SERS method for analyzing a viscous biofluid
The invention relates to a SERS method for analyzing a biological sample, the method comprising the following step of: a. obtaining a biological sample which is viscous biofluid, b. depositing at least one droplet of the biological sample onto a microscope slide, and drying the droplet, c. depositing a drop of an aqueous dispersion of metallic nanoparticles above the droplet dried in step b), to have a dense distribution of nanoparticles on the surface of the dried droplet and to obtain a SERS-activated biological sample, d. drying the SERS-activated biological sample, e. irradiating the SERS-activated biological sample using a light source to obtain a SERS spectrum, and f. collecting the SERS spectrum.
1 . A SERS method for analyzing a biological sample which is a viscous biofluid, the method comprising the step of:
a. depositing at least one droplet of the biological sample onto a microscope slide, and drying the droplet,
b. depositing a drop of an aqueous dispersion of metallic nanoparticles above the droplet dried in step a), to have a dense distribution of nanoparticles on a surface of the dried droplet and to obtain a SERS-activated biological sample,
c. drying the SERS-activated biological sample,
d. irradiating the SERS-activated biological sample using a light source to obtain a SERS spectrum, and
e. collecting the SERS spectrum.
2 . The method according to claim 1 , wherein the biological sample which is a viscous biofluid has a viscosity between 0.6 to 14 poise.
3 . The method according to claim 1 , wherein the dense distribution of nanoparticles is 3×10 16 nanoparticles/m 2 .
4 . The method according to claim 1 , wherein the biological sample which is a viscous biofluid is a synovial fluid previously obtained from a patient.
5 . The method according to claim 1 , wherein the metallic nanoparticles are colloidal metallic nanoparticles.
6 . The method according to claim 5 , wherein the colloidal metallic nanoparticles comprise silver.
7 . The method according to claim 1 , wherein in the step a) of drying the droplet of the biological sample is realized at least two hours before depositing the nanoparticles onto the top of the dried biological sample.
8 . An in vitro method for diagnosing or identifying, from a biological sample which is a viscous biofluid, a joint disease, wherein the method comprises the steps of:
a. depositing at least one droplet of said biological sample onto a microscope slide, and drying the droplet,
b. depositing a drop of an aqueous dispersion of metallic nanoparticles above the droplet dried in step a), to have a dense distribution of nanoparticles on a surface of the dried droplet and to obtain a SERS-activated biological sample,
c. drying the SERS-activated biological sample,
d. irradiating the SERS-activated biological sample using a light source to obtain a SERS spectrum, and
e. collecting and analyzing the SERS spectrum.
9 . The method according to claim 8 , wherein analyzing the SERS spectrum comprises the following sequential steps:
i. a pre-processing step for correcting spectral interferences of said SERS spectrum to normalize them,
ii. a step of selecting features and/or reducing data to identify discriminant wavenumbers, and
iii. a step of construction of a supervised classification model using machine learning for automatic prediction of new samples.
10 . A method utilizing a kit having a SERS substrate, a Raman device, and a computing device configured to determine or identify a joint disease in a biological sample based on a spectral content information, the method including analyzing a biological sample, which is a viscous biofluid, the analyzing, comprising the steps of:
a. depositing at least one droplet of the biological sample onto a microscope slide, and drying the droplet,
b. depositing a drop of an aqueous dispersion of metallic nanoparticles above the droplet dried in step a), to have a dense distribution of nanoparticles on a surface of the dried droplet and to obtain a SERS-activated biological sample,
c. drying the SERS-activated biological sample,
d. irradiating the SERS-activated biological sample using a light source to obtain a SERS spectrum, and
e. collecting the SERS spectrum.
11 . The method of claim 10 , wherein the computing device is further configured to execute the following sequential steps:
i. a pre-processing step for correcting spectral interferences of a SERS spectrum and normalize them,
ii. a step of selecting features and/or reducing data to identify discriminant wavenumbers, and
iii. a step of construction of a supervised classification model using machine learning approaches for automatic prediction of new samples.
12 . The method of claim 10 , further comprising a step of using the kit to diagnose or identify a joint disease in a subject.