Identification of viral particles and extracellular vesicles using nanopore in combination with optical detection

Abstract

The present invention relates to methods for identifying the type and / or subtype of viral particles and / or extracellular vesicles using nanoporous membranes in combination with optical detection. The invention also relates to the use of this method, in particular for identifying / naming the type and / or subtype of viral particles and / or extracellular vesicles present in a biological sample. More specifically, the transport of viral particles and extracellular vesicles through the nanopore enables identification of the type of particles by specific interactions of viral particles and extracellular vesicles with said nanopore and modeling of this phenomenon, in particular using a bottleneck model.

Description

[0001] This invention relates to a method for identifying the type and / or subtype of viral particles and / or extracellular vesicles using nanoporous membranes combined with optical detection.

[0002] The present invention also relates to the use of this method, particularly for the identification, i.e., especially for the naming of types and / or subtypes of viral particles and / or extracellular vesicles present in biological samples. More specifically, the transport of viral particles and extracellular vesicles via nanopores enables the identification of particle types through their specific interactions with the nanopores and the modeling of this phenomenon, for example, through a bottleneck model.

[0003] Currently, rapid and accurate identification of viral particles and extracellular vesicles remains a challenging goal at the industrial, clinical, and academic research levels. Commercially available detection methods do not fully meet the required specifications in several application areas: for example, basic research (to study the mechanisms of viral cycle and extracellular vesicle production, etc.), industrial bioengineering (for quality assessment and production yield monitoring of viral vectors or extracellular vesicles for vaccines or gene therapy), and even in clinical diagnostics (e.g., real-time monitoring of viral levels in patient samples). In making this invention, a trade-off had to be struck between rapid techniques and expensive, time-consuming techniques (qPCR, ELISA), where rapid techniques are not very accurate and are too specific to detect several viral types (antigen detection), and rapid techniques require complex intermediate steps. Even though various improvements to these methods have been proposed in the scientific literature (J. Eid, M. Mougel and M. Socol, "Advances in Continuous Microfluidics-Based Technologies for the Study of HIV Infection," Viruses, vol. 12, no. 9, pp. 1-16, 2020; J. Deng et al., "Rapid One-Step Detection of Viral Particles Using an Aptamer-Based Thermophoretic Assay," J. Am. Chem. Soc., vol. 143, no. 19, pp. 7261-7266, Mai 2021), the problem that they cannot detect multiple viral types in the same sample still exists.

[0004] Nanopore-based systems offer the ability to detect at the single molecule (or particle) level and in real time. Such systems are useful in studying virus particles (L. Yang and T. Yamamoto, "Quantification of virus particles using nanopore-based resistive-pulse sensing techniques," Front. Microbiol., vol. 7, pp. 1-7, 2016; A. Arima et al., "Selective detections of single-viruses using solid-state nanopores," Sci. Rep., vol. 8, no. 1, pp. 1-7, 2018; L.Chazot-Franguiadakis et al., "Optical Quantification by Nanopores of Viruses,Extracellular Vesicles, and Nanoparticles," Nano Lett., 2022; ZD Harms, L.Selzer, A. Zlotnick, and SC Jacobson, "Monitoring Assembly of Virus Capsidswith Nanofluidic Devices," ACS Nano, vol. 9, no. 9, pp. 9087−9096, 2015) and extracellular vesicles (L. Chazot-Franguiadakis et al., "Optical Quantification by Nanopores ofViruses, Extracellular Vesicles, and Nanoparticles," Nano Lett., 2022; SLN Maas, J. De Vrij and MLD Broekman, "Quantification and Size-profiling ofExtracellular Vesicles Using Tunable Resistive Pulse Sensing," J. Vis. Exp.,vol. 92, pp. 1−7, Oct. 2014; R.The characteristics of extracellular vesicles are also receiving increasing attention. (Vogel et al., "A standardized method to determine the concentration of extracellular vesicles using tunable resistive pulse sensing," J. Extracell. Vesicles, vol. 5, no. 1, pp. 1−13, 2016; S. Ryuzaki et al., "Rapid Discrimination of Extracellular Vesicles by ShapeDistribution Analysis," Anal. Chem., vol. 93, no. 18, pp. 7037−7044, 2021) In fact, the small diameter and high surface area / volume ratio of nanopores allow for precise detection of the characteristics of viruses and extracellular vesicles (A. Arima, M. Tsutsui, T. Washio, Y. Baba, and T. Kawai, "Solid-State Nanopore Platform Integrated with Machine Learning for Digital Diagnosis of Virus Infection," Anal. Chem., vol. 93, no. 1, pp. 215−227, Jan. 2021).

[0005] To detect intact viral particles or extracellular vesicles, most nanopore-based methods rely on detecting particles via electrical actuation and detection. These techniques, known as resistive pulse sensing (RPS), are based on analyzing changes in resistance using nanoscale shrinkage. RPS is a versatile technique that can be used to detect nanoparticles (M. Platt, GR Willmott, and GU Lee, "Resistive Pulse Sensing of Analyte-Induced Multicomponent Rod Aggregation Using Tunable Pores," Small, vol. 8, no. 15, pp. 2436−2444, Aug. 2012) or biological objects such as DNA, bacteria, proteins, viruses, and extracellular vesicles (S. Howorka and Z. Siwy, "Nanopore analytics: Sensing of single molecules," Chem. Soc. Rev., vol. 38, no. 8, pp. 2360−2384, Jul. 2009; JAOukhaled, L. Bacri, M. Pastoriza-Gallego, JM Betton, and J. Pelta, "Sensing proteins through nanopores: Fundamental to applications," ACS Chem. Biol., vol. 7, no. 12, pp. 1935−1949, Dec. 2012; DH Stoloff and M. Wanunu, "Recenttrends in nanopores for biotechnology," Curr. Opin. Biotechnol., vol. 24, no.4, pp. 699−704, Aug. 2013; GR Willmott and BG Smith, "Modelling ofResistive Pulse Sensing: Flexible Methods for Submicron Particles," ANZIAMJ., vol. 55, no. 3, pp.(197−213, 2014). More precisely, the RPS technique uses a system consisting of two chambers filled with electrolyte solution and connected by nanopores. In practice, a voltage is applied across the pore, and the change in ionic current is measured. When an object passes through the pore, electrolyte solution ions are instantaneously expelled from the pore, causing the ionic current to drop in a pulsed manner. The measured signals contain representations of the shape (EC Yusko et al., "Real-time shape approximation and fingerprinting of single proteins using a nanopore," Nat. Nanotechnol, vol. 12, no. 4, pp. 360−367, Dec. 2016) and volume (H. Yasaki et al., "Substantial Expansion of Detectable Size Range in Ionie Current Sensingthrough Pores by Using a Microfluidic Bridge Circuit," J. Am. Chem. Soc., vol. 139, no. 40, pp. 14137−14142, Oct. 2017; A. Arima, M. Tsutsui and M. Taniguchi, "Volume discrimination of nanoparticles via electrical trapping using nanopores," J. Nanobiotechnology, vol. 17, no. 1, pp. 1−6, Mar.) of the transported particles. 2019; R. Vogel et al., "Quantitative sizing of nano / microparticles with a tunable elastomeric pore sensor," Anal. Chem., vol. 83, no. 9, pp. 3499−3506, 2011), weight (M. Tsutsui, K. Yokota, A. Arima, Y. He, and T. Kawai, "Solid-State Nanopore Time-of-Flight Mass Spectrometer," ACS Sensors, vol. 4, no. 11, pp.2974−2979, Nov. 2019) and surface charge (A).Sikora, AG Shard and C. Minelli, "Size and ζ-Potential Measurement of Silica Nanoparticles in Serum Using TunableResistive Pulse Sensing," Langmuir, vol. 32, no. 9, pp. 2216−2224, Mar. 2016; A. Arima, M. Tsutsui and M. Taniguchi, "Discrimination of equi-sized nanoparticles by surface charge State using low-aspect-ratio pore sensors," Appl. Phys. Lett., vol. 104, no. 16, pp. 1−4, Apr. 2014; N. Arjmandi, W. VanRoy, L. Lagae, and G. Borghs, "Measuring the electric charge and zeta potential of nanometer-sized objects using pyramidal-shaped nanopores," Anal. Chem.,vol. 84, no. A wealth of information is available in (IW Leong, M. Tsutsui, S. Murayama, Y. He and M. Taniguchi, "Electroosmosis-Driven Nanofluidic Diodes," J. Phys. Chem. B, vol.124, no. 32, pp. 7086−7092, Aug. 2020). The effectiveness of the detection signal is usually evaluated by comparison with several physical models (IW Leong, M. Tsutsui, S. Murayama, Y. He and M. Taniguchi, "Electroosmosis-Driven Nanofluidic Diodes," J. Phys. Chem. B, vol.124, no. 32, pp. 7086−7092, Aug. 2020).

[0006] RPS has many applications in studying the properties of viral particles and extracellular vesicles. In particular, RPS has been used to measure: size (known pore characteristics) (L. Yang and T. Yamamoto, "Quantification of virus particles using nanopore-based resistive-pulse sensing techniques," Front. Microbiol., vol. 7, pp. 1−7, 2016; R. Vogel et al., "Quantitative sizing of nano / microparticles with a tunable elastomeric pore sensor," Anal. Chem., vol. 83, no. 9, pp. 3499−3506, 2011; ZD Harms, DG Haywood, AR Kneller, L. Selzer, A. Zlotnick and SC Jacobson, "Single-particle electrophoresis in nanochannels," Anal. Chem., vol. 87, no. 1, pp. 699−705, 2015; W. Anderson, D. Kozak, VA Coleman, Å. K. Jàmting and M. Trau, "A comparative study of submicron particle sizing platforms: Accuracy, precision and resolution analysis of polydisperse particle size distributions," J. Colloid InterfaceSci., vol. 405, pp. 322−330, Sep. 2013), ζ potential (N. Arjmandi, W. Van Roy, L. Lagae, and G. Borghs, "Measuring the electric charge and zeta potential ofnanometer-sized objects using pyramidal-shaped nanopores," Anal. Chem., vol.84, no. 20, pp.8490−8496, Oct. 2012) and electrophoretic mobility (ZD Harms, DG Haywood, AR Kneller, L. Selzer, A. Zlotnick and SC Jacobson, "Single-particle electrophoresis in nanochannels," Anal. Chem., vol. 87, no. 1, pp. 699-705, 2015) and particle concentration (L. Yang and T. Yamamoto, "Quantification of virus particles using nanopore-based resistive-pulse sensing techniques," Front. Microbiol., vol. 7, pp. 1−7, 2016; RW DeBlois and RK Wesley, "Sizes and concentrations of several type C oncornaviruses and bacteriophage T2 by theresistive-pulse technique.," J. Virol., vol. 23, no. 2, pp. ). 227-233, 1977). RPS can also qualitatively detect the mechanical properties of viral particles (A. Darvish et al., "Mechanical characterization of HIV-1 with a solid-state nanopore sensor," Electrophoresis, vol. 40, no. 5, pp. 776−783, Mar. 2019; BI Karawdeniya et al., "Adeno-associated virus characterization for cargo discrimination through nanopore responsiveness," Nanoscale, vol. 12, no. 46, pp. 23721−23731, Dec. 2020) and viral capsid assembly dynamics (ZD Harms, L. Selzer, A. Zlotnick and SC...).Jacobson, "Monitoring Assembly of Virus Capsids with Nanofluidic Devices," ACS Nano, vol. 9, no. 9, pp. 9087−9096, 2015). Ultimately, identifying virus types, or even subtypes, via RPS remains a significant challenge. The ability to distinguish particles with similar characteristics requires detecting the unique features of each particle type in the electrical signal. One possible strategy employed by Alrima et al. is to combine the detection system with computational data analysis techniques such as deep learning (A. Arima et al., "Selective detections of single-viruses using solid-state nanopores," Sci. Rep., vol. 8, no. 1, pp. 1−7, 2018; A. Arima et al., "Digital Pathology Platform for Respiratory Tract Infection Diagnosis via Multiplex Single-Particle Detections," ACS Sensors, vol. 5, no. 11, pp. 3398−3403, 2020). Using this type of analysis, Alrima et al. were able to construct classification models that are functions of the electrical signal characteristics of a given virus type (A. Arima et al., "Selective detections of single-viruses using solid-state nanopores," Sci. Rep., vol. 8, no. 1, pp. 1−7, 2018; A. Arima et al., "Digital Pathology Platform for Respiratory Tract Infection Diagnosis via Multiplex Single-Particle Detections," ACS Sensors, vol. 5, no. 11, pp. 3398−3403, 2020).

[0007] However, overall, RPS technology has significant drawbacks. First, regarding detection specificity, since any object passing through the pore (including irrelevant particles) will be detected, there is no specific labeling for object type. Therefore, the measurement results cannot directly provide information about the general properties of the object. Second, RPS technology is based on the use of a single nanopore (without systematic parallelization), which is detrimental to the sensitivity of concentration measurements (10...). 7The lower limit of concentration (LDC) of particles per mL (RW DeBlois and RK Wesley, "Sizes and concentrations of several type oncornaviruses and bacteriophage T2 by the resistive-pulse technique," J. Virol., vol. 23, no. 2, pp. 227-233, 1977) and experimental lifetime both pose limitations because the pores quickly become clogged, preventing measurements (A. Arima et al., "Selective detections of single-viruses using solid-state nanopores," Sci. Rep., vol. 8, no. 1, pp. 1−7, 2018). This clogging is irreversible, therefore the nanopores must be replaced. Finally, the size of the pores must be close to the size of the particles to be detected; otherwise, there will be no signal (L. Yang and T. Yamamoto, "Quantification of virus particles using nanopore-based resistive-pulse sensing techniques," Front. Microbiol., vol. 7, pp. 1−7, 2016). This indicates that the nanopores need to be adjusted according to the particle diameter. Nanopore size adjustment can also be performed using an adjustable RPS system, which consists of elastically tunable nanopores whose pore size can be adjusted in situ.However, such systems are more complex and have a high size detection limit (approximately 80 nm; L. Yang and T. Yamamoto, "Quantification of virus particles using nanopore-based resistive-pulse sensing techniques," Front. Microbiol., vol. 7, pp. 1-7, 2016; R. Vogel et al., "A standardized method to determine the concentration of extracellular vesicles using tunable resistive pulse sensing," J. Extracell. Vesicles, vol. 5, no. 1, pp. 1−13, 2016; R. Vogel et al., "Quantitative sizing of nano / microparticles with a tunable elastomericpore sensor," Anal. Chem., vol. 83, no. 9, pp. 3499-3506, 2011), while some virus particles or extracellular vesicles may have diameters of 30 nm or less.

[0008] In the context of this invention, the inventors have developed a method using functionalized nanoporous membranes, combined with an optical detection system, enabling the identification of fluorescently labeled viral particles and extracellular vesicles. The advantage and originality of this method lies in its ability to directly detect and identify a wide range of viral particles (enveloped or non-enveloped, DNA or RNA, and of all sizes) and extracellular vesicles from biological samples with high concentration sensitivity. Viral particles and extracellular vesicles are detected as a whole, i.e., the entire particle is detected, not just genetic material as in PCR (polymerase chain reaction) assays or specific proteins as in ELISA (enzyme-linked immunosorbent assay). The method disclosed in this invention does not require intermediate purification or amplification steps. Compared to existing methods, the target method of this invention enables the identification of viral particles and extracellular vesicles without the need to design assays for specific types / subtypes of viral particles or extracellular vesicles.

[0009] Furthermore, unlike RPS technology, the method disclosed in this invention has adjustable detection specificity. In fact, in the RPS method, particles are unlabeled, which is a major drawback when studying complex samples because any object present in the solution (cell debris, DNA, proteins, etc.) will be detected. This greatly interferes with the measurement with a large number of interfering signals. In contrast, the method according to the invention involves fluorescent labeling of the particles to be detected. This labeling is performed before the measurement begins, and its detection specificity can be adjusted because precise viral particles or extracellular vesicles (via antibody-fluorophore combinations), or groups of particles, can be labeled. This labeling represents a first-level detection, enabling the differentiation of particle groups from the remaining biological objects that are still part of a complex sample.

[0010] Furthermore, RPS technology relies on the use of a single nanopore, while the nanoporous membrane disclosed in this invention has the significant advantage of a large number of parallel pores (approximately 10,000 times more than RPS). The abundance of parallel nanopores greatly increases measurement statistics, resulting in higher concentration sensitivity (10). 3 Particles / mL to 10 5 (A lower lower limit of concentration per particle / mL). Furthermore, high parallelism avoids the irreversible nanopore blockage that often hinders measurements in RPS techniques. Finally, this method does not require nanopore sizes close to the particle diameter. Therefore, the same nanopore diameter can be used for different types of viral particles or extracellular vesicles.

[0011] Using the method according to the invention, the identification of viral particles and extracellular vesicles is simpler, faster (e.g., only minutes compared to hours for qPCR) and more accurate.

[0012] The inventors of this invention have developed a method for improving virus detection sensitivity using nanoporous membranes. By measuring the passage frequency (the number of fluorescent particles passing through the nanopore per unit time) through the nanoporous membrane based on control parameters (pressure, concentration of viral particles or extracellular vesicles, etc.), kinetic and / or physicochemical parameters of the interaction between viral particles or extracellular vesicles and the nanopore, and / or the rate of interaction between the virus and / or extracellular vesicles themselves, or between the virus and / or extracellular vesicles and the nanopore surface, can be extracted. Since these parameters are unique for a given virus (or extracellular vesicle) type / subtype, their measurement enables the creation of specific features and databases, thereby enabling the identification of viral particle and / or extracellular vesicle types and / or subtypes.

[0013] Therefore, the present invention relates to a method for identifying the type and / or subtype of viral particles and / or extracellular vesicles, comprising:

[0014] a. Labeling viral particles and / or extracellular vesicles present in biological samples with fluorescent markers;

[0015] b. Pass the sample containing labeled viral particles and / or extracellular vesicles through a nanoporous membrane;

[0016] c. Measuring the transport of the virus particles and / or extracellular vesicles across the nanoporous membrane by optical detection based on at least one control parameter; and

[0017] d. Extract physicochemical and / or kinetic and / or interaction rate parameters between the virus and / or extracellular vesicles themselves, or between the virus and / or extracellular vesicles and the nanopore surface, which characterize the transport of the viral particles and / or extracellular vesicles by a physical model, such as a bottleneck model, that correlates the transport with the control parameters.

[0018] Advantageously, these parameters extracted in step d can identify the type and / or subtype of viral particles and / or extracellular vesicles, particularly by comparison with a database.

[0019] The present invention also relates to the use of the method according to the invention for identifying the type and / or subtype of virus present in a patient, the quality of viral vector production, or the identification of zoonotic diseases in biological samples.

[0020] The present invention also relates to the use of the method according to the invention for identifying the type and / or subtype of extracellular vesicles in a patient, or the quality of vesicle carrier production. Summary of the Invention

[0021] As previously stated, this invention relates to a method for identifying viral particles and / or extracellular vesicles, wherein:

[0022] a. Labeling viral particles and / or extracellular vesicles present in biological samples with fluorescent markers;

[0023] b. Pass the sample containing labeled viral particles and / or extracellular vesicles through a nanoporous membrane;

[0024] c. Measuring the transport of the virus particles and / or extracellular vesicles across the nanoporous membrane by optical detection based on at least one control parameter; and

[0025] d. Extract physicochemical parameters and / or kinetic parameters and / or interaction rate parameters that characterize the transport of the viral particles and / or extracellular vesicles by means of a physical model, such as a bottleneck model, that correlates the transport with the control parameters.

[0026] Advantageously, these parameters can be correlated with a database, enabling the identification of the type and / or subtype of viral particles and / or extracellular vesicles.

[0027] "Identification" refers to the process of associating detected viral particles and / or extracellular vesicles with one or more corresponding types and / or one or more subtypes. Identification corresponds not only to the presence of viral particles and / or extracellular vesicles in a sample, but also to one or more types and / or one or more subtypes of viruses and / or extracellular vesicles present in a specifically named sample. For example, this might involve identifying whether a sample contains influenza virus (InfV) or hepatitis B virus (HBV), which corresponds to identifying the virus type. Another example involves identifying whether a sample contains influenza A (H1N1) or influenza A (H3N2), which corresponds to identifying the virus subtype.

[0028] "Virus particles" refer to biological entities associated with a virus category. This includes all types of viruses (enveloped, non-enveloped, DNA viruses, RNA viruses, etc.) and all derivatives, i.e., viruses acquired through means other than natural infection. This refers to laboratory-produced viruses (in whole or partial forms, such as virus-like particles or assemblies of viral proteins), as well as viral vectors used in vaccination processes and bioproduced viruses. Viruses vary in size (typically approximately 20 nm to 400 nm in diameter), geometry, and surface and mechanical properties.

[0029] In one embodiment of the invention, the virus particles are selected from enveloped or non-enveloped virus particles, and may contain DNA or RNA.

[0030] "Virus type" refers to the type of virus identified in the official classification (International Committee on Taxonomy of Viruses). For example, influenza A virus corresponds to virus type .

[0031] "Virus subtype" refers to the serotype of a virus as defined in the official classification (International Committee on Taxonomy of Viruses). For example, influenza A virus (H1N1) or influenza A virus (H3N2) both correspond to the viral subtypes of influenza A virus.

[0032] Extracellular vesicles are biological particles composed of a lipid bilayer naturally released by almost all cell types. Extracellular vesicles are complex and heterogeneous biological nanoparticles present in many biological fluids, including, for example, urine and blood. Extracellular vesicles are highly variable in size, typically ranging from 30 nm to 300 nm in diameter. They vary greatly in size, morphology, mechanical and biochemical properties. Extracellular vesicles and their synthetic pathways are also used by tumors and infectious diseases to promote their spread and evade the immune system. Within the scope of this invention, this includes naturally secreted extracellular vesicles, laboratory-produced extracellular vesicles, and bioactive macromolecular carriers.

[0033] In one implementation, the extracellular vesicles are selected from vesicles produced by healthy cells or cancer cells, or from vesicles produced by non-cellular organisms.

[0034] "Extracellular vesicle type" refers to a group of extracellular vesicles originating from the same cell line / type. Extracellular vesicles are usually classified according to their cellular origin and the presence of viral infection or tumors in the cellular environment.

[0035] "Extracellular vesicle subtypes" refer to groups of different extracellular vesicles existing within the same extracellular vesicle type (i.e., extracellular vesicles originating from the same cell line). Subtypes are defined based on size, morphology, mechanical and biochemical properties.

[0036] "Biosample" refers to a sample derived from the biological environment.

[0037] "Bioenvironment" refers to the specific conditions and components of a medium that can store biological particles such as viral particles or extracellular vesicles. Typically, it is a complex medium that includes buffer solutions, cell culture media, cell supernatants, the actual medium (water samples, agricultural and food samples), and biological samples from living organisms (including patient samples).

[0038] Biological samples can be, for example, samples collected from patients, samples derived from biological production processes (in laboratory or industrial production), or even samples collected from the environment, particularly samples collected from river water, or agricultural food samples.

[0039] "Fluorescent marker" should be understood by those skilled in the art to have its common meaning. In particular, examples such as: membrane (lipid bilayer) fluorescent markers, markers of genetic material, markers binding to proteins, markers binding to specific proteins, and markers binding to primary or secondary antibodies may be cited; this list is not exhaustive. Examples are given in Table 5 of the Experimental Section. Examples include:

[0040] - YOYO-1 (Molecular Proves) can penetrate the viral capsid and mark viral genetic material.

[0041] - SYTO-13 (Molecular Probes), which can penetrate the viral envelope and mark viral genetic material.

[0042] - DiO (Vybrant Cell Labeling, Molecular Probes) is a lipophilic carbocyanine that emits weak fluorescence in water, but exhibits high fluorescence and photostability when incorporated into membranes or lipid environments.

[0043] - GFP, fused to the C-terminus of the Gag structural protein.

[0044] - Alexa Fluor 488 ®It binds to the gp64-baculovirus antibody (AcV1, Biotechne), which corresponds to the fluorophore that binds to the primary antibody targeting the gp64 envelope protein of baculovirus.

[0045] Labeling can be performed by any method known to those skilled in the art, such as by incubation of the sample with the selected fluorophore for a variable period of time, or by incorporation of the fluorophore during bioproduction. Fluorescent labeling enables tunable specificity in detection.

[0046] "Detection specificity" refers to the ability of a measurement to accurately detect a specific group / class of particles while eliminating or minimizing interference from other particle types or unwanted noise. Therefore, specific measurements can provide accurate and reliable results for the specific group / class of particles under study (in this invention, extracellular vesicles and / or viral particles), unaffected by the significant influence of other particles or error sources, such as the presence of cell debris, bacteria, or contaminants.

[0047] In the context of this invention, the detection specificity can be adjusted.

[0048] "Adjustable detection specificity level" refers to the ability to adjust the measured detection specificity level. In this invention, detection specificity is ensured by several factors, particularly the presence of a nanoporous membrane, which can be functionalized, and the use of fluorescent labeling. Functionalized nanoporous membranes with pore sizes ranging from tens to hundreds of nanometers (e.g., nanopores with diameters of 50 nm to 400 nm) are used as filters to prevent unwanted features (e.g., bacteria or large cell debris) from passing through the pores. Furthermore, fluorescent labeling enables the labeling of precise groups of particles (viral particles and / or extracellular vesicles) with adjustable precision, as highly specific labels, such as antibody-fluorophore combinations, can also be used to label the precise type or subtype of viral particles and / or extracellular vesicles.

[0049] "Nanoporous membrane" refers to a solid organic or inorganic membrane with pores at the nanoscale.

[0050] In the context of this invention, the membrane includes a plurality of parallel pores, for example, 10. 6 Hole / cm 2 Up to 10 9 Hole / cm 2 The membrane has pores with diameters ranging from 10 nm to 600 nm.

[0051] In one implementation, the nanoporous membrane is functionalized.

[0052] "Functionalized nanoporous membranes" refer to solid organic or inorganic membranes whose nanoscale pores are grafted with synthetic polymers.

[0053] "Electroplated polymer" refers to a polymer containing at least one electroactive functional group that enables the polymer to be grafted onto a surface. More specifically, the electrografted polymer is selected from "poly(2-alkyl-2-oxazoline) polymers" well known in the art.

[0054] Specifically, "alkyl" refers to an alkyl chain containing one to four carbon atoms. Examples include poly(2-methyl-2-oxazoline), poly(2-ethyl-2-oxazoline), poly(2-isopropyl-2-oxazoline), and poly(2-n-propyl-2-oxazoline).

[0055] In particular, the polymer according to the invention comprises 2 to 1000 monomers, more particularly 10 to 800 monomers.

[0056] In one embodiment, the polymer is attached to an electroactive probe, particularly a diazo-type probe. This enables the electrochemical grafting of these polymers onto nanoporous surfaces.

[0057] A method for electrografting polymers onto functionalized nanoporous membranes is disclosed in patent application WO2022195059A1.

[0058] Nanoporous membranes can also be functionalized through non-covalent grafting, particularly with Pluronic® or poly(vinylpyrrolidone), methylcellulose, albumin or fetal bovine serum.

[0059] "Detection" refers to the process of determining the presence of viral particles or extracellular vesicles in a sample. Detection is different from identifying the type / subtype of viral particles and / or extracellular vesicles. In particular, detection is performed in real time.

[0060] "Real-time detection" refers to observing the transport of particles (virus particles or extracellular vesicles) through nanopores in real time at the level of individual particles.

[0061] In the context of this invention, the method is capable of detecting viruses and / or extracellular vesicles in biological samples, as well as detecting mixtures of viruses and / or extracellular vesicles present in the same sample.

[0062] As previously stated, the measurement of the transport of the viral particles and / or extracellular vesicles through the nanoporous membrane is performed by optical detection based on at least one control parameter.

[0063] In particular, a zero-mode waveguide microscope (which enhances the electromagnetic field near nanopores coated with metal films) can be used. This highly sensitive optical system enables the detection of the transport of individual fluorescent particles at the level of a single pore and in real time.

[0064] "Control parameters" refer to parameters that can be controlled by the experimenter, particularly the concentration, pressure, temperature, salinity, and pH of viral particles and / or extracellular vesicles.

[0065] "Measurement parameters" refer to parameters observed and / or quantified during the experiment, particularly the frequency of fluorescent particle passage, i.e., the number of particles passing through the pore per unit time, the intensity of the transport event, the duration of the transport event, or the trajectory of the transport event.

[0066] In the context of this invention, the terms “translocation,” “translocation,” “passage,” and “event” are used interchangeably for the transport / passage / translocation of viral particles / extracellular vesicle fluid through a nanoporous membrane. Advantageously, in step b), the sample passage through the nanoporous membrane can be performed by hydrodynamic actuation. Compared to electrodynamic actuation (used in the RPS case described in the introduction), hydrodynamic actuation makes it possible to apply weak and controlled forces on the transported particles. For example, forces on the order of thermal agitation can be used and controlled. Furthermore, modeling hydrodynamic forces is straightforward and simple. It does not involve coupling with other physical quantities / phenomena, as in the case of electrodynamic actuation (e.g., electroosmosis, dielectrophoresis).

[0067] "Intensity of a transport event" refers to the spatiotemporal distribution of light intensity during the event. In the case of an event, i.e., the passage of fluorescently labeled viral particles or extracellular vesicles, the light intensity is extracted from the original signal.

[0068] "Transfer event trajectory" refers to the evolution of the event's spatial coordinates over time and the intensity of the transfer event. Once an event is identified, such as the coordinates of the event center, it can be recorded as a function of time.

[0069] Subsequently, physicochemical and / or kinetic parameters and / or interaction rate parameters are extracted between the virus and / or extracellular vesicles themselves, or between the virus and / or extracellular vesicles and the nanopore surface, which characterize the transport of the viral particles and / or extracellular vesicles by a physical model, such as a bottleneck model, associated with the transport and the control parameters.

[0070] "Extracting physicochemical and / or kinetic and / or interaction rate parameters" refers to quantifying physicochemical and / or kinetic and / or interaction rate parameters by using a physical model of transport based on one or more control parameters, and in particular by fitting that physical model. The physicochemical and / or kinetic and / or interaction rate parameters specifically correspond to at least one parameter selected from the transport rate, affinity, and binding or dissociation rate between viral particles and / or extracellular vesicles and each other, or between viral particles and / or extracellular vesicles and nanopore surfaces.

[0071] "Interaction rate" refers to the binding and / or dissociation rates between viral particles and / or extracellular vesicles or between viral particles and / or extracellular vesicles and the nanopore surface.

[0072] Preferably, step d includes modeling the transport measurements of the viral particles and / or extracellular vesicles by associating transport with control parameters, preferably a bottleneck model, and extracting physicochemical parameters and / or interaction rate parameters between the viruses and / or extracellular vesicles themselves or between the viruses and / or extracellular vesicles and the nanopore surface. These parameters are bound to a database to identify the type and / or subtype of the viral particles and / or extracellular vesicles.

[0073] A “bottleneck,” “blockage,” or even “obstruction” refers to a congestion state caused by the presence of viruses and / or extracellular vesicles within the nanopore, which slows down the transport of viruses and / or extracellular vesicles through the nanopore. From an experimental perspective, this bottleneck phenomenon is particularly reflected in the decrease in the frequency of passage as the concentration of viruses and / or extracellular vesicles increases.

[0074] "Transportation measurements" refer to the measurement of the transit frequency of viruses and / or extracellular vesicles through nanopores and / or another parameter based on control parameters. In particular, these measurements correspond to the relationship between translocation frequency and applied pressure, the relationship between translocation frequency and particle or vesicle concentration, and the measurement of fluorescence intensity over time.

[0075] "Physical model," "identification model," or "transportation measurement model" refers to a transport model used to extract specific physicochemical and / or kinetic parameters by measuring the transport of viral particles or extracellular vesicles through a nanoporous membrane. Specifically, a model describing particle blockage in nanopores can be used, which correlates the particle passage frequency with its physicochemical and / or kinetic interaction parameters.

[0076] The "physical bottleneck model" corresponds to a physical transport model describing the relationship between the transit frequency of viruses and / or extracellular vesicles and control parameters. This model relies on a limited set of physicochemical parameters and / or interaction rate parameters between viruses and / or extracellular vesicles themselves or between viruses and / or extracellular vesicles and nanopore surfaces. Each type / subtype of virus or extracellular vesicle can be associated with a given set of these physicochemical parameters and / or interaction rate parameters. Therefore, a given set of these physicochemical parameters and / or interaction rate parameters corresponds to a specific characteristic of the virus or extracellular vesicle type / subtype.

[0077] "Extracting physicochemical and / or interaction rate parameters between viruses themselves and between viruses and nanopore surfaces" refers to quantifying the parameters by fitting experimental data with a physical bottleneck model ("transport measurement").

[0078] Transport measurements can correspond to curves performed when viral particles or extracellular vesicles are transported through a nanoporous membrane. Specifically, these curves correspond to translocation frequencies as a function of applied pressure (plotted on a system curve), translocation frequency curves based on particle or vesicle concentration, and curves showing event intensity over time. In particular, physicochemical parameters can be extracted from the transport measurements by fitting an "identification model," particularly a "physical bottleneck model." Transport parameters constitute the "identity card" of the particle (viral particle or extracellular vesicle) because they allow for tracing the associated particle type or subtype using the methods disclosed in this invention.

[0079] In particular, the identification model enables the extraction of these physicochemical and / or kinetic parameters and / or interaction rate parameters between viruses themselves and between viruses and nanopore surfaces. These parameters are correlated with the definitions of unique domains of viral particle or extracellular particle types or subtypes in the identification database.

[0080] Advantageously, the method of the present invention includes step e, which identifies the viral particles and / or extracellular vesicles present in the sample by comparing the parameters extracted in step d with a database including specific features of each viral type or subtype and / or each extracellular vesicle type or subtype.

[0081] Specific features may be associated with a “reference sample” that corresponds to a biological sample of a precise type or subtype of viral particles or extracellular vesicles used to establish the database disclosed in the context of this invention.

[0082] Specifically, the physicochemical parameters and / or interaction rate parameters between the virus particles and / or extracellular vesicles themselves, or between the virus and / or extracellular vesicles and the nanopore surface, are compared with a database containing specific characteristics of each virus type or subtype and / or each extracellular vesicle type or subtype to identify the virus particles and / or extracellular vesicles present in the sample.

[0083] More specifically, by performing steps a) through d) on samples containing known viral particles and / or extracellular vesicles, specific characteristics are obtained that enable identification of each viral type and / or each extracellular vesicle type of labeled viral particles and / or extracellular vesicles.

[0084] A “database” refers to a structured collection of different viral particles or extracellular vesicles in which physicochemical parameters and / or interaction rate parameters and / or specific kinetic parameters have been measured for each type / subtype of particle or vesicle, between the virus and / or extracellular vesicle itself, or between the virus and / or extracellular vesicle and the nanopore surface, enabling the construction of discrete domains for each precise type or subtype of particle or vesicle throughout the database. The features constituting this database correspond to reference samples of precise types or subtypes of viral particles or extracellular vesicles for which specific transport parameters have been measured according to the method disclosed in this invention.

[0085] As previously stated, the present invention also relates to the use of the method according to the present invention for identifying the type and / or subtype of virus present in a patient, the quality of viral vector production, or the identification of zoonotic diseases in biological samples.

[0086] The present invention also relates to the use of the method according to the invention for identifying the type and / or subtype of extracellular vesicles or the quality of vesicle carrier production in patients.

[0087] Therefore, this method is applied in the medical field, particularly in clinical or diagnostic fields, in the bioproduction field, particularly in vaccine manufacturing and gene therapy, in the public health surveillance of human, plant or animal diseases, and in the agri-food field, to identify the presence of viral particles.

[0088] The present invention will be described with reference to the following figures and embodiments. Attached Figure Description

[0089] [ Figure 1 [This describes a zero-mode waveguide configuration for the translocation of viral particles or extracellular vesicles through a functionalized nanoporous membrane. The cis-chamber is connected to a pressure controller and contains fluorescently labeled viral particles or extracellular vesicles. When a pressure difference is applied between the two chambers, the particles are transported through the nanopores to the inverse chamber. A laser is directed to the inverse side, and the particles are illuminated once they pass through the evanescent field region at the tip of the pore. The zero-mode waveguide effect consists of amplification of the fluorescence signal near the gold layer. Once exiting the nanopore, the fluorescently labeled particles become blurred (defocused) and photobleached.]

[0090] [ Figure 2 A. Changes in pressure-based translocation frequencies of HIV particles (Gag-GFP, IRIM) at different concentrations, where f is the translocation frequency divided by a factor k, and P is the applied pressure divided by the critical pressure Pc.

[0091] B. Variation of translocation frequency based on viral concentration for different virus types. Pressure set at 8 mbar. C. Variation of translocation frequency based on pressure for extracellular vesicles (HeLa cells (without virus production), IRIM) at different concentrations. For A, B, and C, the solid lines represent instances adjusted using an identification model (here, a bottleneck model, or a blockage model). The nanoporous membrane has a pore size of 200 nm. Experimental error is the standard error of the mean, with more than 8 replicates per experimental series.

[0092] [ Figure 3 A. HIV particles (Gag-GFP, IRIM, 70.10) under different experimental conditions 5 The change in translocation frequency (particles / mL) based on pressure was measured under the following experimental conditions: a nanoporous membrane with a conventional buffer (containing 5 mM NaCl), a functionalized nanoporous membrane (grafted with poly(2-methyl-2-oxazoline) with a degree of polymerization of 387) with a conventional buffer (containing 5 mM NaCl), and a nanoporous membrane with a buffer containing more salt (containing 150 mM NaCl). f is the translocation frequency divided by the factor k. P is the applied pressure divided by the critical pressure Pc.

[0093] B. Extracellular vesicles under different experimental conditions (HeLa cells (without virus production), IRIM, 30.10) 5 The change in translocation frequency (particles / mL) based on pressure, under the experimental conditions of a nanoporous membrane with a standard buffer (containing 5 mM NaCl) or a buffer containing more salt (containing 150 mM NaCl). For A and B, the solid lines represent instances adjusted using a validation model (here, the bottleneck model, i.e., the blockage model). The nanoporous membrane pore size is 200 nm. Experimental error is the standard error of the mean, with more than 8 replicates per experimental series.

[0094] [ Figure 4 [: A Voronoi diagram depicting multiple domains, each associated with a type of virus particle. Each domain is associated with a color, with its boundaries shown in black. Certain points correspond to the nucleus of each viral domain, i.e., the average physicochemical parameters associated with the precise virus type. A specific point corresponds to an "unknown sample" that was correctly classified into the influenza domain (influenza, IntV).]

[0095] definition

[0096] "Unknown sample or unknown particle" refers to "biological sample of viral particles or extracellular vesicles", where the type or subtype of viral particles and / or extracellular vesicles present in the sample is unknown.

[0097] "Identification specificity" refers to the probability of obtaining a negative test if the particle of interest is not present (in other words, the probability of obtaining a negative test in a disease-free individual). Specifically, it can be given by the following formula: Specificity = (True Negative) / (True Negative + False Positive), which will be detailed in the experimental section.

[0098] "Concentration sensitivity" refers to the system's detection limit, which is the lowest concentration (of viral particles or extracellular vesicles) that can be detected and have an effect.

[0099] "Identification sensitivity" refers to the probability that a test correctly classifies an unknown particle into the correct region (in other words, the probability that the test will be positive if the disease is present). Specifically, it can be given by the following formula: Sensitivity = (True Positive) / (True Positive + False Negative), which will be detailed in the experimental section.

[0100] "Precision" refers to the probability that an unknown sample is correctly classified into the correct viral particle or extracellular vesicle type / subtype among all unknown samples classified into that type / subtype (in other words, it corresponds to the number of samples correctly classified as positive out of all positives). Specifically, it can be given by the following formula: Precision = (True Positives) / (True Positives + False Positives), as detailed in the experimental section.

[0101] The "F1 score" refers to the harmonic mean of accuracy and sensitivity, which is a measure of a model's classification performance. Specifically, it can be given by the following formula: F1 score = 2 × (accuracy × sensitivity) / (accuracy + sensitivity), which will be detailed in the experimental section. Example

[0102] Examples of nanoporous membrane preparation according to the present invention

[0103] The nanoporous membranes used were track-etched polycarbonate membranes from Whatman Nucleopore (GE Healthcare). Polycarbonate was chosen because of its low protein interaction. These commercially available nanoporous membranes consist of polycarbonate layers ranging from 6 µm to 10 µm in thickness and have diameters and densities ranging from 50 nm to 400 nm and 1 × 10⁻⁶, respectively. 8 Hole / cm 2 Up to 6×10 8 Hole / cm 2 Cylindrical nanopores.

[0104] The membrane specifications are given in Table 1.

[0105] [Table 1]

[0106]

[0107] Table 1: Track Etch Membrane Specifications (Source: Whatman, GE Healthcare) ("Membrane GE Polycarbonate Track Etch (PCTE)", Ge Water Process Technol., pp. 1-2, 2010).

[0108] To visualize the translocation of viral particles and extracellular vesicles through nanopores, a 50 nm thick gold layer was deposited on top of the membrane using a cleanroom evaporator. The evaporation process was carried out at temperatures below 10 nm. -6 The deposition was carried out under a pressure of Pa, and gold was deposited at a rate of 0.1 nm / s. The deposition rate and thickness were controlled by a quartz balance. The gold-coated films were characterized using scanning electron microscopy (SEM) and atomic force microscopy (AFM) (PJ Kolbeck et al., "Thermally Switchable Nanogate Based on Polymer Phase Transition," Nano Lett., 2023). The diameters measured by SEM are shown in Table 2 (L. Chazot-Franguiadakis et al., "Optical Quantification by Nanopores of Viruses, Extracellular Vesicles, and Nanoparticles," Nano Lett. 2022; PJ Kolbeck et al., "Thermally Switchable Nanogate Based on Polymer PhaseTransition," Nano Lett. 2023; T. Auger et al., "Zero-mode waveguide detection of flow-driven DNA translocation through nanopores," Phys. Rev. Lett., vol. 113, no. 2, pp. 1−5, 2014).

[0109] [Table 2]

[0110]

[0111] Table 2: The aperture determined by SEM measurement is the average value calculated after measuring more than 200 apertures.

[0112] To improve measurement sensitivity, the use of functionalized nanoporous membranes is advantageous. For example, different types of polymers have been used: two electrochemically grafted poly(2-alkyl-2-oxazoline) ((poly(methyl-2-oxazoline) and poly(2-n-propyl-2-oxazoline)). The synthesis of these polymers (with degrees of polymerization varying between 2 and 1000, more specifically between 10 and 800) and their grafting are disclosed in patent application WO2022195059, which provides precise details of their preparation. In short, polymer grafting is performed on the gold layer of the nanoporous membrane via electrografting (connecting the polymer and the gold surface via azo coupling).

[0113] Embodiments of the present invention combining nanoporous membranes with optical systems

[0114] Experimental setup

[0115] The experimental setup consists of an optical detection and pressure control system used to induce the transport of viral particles and extracellular vesicles through nanoporous membranes, as previously disclosed (L. Chazot-Franguiadakis et al., "Optical Quantification by Nanopores of Viruses, Extracellular Vesicles, and Nanoparticles," Nano Lett., 2022; T. Auger et al., "Zero-mode waveguide detection of flow-driven DNA translocation through nanopores," Phys. Rev. Lett., vol. 113, no. 2, pp. 1-5, 2014; T. Auger et al., "Zero-mode waveguide detection of DNA translocation through FIB-organized arrays of engineered nanopores," Microelectron. Eng., vol. 187−188, pp. 90−94, 2018). Figure 1More specifically, the device consists of two chambers separated by a nanoporous membrane. The optical system comprises a conventional epifluorescence microscope (Axiovert 200, ZEISS) with a water immersion objective (ZEISS C-Apochromat, Korr M27) ​​mounted on it. This lens is equipped with chromatic aberration correction, a numerical aperture of 1.2, and a magnification of 63x. The microscope also includes a filter box (combining multiple pairs of emission / excitation filters and dichroic mirrors). The laser (Cobolt blues 50, Cobolt AB) emits at a wavelength of 473 nm and has a power of 50 mW, making it suitable for fluorescence measurements of biomolecules. The laser follows the optical path and is beam-expanded, particularly through a telescope, so that the beam is parallel to the lens output. Finally, the microscope is connected to an EMCDD camera (electron multiplication charge-coupled device (Andor, iXon+)). Typically, a frame rate of 33 Hz (using 512 x 512 pixels) is used in experiments. In addition, cooling the camera to -60°C is crucial for limiting thermal noise. The entire setup, including the laser, optical path, fluorescence microscope, dual-chamber system, and camera, is situated on a stable air-cushioned optical platform.

[0116] The core of the experimental setup consists of two chambers connected by a functionalized nanoporous membrane, such as... Figure 1 As shown:

[0117] The lower chamber (reverse chamber) consists of Teflon rings to which a 0.17 mm thick glass slide is adhered. The adhesive used is always a biocompatible polysiloxane adhesive (Silcomet JS 533 Red, Loctite).

[0118] The upper chamber (cis-chamber) consists of a lid with a perforated end and a functionalized nanoporous membrane attached. The upper part of the lid serves as a reservoir for storing viral particles or extracellular vesicle solutions. The upper chamber is also connected to a pressure controller (MFCS-FLex, Fluigent). The pressure controller has four channels, three of which operate from 0 mbar to 1 bar with an accuracy of 1 mbar, and a more precise channel operates from 0 mbar to 69 mbar with an accuracy of 0.1 mbar.

[0119] The inverse and cis chambers are assembled above the objective lens. This assembly can be moved in three spatial directions with micrometer-level precision using a rotating stage.

[0120] In a typical experiment, fluorescently labeled viral particles or extracellular vesicles are placed in the cis chamber. By applying a pressure difference between the two chambers, they are transported through a functionalized nanoporous membrane into the trans chamber. The nanoporous membrane is irradiated with a laser beam on the trans side, and particles are observed at the pore outlet. The detection is based on the zero-mode waveguide effect, which includes amplifying the fluorescence signal near the gold layer (L. Chazot-Franguiadakis et al., "Optical Quantification by Nanopores of Viruses, Extracellular Vesicles, and Nanoparticles," Nano Lett., 2022; PJ Kolbeck et al., "Thermally Switchable Nanogate Based on PolymerPhase Transition," Nano Lett., 2023; T. Auger et al., "Zero-mode waveguide detection of flow-driven DNA translocation through nanopores," Phys. Rev. Lett., vol. 113, no. 2, pp. 1-5, 2014; T. Auger, "Translocation de biopolymères à travers des pores naturels ou artificiels," Paris Cité, 2016; B. Molcrette et al., "Experimental study of a nanoscale translocation ratchet," Proc.). Natl. Acad. Sci., vol. 119, no. 30, pp. 1−10, Juil. 2022; N. Klughammer and C. Dekker, "Palladium zero-mode waveguides for optical single-molecule detection with nanopores," Nanotechnology, vol. 32, no. 18, pp. 1−14, Feb.2021). This effect enables operation at the single-particle scale.Therefore, the presence of the gold layer is crucial for promoting the zero-mode waveguide effect, and the gold layer also acts as a mirror, blocking the view of fluorescent particles upstream of the nanopore, so that the only visible particles are those that emerge from the pore. Figure 1 As the particles move away from the membrane, their fluorescence diminishes due to photobleaching and defocusing. Finally, the field of view on the nanoporous membrane is 125 x 125 µm. 2 (Measured using a Thorlabs R1L3S2P micrometer). For 1×10 8 Hole / cm 2 Up to 6×10 8 Hole / cm 2 The pore density (consistent with pore size, see Table 1) estimates the number of parallel pores in the field of view to be approximately 15,600 (±2,500) to 93,700 (±15,000). This large number of pores provides good statistics for measuring the average translocation frequency of viral particles and extracellular vesicles, and thus provides high concentration sensitivity. This is a significant advantage of the method disclosed in this paper over RPS techniques.

[0121] Examples of preparation of virus particle and extracellular vesicle samples according to the present invention

[0122] Viral particle and extracellular vesicle samples were provided by various virology research laboratories. In short, viral particles were obtained through cellular expression of viral proteins, plasmid transfection, or viral cell culture. Extracellular vesicles were harvested from the culture medium of HeLa, NIH3T3, or 293THEK cells, which produced or did not produce viral particles. Examples of the types and subtypes of viral particles tested are summarized in Table 3, while examples of the extracellular vesicles tested are summarized in Table 4.

[0123] [Table 3]

[0124]

[0125] Table 3: Examples of virus types and subtypes used in this method.

[0126] [Table 4]

[0127]

[0128] Table 4: Examples of extracellular vesicle (EV) types used in this method.

[0129] In general, these samples are complex biological samples collected from unpurified biological media (e.g., cell supernatants) without intermediate purification steps. Additionally, water samples collected, for example, from Rhône were tested (the collected samples were filtered through a 450 nm pore size membrane prior to the experiments).

[0130] Experimental conditions

[0131] Typically, experiments are performed in a buffer medium (at a fixed pH). The buffer used is a standard Tris-EDTA buffer: composed of 10 mM potassium tris(hydroxymethyl)aminomethane buffer (Tris-KCl) and 1 mM ethylenediaminetetraacetic acid (EDTA). Once mixed, the pH is adjusted to 7.5 using 0.1 M hydrochloric acid solution (Sigma Aldrich). It is then filtered through a Whatman nylon membrane (0.2 µm pore size) to avoid impurities. The buffer is stored at 4°C. Alternatively, PBS (1X, magnesium-free, calcium-free, Sigma Aldrich) can be used.

[0132] Fluorescent labeling

[0133] To visualize viral particles or extracellular vesicles emerging from ZMW-functionalized nanoporous membranes, fluorescent labeling of the particles is required. Different strategies can be employed depending on the target of the fluorescent label. For example, four types of labels were used. They target: genetic material (DNA, RNA) inside the viral capsid or across the lipid envelope, the lipid envelope itself, or those inherent to particle production, as shown in Table 5. The chosen labels should have excitation and emission wavelengths compatible with the experimental setup. Furthermore, the fluorescent labels used are passive labels that do not interact with the nanopores and do not alter the translocation of viral particles and extracellular vesicles.

[0134] [Table 5]

[0135]

[0136] Table 5: Overview of different fluorescent labels that can be used for viral particles and extracellular vesicles.

[0137] YOYO-1 mark

[0138] YOYO-1 (Molecular Probes) can penetrate viral capsids and label viral genetic material. It has been used to label non-enveloped viruses (e.g., AAV, HBV, and AdV) (L. Chazot-Franguiadakis et al., "Optical Quantification by Nanopores of Viruses, Extracellular Vesicles, and Nanoparticles," Nano Lett., 2022). The aim was to obtain the strongest fluorescence emission by saturating DNA molecules with YOYO-1. For example, the same protocol was used for all viral particle types, which involved adding 0.7 µL of YOYO-1 (1 mM) directly to 1 µL of viral solution. The solution was incubated at room temperature for 10 minutes, after which it could be diluted to the required concentration for the experiment.

[0139] SYTO-13 marking

[0140] SYTO-13 (Molecular Probes) can penetrate viral envelopes and label viral genetic material. It has been used to label enveloped viral particles (e.g., HIV, HSV, MLV, MeV, SC2, etc.). For example, the same protocol is used for all viral particle types: 0.5 µL of SYTO-13 (5 mM) is added directly to 1 µL of viral solution. The solution is incubated at room temperature for 10 minutes, after which it can be diluted to the required concentration for the experiment.

[0141] DiO Marker

[0142] In cases where genetic material is unavailable or absent, the lipid envelope of particles can be fluorescently labeled. This is accomplished using DiO (Vybrant Cell Labeling, Molecular Probes), a lipophilic carbonyl cyanide that emits weak fluorescence in water but exhibits high fluorescence and photostability when incorporated into membranes or lipid environments. This type of fluorophore has been used to label extracellular vesicles and enveloped viral particles (e.g., HIV, MLV, etc.) (L. Chazot-Franguiadakis et al., "Optical Quantification by Nanopores of Viruses, Extracellular Vesicles, and Nanoparticles," Nano Lett., 2022). For example, a protocol for labeling enveloped viral particles or extracellular vesicles corresponds to a conventional lipid envelope labeling protocol, which involves adding 0.5 µL of DiO (0.1 mM) to a solution containing 1 µL of vesicles or virus. The entire solution is then incubated at 37 °C (in a constant-temperature metal bath) for 15 minutes. The solution can then be rediluted according to the concentration required for the experiment.

[0143] GFP marker

[0144] Fluorescein can be directly incorporated during production, as in the cases of HIV and MLV, where GFP is used for fluorescent labeling, with GFP fused to the C-terminus of the Gag structural protein (J. Eid, "Etude du relargage du VIH-1 entemps réel à l'échelle de la cellule unique par la Viro-fluidique," Montpellier, 2022; S. Nydegger, M. Foti, A. Derdowski, P. Spearman, and M. Thali, "HIV-1 egress is gated through late endosomal membranes," Traffic, vol. 4, no. 12, pp. 902-910, Dec. 2003).

[0145] Fluorescent (primary or secondary antibody) labeling

[0146] Specific (primary or secondary) antibodies, typically labeled with Alexa Fluor 488® or FITC fluorescence, can also be used. This type of labeling helps improve the specificity of the measurement and can be used to identify virus type / subtype or extracellular vesicles. Incubation time varies depending on the fluorescent antibody / antigen pair considered. For example, to fluorescently label a baculovirus sample, gp64-baculovirus antibody (AcV1, Biotechne) conjugated with Alexa Fluor 488® is incubated with the baculovirus sample at a ratio of 1:5000 (virus:antibody) at 20°C for 15 minutes.

[0147] Embodiments of the present invention using nanopore systems combined with optical detection

[0148] In a typical experiment, fluorescently labeled viral particles or extracellular vesicles are placed in the cis-chamber. By applying a pressure difference between the two chambers, they are transported across the membrane to the trans-chamber. More precisely, after adding a water droplet to the objective lens, the trans-chamber (Teflon ring + glass surface) is placed on the stage in contact with the objective lens and filled with 500 µL of Tris-EDTA buffer. The inner cavity of the cap (cis-chamber) is filled with a solution containing the biological sample of interest. The smallest chamber has a capacity of approximately 1 µL, while the largest has a capacity of approximately 1000 µL. The exact volume and concentration used vary depending on the experiment. The cap (cis-chamber) is screwed onto a holder connected to a pressure controller and placed on the trans-chamber. The particles are observed at the well outlet by irradiating the membrane (trans-side) with a laser beam. Detection is based on the zero-mode waveguide effect, which includes amplification of the fluorescence signal near the gold layer.

[0149] Data collection

[0150] Data acquisition and parameter selection are performed using Andor Solis software connected to the camera. As mentioned earlier, the camera is equipped with an integrated cooling system, enabling all measurements to be performed at -60°C, thereby reducing thermal noise. Furthermore, the gain value corresponding to the system signal amplification ratio can be adjusted; a gain value of 300 was selected.

[0151] The event corresponds to the complete translocation of viral particles or extracellular vesicles through nanopores. In the experiment, the aim is to assess changes in translocation frequency based on the pressure difference applied to the system or the concentration of the object in the system. The experiment corresponds to single-particle measurements, based on the detection and quantification of isolated events during acquisition. Image sequences are acquired using a camera, and the resolution, number of images, and time interval between each frame are defined. Typically, a real-time display mode is used, with an image resolution of 125 x 125 μm. 2 The image was captured at 512 x 512 pixels at a frequency of 33 Hz. The sequence was typically 250 frames long, corresponding to a 7.6-second video. Four sequences were run consecutively to increase statistics. Finally, the temporal resolution could be improved by increasing pixel merging and reducing the observation area. These parameters were adjusted (at 62.5 x 62.5 pm). 2 Using 256x256 pixels (with pixel binning set to 2), the acquisition frequency of the detection system can be increased to 112Hz. Furthermore, it is possible to track the intensity changes of each individual event over time.

[0152] Pressure-based displacement frequency

[0153] Once the biological sample is loaded into the cis-chamber, a series of videos covering a pressure range are generated, from minimum pressure (typically 20 mbar for a 50 nm pore size and 0.1 mbar for a 400 nm pore size) to maximum pressure (typically 80 mbar for a 50 nm pore size and 2 mbar for a 400 nm pore size). The minimum and maximum pressure values, as well as the interval between each measurement, vary depending on the pore size considered. Furthermore, it is generally recommended to wait 3 minutes before acquiring the first point and typically 1 minute between each new point to allow the pressure to stabilize. For each point, four videos are consecutively generated at a given pressure. For the same cap (cis-chamber), two sequences are also typically acquired in two different regions to increase statistical value.

[0154] Translocation frequency based on viral particle or extracellular vesicle concentration

[0155] For this type of measurement, the pressure value is constant. Initially, the concentration in the cap (cis-chamber) is lowest, and then gradually increased. More precisely, for each concentration, four video clips are first acquired in the first region, then the region on the membrane is changed so that another four video clips are acquired. Once the concentration has been measured, a certain volume of viral particles and / or extracellular vesicle solution can be added to the cis-chamber to obtain a new concentration. After homogenizing the contents of the cap, allow it to equilibrate for 3 minutes, and then begin a new acquisition. This step is repeated the corresponding number of times depending on the different concentrations.

[0156] Event intensity changes over time

[0157] For this type of measurement, pressure and particle concentration are constant. The intensity of each individual event in the video can be analyzed over time.

[0158] Typically, the normalization strength of an event is determined by... This indicates that I (time) is the intensity of the event over time. 最大 This represents the maximum intensity of the event. The obtained curve can then be fitted using a model to extract parameters related to the intensity signal. For example, a decreasing exponential function (emax) can be used. -t / a (The correction is performed.)

[0159] Image analysis

[0160] Once measurements are taken, the data is analyzed by counting the number of events in each video. For example, the simplest method for detecting and counting events in an image sequence is manual inspection by eye. To facilitate event detection, several operations are performed on the sequence to be analyzed using ImageJ software:

[0161] Starting with the initial sequence, the Z-project function performs a time-averaged calculation on all images to obtain the average intensity of each pixel. The result is an image containing the average noise value for each pixel, while also taking static features into account.

[0162] Then, using the Image Calculator function in ImageJ, the generated image is subtracted from the initial image sequence. This subtraction eliminates parasitic features (membrane defects, adhesion objects) fixed during the acquisition process and reduces noise.

[0163] In addition, a program specifically designed for automatic event detection can be used. An automated algorithm for tracking and quantifying translocation events is employed. This program is written in Matlab and partially utilizes existing functions within Matlab. In short, this involves processing the image using several filters (Gaussian filter, Wiener filter, etc.) and then tracking the events.

[0164] Finally, based on the number of events obtained during image analysis, we calculate the corresponding translocation frequency using the following formula: Where N is the number of events, f 采集 It is the sampling frequency (f) 采集 =33Hz), σ is the pore density (σ=1×10⁻⁶). 8 Hole / cm 2 Up to 6×10 8 Hole / cm 2 (See Table 1), where S is the field of view (S = 125 x 125µm). 2 ).

[0165] Normalization allows us to work using translocation frequencies rather than the number of events, thus enabling comparisons of results obtained across different pore sizes. In fact, membranes with different pore sizes do not have the same pore density (see Table 1).

[0166] Embodiments of the present invention for data analysis and results for identifying viral particles and extracellular vesicles.

[0167] This invention relates to the identification of viral particles and extracellular vesicles based on their specific interactions with nanopores during transport. By measuring the translocation frequency across the nanoporous membrane based on control parameters (pressure, particle concentration) and changes in event intensity over time, a bottleneck model can be used to extract physicochemical parameters and / or interaction rate parameters between the virus and / or extracellular vesicles themselves or between the virus and / or extracellular vesicles and the nanopore surface. These parameters are specific to the type / subtype of the viral particle or extracellular vesicle. Because these parameters are specific to a given particle type, their measurements can be used to establish specific characteristics, thereby building an identification database.

[0168] Construction of the identification database

[0169] The identification database consists of experimental transport measurements collected from reference samples. These data are then analyzed, and physical models are used to extract physicochemical parameters (and / or interaction rate parameters between viruses and / or extracellular vesicles themselves or between viruses and / or extracellular vesicles and nanopore surfaces) specific to each type or subtype of virus and extracellular vesicle. Typically, once parameters have been measured for all particles included in the initial database disclosed in this invention (see Tables 3 and 4), specific domains are associated with each virus type / subtype (or extracellular vesicle type / subtype) within a space common to all virus types (or extracellular vesicle types). This representation constitutes the database required for identifying unknown particles, as described below.

[0170] Collect reference measurements

[0171] For each reference sample corresponding to each virus type / subtype (or each extracellular vesicle type / subtype), several experimental measurements were performed, detailed below. For all these experiments, nanoporous membranes or functionalized nanoporous membranes were used.

[0172] - Changes in translocation frequency with pressure at a fixed particle concentration.

[0173] At least four curves were run, each at a fixed concentration of viral particles (or extracellular vesicles), with the pressure range varying according to pore size (see Table 6). For example, for a 200 nm pore, the pressure could be varied from 0 mbar to 8 mbar in steps of 0.2 mbar, then 0.5 mbar, and finally 1 mbar. Each pressure point was measured four times consecutively for each curve. Each curve was also run at least twice (in two different observation regions), meaning the total number of replicates for each point was greater than eight.

[0174] [Table 6]

[0175]

[0176] Table 6: Pressure ranges typically used in translocation frequencies based on pressure experiments.

[0177] - The change of displacement frequency with particle concentration under constant pressure.

[0178] For this type of measurement, the pressure value is set to a high pressure value related to the nanopore diameter (e.g., 8 mbar for 200 nm pores or 2 mbar for 400 nm pores). Initially, the concentration in the lid (cis chamber) is minimum, and then gradually increased. Depending on the particle type, the concentration range used is from 10... 3 Particles / mL to 10 9 The number of particles / mL varied. For each concentration, four video segments were first acquired on the first region, then the region on the membrane was changed, and four more video segments were acquired. Therefore, the total number of replicates for each point was greater than eight.

[0179] Figure 2 Examples of the changes in translocation frequency of viral particles and extracellular vesicles with pressure and the changes in translocation frequency with particle concentration are given.

[0180] Other experimental conditions

[0181] Several experiments were conducted to test variations in experimental conditions, including the type of medium or membrane. Specifically, experiments were performed by directly adding salt to the reference buffer medium (Tris-EDTA) (initially 5 mM NaCl) to alter the medium, resulting in a final solution containing an additional 150 mM NaCl. In this case, as previously described, the transport of viral particles or extracellular vesicles across the nanoporous membrane could be monitored. An example of the change in translocation frequency versus pressure is shown in... Figure 3 As shown in the image.

[0182] Furthermore, functionalized nanoporous membranes, particularly those functionalized with poly(2-methyl-2-oxazoline), were used. Nanoporous membranes, whether functionalized or not, were used in the same manner under the same experimental conditions. For example, poly(2-methyl-2-oxazoline) with a degree of polymerization of 387 was synthesized and then grafted onto a nanoporous membrane with a pore size of 200 nm. Further details regarding the synthesis, characterization, and grafting of these polymers are disclosed in patent application WO2022195059. Examples of viral particles passing through such functionalized nanoporous membranes are also included. Figure 3 The information is provided in the text.

[0183] Data Analysis: Parameter Extraction

[0184] Once experimental data from the reference sample is generated, the aim is to extract a set of physicochemical parameters and / or interaction rate parameters between the virus and / or extracellular vesicles themselves, or between the virus and / or extracellular vesicles and the nanopore surface, said parameters being specific to each virus type / subtype (or extracellular vesicle type / subtype).

[0185] For example, a physical model was developed to simulate the curves (frequency / pressure and frequency / concentration) obtained from experiments. This model is called a "bottleneck (or 'blockage' or 'clogging') model." It describes the accumulation of particles within nanopores observed in experiments. It is based on the following experimental observations:

[0186] - There exists a critical pressure below which no viral particles or extracellular vesicles leave the pore. This critical pressure (Pc) is thought to be related to particle adhesion within the pore (the minimum pressure required to prevent particles from adhering to the pore surface).

[0187] - Under high pressure, we observed a frequency plateau (frequency / pressure curve), which was more pronounced at higher concentrations of viral particles or extracellular vesicles.

[0188] An increase in the concentration of viral particles or extracellular vesicles is accompanied by a decrease in the translocation frequency of viral particles or extracellular vesicles and greater saturation, which is a prominent feature of the bottleneck (also known as blockage) phenomenon.

[0189] Overall, these observations are consistent with the existence of a bottleneck (also known as blockage) phenomenon, which is associated with strong confinement of viral particles or extracellular vesicles under flow. The proposed physical model describes this phenomenon as aggregation under flow. Therefore, the total translocation time of the virus through the nanopore can be decomposed into two durations, τ1 (the time for two consecutive viral particles or extracellular vesicles to enter the pore) and τ2 (the time for two consecutive viral particles or extracellular vesicles to exit the pore).

[0190] - For the pore entry portion, it is assumed that a viral particle or extracellular vesicle can only enter the nanopore if another viral particle or extracellular vesicle that has already occupied the pore has left. This depends on the probability (probability P) that the pore is occupied by another viral particle or extracellular vesicle. 颗粒 The second assumption is that the time required to bring viral particles or extracellular vesicles to the pore inlet via advection is negligible. Then, assuming the equilibrium between free viral particles or extracellular vesicles in solution and those particles associated with the bottleneck (i.e., blockage) is given by Langmuir equilibrium, time τ1 can be expressed as:

[0191]

[0192] in, It is the characteristic dissociation rate of viral particles or extracellular vesicles from the bottleneck (i.e., the blockage). It is the dissociation constant between viral particles or extracellular vesicles and the bottleneck (i.e., blockage) portion.

[0193] - Regarding the pore outlet portion, the first assumption is that, under steady state, the concentration C of viral particles or extracellular vesicles within the nanopore is... n (x, t) can be expressed as the competition between particle attachment to the pore surface and the advection of the flow. According to the conservation equations for viral particles or extracellular vesicles: Where v is the flow velocity, This is the characteristic rate at which viral particles or extracellular vesicles attach to the central channel of the nanopore, from which the concentration of viral particles or extracellular vesicles in the nanopore can be obtained:

[0194]

[0195] Where C is the concentration of viral particles or extracellular vesicles in the cis compartment (at x=0).

[0196] Then, using the Hagen-Poiseuille law in cylindrical geometry (e.g., the shape of the hole), the experimental parameter of the pressure difference between the two chambers can be introduced. ;in R and L are the radius and length of the pore, respectively, and η is the viscosity of water.

[0197] Therefore, the time (τ2) for two consecutive viral particles or extracellular vesicles to emerge from the pore can be expressed as:

[0198]

[0199] By introducing the pre-factor CLR 2 and critical pressure .

[0200] Finally, the translocation frequency of viral particles or extracellular vesicles through nanopores can be written as:

[0201]

[0202] All experimental data (frequency / pressure and frequency / concentration curves) for different viral particles (or extracellular vesicles) successfully conformed to this bottleneck (i.e., blockage) model, such as Figure 2 and Figure 3 The solid line depicted in the example is shown. More specifically, Matlab was used to fit the data with a bottleneck (i.e., blockage) model. Analysis scripts were developed, and integrated tools such as the curve fitting toolbox were also used.

[0203] Typically, three physicochemical parameters and / or interaction rate parameters are extracted between the virus and / or extracellular vesicles themselves, or between the virus and / or extracellular vesicles and the nanopore surface. These parameters are specific to each type / subtype of viral particle or extracellular vesicle (P...). c This involves the interaction between viral particles or extracellular vesicles and the pore surface; and , This involves the interaction between viral particles or extracellular vesicles within the bottleneck (i.e., blockage) section. The process is as follows:

[0204] - Fit the initial point of the frequency / pressure curve using the following parts of the model (before reaching saturation): This makes it possible to extract the critical pressure parameter P. c Perform this process on all frequency / pressure curves, and the final P... c By adjusting all P found c The average is obtained.

[0205] - Fit the frequency / concentration curve using a complete bottleneck (i.e., blockage) model (since the pressure is constant, the terminology will be...) (Consider it as a constant). This fitting allows us to obtain two parameters. and .

[0206] Examples of physicochemical parameters and / or interaction rate parameters between the virus and / or extracellular vesicles themselves, or between the virus and / or extracellular vesicles and the nanopore surface, extracted from reference samples for different virus particle and extracellular vesicle types, are given in Table 7. Similar experiments were performed for subtypes.

[0207] [Table 7]

[0208]

[0209] Table 7: Examples of transport (or physicochemical) parameters extracted from the bottleneck (i.e., blockage) model for different virus types and extracellular vesicle types. For virus types, the results are averages of different viral subtypes that make up the same virus type (the results for these subtypes have also been averaged from different experiments). Error bars were calculated using error propagation and the jackknifing process.

[0210] This step is crucial for database construction because the physicochemical parameters and / or interaction rate parameters between the collected viruses and / or extracellular vesicles themselves, or between the viruses and / or extracellular vesicles and the nanopore surface, are specific to each type / subtype of virus particle or extracellular vesicle, thus enabling the construction of unique type / subtype characteristics.

[0211] Alternatively, other models can be used to fit the data, such as the following Gaussian model:

[0212]

[0213] The four parameters (a, b, c, and d) collected by fitting were then used to construct transport parameters associated with each virus type / subtype (or extracellular vesicle type / subtype).

[0214] Database representation

[0215] Based on the parameters extracted above, an nD plot representation of the database is generated. For example, we choose to create a 3D plot using transport (or physicochemical) parameters extracted from a bottleneck (i.e., blockage) model. Each axis corresponds to a parameter (P). c , , The goal is to establish specific domains for each virus type / subtype (or extracellular vesicle type / subtype). For this purpose, the Voronoi algorithm was developed. This algorithm is adapted from an existing Matlab algorithm. The principle of the Voronoi diagram is based on dividing a cube into multiple domains (neighboring domains) based on a set of points called "sites". Each domain contains a site and forms a set of points on the plane that are closer to that site than to any other site. The boundaries between domains are designed to be perfectly equidistant from the nearest site.

[0216] This yields a cube (containing the entire database of viral particles and / or extracellular vesicles) and divides it into multiple domains. Each domain is associated with a subtype or type of viral particle (or extracellular vesicle). For better readability, axes have been normalized by the maximum and minimum values ​​of physicochemical parameters and / or interaction rate parameters between viruses and / or extracellular vesicles themselves, or between viruses and / or extracellular vesicles and the nanopore surface. Examples of Voronoi diagrams for different viral types are depicted in... Figure 4 In the diagram, physicochemical parameter values ​​correspond to average parameters measured for different subtypes associated with the same virus type. Red dots represent sites for each viral domain, i.e., average parameters associated with the precise virus type. A Voronoi diagram containing all viral subtypes was also generated.

[0217] In summary, this representation constitutes the identification database needed to identify unknown particles, as described below.

[0218] In addition, other types of representations besides the Voronoi diagram can be used, such as the k-nearest neighbor algorithm or support vector machines as supervised learning models.

[0219] Identification process testing

[0220] 1- "Unknown Sample"

[0221] Once the identification database is established, the next step involves creating a protocol for identifying “unknown samples,” which are samples that may contain viral particles and / or extracellular vesicles of unknown types and / or subtypes. The first step of this protocol involves measuring the physicochemical parameters associated with the sample (and / or the interaction rate parameters between the virus and / or extracellular vesicles themselves, or between the virus and / or extracellular vesicles and the nanopore surface) in the same manner as when creating the identification database (see the section on collecting reference measurements). However, in the case of unknown samples, single-pass frequency / pressure and single-pass frequency / concentration curves are performed to limit experimental and processing time. The next step involves extracting the physicochemical parameters and / or interaction rate parameters between the virus and / or extracellular vesicles themselves, or between the virus and / or extracellular vesicles and the nanopore surface (by fitting with a bottleneck model), and then representing these new parameters in the identification database (e.g., in a Voronoi diagram). Examples of “unknown viral particle samples” are shown in... Figure 4 The asterisk is used to indicate this. Similar measurements can be performed on extracellular vesicles.

[0222] To determine which domain an "unknown sample" resides in and how close it is to its different neighboring domains (which may indicate measurement error), the distances between the "unknown sample" and all domain sites are measured. The minimum distance is associated with the domain in which the sample resides and is therefore identified as the type / subtype associated with that domain.

[0223] 2- Measurement to assess robustness

[0224] Testing using "unknown samples" provides a standard for evaluating the robustness of the methods disclosed in this invention. For example, four "unknown virus particle samples" were tested for each viral subtype. The identification results are given in Table 8.

[0225] [Table 8]

[0226]

[0227] Table 8: Confusion matrix of “unknown virus samples” used to evaluate the identification process. Data is typically distributed according to a standard process (3 / 4 for database construction and 1 / 4 for testing). Inputs (“Reference True Values” or “Baseline True Values”) correspond to the actual subtypes of the virus particles, while outputs (“Experimental Values”) correspond to the results of the identification process developed in this invention. Results are given as percentages. Four “unknown virus particle samples” were tested for each virus subtype.

[0228] Based on these results, the identification specificity, identification sensitivity, and precision of the method can be calculated (see Table 9). Identification specificity refers to the probability of obtaining a negative test in the absence of the viral particle of interest (in other words, the probability of obtaining a negative test in uninfected individuals). Identification sensitivity refers to the probability that the test correctly classifies an "unknown" viral particle into the correct domain (in other words, the probability of a positive test in the presence of disease). Precision refers to the probability corresponding to the number of samples that are actually correctly classified into a certain subtype out of all samples classified into that subtype.

[0229] [Table 9]

[0230]

[0231] Table 9: Confusion matrix of possible results when measuring the intrinsic validity of a test.

[0232] According to Table 9, the identification specificity, identification sensitivity, and accuracy of the method can be expressed as follows:

[0233]

[0234] For the aforementioned examples (testing unknown samples for each viral subtype) and using the method disclosed in this invention, the identification specificity was found to be 98%, the identification sensitivity to be 95%, and the accuracy to be 98%.

[0235] Furthermore, the harmonic mean of precision and identification sensitivity yields a score called the F1 score, which is a measure of the model's classification ability. The F1 score is expressed as follows:

[0236]

[0237] For the aforementioned examples (testing unknown samples for each viral subtype) and using the method disclosed in this invention, the F1 score reached 96%.

[0238] 3- Samples containing a mixture of multiple types of viral particles and / or extracellular vesicles

[0239] Complex samples corresponding to mixtures of different types / subtypes of viral particles and / or extracellular vesicles present at different concentrations were tested.

[0240] For example, samples containing viral particles (HIV Gag-GFP) and extracellular vesicles (EVs from HeLa cells used for HIV Gag-GFP production, fluorescently labeled with DiO) were prepared according to the aforementioned protocol. First, the viral particles (1×10⁻⁶) were quantified. 11 Particles / mL) and extracellular vesicles (2×10⁻⁶) 10 The concentration of viral particles (particles / mL) was then measured. Subsequently, throughput frequency based on pressure and viral particle concentration was measured to extract viral particle-specific transport parameters (derived from fitting a bottleneck (i.e., blockage) model). Finally, the viral type was identified using a database. The identification results corresponded to most viral types (HIV Gag-GFP), demonstrating the ability to identify viral types in mixtures containing extracellular vesicles.

[0241] In addition, samples containing two types of viruses at a concentration ratio of 1:100, AAV-8 (fluorescently labeled with YOYO-1) and human type A InfV ​​(H1N1) (fluorescently labeled with DiO), were tested. The major virus types were identified using the aforementioned developed methods (transportation of virus particles through nanopores / measurement of transport parameters / use of an identification database). The identification results corresponded to most types of human type A InfV ​​(H1N1) virus and demonstrated the ability to identify virus types in mixtures containing other virus types.

[0242] It should be noted that the concentration ratios described above are relevant in the case of viral particles and / or extracellular vesicles in patient samples. In fact, in cases of co-infection, there are significant differences in concentration between different viral types (typically a factor greater than 1:1000). This also applies to the bioproduction of viral particles and / or extracellular vesicles, where the presence of unwanted particles is low relative to the amount of target particles being produced.

[0243] 4- Patient Samples

[0244] Patient samples corresponding to respiratory swabs were tested. More precisely, a sample pretreatment process was established using patient respiratory swabs stored in a universal transport medium (Becton Dickinson) (corresponding to a negative matrix (free from virus or bacterial infection)). In fact, highly purified samples are not required, as the nanoporous membrane itself acts as a filter, selectively allowing particles smaller than the pore size (200 nm or 400 nm in diameter) to pass through. This method also requires very low concentrations, allowing samples to be diluted at least 10-fold to avoid some of the problems associated with over-concentrated media. Furthermore, our method requires fluorescent labeling to specifically observe viral and / or extracellular vesicle translocations with adjustable specificity (standard labels for viral lipid bilayers or viral genetic material, but also specific labels using antibodies). Nevertheless, the shift to complex samples such as patient samples (e.g., respiratory swabs) represents a technological advancement, potentially requiring additional pretreatment. Therefore, the purpose of the pretreatment step is to make the sample suitable for analysis by removing various components that could completely clog the nanopores, such as bacteria, cells, and cell debris.

[0245] A simple pre-filtration step was performed using a 0.45 µm filter membrane (Agilent), with optional additional centrifugation (affinity column or simple centrifugation). Fluorescent beads (polystyrene beads, 260 nm in diameter, carboxyl-functionalized, Spherotech Inc.) were then added to the filtered test sample (negative matrix from patient respiratory swabs), and the transport of these beads through the pores in this type of medium was measured. For the same bead concentration, the passage frequency was measured based on the pressure applied under three conditions: beads in PBS buffer; beads passing through the transport medium; and beads in the patient respiratory swab (negative matrix). Finally, the curves obtained under the three conditions overlapped. This highlights the effectiveness of the pretreatment protocol, enabling experiments to be conducted under the same conditions as before while overcoming the complexity of the medium.

Claims

1. A method for identifying the type and / or subtype of viral particles and / or extracellular vesicles, comprising: a. Labeling viral particles and / or extracellular vesicles present in biological samples with fluorescent markers; b. Pass the sample containing the labeled virus particles and / or extracellular vesicles through a nanoporous membrane; c. Measuring the transport of the virus particles and / or extracellular vesicles across the nanoporous membrane by optical detection based on at least one control parameter; and d. Extract physicochemical and / or kinetic and / or interaction rate parameters between the virus particles and / or extracellular vesicles themselves, or between the virus particles and / or extracellular vesicles and the nanopore surface, which characterize the transport of the virus particles and / or extracellular vesicles by a physical model that associates the transport with the control parameters.

2. The method according to claim 1, wherein the parameters extracted in step d are capable of identifying the type and / or subtype of viral particles and / or extracellular vesicles.

3. The method according to claim 1 or 2, wherein step d comprises modeling the transport measurements of the viral particles and / or extracellular vesicles by associating the transport with the control parameters, and extracting physicochemical and / or kinetic parameters and / or interaction rate parameters between the viral particles and / or extracellular vesicles themselves, or between the viral particles and / or extracellular vesicles and the nanopore surface.

4. The method according to any one of the preceding claims, wherein physicochemical parameters and / or interaction rate parameters between the virus particles and / or extracellular vesicles themselves, or between the virus and / or extracellular vesicles and the nanopore surface, are compared with a database including specific characteristics of each virus type or subtype and / or each extracellular vesicle type or subtype to identify the virus particles and / or extracellular vesicles present in the sample.

5. The method according to any one of the preceding claims, the method comprising step e, which identifies the viral particles and / or extracellular vesicles present in the sample by comparing the physicochemical parameters and / or interaction rate parameters extracted in step d with a database including specific features of each viral type or subtype and / or each extracellular vesicle type or subtype.

6. The method according to any one of the preceding claims, wherein the at least one control parameter is selected from the concentration of viral particles and / or extracellular vesicles, pressure, duration of transport event, intensity of transport event, trajectory of transport event, temperature, salinity, and pH.

7. The method according to any one of the preceding claims, wherein steps a) to d) are performed on a sample containing known viral particles and / or extracellular vesicles to obtain specific characteristics capable of identifying each type of virus and / or each type of extracellular vesicle of the labeled viral particles and / or extracellular vesicles.

8. The method according to any one of the preceding claims, wherein the nanoporous membrane is functionalized, particularly with synthetic polymers, such as poly-2-alkyl-2-oxazoline, or by non-covalent grafting, particularly with Pluronic®, poly(vinylpyrrolidone), methylcellulose, albumin or fetal bovine serum.

9. The method of claim 8, wherein the polymer is attached to an electroactive probe, particularly a diazo-type electroactive probe.

10. The method according to any one of the preceding claims, wherein in step b), passing the sample through the nanoporous membrane is performed by hydrodynamic driving.

11. The method according to any one of the preceding claims, wherein the virus particles are selected from enveloped DNA virus particles or RNA virus particles, or non-enveloped DNA virus particles or RNA virus particles.

12. The method according to any one of claims 1 to 10, wherein the extracellular vesicle is selected from vesicles produced by healthy cells or cancer cells, or from vesicles produced by non-cellular organisms.

13. The method according to any one of claims 1 to 9 is used for identifying the type and / or subtype of virus present in a patient, the quality of viral vector production, or as part of public health surveillance for identifying human, plant, or animal diseases in biological samples, or even for identifying viral particles in agricultural food samples.

14. The method according to any one of claims 1 to 10 and 12 is used for identifying the type and / or subtype of extracellular vesicles in a patient or for identifying the quality of vesicle carrier production.

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