus type and titer includes acquiring a spectrum with an exposure time of about 100 ms, 150 ms, 200 ms, 250 ms, 300 ms, 350 ms, 400 ms, 450 ms, 500 ms, 550 ms, 600 ms, 650 ms, 700 ms, 750 ms, 800 ms, 850 ms, 900 ms, 950 ms, 1000 ms, or greater than 1000 ms. In any embodiment, the Raman spectrum is acquired using an exposure time of about 250 ms.

[0060] In any embodiment, subjecting each sample to Raman spectroscopy to generate a reference spectrum for each known virus type and titer comprises acquiring a spectrum using a Raman laser having a power of about 1.9 mW, about 1.8 mW, about 1.7 mW, about 1.6 mW, about 1.50 mW, about 1.4 mW, about 1.3 mW, about 1.2 mW, about 1.1 mW, about 1 mW, about 0.9 mW, about 0.8 mW, about 0.7 mW, about 0.6 mW, about 0.5 mW, about 0.4 mW, about 0.3 mW, about 0.2 mW, or about 0.1 mW. In any embodiment, the spectrum is acquired with a Raman laser having a power of about 1.50 mW or less. In any embodiment, the spectrum is acquired with a Raman laser having a power of 0.6 mW or less.

[0061] The Raman spectra collected from samples containing known virus types and corresponding virus titers of known amounts are then analyzed to identify virus type-specific components from the reference spectra. In some embodiments, the reference spectrum for each sample is generated by analyzing two or more spectra of the sample. Alternatively, the reference spectrum for each sample is generated by analyzing 3 or more, 4 or more, 5 or more, 10 or more, 20 or more, 30 or more, 40 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, 100 or more, 200 or more, 300 or more, 400 or more, or 500 or more spectra of the sample.

[0062] In some embodiments, one or more background Raman spectra are collected. Such background spectra can be collected from a control sample, i.e., a sample that corresponds to the virus sample in the composition but does not contain the virus. For example, a suitable background spectrum can be obtained from a sample of water, culture medium, or other buffer that matches the virus-containing sample in the composition but does not contain the virus. In some embodiments, two or more spectra from the background are averaged to generate a background Raman spectrum. In some embodiments, the background spectrum is subtracted from each of the reference spectra before the spectrum from the virus-containing sample is analyzed.

[0063] In some embodiments, the spectrum collected from the sample is truncated before being analyzed. Preferably, the spectrum is truncated, e.g., at 300 cm -1 ~2000cm -1 , 300cm -1 ~1900cm -1 , 300cm -1 ~1800cm -1 , 300cm -1 ~1700cm -1 , 300cm -1 ~1600cm -1 , 300cm -1 ~1500cm -1 , 350cm -1 ~1700cm -1 , 350cm -1 ~1600cm -1 , 400cm -1 ~1700cm -1 , 400cm -1 ~1600cm -1 , 450cm -1 ~1700cm -1 , 450cm -1 ~1600cm -1 , 500cm -1 ~1700cm -1 , 500cm -1 ~1600cm -1 , 550cm -1 ~1700cm -1, 550cm -1 ~1600cm -1 , 600cm -1 ~1700cm -1 , or 600cm -1 ~1600cm -1 More preferably, the spectrum is truncated to a spectral range of 500 cm -1 ~1600cm -1 The spectral range is cut off.

[0064] To identify virus type-specific components from a reference spectrum (i.e., a spectrum from a sample of known virus type and known titer), a chemometric analysis is used to analyze (decompose) two or more reference spectra generated from each of two or more samples. Using this chemometric analysis, one or more components of variation between the reference spectra are identified and a score for each component is evaluated. Suitable chemometric analyses that can be utilized to analyze the reference spectra generated from a sample are known in the art and include, but are not limited to, multivariate curve resolution, principal component analysis, discriminant analysis (e.g., linear discriminant analysis, partial least squares discriminant analysis), K-means clustering, neural network analysis, regression analysis (e.g., principal component regression analysis, partial least squares regression analysis), and class modeling methods (e.g., soft independent modeling of class similarity) (see, e.g., Biancolillo and Marini, “Chemometric Methods for Spectroscopy-Based Pharmaceutical Analysis,” Front.Chem.6:576 (2018), which is incorporated herein by reference in its entirety). Based on these analyses, one or more spectral components that correlate with a particular virus type (ie, virus type-specific components) are identified and scored.

[0065] The scores of the virus type specific components for each sample containing a known virus type and a known amount of the corresponding virus titer are used to prepare a model suitable for quantifying the virus titer of the unknown virus titer in the test sample or experimental sample. In any embodiment, the model generated is a virus type specific calibration curve or standard curve. Thus, the criteria for selecting one virus particle specific component over another depends on the ability of the component to distinguish the virus particles or empty capsids that are not of interest from the ones of interest as well as the medium in which the virus particles are present. It is also important to check the components for features that are characteristic (i.e., specificity) of the virus particles, empty capsids, or genetic material of interest to ensure that the spectrum obtained looks like the reference spectrum of the virus particles or empty capsid of interest or has the expected characteristics.

[0066] The reference spectrum used to develop a model suitable for quantifying viral titer in a sample is preferably generated from two or more samples containing known target viral type. In some embodiments, the two or more samples contain two or more different viral types, for example, viral types that differ by one or more genetic elements (e.g., gene insertion, deletion, substitution, or translocation). For example, if the model suitable for quantifying viral titer in a sample is suitable for identifying lentiviruses that contain exogenous gene insertions, the reference spectrum used to develop the model is collected from a sample containing a known amount of the target modified lentivirus, and optionally a sample containing a known amount of wild-type lentivirus. This allows the identification of spectral components that are specific to the modified lentivirus compared to wild-type lentivirus, and the generation of a specific calibration curve to determine the titer of the modified lentivirus in a sample.

[0067] As mentioned above, the method of generating a suitable model for quantifying the viral titer in a sample is carried out by utilizing a sample that contains any type of virus of known type and amount.For example, suitable samples include, but are not limited to, samples that contain retrovirus particles, retrovirus-like particles, adenovirus particles, adenovirus-like particles, adeno-associated virus particles, adeno-associated virus-like particles, herpes simplex virus particles, and herpes simplex virus-like particles.In some embodiments, the virus is a retrovirus particle, such as a lentivirus particle. EXAMPLES

[0068] The following examples are intended to illustrate the practice of embodiments of the present disclosure, but are not intended to limit its scope in any way.

[0069] Materials and Methods for Examples 1 and 2 Materials and Reagents: Gold and silver SERS substrates were purchased from Silmeco (SERStrate). Ultrapure water (18.2 MΩcm) was obtained from a Milli-Q system. Fused silica capillaries with an inner diameter of 75.9 μm and an outer diameter of 150.0 μm were purchased from Polymicro Technologies. Hydrochloric acid, LV-MAX production medium, fetal bovine serum (FBS), phosphate-buffered saline (PBS), Eagle's minimum essential medium (EMEM), and human non-targeted, LentiArray CRISPR negative control lentivirus with and without green fluorescent protein (GFP) were purchased from ThermoFisher Scientific. Human HT-1080 fibrosarcoma cells (ATCC CCL-121) were obtained from the American Type Culture Collection (ATCC). Polybrene and isopropanol were purchased from Sigma Aldrich. Orange Tough resin was purchased from Prusa Research.

[0070] Sample preparation: All virus solutions were prepared in LV-MAX production medium. For SERS calibration, solutions of human non-targeted, LentiArray CRISPR negative control lentivirus with and without GFP were prepared at concentrations ranging from 500 TU / mL to 50,000 TU / mL, respectively.

[0071] 3D Printing: A 3D printed SERS substrate holder was generated using an Original Prusa SL1 for use with gold substrates. For silver substrates, a 3D printed flow cell was developed and used. CAD designs were generated in Autodesk Fusion 360 and then sliced ​​using PrusaSlicer software. Orange Tough resin was used for all printed objects. Objects were then washed in isopropanol for 10 minutes, dried for 2 minutes, and cured in an Original Prusa CW1 Curing and Washing Machine for 2 minutes. Objects were then rinsed with Milli-Q water.

[0072] SERS flow cell preparation: The silver and gold substrates were heated on a hot plate at 175° C. for 10 minutes. The gold Silmeco substrate was placed in a 3D-printed SERS substrate holder with a fused silica capillary fixed on top. This substrate holder was then placed in a sheath flow SERS cell as described above in place of a glass slide (Negri et al., “Ultrasensitive Surface-Enhanced Raman Scattering Flow Detector Using Hydrodynamic Focusing,” Analytical Chemistry 85(21):10159-10166 (2003), which is incorporated herein by reference in its entirety). The silver Silmeco substrate was placed in the 3D-printed flow cell, which was then glued together using clear Gorilla glue. Prior to Raman detection, 0.1 M HCl was flowed over the silver substrate to wash away any contaminants remaining on the surface. Water was passed through the flow cell to rinse both the silver and gold surfaces before Raman measurements.

[0073] Raman measurements: Raman spectroscopy was carried out using a home-made instrument. A 785 nm or 532 nm laser (Oxxius) was focused onto the SERS substrate using a 40× water immersion objective (NA=0.8). Raman scattering was collected through the same objective and analyzed with a ProEM:1600 2 The samples were directed to an Isoplane SCT-320 spectrometer equipped with an eXcelon 3 CCD detector (Princeton Instruments). Spectra were acquired at the sample with an exposure time of 250 ms and a laser power of 1.50 mW (785 nm) or 0.60 mW (532 nm). 240 spectra were acquired consecutively per acquisition. For experiments with gold substrates, the virus sample was injected through a fused silica capillary with a water sheath fluid. The sheath and sample flows were stopped before and during SERS spectrum acquisition. After spectrum acquisition, the sheath flow was restarted and the sample flow was switched to water to clean the surface before the next sample was injected.

[0074] Data analysis: All spectra were processed using Matlab R2018b (Mathworks). Multivariate curve resolution (MCR) was performed using the PLS Toolbox (Eigenvector Research Inc.) in Matlab. Prior to MCR analysis, the background spectrum of water on the SERS substrate was subtracted from each lentivirus spectrum, and the spectra were then analyzed at 500 cm. -1 ~1600cm -1 The spectral range was cut to 0.05 μm. The average scores for component 2 were plotted against concentration to generate a calibration curve.

[0075] Lentiviral transduction: HT-1080 cells were cultured in EMEM supplemented with 10% FBS. 10,000 cells were seeded in 100 μL of culture medium per well in a 96-well plate and incubated overnight in a humidified atmosphere of 5% CO2 and at a temperature of 37°C. Human non-targeted, LentiArray™ CRISPR negative control lentivirus with GFP was transduced after thawing on ice for 1 hour. Transduction medium was prepared by adding polybrene to EMEM supplemented with FBS to a final concentration of 8 μg / mL. 4 logarithmic serial dilutions were prepared in triplicate by serially diluting the virus solution in transduction medium. The medium was removed from the cells and 100 μL of each dilution was added to the wells. The plate was swirled for 5 minutes to distribute the virus. The cells were then incubated for 24 hours. The lentiviral transduction medium was then replaced with EMEM supplemented with 10% FBS, and the cells were then incubated for another 24 hours. The medium was then removed from the cells and replaced with PBS before imaging.

[0076] Fluorescence imaging: Cells were imaged on an upright Olympus microscope using a GFP filter cube and a blue LED (Thorlabs) for excitation. Data analysis of brightfield and fluorescent images was performed using ImageJ, and cells were counted using the cell counter plugin. Lentiviral titers were then calculated using the following formula 1:

number

[0077] Example 1: Detection and differentiation of lentiviral particles using gold SERS substrates The experimental design is shown in FIG. 1. SERS spectra from lentiviral particles, either with or without the GFP gene coding, were acquired in LV-MAX virus production medium. Each virus type was injected into the SERS substrate in the medium and measured without flow. By measuring in the medium, minimal sample pretreatment is required. Additionally, the aqueous environment has been shown to improve heat dissipation from excited nanostructures, which allows for improved signal generation without thermal damage to the sample or substrate (Zeng et al., “Photothermal Microscopy of Coupled Nanostructures and the Impact of Nanoscale Heating in Surface Enhanced Raman Spectroscopy,” J. Phys. Chem. C 121(21):11623-11631 (2017), which is incorporated herein by reference in its entirety).

[0078] The spectra obtained from the SERS measurements of the particles were used to generate a multivariate curve resolution (MCR) model that represents the number of particles containing the GFP gene. Prior to SERS acquisition, the flow cell was rinsed with water to clean the surface and capillary, and then the virus sample was injected through the capillary and both the sheath and sample flows were stopped. After SERS acquisition, the surface of the substrate was washed using water. A spectrum of water on the substrate was acquired before analyzing each concentration and then subtracted from the SERS spectrum before analysis to account for the background signal of the substrate.

[0079] SERS spectra of virus particles obtained by injecting lentivirus samples into a sheath flow SERS cell with a gold substrate are shown in Figures 2A-2B. Figure 2A compares the average SERS spectra obtained from viruses with and without the GFP gene at the highest concentration analyzed. The spectrum of each type of virus particle shows Raman bands associated with amino acids and nucleic acids. A common peak shared between the two types of particles is the 813 cm associated with the phosphate backbone extension in RNA. -1(Shanmukh et al., “Rapid and Sensitive Detection of Respiratory Virus Molecular Signatures Using a Silver Nanorod Array SERS Substrate,” Nano Letters 6(11):2630-2636(2006), which is incorporated herein by reference in its entirety)) and the 851 cm -1 (Shanmukh et al., “Rapid and Sensitive Detection of Respiratory Virus Molecular Signatures Using a Silver Nanorod Array SERS Substrate,” Nano Letters 6(11):2630-2636 (2006), which is incorporated herein by reference in its entirety)) and the 1007 cm assigned to phenylalanine. -1 (Ashton et al., “pH-Induced Conformational Transitions in α-Lactalbumin Investigated with Two-Dimensional Raman Correlation Variance Plots and Moving Windows,” Journal of Molecular Structure 974(1-3):132-138(2010), which is incorporated herein by reference in its entirety) and the 1046 cm assigned to cytosine. -1(Shanmukh et al., “Rapid and Sensitive Detection of Respiratory Virus Molecular Signatures Using a Silver Nanorod Array SERS Substrate,” Nano Letters 6(11):2630-2636(2006); Negri et al., “Detection of Genetic Markers Related to High Pathogenicity in Influenza by SERS,” The Analyst 138(17):4877-4884(2013) (which are incorporated herein by reference in their entireties)) -1 (Ashton et al., “pH-Induced Conformational Transitions in α-Lactalbumin Investigated with Two-Dimensional Raman Correlation Variance Plots and Moving Windows,” Journal of Molecular Structure 974(1-3):132-138(2010), which is incorporated herein by reference in its entirety), and the 1475 cm associated with the CH scissor vibrations of glutamic acid and aspartic acid. -1(Negri et al., “Online SERS Detection of the 20 Proteinogenic l-Amino Acids Separated by Capillary Zone Electrophoresis,” The Analyst 139(22):5989-5998 (2014), which is incorporated herein by reference in its entirety.) Despite the similar structures of the two different virus particles, FIG. 2A also shows that there are differences in the SERS signals. Notable differences in the spectra are the 812 and 921 cm for particles with GFP, arising from the phosphate backbone (Shanmukh et al., “Rapid and Sensitive Detection of Respiratory Virus Molecular Signatures Using a Silver Nanorod Array SERS Substrate,” Nano Letters 6(11):2630-2636 (2006), which is incorporated herein by reference in its entirety), and adenine (Suh et al., “Surface-Enhanced Raman Spectroscopy of Amino Acids and Nucleotide Bases Adsorbed on Silver,” J. Am. Chem. Soc. 108(16):4711-4718 (1986), which is incorporated herein by reference in its entirety). -1 These additional bands may arise due to additional RNA in these particles compared to those without GFP. Also included are strong peaks at 1164 cm arising from phenylalanine and tryptophan. -1 Shoulder length: 1141cm -1Intense bands at 1280, 1410, and 1567 cm arising from arginine, histidine, and phenylalanine / tyrosine, respectively, are observed in the spectrum of particles bearing GFP (Negri et al., “Online SERS Detection of the 20 Proteinogenic l-Amino Acids Separated by Capillary Zone Electrophoresis,” The Analyst 139(22):5989-5998 (2014), which is incorporated herein by reference in its entirety). Additionally, strong bands at 1280, 1410, and 1567 cm arising from arginine, histidine, and phenylalanine / tyrosine, respectively, are observed in the spectrum of particles bearing GFP (Negri et al., “Online SERS Detection of the 20 Proteinogenic l-Amino Acids Separated by Capillary Zone Electrophoresis,” The Analyst 139(22):5989-5998 (2014), which is incorporated herein by reference in its entirety). -1 bands are seen strongly in the spectra of particles containing GFP (Negri et al., “Online SERS Detection of the 20 Proteinogenic l-Amino Acids Separated by Capillary Zone Electrophoresis,” The Analyst 139(22):5989-5998 (2014), which is incorporated herein by reference in its entirety). These differences indicate that SERS can detect modifications of genetic material within viral particles at concentrations of 50,000 TU / mL.

[0080] The spectra of virus particles with GFP at various concentrations are shown in FIG. 2B with the standard deviation shaded around it. Due to the complex structure of the virus particles, there is variation in the average signal obtained for the same particle type. The 1164 and 1259 cm peaks arising from tryptophan (Negri et al., “Online SERS Detection of the 20 Proteinogenic l-Amino Acids Separated by Capillary Zone Electrophoresis,” The Analyst 139(22):5989-5998(2014), which is incorporated herein by reference in its entirety), and amide III stretching (Ashton et al., “pH-Induced Conformational Transitions in α-Lactalbumin Investigated with Two-Dimensional Raman Correlation Variance Plots and Moving Windows,” Journal of Molecular Structure 974(1-3):132-138(2010), which is incorporated herein by reference in its entirety) are seen. -1 A peak at 813 cm was detected at all four concentrations. -1 (phosphate backbone) (Shanmukh et al., “Rapid and Sensitive Detection of Respiratory Virus Molecular Signatures Using a Silver Nanorod Array SERS Substrate,” Nano Letters 6(11):2630-2636(2006), which is incorporated herein by reference in its entirety), 923 cm -1(adenine) (Suh et al., “Surface-Enhanced Raman Spectroscopy of Amino Acids and Nucleotide Bases Adsorbed on Silver,” J. Am. Chem. Soc. 108(16):4711-4718 (1986), which is incorporated herein by reference in its entirety), 1095 cm -1 (phosphate backbone) (Negri et al., “Detection of Genetic Markers Related to High Pathogenicity in Influenza by SERS,” The Analyst 138(17):4877-4884(2013) (which is incorporated herein by reference in its entirety)), 1475 cm -1 (glutamic acid / aspartic acid) (Negri et al., “Online SERS Detection of the 20 Proteinogenic l-Amino Acids Separated by Capillary Zone Electrophoresis,” The Analyst 139(22):5989-5998 (2014), which is incorporated herein by reference in its entirety), and 1575 cm -1The peak with tryptophan (Shanmukh et al., “Rapid and Sensitive Detection of Respiratory Virus Molecular Signatures Using a Silver Nanorod Array SERS Substrate,” Nano Letters 6(11):2630-2636 (2006), which is incorporated herein by reference in its entirety, showed the greatest increase in intensity at the highest concentration. The peaks that showed consistent averages likely arise from proteins found on the surface or capsid of the virus particle. On the other hand, the majority of the peaks that showed an increase in intensity arise from the nucleic acid and phosphate backbone of the genetic material within the capsid. Virus particles contain proteins and lipids on their surface that surround the capsid, which contains the genetic material. As expected, more of the signal is coming from molecules on the surface of the virus than from molecules within the capsid. Modes arising from the SERS hotspot were observed more frequently. This is due to the direct interaction of the surface of the virus with the SERS substrate. However, aromatic nucleic acids are expected to be close enough to the substrate to be detected by SERS. Differences in the average signal for different concentrations of virus particles could arise from different orientations of the particles on the SERS-active surface, or from different molecular components of the virus that are within the hotspot, leading to different enhancements of the vibrational modes seen. The analyzed virus particles were spherical to elliptical and 80-100 nm in diameter, with a diameter of approximately 1 nm associated with the hotspot on the SERS substrate. 3 The size of the hotspot is much larger than the volume of the GFP, and therefore only a portion of the viral particles is within a given hotspot. The average spectra for each concentration show differences in the observed bands, complicating the use of univariate analysis to determine concentration dependence. The average SERS spectra for each concentration of lentiviral particles without GFP are shown in Figure 3 and show a similar trend.

[0081] Lentiviral particles were diluted and stored in LV-MAX production medium. Pure LV-MAX medium was injected into the flow cell and the spectrum obtained is shown in Figure 4. The medium has a peak at 653 cm -1 and 1030cm-1 , and therefore the signal obtained for lentiviral particles arises from the viral particles themselves and not from the medium.

[0082] To determine the viral titer, multivariate curve resolution (MCR) was performed to extract components that correlate with the GFP vector. Figures 5A-5D show a three-component model developed using spectra acquired from lentivirus particles with GFP at concentrations of 500 TU / mL, 5,000 TU / mL, and 50,000 TU / mL. The model captured 95.47% of the variance in the data. The GFP particle spectrum at 10,000 TU / mL was used to validate the model. The model shows a linear relationship between the score at component 2 and the concentration of lentivirus with GFP (Figure 5B). The average score at component 2 for each concentration was then plotted against the viral titer to generate a calibration for determining the viral titer (Figure 5C). The error bars on this curve represent the standard deviation of the measurements.

[0083] Loading for component 2 shows a similar spectrum to lentiviral particles containing GFP vector at the highest concentration analyzed (FIG. 6). Peak assignments for the SERS bands observed for component 2 are shown in Table 1. To further confirm that this component spectrum arises from particles with GFP, the spectra of particles without GFP at a concentration of 50,000 TU / mL were tested in the model. These spectra are lower for component 2 than particles with GFP at the same concentration, and the difference is statistically significant. This further suggests that this component is related to the GFP vector. The spectra of LV-MAX medium shown in FIG. 4 were also tested in the model, and the scores for these spectra for component 2 are much lower than both lentiviral particle types. The bar graph shown in FIG. 5D compares the scores for each of these samples and shows that SERS can distinguish between viral particles with and without inserted genes. Particles without GFP have an average score of 2500 for component 2, which is comparable to the scores for particles with GFP at concentrations of 5,000 and 10,000 TU / mL. This is due to the high level of scattering from particles that do not carry the GFP gene, which may interfere with quantification. However, commonly used viral titers are around 10 5TU / mL or more, which is greater than the titer shown in this study (Pivert et al., “A First Experience of Transduction for Differentiated HepaRG Cells using Lentiviral Technology,” Scientific Reports 9(1):12910(2019); Lim et al., “Identification of Newly Emerging Influenza Viruses by Surface-Enhanced Raman Spectroscopy,” Analytical Chemistry 87(23):11652-11659(2015); Bagnall et al., “Quantitative Dynamic Imaging of Immune Cell Signalling Using Lentiviral Gene Transfer,” Integrative Biology 7(6):713-725(2015); Guerreiro et al., “Detection and Quantification of Label-Free Infectious Adenovirus Using a Switch-On Cell-Based Fluorescent Biosensor,” ACS Sensors 4(6):1654-1661 (2019), which are incorporated by reference in their entireties. [Table 1]

[0084] Fluorescence imaging was used to validate the viral titers determined by SERS. HT-1080 fibrosarcoma cells were transduced with lentivirus encoding GFP. A dose-response curve (Figure 7) was generated using the percentage of cells that were fluorescent and the logarithm of the amount of lentiviral particles used for transduction. These results confirm the viral titers used for the SERS experiments. These images also show that the lentivirus used was 10 4Concentrations above TU / mL indicate high infectivity, and these concentrations are similar to those at which SERS was used to distinguish between the two particle types.

[0085] Example 2: Detection and differentiation of lentiviral particles using silver SERS substrates A SERS method for detecting and differentiating lentiviral particles was also performed using a silver SERS substrate and 532 nm excitation. Using the same lentiviruses previously studied, reference spectra were collected for each particle type at a concentration of 50,000 TU / mL. The collected raw spectra of lentiviral particles with and without the GFP gene are shown in FIG. 8A. A three-component MCR model was constructed using all the spectra collected for each particle, a total of 740 spectra for each type. The loading spectrum for this model is shown in FIG. 8B, and the scores for each component are shown in FIG. 8C. In this model, components 1, 2, and 3 accounted for 53.67%, 44.74%, and 1.29% of the total variance of the data, respectively. The only spectra that score high on component 3 are those from lentiviral particles with GFP. Since these spectra can be separated from the spectra of lentiviral particles without GFP, this component may be related to the GFP vector. Most of the spectra have similar scores in components 1 and 2, thus indicating that the detection of the inserted gene may be complicated by background signals from the lentivirus itself. The large and complex molecular structure of the virus particle may scatter a lot of light and generate complex SERS signals. These signals may therefore complicate the spectrum showing the inserted gene, making it more difficult to detect and quantify. Most of the spectra for both lentiviruses have similar scores in components 1 and 2, but component 3 can be utilized to detect the inserted gene.

[0086] The component spectra associated with the GFP gene on silver and gold substrates are shown in Figure 8D. These component spectra show many spectral differences, sharing only three peaks of the same frequency but different intensities. The peaks correspond to amide III extension (Ashton et al., “pH-Induced Conformational Transitions in α-Lactalbumin Investigated with Two-Dimensional Raman Correlation Variance Plots and Moving Windows,” Journal of Molecular Structure 974(1-3):132-138(2010), which is incorporated herein by reference in its entirety), arginine (Negri et al., “Online SERS Detection of the 20 Proteinogenic l-Amino Acids Separated by Capillary Zone Electrophoresis,” The Analyst 139(22):5989-5998(2014), which is incorporated herein by reference in its entirety), and tryptophan (Ashton et al., “pH-Induced Conformational Transitions in α-Lactalbumin Investigated with Two-Dimensional Raman Correlation Variance Plots and Moving Windows,” Journal of Molecular Structure 974(1-3):132-138(2010), which is incorporated herein by reference in its entirety). of Molecular Structure 974(1-3):132-138(2010), which is incorporated herein by reference in its entirety. -1 A complete list of peak assignments is shown in Tables 1 and 2. The spectrum on the silver substrate shows more bands associated with nucleic acids than amino acids compared to the gold substrate. [Table 2]

[0087] Considerations for Examples 1 and 2: The results show that SERS can detect and quantify a single virus particle type that differs only by the insertion of a GFP gene. These results provide a significantly more rapid method of quantifying virus particles that have been successfully transformed to encode a specific gene, with less sample preparation than existing techniques. Quantification of these particles depends on the virus particles interacting with hot spots on the surface of the SERS-active substrate. Quantification can be difficult to achieve using a single peak, as the observed spectrum depends on the orientation and the specific portion of the virus that interacts with the hot spot. MCR is shown to be useful in quantifying these complex signals by breaking down the SERS spectrum into components, so that the score can be used to determine the amount of virus present. Functionalizing the substrate surface can also be explored to force a specific orientation of the virus to improve signal uniformity.

[0088] The signal associated with particles containing the GFP gene varies depending on the excitation wavelength and metal used for the SERS substrate. When using a 532 nm excitation laser and a silver substrate, more peaks are associated with nucleic acids compared to the spectrum using 785 nm excitation and a gold substrate. Previous work on SERS for virus particles has used a silver substrate with 785 nm excitation and shows that both nucleic acids and amino acids can be detected (Shanmukh et al., "Rapid and Sensitive Detection of Respiratory Virus Molecular Signatures Using a Silver Nanorod Array SERS Substrate," Nano Letters 6(11):2630-2636 (2006), which is incorporated herein by reference in its entirety). However, studies using gold substrates with 785 nm excitation indicate that the majority of the SERS signal arises from the amino acid and lipid components of the viral envelope (Lim et al., “Identification of Newly Emerging Influenza Viruses by Surface-Enhanced Raman Spectroscopy,” Analytical Chemistry 87(23):11652-11659 (2015), which is incorporated herein by reference in its entirety). The component spectra of viruses with GFP on silver substrates show more nucleic acid signal than those on gold substrates. However, nucleic acid and amino acid signals are observed on both types of substrates.

[0089] The interaction between the metal and the analyte may also affect the way the molecules adsorb to the surface, which therefore affects the observed SERS signal. This may be due to a preferred orientation of the analyte on the surface, which enhances the bands closest to the surface over those further away from the substrate. The SERS spectrum of red blood cells appears different when silver nanoparticles are used instead of gold, which is explained by the different interactions between the analyte and each metal (Drescher et al., "SERS Reveals the Specific Interaction of Silver and Gold Nanoparticles with Hemoglobin and Red Blood Cell Components," Physical Chemistry Chemical Physics 15:5364-5373 (2013) (which is incorporated herein by reference in its entirety)). A similar effect is likely responsible for the different signals of GFP-containing virus particles on silver and gold substrates.

[0090] SERS offers a more rapid approach to viral titer determination, with less sample preparation than current methods. SERS has been shown to distinguish between virus types and strains (Shanmukh et al., “Rapid and Sensitive Detection of Respiratory Virus Molecular Signatures Using a Silver Nanorod Array SERS Substrate,” Nano Letters 6(11):2630-2636 (2006); Lim et al., “Identification of Newly Emerging Influenza Viruses by Surface-Enhanced Raman Spectroscopy,” Analytical Chemistry 87(23):11652-11659 (2015), which are incorporated by reference in their entireties), as well as using DNA hairpins to detect viral mutations (Dardir et al., “SERS Nanoprobe for Intracellular Monitoring of Viral Mutations,” The Journal of Physical Chemistry C 124(5):3211-3217 (2020), which are incorporated by reference in their entireties). The method described in this application for determining viral titer using direct SERS measurements can be applied to other virus types and can also be used to quantify modifications to the viral genome.

[0091] conclusion As demonstrated in the preceding examples, viral titers can be determined without modification to the SERS substrate, thus providing a rapid and simple method using commercially available SERS substrates. Due to the complexity of the viral particle structure, chemometric analysis, e.g., MCR, is useful to resolve the spectrum so that the virus-specific components can be determined and used for quantification. The viral titers used in the preceding examples are an order of magnitude lower than those commonly used in biomedical applications. The use of different metals and excitation wavelengths to analyze these particles was also studied, showing that a silver substrate with 532 nm excitation produced a spectrum with more nucleic acid features than a gold substrate with 785 nm excitation. SERS provides a rapid method to determine viral titers with minimal sample preparation, and these results confirm that the method can be applied to a variety of virus types.

[0092] Example 3: Detection and differentiation of complete and empty lentiviral capsids For the analysis of lentiviruses containing either full or empty lentivirus capsids, SERS spectra were acquired using 785 nm excitation with a gold SERS substrate. Commercially available lentiviruses (human non-targeted, LentiArray CRISPR negative control lentiviruses with and without green fluorescent protein (GFP)) were purchased from ThermoFisher Scientific, and gold SERS substrates were purchased from Silmeco for full lentivirus measurements. Empty capsid lentiviruses were purchased from SignaGen. Thermally evaporated gold substrates were generated using a previously described procedure and used for empty capsid experiments (Asiala et al., “Characterization of Hotspots in a Highly Enhancing SERS Substrate,” Analyst 136(21):4472-4479 (2011), which is incorporated herein by reference in its entirety). Raman spectroscopy was performed using a home-made Raman instrument by focusing a laser onto the SERS substrate through a 40x water immersion objective (NA=0.8). Raman scattering was measured using a ProEM:1600 2 The SERS signals were then directed onto an Isoplane SCT-320 spectrometer with an eXcelon 3 CCD detector (Princeton Instruments). The virus solution was injected into the SERS substrate and spectra were acquired with an exposure time of 250 ms and a laser power of 1.50 mW for the full lentivirus and with an exposure time of 500 ms and a laser power of 0.50 mW for the empty lentivirus. The spectrum shown in FIG. 9 is an average of 720 spectra. The difference in SERS signal between the full and empty virus particles may be related to the absence of genetic material within the capsid. The peaks highlighted by the grey boxes are seen across all three virus particles (Table 3) and are consistent with tyrosine (853 cm -1 ), phenylalanine (1004cm -1 ), tryptophan (1127cm -1 ), and amide III stretch (1240 cm -1) (Shanmukh et al., "Rapid and Sensitive Detection of Respiratory Virus Molecular Signatures Using a Silver Nanorod Array SERS Substrate," Nano Letters 6(11):2630-2636(2006); Ashton et al., "pH-Induced Conformational Transitions in α-Lactalbumin Investigated with Two-Dimensional Raman Correlation Variance Plots and Moving Windows," Journal of Molecular Structure 974(1-3):132-138(2010); Verduin et al., "RNA-Protein Interactions and Secondary Structures of Cowpea Chlorotic Mottle Virus for in Vitro Assembly," Biochemistry 23(19):4301-4308(1984) (which are incorporated herein by reference in their entireties)). An additional band seen in the empty capsid spectrum (Table 4) is at 719 cm -1 (ν(CS), tryptophan), 1362cm -1 (tryptophan), 1454cm -1 (CH2 modification of protein, tryptophan), and 1565 cm -1(phenylalanine / tryptophan, amide II) (Shanmukh et al., “Rapid and Sensitive Detection of Respiratory Virus Molecular Signatures Using a Silver Nanorod Array SERS Substrate,” Nano Letters 6(11):2630-2636(2006); Ashton et al., “pH-Induced Conformational Transitions in α-Lactalbumin Investigated with Two-Dimensional Raman Correlation Variance Plots and Moving Windows,” Journal of Molecular Structure 974(1-3):132-138(2010); Verduin et al., “RNA-Protein Interactions and Secondary Structures of Cowpea Chlorotic Mottle Virus for in Vitro Assembly,” Biochemistry 23(19):4301-4308(1984); Negri et al., “Online SERS Detection of the 20 Proteinogenic L-Amino Acids Separated by Capillary Zone Electrophoresis,” The Analyst 139(22):5989-5998(2014); Szekeres et al., “SERS Probing of Proteins in Gold Nanoparticle Agglomerates,” Front. Chem. 7:30(2019) (which are incorporated by reference in their entirety). -1 , 921cm resulting from adenine -1 , and 1046 cm arising from cytosine -1Differences between full and empty capsid spectra, such as bands at 100 nm and 150 nm, can be related to the presence of genetic material within the virus (Shanmukh et al., “Rapid and Sensitive Detection of Respiratory Virus Molecular Signatures Using a Silver Nanorod Array SERS Substrate,” Nano Letters 6(11):2630-2636(2006); Negri et al., “Detection of Genetic Markers Related to High Pathogenicity in Influenza by SERS,” The Analyst 138(17):4877-4884(2013); Suh et al., “Surface-Enhanced Raman Spectroscopy of Amino Acids and Nucleotide Bases Adsorbed on Silver,” Journal of the American Chemical Society 108(16):4711-4718(1986), which are incorporated herein by reference in their entireties). [Table 3] [Table 4]

[0093] Example 4: Generation of a calibration curve for modified lentiviral particles The SERS spectrum of the modified lentiviral particle, designated JLV1, was acquired using a Snowy Range Sierra Raman Spectrometer with 638 nm excitation and a commercially available gold SERS substrate (Silmeco). The virus solution was injected into the SERS substrate through a 3D-printed flow cell and the spectrum was acquired with an exposure time of 250 ms and a laser power of 0.50 mW. The spectrum shown in FIG. 10A is an average of 200 spectra. All spectra were analyzed using a 520 cm NMR spectrum arising from the silicon backing of the SERS substrate prior to analysis. -1 The MCR was used to build a calibration model for determining viral titer, as shown in Figures 10B and 10C, and Figures 10F-H (comparison of scores for components 1, 2, and 3). Figure 10C shows, for example, that guanine (658 and 1380 cm -1 ), the phosphate backbone of RNA (811 and 1173 cm -1 ), uracil (1059 and 1296 cm -1 ), Adenine (1328cm -1 ), and tryptophan (1115, 1380, and 1524 cm -1) shows the component 1 loading spectrum of this model with peaks arising from the amino acids and nucleic acids that compose the virus (Shanmukh et al., “Rapid and Sensitive Detection of Respiratory Virus Molecular Signatures Using a Silver Nanorod Array SERS Substrate,” Nano Letters 6(11):2630-2636(2006); Otto et al., “Surface-Enhanced Raman Spectroscopy of DNA Bases,” Journal of Raman Spectroscopy 17(3):289-298(1986); Suh et al., “Surface-Enhanced Raman Spectroscopy of Amino Acids and Nucleotide Bases Adsorbed on Silver,” Journal of the American Chemical Society 108(16):4711-4718(1986); Sloan-Dennison et al., “Surface Enhanced Raman Scattering Selectivity in Proteins Arises from Electron Capture and Resonant Enhancement of Radical Species,” The Journal of Physical Chemistry C 124(17):9548-9558(2020); Verduin et al., “RNA-Protein Interactions and Secondary Structures of Cowpea Chlorotic Mottle Virus for in Vitro Assembly,” Biochemistry 23(19):4301-4308(1984) (which are incorporated by reference in their entireties). The peaks that appear strongly in the loading spectrum also appear strongly in the average spectrum for each titer, confirming that the components are associated with lentiviral particles.The average score for each sample in component 1 is then plotted against the virus titer (see Figures 10D-E) to generate a calibration curve. 10 being the highest titer analyzed. 6 At TU / mL, the SERS signal decreases (Figure 10D). This is likely due to the surface being saturated with sample, thus blocking additional lentiviral particles from occupying the hotspots, and some of the SERS signal is observed. However, below that titer level, there is a linear trend between the score on component 1 and the viral titer, which was used to create a calibration to determine the viral titer of JLV1 shown in Figure 10E (R 2 = 0.974). Error bars represent the standard deviation of the SERS measurements.

[0094] Based on the above, the calibration curve was set up with the same settings and the 10 5 Viral titers below TU / mL can be assessed, and optionally higher viral titers at a given sample dilution (e.g., 10x, 100x, or more). Both diluted and undiluted samples can be assessed, optionally in parallel.

[0095] While preferred embodiments have been shown and described in detail herein, it will be apparent to those skilled in the art that various modifications, additions, substitutions, and the like, can be made therein without departing from the spirit of the disclosure, and therefore are deemed to be within the scope of the present disclosure as defined in the following claims.

Claims

1. 1. A method for quantifying viral titer in a sample using Raman spectroscopy, said method comprising: Providing a sample; providing a model for determining viral titer in said sample; illuminating the sample with a light source; obtaining a Raman spectrum of the sample; and quantifying the viral titer of the sample by applying the viral component of the Raman spectrum to the model for determining viral titer.

2. 10. The method of claim 1, wherein the sample comprises one or more virus particle types.

3. 3. The method of claim 2, wherein the sample is a fluid sample containing two or more virus particle types, the types differing by one or more genetic elements.

4. 4. The method of claim 3, wherein the one or more genetic elements comprise a gene insertion, substitution, or deletion in the viral particle genome.

5. 5. The method of claim 4, wherein the one or more genetic elements comprise an exogenous gene insertion into the viral particle genome.

6. 3. The method of claim 2, wherein the viral particle is selected from a retrovirus particle, a retrovirus-like particle, an adenovirus particle, an adenovirus-like particle, an adeno-associated virus particle, an adeno-associated virus-like particle, a herpes simplex virus particle, and a herpes simplex virus-like particle.

7. The method of claim 6 , wherein the viral particle is a retroviral particle.

8. 8. The method of claim 7, wherein the retroviral particle is a lentiviral particle.

9. The method according to any one of claims 1 to 8, wherein the Raman spectroscopy is surface-enhanced Raman (SER) spectroscopy.

10. 10. The method of claim 9, wherein the SER spectroscopy is carried out using a metal substrate, the metal being selected from gold, silver, copper, and platinum, and alloys thereof.

11. The method of claim 10 , wherein the SER spectroscopy is performed on a gold substrate.

12. The method of claim 11, wherein the light source is a narrow bandwidth laser having a wavelength between 550 and 1064 nm.

13. The method of claim 10 , wherein the SER spectroscopy is performed using a silver substrate.

14. The method of claim 13, wherein the light source is a narrow bandwidth laser having a wavelength between 400 and 1064 nm.

15. extracting scores of the viral components from the Raman spectra; and applying the scores extracted from the Raman spectrum of the sample to the model to quantify the viral titer in the sample.

16. 16. The method of claim 15, wherein the model is a virus particle type-specific calibration curve, and virus particle type-specific component scores are extracted and applied to the model to quantify the virus particle type-specific titer in the sample.

17. 1. A method for generating a model suitable for quantifying viral titer in a sample, said method comprising: providing two or more samples, each sample containing a known virus type and a known titer; subjecting each sample to Raman spectroscopy to generate a reference spectrum for each known virus type and titer; identifying virus type-specific components from the reference spectrum; determining a score for said component for each sample of known virus type and known titer; generating the model for quantifying viral titer based on the score.

18. 18. The method of claim 17, wherein the reference spectrum for each sample is generated by analyzing two or more spectra of the sample.

19. 18. The method of claim 17, further comprising subtracting a background spectrum from the reference spectrum prior to said identifying.

20. The identifying step applying chemometric analysis to the reference spectra generated from the two or more samples to identify one or more components of variation between the reference spectra; assessing the scores of the one or more identified components in the spectra from two or more samples of each known virus type having different known titers; and selecting, based on said evaluating, as said virus type-specific component, said component that correlates with known virus titers.

21. 21. The method of claim 20, wherein the chemometric analysis is multivariate curve analysis.

22. The method of any one of claims 17 to 21, wherein the generated model is a virus type-specific calibration curve.

23. 18. The method of claim 17, wherein the two or more samples comprise at least two different virus types, the virus types differing by one or more genetic elements.

24. 24. The method of claim 23, wherein the one or more genetic elements comprise a gene insertion, substitution, or deletion in the viral genome.

25. 25. The method of claim 24, wherein the one or more genetic elements comprise an exogenous gene insertion into the viral genome.

26. 26. The method of any one of claims 17 to 21 and 23 to 25, wherein the virus is selected from a retrovirus particle, a retrovirus-like particle, an adenovirus particle, an adenovirus-like particle, an adeno-associated virus particle, an adeno-associated virus-like particle, a herpes simplex virus particle, and a herpes simplex virus-like particle.

27. 27. The method of claim 26, wherein the virus is a retroviral particle.

28. 28. The method of claim 27, wherein the retroviral particle is a lentiviral particle.

29. 18. The method of claim 17, wherein the Raman spectroscopy is surface-enhanced Raman (SER) spectroscopy.

30. 30. The method of claim 29, wherein the SER spectroscopy is carried out using a metal substrate, the metal being selected from gold, silver, copper, and platinum, and alloys thereof.

31. 31. The method of claim 30, wherein the SER spectroscopy is performed using a gold substrate.

32. 32. The method of claim 31, wherein said subjecting comprises illuminating each sample with a narrow bandwidth laser having a wavelength between 550 and 1064 nm.

33. 33. The method of claim 32, wherein the wavelength is about 785 nm.

34. 34. The method of any one of claims 31-33, wherein said subjecting further comprises acquiring a spectrum at an exposure time of about 250 ms and at about 1.50 mW.

35. 31. The method of claim 30, wherein the SER spectroscopy is performed using a silver substrate.

36. 36. The method of claim 35, wherein said subjecting comprises illuminating each sample with a narrow bandwidth laser having a wavelength between 400 and 1064 nm.

37. 37. The method of claim 36, wherein the wavelength is about 532 nm.

38. 38. The method of any one of claims 35-37, wherein said subjecting further comprises acquiring a spectrum at an exposure time of about 250 ms and at about 0.6 mW.

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